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\begin{document}

\title{Emerging technologies}



\author[1]{Anna Biskup}%
\affil[1]{Uniwersytet Ekonomiczny w Krakowie}%


\vspace{-1em}



  
  \date{May 08, 2024}


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\par\null

Sources:

\url{https://en.wikipedia.org/wiki/Internet\_of\_things}

\url{https://en.wikipedia.org/wiki/3D\_printing}

\url{https://en.wikipedia.org/wiki/Self-driving\_car}

Fig.1
\url{https://en.wikipedia.org/wiki/Internet\_of\_things\#/media/File:Internet\_of\_Things.jpg}

Fig.2
\url{https://en.wikipedia.org/wiki/Internet\_of\_things\#/media/File:Internet\_of\_Things.svg}

Fig.3
\url{https://en.wikipedia.org/wiki/3D\_printing\#/media/File:MakerBot\_ThingOMatic\_Bre\_Pettis.jpg}

Fig.4
\url{https://en.wikipedia.org/wiki/3D\_printing\#/media/File:84530877\_FillingSys\_(9415669149).jpg}

Fig.5
\url{https://upload.wikimedia.org/wikipedia/commons/b/ba/3D\_selfie\_in\_1\_20\_scale\_in\_my\_palm\_after\_one\_spray\_of\_clear\_satin\_acrylic\_varnish\_IMG\_4751\_FRD.jpg}

Fig.6
\url{https://en.wikipedia.org/wiki/3D\_printing\#/media/File:HCC\_3D\_printed\_turbine\_view\_1.jpg}

Fig.7 \url{https://en.wikipedia.org/wiki/Self-driving\_car}

Table~\cite{2018c}

\section*{Internet of things}

{\label{862729}}

\subsection*{Outline}

{\label{593073}}

The Internet of things (IoT) is the network of physical devices,
vehicles, home appliances, and other items embedded with electronics,
software, sensors, actuators, and connectivity which enables these
things to connect, collect and exchange data. IoT involves extending
Internet connectivity beyond standard devices, such as desktops,
laptops, smartphones and tablets, to any range of traditionally dumb or
non-internet-enabled physical devices and everyday objects. Embedded
with technology, these devices can communicate and interact over the
Internet, and they can be remotely monitored and controlled. With the
arrival of driverless vehicles, a branch of IoT, i.e.~the Internet of
Vehicle starts to gain more attention.

\par\null

\subsection*{History}

{\label{751141}}

The definition of the Internet of things has evolved due to convergence
of multiple technologies, real-time analytics, machine learning,
commodity sensors, and embedded systems. Traditional fields of embedded
systems, wireless sensor networks, control systems, automation
(including home and building automation), and others all contribute to
enabling the Internet of things.

The concept of a network of smart devices was discussed as early as
1982, with a modified Coke machine at Carnegie Mellon University
becoming the first Internet-connected appliance, able to report its
inventory and whether newly loaded drinks were cold. Mark Weiser's 1991
paper on ubiquitous computing, ``The Computer of the 21st Century'', as
well as academic venues such as UbiComp and PerCom produced the
contemporary vision of IoT. In 1994, Reza Raji described the concept in
IEEE Spectrum as ``{[}moving{]} small packets of data to a large set of
nodes, so as to integrate and automate everything from home appliances
to entire factories''. Between 1993 and 1997, several companies proposed
solutions like Microsoft's at Work or Novell's NEST. The field gained
momentum when Bill Joy envisioned Device to Device (D2D) communication
as part of his ``Six Webs'' framework, presented at the World Economic
Forum at Davos in 1999.

The term ``Internet of things'' was likely coined by Kevin Ashton of
Procter \& Gamble, later MIT's Auto-ID Center, in 1999, though he
prefers the phrase ``Internet for things''. At that point, he viewed
Radio-frequency identification (RFID) as essential to the Internet of
things, which would allow computers to manage all individual things.

A research article mentioning the Internet of things was submitted to
the conference for Nordic Researchers in Logistics, Norway, in June
2002, which was preceded by an article published in Finnish in January
2002. The implementation described there was developed by Kary Fr\selectlanguage{ngerman}ämling
and his team at Helsinki University of Technology and more closely
matches the modern one, i.e.~an information system infrastructure for
implementing smart, connected objects. (Fig.
{\ref{664155}})

Defining the Internet of things as ``simply the point in time when more
`things or objects' were connected to the Internet than people'', Cisco
Systems estimated that IoT was ``born'' between 2008 and 2009, with the
things/people ratio growing from 0.08 in 2003 to 1.84 in 2010.

\subsection*{Applications}

{\label{989938}}

\subsubsection*{Consumer applications}

{\label{917708}}

\emph{Smart home}

IoT devices are a part of the larger concept of home automation, which
can include lighting, heating and air conditioning, media and security
systems. Long term benefits could include energy savings by
automatically ensuring lights and electronics are turned off.

A smart home or automated home could be based on a platform or hubs that
control smart devices and appliances. For instance, using Apple's
HomeKit, manufacturers can get their home products and accessories be
controlled by an application in iOS devices such as the iPhone and the
Apple Watch. This could be a dedicated app or iOS native applications
such as Siri. This can be demonstrated in the case of Lenovo's Smart
Home Essentials, which is a line of smart home devices that are
controlled through Apple's Home app or Siri without the need for a Wi-Fi
bridge. There are also dedicated smart home hubs that are offered as
standalone platforms to connect different smart home products and these
include the Amazon Echo, Apple's HomePod, and Samsung's SmartThings Hub.

Elder care

One key application of smart home is to provide assistance for those
with disabilities and elderly individuals. These home systems use
assistive technology to accommodate an owner's specific disabilities.
Voice control can assist users with sight and mobility limitations while
alert systems can be connected directly to cochlear

\par\null\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure1/Figure1}
\caption{{Internet of Things.
{\label{664155}}%
}}
\end{center}
\end{figure}

implants worn by hearing impaired users. They can also be equipped with
additional safety features. These features can include sensors that
monitor for medical emergencies such as falls or seizures. Smart

home technology applied in this way can provide users with more freedom
and a higher quality of life.

\subsection*{Commercial applications}

{\label{520225}}

\emph{Medical and healthcare}

The Internet of Medical Things (also called the internet of health
things) is an application of the IoT for medical and health related
purposes, data collection and analysis for research, and monitoring.
This `Smart Healthcare', as it can also be called, led to the creation
of a digitized healthcare system, connecting available medical resources
and healthcare services.

IoT devices can be used to enable remote health monitoring and emergency
notification systems. These health monitoring devices can range from
blood pressure and heart rate monitors to advanced devices capable of
monitoring specialized implants, such as pacemakers, Fitbit electronic
wristbands, or advanced hearing aids. Some hospitals have begun
implementing ``smart beds'' that can detect when they are occupied and
when a patient is attempting to get up. It can also adjust itself to
ensure appropriate pressure and support is applied to the patient
without the manual interaction of nurses. A 2015 Goldman Sachs report
indicated that healthcare IoT devices ``can save the United States more
than \$300 billion in annual healthcare expenditures by increasing
revenue and decreasing cost.'' Moreover, the use of mobile devices to
support medical follow-up led to the creation of `m-health', used ``to
analyze, capture, transmit and store health statistics from multiple
resources, including sensors and other biomedical acquisition systems''.

Specialized sensors can also be equipped within living spaces to monitor
the health and general well-being of senior citizens, while also
ensuring that proper treatment is being administered and assisting
people regain lost mobility via therapy as well. These sensors create a
network of intelligent sensors that are able to collect, process,
transfer and analyse valuable information in different environments,
such as connecting in-home monitoring devices to hospital-based systems.
Other consumer devices to encourage healthy living, such as connected
scales or wearable heart monitors, are also a possibility with the IoT.
End-to-end health monitoring IoT platforms are also available for
antenatal and chronic patients, helping one manage health vitals and
recurring medication requirements.

As of 2018 IoMT was not only being applied in the clinical laboratory
industry, but also in the healthcare and health insurance industries.
IoMT in the healthcare industry is now permitting doctors, patients and
others involved (i.e.~guardians of patients, nurses, families, etc.) to
be part of a system, where patient records are saved in a database,
allowing doctors and the rest of the medical staff to have access to the
patient's information. Moreover, IoT-based systems are patient-centered,
which involves being flexible to the patient's medical conditions. IoMT
in the insurance industry provides access to better and new types of
dynamic information. This includes sensor-based solutions such as
biosensors, wearables, connected health devices and mobile apps to track
customer behaviour. This can lead to more accurate underwriting and new
pricing models.

\emph{Transportation}

The IoT can assist in the integration of communications, control, and
information processing across various transportation systems.
Application of the IoT extends to all aspects of transportation systems
(i.e.~the vehicle, the infrastructure, and the driver or user). Dynamic
interaction between these components of a transport system enables inter
and intra vehicular communication, smart traffic control, smart parking,
electronic toll collection systems, logistic and fleet management,
vehicle control, and safety and road assistance. In Logistics and Fleet
Management for example, The IoT platform can continuously monitor the
location and conditions of cargo and assets via wireless sensors and
send specific alerts when management exceptions occur (delays, damages,
thefts, etc.). If combined with Machine Learning then it also helps in
reducing traffic accidents by introducing drowsiness alerts to drivers
and providing self driven cars too.

\emph{Building and home automation}

IoT devices can be used to monitor and control the mechanical,
electrical and electronic systems used in various types of buildings
(e.g., public and private, industrial, institutions, or residential) in
home automation and building automation systems. In this context, three
main areas are being covered in literature:

The integration of the Internet with building energy management systems
in order to create energy efficient and IOT driven ``smart buildings''.

The possible means of real-time monitoring for reducing energy
consumption and monitoring occupant behaviors.

The integration of smart devices in the built environment and how they
might to know who to be used in future applications.

\subsubsection*{Industrial applications}

{\label{636975}}

\emph{Manufacturing}

The IoT can realize the seamless integration of various manufacturing
devices equipped with sensing, identification, processing,
communication, actuation, and networking capabilities. Based on such a
highly integrated smart cyberphysical space, it opens the door to create
whole new business and market opportunities for manufacturing. Network
control and management of manufacturing equipment, asset and situation
management, or manufacturing process control bring the IoT within the
realm of industrial applications and smart manufacturing as well. The
IoT intelligent systems enable rapid manufacturing of new products,
dynamic response

to product demands, and real-time optimization of manufacturing
production and supply chain networks, by networking machinery, sensors
and control systems together.

Digital control systems to automate process controls, operator tools and
service information systems to optimize plant safety and security are
within the purview of the IoT. But it also extends itself to asset
management via predictive maintenance, statistical evaluation, and
measurements to maximize reliability. Smart industrial management
systems can also be integrated with the Smart Grid, thereby enabling
real-time energy optimization. Measurements, automated controls, plant
optimization, health and safety management, and other functions are
provided by a large number of networked sensors.

The term industrial Internet of things (IIoT) is often encountered in
the manufacturing industries, referring to the industrial subset of the
IoT. IIoT in manufacturing could generate so much business value that it
will eventually lead to the fourth industrial revolution, so the
so-called Industry 4.0. It is estimated that in the future, successful
companies will be able to increase their revenue through Internet of
things by creating new business models and improve productivity, exploit
analytics for innovation, and transform workforce. The potential of
growth by implementing IIoT may generate \$12 trillion of global GDP by
2030.

\emph{Agriculture}

There are numerous IoT applications in farming such as collecting data
on temperature, rainfall, humidity, wind speed, pest infestation, and
soil content. This data can be used to automate farming techniques, take
informed decisions to improve quality and quantity, minimize risk and
waste, and reduce effort required to manage crops. For example, farmers
can now monitor soil temperature and moisture from afar, and even apply
IoT-acquired data to precision fertilization programs.

In August 2018, Toyota Tsusho began a partnership with Microsoft to
create fish farming tools using the Microsoft Azure application suite
for IoT technologies related to water management. Developed in part by
researchers from Kindai University, the water pump mechanisms use
artificial intelligence to count the number of fish on a conveyor belt,
analyze the number of fish, and deduce the effectiveness of water flow
from the data the fish provide. The specific computer programs used in
the process fall under the Azure Machine Learning and the Azure IoT Hub
platforms.

\subsubsection*{Infrastructure
application}

{\label{804431}}

Monitoring and controlling operations of sustainable urban and rural
infrastructures like bridges, railway tracks and on- and offshore
wind-farms is a key application of the IoT. The IoT infrastructure can
be used for monitoring any events or changes in structural conditions
that can compromise safety and increase risk. IoT can benefit the
construction industry by cost saving, time reduction, better quality
workday, paperless workflow and increase in productivity. It can help in
taking faster decisions and save money with Real-Time Data Analytics. It
can also be used for scheduling repair and maintenance activities in an
efficient manner, by coordinating tasks between different service
providers and users of these facilities. IoT devices can also be used to
control critical infrastructure like bridges to provide access to ships.
Usage of IoT devices for monitoring and operating infrastructure is
likely to improve incident management and emergency response
coordination, and quality of service, up-times and reduce costs of
operation in all infrastructure related areas. Even areas such as waste
management can benefit from automation and optimization that could be
brought in by the IoT.

\emph{Metropolitan scale deployments}

There are several planned or ongoing large-scale deployments of the IoT,
to enable better management of cities and systems. For example, Songdo,
South Korea, the first of its kind fully equipped and wired smart city,
is gradually being built, with approximately 70 percent of the business
district completed as of June 2018. Much of the city is planned to be
wired and automated, with little or no human intervention.

Another application is a currently undergoing project in Santander,
Spain. For this deployment, two approaches have been adopted. This city
of 180,000 inhabitants has already seen 18,000 downloads of its city

smartphone app. The app is connected to 10,000 sensors that enable
services like parking search, environmental monitoring, digital city
agenda, and more. City context information is used in this deployment so
as to benefit merchants through a spark deals mechanism based on city
behavior that aims at maximizing the impact of each notification.

Other examples of large-scale deployments underway include the
Sino-Singapore Guangzhou Knowledge City; work on improving air and water
quality, reducing noise pollution, and increasing transportation
efficiency in San Jose, California; and smart traffic management in
western Singapore. French company, Sigfox, commenced building an
ultra-narrowband wireless data network in the San Francisco Bay Area in
2014, the first business to achieve such a deployment in the U.S. It
subsequently announced it would set up a total of 4000 base stations to
cover a total of 30 cities in the U.S. by the end of 2016, making it the
largest IoT network coverage provider in the country thus far.

Another example of a large deployment is the one completed by New York
Waterways in New York City to connect all the city's vessels and be able
to monitor them live 24/7. The network was designed and engineered by
Fluidmesh Networks, a Chicago-based company developing wireless networks
for critical applications. The NYWW network is currently providing
coverage on the Hudson River, East River, and Upper New York Bay. With
the wireless network in place, NY Waterway is able to take control of
its fleet and passengers in a way that was not previously possible. New
applications can include security, energy and fleet management, digital
signage, public Wi-Fi, paperless ticketing and others.

\emph{Energy management}

Significant numbers of energy-consuming devices (e.g.~switches, power
outlets, bulbs, televisions, etc.) already integrate Internet
connectivity, which can allow them to communicate with utilities to
balance power generation and energy usage and optimize energy
consumption as a whole. These devices allow for remote control by users,
or central management via a cloud-based interface, and enable functions
like scheduling (e.g., remotely powering on or off heating systems,
controlling ovens, changing lighting conditions etc.). The smart grid is
a utility-side IoT application; systems gather and act on energy and
power-related information to improve the efficiency of the production
and distribution of electricity. Using advanced metering infrastructure
(AMI) Internet-connected devices, electric utilities not only collect
data from end-users, but also manage distribution automation devices
like transformers.

\emph{Environmental monitoring}

Environmental monitoring applications of the IoT typically use sensors
to assist in environmental protection by monitoring air or water
quality, atmospheric or soil conditions, and can even include areas like
monitoring the movements of wildlife and their habitats. Development of
resource-constrained devices connected to the Internet also means that
other applications like earthquake or tsunami early-warning systems can
also be used by emergency services to provide more effective aid. IoT
devices in this application typically span a large geographic area and
can also be mobile. It has been argued that the standardization IoT
brings to wireless sensing will revolutionize this area.

\subsection*{Trends and
characteristics}

{\label{130353}}

The IoT's major significant trend in recent years is the explosive
growth of devices connected and controlled by the Internet. The wide
range of applications for IoT technology mean that the specifics can be
very different from one device to the next but there are basic
characteristics shared by most. IoT creates opportunities for more
direct integration of the physical world into computer-based systems,
resulting in efficiency improvements, economic benefits, and reduced
human exertions.

The number of IoT devices increased 31\% year-over-year to 8.4 billion
in the year 2017 and it is estimated that there will be 30 billion
devices by 2020. The global market value of IoT is projected to reach
\$7.1 trillion by 2020 (Fig. {\ref{839566}}).

\par\null\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure2/Figure2}
\caption{{Technology roadmap: Internet of things.
{\label{839566}}%
}}
\end{center}
\end{figure}

\emph{Intelligence}

Ambient intelligence and autonomous control are not part of the original
concept of the Internet of things. Ambient intelligence and autonomous
control do not necessarily require Internet structures, either. However,
there is a shift in research (by companies such as Intel) to integrate
the concepts of IoT and autonomous control, with initial outcomes
towards this direction considering objects as the driving force for
autonomous IoT.

In the future, the Internet of Things may be a non-deterministic and
open network in which auto-organized or intelligent entities (web
services, SOA components) and virtual objects (avatars) will be
interoperable and able to act independently (pursuing their own
objectives or shared ones) depending on the context, circumstances or
environments. Autonomous behavior through the collection and reasoning
of context information as well as the object's ability to detect changes
in the environment (faults affecting sensors) and introduce suitable
mitigation measures constitutes a major research trend, clearly needed
to provide credibility to the IoT technology. Modern IoT products and
solutions in the marketplace use a variety of different technologies to
support such context-aware automation, but more sophisticated forms of
intelligence are requested to permit sensor units and intelligent
cyber-physical systems to be deployed in real environments.

\emph{Architecture}

Tier 1 of the IIoT architecture consists of networked things, typically
sensors and actuators, from the IIoT equipment, which use protocols such
as Modbus, Zigbee, or proprietary protocols, to connect to an Edge
Gateway. Tier 2 includes sensor data aggregation systems called Edge
Gateways that provide functionality, such as pre-processing of the data,
securing connectivity to cloud, using systems such as WebSockets, the
event hub, and, even in some cases, edge analytics or fog computing.
Tier 3 includes the cloud application built for IIoT using the
microservices architecture, which are usually polyglot and inherently
secure in nature using HTTPS/OAuth. Tier 3 also includes storage of
sensor data using various database systems, such as time series
databases or asset stores using backend data storage systems such as
Cassandra or Postgres. In addition to the data storage, we analyze the
data using various analytics, predictive or threshold-based or
regression-based, to get more insights on the IIoT equipment.

Building on the Internet of things, the web of things is an architecture
for the application layer of the Internet of things looking at the
convergence of data from IoT devices into Web applications to create
innovative

\par\null

use-cases. In order to program and control the flow of information in
the Internet of things, a predicted architectural direction is being
called BPM Everywhere which is a blending of traditional process
management with process mining and special capabilities to automate the
control of large numbers of coordinated devices.

The Internet of things requires huge scalability in the network space to
handle the surge of devices. IETF 6LoWPAN would be used to connect
devices to IP networks. With billions of devices being added to the
Internet space, IPv6 will play a major role in handling the network
layer scalability. IETF's Constrained Application Protocol, ZeroMQ, and
MQTT would provide lightweight data transport.

\emph{Complexity}

In semi-open or closed loops (i.e.~value chains, whenever a global
finality can be settled) IoT will often be considered and studied as a
complex system due to the huge number of different links, interactions
between autonomous actors, and its capacity to integrate new actors. At
the overall stage (full open loop) it will likely be seen as a chaotic
environment (since systems always have finality). As a practical
approach, not all elements in the Internet of things run in a global,
public space. Subsystems are often implemented to mitigate the risks of
privacy, control and reliability. For example, domestic robotics
(domotics) running inside a smart home might only share data within and
be available via a local network. Managing and controlling high dynamic
ad hoc IoT things/devices network is a tough task with the traditional
networks architecture, Software Defined Networking (SDN) provides the
agile dynamic solution that can cope with the special requirements of
the diversity of innovative IoT applications.

\emph{Size considerations}

The Internet of things would encode 50 to 100 trillion objects, and be
able to follow the movement of those objects. Human beings in surveyed
urban environments are each surrounded by 1000 to 5000 trackable
objects. In 2015 there were already 83 million smart devices in people`s
homes. This number is about to grow up to 193 million devices in 2020
and will for sure go on growing in the near future. The figure of online
capable devices grew 31\% from 2016 to 8.4 billion in 2017. Space
considerations In the Internet of things, the precise geographic
location of a thing---and also the precise geographic dimensions of a
thing---will be critical. Therefore, facts about a thing, such as its
location in time and space, have been less critical to track because the
person processing the information can decide whether or not that
information was important to the action being taken, and if so, add the
missing information (or decide to not take the action). (Note that some
things in the Internet of things will be sensors, and sensor location is
usually important.) The GeoWeb and Digital Earth are promising
applications that become possible when things can become organized and
connected by location. However, the challenges that remain include the
constraints of variable spatial scales, the need to handle massive
amounts of data, and an indexing for fast search and neighbor
operations. In the Internet of things, if things are able to take
actions on their own initiative, this human-centric mediation role is
eliminated. Thus, the time-space context that we as humans take for
granted must be given a central role in this information ecosystem. Just
as standards play a key role in the Internet and the Web, geospatial
standards will play a key role in the Internet of things.

\emph{A solution to ``basket of remotes''}

Many IoT devices have a potential to take a piece of this market.
Jean-Louis Gass\selectlanguage{ngerman}ée (Apple initial alumni team, and BeOS co-founder) has
addressed this topic in an article on Monday Note, where he predicts
that the most likely problem will be what he calls the ``basket of
remotes'' problem, where we'll have hundreds of applications to
interface with hundreds of devices that don't share protocols for
speaking with one another. For improved user interaction, some
technology leaders are joining forces to create standards for
communication between devices to solve this problem. Others are turning
to the concept of predictive interaction of devices, ``where collected
data is used to predict and trigger actions on the specific devices''
while making them work together.

\subsubsection*{Frameworks}

{\label{771478}}

IoT frameworks might help support the interaction between ``things'' and
allow for more complex structures like distributed computing and the
development of distributed applications. Currently, some IoT frameworks
seem to focus on real-time data logging solutions, offering some basis
to work with many ``things'' and have them interact. Future developments
might lead to specific software-development environments to create the
software to work with the hardware used in the Internet of things.
Companies are developing technology platforms to provide this type of
functionality for the Internet of things. Newer platforms are being
developed, which add more intelligence.

REST is a scalable architecture that allows things to communicate over
Hypertext Transfer Protocol and is easily adopted for IoT applications
to provide communication from a thing to a central web server.

\subsection*{3D printing}

{\label{841944}}

\subsubsection*{Outline}

{\label{537024}}

3D printing is any of various processes in which material is joined or
solidified under computer control to create a three-dimensional object,
with material being added together (such as liquid molecules or powder
grains being fused together). 3D printing is used in both rapid
prototyping and additive manufacturing. Objects can be of almost any
shape or geometry and typically are produced using digital model data
from a 3D model or another electronic data source such as an Additive
Manufacturing File (AMF) file (usually in sequential layers). There are
many different technologies, like stereolithography (SLA) or fused
deposit modeling (FDM). Thus, unlike material removed from a stock in
the conventional machining process, 3D printing or Additive
Manufacturing builds a three-dimensional object from a computer-aided
design (CAD) model or AMF file, usually by successively adding material
layer by layer.

The term ``3D printing'' originally referred to a process that deposits
a binder material onto a powder bed with inkjet printer heads layer by
layer. More recently, the term is being used in popular vernacular to
encompass a wider variety of additive manufacturing techniques. United
States and global technical standards use the official term additive
manufacturing for this broader sense (Fig.
{\ref{749370}}).

\subsubsection*{Terminology}

{\label{168498}}

The umbrella term additive manufacturing (AM) gained wide currency in
the 2000s, inspired by the theme of material being added together (in
any of various ways). In contrast, the term subtractive manufacturing
appeared as a retronym for the large family of machining processes with
material removal as their common theme. The term 3D printing still
referred only to the polymer technologies in most minds, and the term AM
was likelier to be used in metalworking and end use part production
contexts than among polymer, inkjet, or stereolithography enthusiasts.

By the early 2010s, the terms 3D printing and additive manufacturing
evolved senses in which they were alternate umbrella terms for additive
technologies, one being used in popular vernacular by consumer-maker
communities and the media, and the other used more formally by
industrial end-use part producers, machine manufacturers, and global
technical standards organizations. Until recently, the term 3D printing
has been associated with machines low-end in price or in capability.
Both terms reflect that the technologies share the theme of material
addition or joining throughout a 3D work envelope under automated
control. Peter Zelinski, the editor-in-chief of Additive Manufacturing
magazine, pointed out in 2017 that the terms are still often synonymous
in casual usage but that some manufacturing industry experts are
increasingly making a sense distinction whereby Additive Manufacturing
comprises 3D printing plus other technologies or other aspects of a
manufacturing process.

\par\null\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure3/Figure3}
\caption{{A makerBot three-dimensional printer.
{\label{749370}}%
}}
\end{center}
\end{figure}

Other terms that have been used as synonyms or hypernyms have included
desktop manufacturing, rapid manufacturing (as the logical
production-level successor to rapid prototyping), and on-demand
manufacturing (which echoes on-demand printing in the 2D sense of
printing). That such application of the adjectives rapid and on-demand
to the noun manufacturing was novel in the 2000s reveals the prevailing
mental model of the long industrial era in which almost all production
manufacturing involved long lead times for laborious tooling
development. Today, the term subtractive has not replaced the term
machining, instead complementing it when a term that covers any removal
method is needed. Agile tooling is the use of modular means to design

tooling that is produced by additive manufacturing or 3D printing
methods to enable quick prototyping and responses to tooling and fixture
needs. Agile tooling uses a cost effective and high quality method to
quickly respond to customer and market needs, and it can be used in
hydro-forming, stamping, injection molding and other manufacturing
processes.

\subsection*{History}

{\label{148293}}

1981

Early additive manufacturing equipment and materials were developed in
the 1980s. In 1981, Hideo Kodama of Nagoya Municipal Industrial Research
Institute invented two additive methods for fabricating
three-dimensional plastic models with photo-hardening thermoset polymer,
where the UV exposure area is controlled by a mask pattern or a scanning
fiber transmitter.

1984

On 16 July 1984, Alain Le M\selectlanguage{ngerman}éhauté, Olivier de Witte, and Jean Claude
André filed their patent for the stereolithography process. The
application of the French inventors was abandoned by the French General
Electric Company (now Alcatel-Alsthom) and CILAS (The Laser Consortium).
The claimed reason was ``for lack of business perspective''.

Three weeks later in 1984, Chuck Hull of 3D Systems Corporation filed
his own patent for a stereolithography fabrication system, in which
layers are added by curing photopolymers with ultraviolet light lasers.
Hull defined the process as a ``system for generating three-dimensional
objects by creating a cross-sectional pattern of the object to be
formed,''. Hull's contribution was the STL (Stereolithography) file
format and the digital slicing and infill strategies common to many
processes today.

1988

The technology used by most 3D printers to date---especially hobbyist
and consumer-oriented models---is fused deposition modeling, a special
application of plastic extrusion, developed in 1988 by S. Scott Crump
and commercialized by his company Stratasys, which marketed its first
FDM machine in 1992.

AM processes for metal sintering or melting (such as selective laser
sintering, direct metal laser sintering, and selective laser melting)
usually went by their own individual names in the 1980s and 1990s. At
the time, all metalworking was done by processes that we now call
non-additive (casting, fabrication, stamping, and machining); although
plenty of automation was applied to those technologies (such as by robot
welding and CNC), the idea of a tool or head moving through a 3D work
envelope transforming a mass of raw material into a desired shape with a
toolpath was associated in metalworking only with processes that removed
metal (rather than adding it), such as CNC milling, CNC EDM, and many
others. But the automated techniques that added metal, which would later
be called additive manufacturing, were beginning to challenge that
assumption. By the mid-1990s, new techniques for material deposition
were developed at Stanford and Carnegie Mellon University, including
microcasting and sprayed materials. Sacrificial and support materials
had also become more common, enabling new object geometries.

1993

The term 3D printing originally referred to a powder bed process
employing standard and custom inkjet print heads, developed at MIT in
1993 and commercialized by Soligen Technologies, Extrude Hone
Corporation, and Z Corporation.

The year 1993 also saw the start of a company called Solidscape,
introducing a high-precision polymer jet fabrication system with soluble
support structures, (categorized as a ``dot-on-dot'' technique).

1995

In 1995 the Fraunhofer Institute developed the selective laser melting
process.

2009

Fused Deposition Modeling (FDM) printing process patents expired in
2009. As the various additive processes matured, it became clear that
soon metal removal would no longer be the only metalworking process done
through a tool or head moving through a 3D work envelope transforming a
mass of raw material into a desired shape layer by layer. The 2010s were
the first decade in which metal end use parts such as engine brackets
and large nuts would be grown (either before or instead of machining) in
job production rather than obligately being machined from bar stock or
plate. It is still the case that casting, fabrication, stamping, and
machining are more prevalent than additive manufacturing in
metalworking, but AM is now beginning to make significant inroads, and
with the advantages of design for additive manufacturing, it is clear to
engineers that much more is to come.

As technology matured, several authors had begun to speculate that 3D
printing could aid in sustainable development in the developing world.

2013

NASA employees Samantha Snabes and Matthew Fiedler create first
prototype of large-format, affordable 3D printer, Gigabot, and launch 3D
printing company re:3D.

2018

re:3D develops a system that uses plastic pellets that can be made by
grinding up waste plastic.

\subsubsection*{General principles}

{\label{880688}}

\emph{Modeling}

3D printable models may be created with a computer-aided design (CAD)
package, via a 3D scanner, or by a plain digital camera and
photogrammetry software. 3D printed models created with CAD result in
reduced errors and can be corrected before printing, allowing
verification in the design of the object before it is printed. The
manual modeling process of preparing geometric data for 3D computer
graphics is similar to plastic arts such as sculpting. 3D scanning is a
process of collecting digital data on the shape and appearance of a real
object, creating a digital model based on it.

\emph{Printing}

Before printing a 3D model from an STL file, it must first be examined
for errors. Most CAD applications (Fig. {\ref{789801}})
produce errors in output STL files of the following types:

\begin{itemize}
\tightlist
\item
  holes,
\item
  faces normal,
\item
  self-intersections,
\item
  noise shells,
\item
  manifold errors.
\end{itemize}

A step in the STL generation known as ``repair'' fixes such problems in
the original model. Generally STLs that have been produced from a model
obtained through 3D scanning often have more of these errors. This is
due to how 3D scanning works-as it is often by point to point
acquisition, reconstruction will include errors in most cases.

Once completed, the STL file needs to be processed by a piece of
software called a ``slicer,'' which converts the model into a series of
thin layers and produces a G-code file containing instructions tailored
to a specific type of 3D printer (FDM printers). This G-code file can
then be printed with 3D printing client software (which loads the
G-code, and uses it to instruct the 3D printer during the 3D printing
process).

Printer resolution describes layer thickness and X--Y resolution in dots
per inch (dpi) or micrometers (\selectlanguage{greek}µ\selectlanguage{english}m). Typical layer thickness is around
100 \selectlanguage{greek}µ\selectlanguage{english}m (250 DPI), although some machines can print layers as thin as 16\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure4/Figure4}
\caption{{CAD model used for 3D printing.
{\label{789801}}%
}}
\end{center}
\end{figure}

\selectlanguage{greek}µ\selectlanguage{english}m (1,600 DPI). X--Y resolution is comparable to that of laser printers.
The particles (3D dots) are around 50 to 100 \selectlanguage{greek}µ\selectlanguage{english}m (510 to 250 DPI) in
diameter. For that printer resolution, specifying a mesh resolution of
0.01--0.03 mm and a chord length ? 0.016 mm generate an optimal STL
output file for a given model input file. Specifying higher resolution
results in larger files without increase in print quality.

Construction of a model with contemporary methods can take anywhere from
several hours to several days, depending on the method used and the size
and complexity of the model. Additive systems can typically reduce this
time to a few hours, although it varies widely depending on the type of
machine used and the size and number of models being produced
simultaneously.

Traditional techniques like injection moulding can be less expensive for
manufacturing polymer products in high quantities, but additive
manufacturing can be faster, more flexible and less expensive when
producing relatively small quantities of parts. 3D printers give
designers and concept development teams the ability to produce parts and
concept models using a desktop size printer.

Seemingly paradoxic, more complex objects can be cheaper for 3D printing
production than less complex objects.

\emph{Finishing}

Though the printer-produced resolution is sufficient for many
applications, printing a slightly oversized version of the desired
object in standard resolution and then removing material with a
higher-resolution subtractive process can achieve greater precision.

The layered structure of all Additive Manufacturing processes leads
inevitably to a strain-stepping effect on part surfaces which are curved
or tilted in respect to the building platform. The effects strongly
depend on the orientation of a part surface inside the building process.

Some printable polymers such as ABS, allow the surface finish to be
smoothed and improved using chemical vapor processes based on acetone or
similar solvents.

Some additive manufacturing techniques are capable of using multiple
materials in the course of constructing parts. These techniques are able
to print in multiple colors and color combinations simultaneously, and
would

not necessarily require painting.

Some printing techniques require internal supports to be built for
overhanging features during construction. These supports must be
mechanically removed or dissolved upon completion of the print.

All of the commercialized metal 3D printers involve cutting the metal
component off the metal substrate after deposition. A new process for
the GMAW 3D printing allows for substrate surface modifications to
remove aluminum or steel.

\subsubsection*{}

{\label{769451}}

\subsubsection*{Processes and printers}

{\label{423593}}

A large number of additive processes are available. The main differences
between processes are in the way layers are deposited to create parts
and in the materials that are used. Each method has its own advantages
and drawbacks, which is why some companies offer a choice of powder and
polymer for the material used to build the object. Others sometimes use
standard, off-the-shelf business paper as the build material to produce
a durable prototype. The main considerations in choosing a machine are
generally speed, costs of the 3D printer, of the printed prototype,
choice and cost of the materials, and color capabilities. Printers that
work directly with metals are generally expensive. However less
expensive printers can be used to make a mold, which is then used to
make metal parts.

ISO/ASTM52900-15 defines seven categories of Additive Manufacturing (AM)
processes within its meaning: binder jetting, directed energy
deposition, material extrusion, material jetting, powder bed fusion,
sheet lamination, and vat photopolymerization.

Some methods melt or soften the material to produce the layers. In Fused
filament fabrication, also known as Fused deposition modeling (FDM), the
model or part is produced by extruding small beads or streams of
material which harden immediately to form layers. A filament of
thermoplastic, metal wire, or other material is fed into an extrusion
nozzle head (3D printer extruder), which heats the material and turns
the flow on and off. FDM is somewhat restricted in the variation of
shapes that may be fabricated. Another technique fuses parts of the
layer and then moves upward in the working area, adding another layer of
granules and repeating the process until the piece has built up. This
process uses the unfused media to support overhangs and thin walls in
the part being produced, which reduces the need for temporary auxiliary
supports for the piece.

Laser sintering techniques include selective laser sintering, with both
metals and polymers, and direct metal laser sintering. Selective laser
melting does not use sintering for the fusion of powder granules but
will completely melt the powder using a high-energy laser to create
fully dense materials in a layer-wise method that has mechanical
properties similar to those of conventional manufactured metals.
Electron beam melting is a similar type of additive manufacturing
technology for metal parts (e.g.~titanium alloys). EBM manufactures
parts by melting metal powder layer by layer with an electron beam in a
high vacuum. Another method consists of an inkjet 3D printing system,
which creates the model one layer at a time by spreading a layer of
powder (plaster, or resins) and printing a binder in the cross-section
of the part using an inkjet-like process. With laminated object
manufacturing, thin layers are cut to shape and joined together.

Schematic representation of Stereolithography; a light-emitting device
a) (laser or DLP) selectively illuminate the transparent bottom c) of a
tank b) filled with a liquid photo-polymerizing resin; the solidified
resin d) is progressively dragged up by a lifting platform e)

Other methods cure liquid materials using different sophisticated
technologies, such as stereolithography. Photopolymerization is
primarily used in stereolithography to produce a solid part from a
liquid. Inkjet printer systems like the Objet PolyJet system spray
photopolymer materials onto a build tray in ultra-thin layers (between
16 and 30 \selectlanguage{greek}µ\selectlanguage{english}m) until the part is completed. Each photopolymer layer is
cured with UV light after it is jetted, producing fully cured models
that can be handled and used immediately, without post-curing.
Ultra-small features can be made with the 3D micro-fabrication technique
used in multiphoton photopolymerisation. Due to the nonlinear nature of
photo excitation, the gel is cured to a solid only in

the places where the laser was focused while the remaining gel is then
washed away. Feature sizes of under 100 nm are easily produced, as well
as complex structures with moving and interlocked parts. Yet another
approach uses a synthetic resin that is solidified using LEDs.

In Mask-image-projection-based stereolithography, a 3D digital model is
sliced by a set of horizontal planes. Each slice is converted into a
two-dimensional mask image. The mask image is then projected onto a
photocurable liquid resin surface and light is projected onto the resin
to cure it in the shape of the layer. Continuous liquid interface
production begins with a pool of liquid photopolymer resin. Part of the
pool bottom is transparent to ultraviolet light (the ``window''), which
causes the resin to solidify. The object rises slowly enough to allow
resin to flow under and maintain contact with the bottom of the object.
In powder-fed directed-energy deposition, a high-power laser is used to
melt metal powder supplied to the focus of the laser beam. The powder
fed directed energy process is similar to Selective Laser Sintering, but
the metal powder is applied only where material is being added to the
part at that moment.

As of December 2017, additive manufacturing systems were on the market
that ranged from \$99 to \$500,000 in price and were employed in
industries including aerospace, architecture, automotive, defense, and
medical replacements, among many others. For example, General Electric
uses the high-end model to build parts for turbines. Many of these
systems are used for rapid prototyping, before mass production methods
are employed. Higher education has proven to be a major buyer of desktop
and professional 3D printers which industry experts generally view as a
positive indicator. Libraries around the world have also become
locations to house smaller 3D printers for educational and community
access. Several projects and companies are making efforts to develop
affordable 3D printers for home desktop use. Much of this work has been
driven by and targeted at DIY/Maker/enthusiast/early adopter
communities, with additional ties to the academic and hacker
communities.

\subsubsection*{}

{\label{100007}}

\subsubsection*{Applications}

{\label{100007}}

In the current scenario, 3D printing or Additive Manufacturing has been
used in manufacturing, medical, industry and sociocultural sectors which
facilitate 3D printing or Additive Manufacturing to become successful
commercial technology(Fig. {\ref{390801}}). The
earliest application of additive manufacturing was on the toolroom end
of the manufacturing spectrum. For example, rapid prototyping was one of
the earliest additive variants, and its mission was to reduce the lead
time and cost of developing prototypes of new parts and devices, which
was earlier only done with subtractive toolroom methods such as CNC
milling, turning, and precision grinding. In the 2010s, additive
manufacturing entered production to a much greater extent.

Additive manufacturing of food is being developed by squeezing out food,
layer by layer, into three-dimensional objects. A large variety of foods
are appropriate candidates, such as chocolate and candy, and flat foods
such as crackers, pasta, and pizza.

A 3D selfie in 1:20 scale printed by Shapeways using gypsum-based
printing.

3D printing has entered the world of clothing, with fashion designers
experimenting with 3D-printed bikinis, shoes, and dresses. In commercial
production Nike is using 3D printing to prototype and manufacture the
2012 Vapor Laser Talon football shoe for players of American football,
and New Balance is 3D manufacturing custom-fit shoes for athletes. 3D
printing has come to the point where companies are printing consumer
grade eyewear with on-demand custom fit and styling (although they
cannot print the lenses). On-demand customization of glasses is possible
with rapid prototyping.

A Jet Engine turbine printed from the Howard Community College Makerbot.

Vanessa Friedman, fashion director and chief fashion critic at The New
York Times, says 3D printing will have a significant value for fashion
companies down the road, especially if it transforms into a
print-it-yourself tool for shoppers. ``There's real sense that this is
not going to happen anytime soon,'' she says, ``but it will happen, and
it will create dramatic change in how we think both about intellectual
property and how things are in the supply chain.'' She adds: ``Certainly
some of the fabrications that brands can use will be dramatically
changed by technology.''

In cars, trucks, and aircraft (Fig. {\ref{301639}}),
Additive Manufacturing is beginning to transform both (1) unibody\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure5/Figure5}
\caption{{A 3D selfie in 1:20 scale printed by Shapeways using gypsum-bades
printing.
{\label{390801}}%
}}
\end{center}
\end{figure}

and fuselage design and production and (2) powertrain design and
production. For example:

\begin{itemize}
\tightlist
\item
  in early 2014, Swedish supercar manufacturer Koenigsegg announced the
  One:1, a supercar that utilizes many components that were 3D printed.
  Urbee is the name of the first car in the world car mounted using the
  technology 3D printing (its bodywork and car windows were
  ``printed''),
\item
  in 2014, Local Motors debuted Strati, a functioning vehicle that was
  entirely 3D Printed using ABS plastic and carbon fiber, except the
  powertrain. In May 2015 Airbus announced that its new Airbus A350 XWB
  included over 1000 components manufactured by 3D printing,
\item
  in 2015, a Royal Air Force Eurofighter Typhoon fighter jet flew with
  printed parts. The United States Air Force has begun to work with 3D
  printers, and the Israeli Air Force has also purchased a 3D printer to
  print spare parts,
\item
  in 2017, GE Aviation revealed that it had used design for additive
  manufacturing to create a helicopter
\end{itemize}

\par\null\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure6/Figure6}
\caption{{A Jet Engine turbine printed from the Howard Community College Makerbot.
{\label{301639}}%
}}
\end{center}
\end{figure}

\begin{quote}
engine with 16 parts instead of 900, with great potential impact on
reducing the complexity of supply chains.
\end{quote}

AM's impact on firearms involves two dimensions: new manufacturing
methods for established companies, and new possibilities for the making
of do-it-yourself firearms. In 2012, the US-based group Defense
Distributed disclosed plans to design a working plastic 3D printed
firearm ``that could be downloaded and reproduced by anybody with a 3D
printer.'' After Defense Distributed released their plans, questions
were raised regarding the effects that 3D printing and widespread
consumer-level CNC machining may have on gun control effectiveness.

Surgical uses of 3D printing-centric therapies have a history beginning
in the mid-1990s with anatomical modeling for bony reconstructive
surgery planning. Patient-matched implants were a natural extension of
this work, leading to truly personalized implants that fit one unique
individual. Virtual planning of surgery and guidance using 3D printed,
personalized instruments have been applied to many areas of surgery
including total joint replacement and craniomaxillofacial reconstruction
with great success. One example of this is the bioresorbable trachial
splint to treat newborns with tracheobronchomalacia developed at the
University of Michigan. The use of additive manufacturing for serialized
production of orthopedic implants (metals) is also increasing due to the
ability to efficiently create porous surface structures that facilitate
osseointegration. The hearing aid and dental industries are expected to
be the biggest area of future development using the custom 3D printing
technology.

In March 2014, surgeons in Swansea used 3D printed parts to rebuild the
face of a motorcyclist who had been seriously injured in a road
accident. In May 2018, 3D printing has been used for the kidney
transplant to save a three-year-old boy. As of 2012, 3D bio-printing
technology has been studied by biotechnology firms and academia for
possible use in tissue engineering applications in which organs and body
parts are built using inkjet printing techniques. In this process,
layers of living cells are deposited onto a gel medium or sugar matrix
and slowly built up to form three-dimensional structures including
vascular systems. Recently, a heart-on-chip has been created which
matches properties of cells.

In 2018, 3D printing technology was used for the first time to create a
matrix for cell immobilization in fermentation. Propionic acid
production by Propionibacterium acidipropionici immobilized on
3D-printed nylon beads was chosen as a model study. It was shown that
those 3D-printed beads were capable to promote high density cell
attachment and propionic acid production, which could be adapted to
other fermentation bioprocesses.

In 2005, academic journals had begun to report on the possible artistic
applications of 3D printing technology. As of 2017, domestic 3D printing
was reaching a consumer audience beyond hobbyists and enthusiasts. Off
the shelf machines were increasingly capable of producing practical
household applications, for example, ornamental objects. Some practical
examples include a working clock and gears printed for home woodworking
machines among other purposes. Web sites associated with home 3D
printing tended to include backscratchers, coat hooks, door knobs, etc.

3D printing, and open source 3D printers in particular, are the latest
technology making inroads into the classroom. Some authors have claimed
that 3D printers offer an unprecedented ``revolution'' in STEM
education. The evidence for such claims comes from both the low cost
ability for rapid prototyping in the classroom by students, but also the
fabrication of low-cost high-quality scientific equipment from open
hardware designs forming open-source labs. Future applications for 3D
printing might include creating open-source scientific equipment.

In the last several years 3D printing has been intensively used by in
the cultural heritage field for preservation, restoration and
dissemination purposes. Many Europeans and North American Museums have
purchased 3D printers and actively recreate missing pieces of their
relics. The Metropolitan Museum of Art and the British Museum have
started using their 3D printers to create museum souvenirs that are
available in the museum shops. Other museums, like the National Museum
of Military History and Varna Historical Museum, have gone further and
sell through the online platform Threeding digital models of their
artifacts, created using Artec 3D scanners, in 3D printing friendly file
format, which everyone can 3D print at home.

3D printed soft actuators is a growing application of 3D printing
technology which has found its place in the 3D printing applications.
These soft actuators are being developed to deal with soft structures
and organs especially in biomedical sectors and where the interaction
between human and robot is inevitable. The majority of the existing soft
actuators are fabricated by conventional methods that require manual
fabrication of devices, post processing/assembly, and lengthy iterations
until maturity in the fabrication is achieved. To avoid the tedious and
time-consuming aspects of the current fabrication processes, researchers
are exploring an appropriate manufacturing approach for effective
fabrication of soft actuators. Thus, 3D printed soft actuators are
introduced to revolutionize the design and fabrication of soft actuators
with custom geometrical, functional, and control properties in a faster
and inexpensive approach. They also enable incorporation of all actuator
components into a single structure eliminating the need to use external
joints, adhesives, and fasteners.

\subsection*{}

{\label{501258}}

\subsection*{Legal aspects}

{\label{501258}}

\subsubsection*{Intellectual property}

{\label{629480}}

3D printing has existed for decades within certain manufacturing
industries where many legal regimes, including patents, industrial
design rights, copyright, and trademark may apply. However, there is not
much jurisprudence to say how these laws will apply if 3D printers
become mainstream and individuals or hobbyist communities begin
manufacturing items for personal use, for non-profit distribution, or
for sale.

Any of the mentioned legal regimes may prohibit the distribution of the
designs used in 3D printing, or the distribution or sale of the printed
item. To be allowed to do these things, where an active intellectual
property was involved, a person would have to contact the owner and ask
for a license, which may come with conditions and a price. However, many
patent, design and copyright laws contain a standard limitation or
exception for `private', `non-commercial' use of inventions, designs or
works of art protected under intellectual property (IP). That standard
limitation or exception may leave such private, non-commercial uses
outside the scope of IP rights.

Patents cover inventions including processes, machines, manufactures,
and compositions of matter and have a finite duration which varies
between countries, but generally 20 years from the date of application.
Therefore, if a type of wheel is patented, printing, using, or selling
such a wheel could be an infringement of the patent.

Copyright covers an expression in a tangible, fixed medium and often
lasts for the life of the author plus 70 years thereafter. If someone
makes a statue, they may have copyright on the look of that statue, so
if someone sees that statue, they cannot then distribute designs to
print an identical or similar statue.

When a feature has both artistic (copyrightable) and functional
(patentable) merits, when the question has appeared in US court, the
courts have often held the feature is not copyrightable unless it can be
separated from the functional aspects of the item. In other countries
the law and the courts may apply a different approach allowing, for
example, the design of a useful device to be registered (as a whole) as
an industrial design on the understanding that, in case of unauthorized
copying, only the non-functional features may be claimed under design
law whereas any technical features could only be claimed if covered by a
valid patent.

\subsubsection*{Gun legislation and
administration}

{\label{518955}}

The US Department of Homeland Security and the Joint Regional
Intelligence Center released a memo stating that ``significant advances
in three-dimensional (3D) printing capabilities, availability of free
digital 3D printable files for firearms components, and difficulty
regulating file sharing may present public safety risks from unqualified
gun seekers who obtain or manufacture 3D printed guns'' and that
``proposed legislation to ban 3D printing of weapons may deter, but
cannot completely prevent, their production. Even if the practice is
prohibited by new legislation, online distribution of these 3D printable
files will be as difficult to control as any other illegally traded
music, movie or software files.''

Attempting to restrict the distribution of gun plans via the Internet
has been likened to the futility of preventing the widespread
distribution of DeCSS, which enabled DVD ripping. After the US
government had Defense Distributed take down the plans, they were still
widely available via the Pirate Bay and other file sharing sites.
Downloads of the plans from the UK, Germany, Spain, and Brazil were
heavy. Some US legislators have proposed regulations on 3D printers to
prevent them from being used for printing guns. 3D printing advocates
have suggested that such regulations would be futile, could cripple the
3D printing industry, and could infringe on free speech rights, with
early pioneer of 3D printing Professor Hod Lipson suggesting that
gunpowder could be controlled instead.

Internationally, where gun controls are generally stricter than in the
United States, some commentators have said the impact may be more
strongly felt since alternative firearms are not as easily obtainable.
Officials in the United Kingdom have noted that producing a 3D printed
gun would be illegal under their gun control laws. Europol stated that
criminals have access to other sources of weapons but noted that as
technology improves, the risks of an effect would increase.

\subsubsection*{Aerospace regulation}

{\label{479613}}

In the United States, the FAA has anticipated a desire to use additive
manufacturing techniques and has been considering how best to regulate
this process. The FAA has jurisdiction over such fabrication because all
aircraft parts must be made under FAA production approval or under other
FAA regulatory categories. In December 2016, the FAA approved the
production of a 3D printed fuel nozzle for the GE LEAP engine. Aviation
attorney Jason Dickstein has suggested that additive manufacturing is
merely a production method, and should be regulated like any other
production method. He has suggested that the FAA's focus should be on
guidance to explain compliance, rather than on changing the existing
rules, and that existing regulations and guidance permit a company ``to
develop a robust quality system that adequately reflects regulatory
needs for quality assurance.''

\subsubsection*{Health and safety}

{\label{363252}}

Research on the health and safety concerns of 3D printing is new and in
development due to the recent proliferation of 3D printing devices. In
2017 the European Agency for Safety and Health at Work has published a
discussion paper on the processes and materials involved in 3D printing,
potential implications of this technology for occupational safety and
health and avenues for controlling potential hazards. Most

concerns involve gas and material exposures, in particular
nanomaterials, material handling, static electricity, moving parts and
pressures.

A National Institute for Occupational Safety and Health (NIOSH) study
noted particle emissions from a fused filament peaked a few minutes
after printing started and returned to baseline levels 100 minutes after
printing ended. Emissions from fused filament printers can include a
large number of ultrafine particles and volatile organic compounds
(VOCs).

The toxicity from emissions varies by source material due to differences
in size, chemical properties, and quantity of emitted particles.
Excessive exposure to VOCs can lead to irritation of the eyes, nose, and
throat, headache, loss of coordination, and nausea and some of the
chemical emissions of fused filament printers have also been linked to
asthma. Based on animal studies, carbon nanotubes and carbon nanofibers
sometimes used in fused filament printing can cause pulmonary effects
including inflammation, granulomas, and pulmonary fibrosis when at the
nanoparticle size.

As of March 2018, the US Government has set 3D printer emission
standards for only a limited number of compounds. Furthermore, the few
established standards address factory conditions, not home or other
environments in which the printers are likely to be used.

Carbon nanoparticle emissions and processes using powder metals are
highly combustible and raise the risk of dust explosions. At least one
case of severe injury was noted from an explosion involved in metal
powders used for fused filament printing. Other general health and
safety concerns include the hot surface of UV lamps and print head
blocks, high voltage, ultraviolet radiation from UV lamps, and potential
for mechanical injury from moving parts.

The problems noted in the NIOSH report were reduced by using
manufacturer-supplied covers and full enclosures, using proper
ventilation, keeping workers away from the printer, using respirators,
turning off the printer if it jammed, and using lower emission printers
and filaments. At least one case of severe injury was noted from an
explosion involved in metal powders used for fused filament. Personal
protective equipment has been found to be the least desirable control
method with a recommendation that it only be used to add further
protection in combination with approved emissions protection.

Hazards to health and safety also exist from post-processing activities
done to finish parts after they have been printed. These post-processing
activities can include chemical baths, sanding, polishing, or vapor
exposure to refine surface finish, as well as general subtractive
manufacturing techniques such as drilling, milling, or turning to modify
the printed geometry. Any technique that removes material from the
printed part has the potential to generate particles that can be inhaled
or cause eye injury if proper personal protective equipment is not used,
such as respirators or safety glasses. Caustic baths are often used to
dissolve support material used by some 3D printers that allows them to
print more complex shapes. These baths require personal protective
equipment to prevent injury to exposed skin.

Although no occupational exposure limits specific to 3D printer
emissions exist, certain source materials used in 3D printing, such as
carbon nanofiber and carbon nanotubes, have established occupational
exposure limits at the nanoparticle size.

Since 3-D imaging creates items by fusing materials together, there runs
the risk of layer separation in some devices made using 3-D Imaging. For
example, in January 2013, the US medical device company, DePuy, recalled
their knee and hip replacement systems. The devices were made from
layers of metal, and shavings had come loose -- potentially harming the
patient.

\subsubsection*{Impact}

{\label{963932}}

Additive manufacturing, starting with today's infancy period, requires
manufacturing firms to be flexible, ever-improving users of all
available technologies to remain competitive. Advocates of additive
manufacturing also predict that this arc of technological development
will counter globalization, as end users will do much

of their own manufacturing rather than engage in trade to buy products
from other people and corporations. The real integration of the newer
additive technologies into commercial production, however, is more a
matter of complementing traditional subtractive methods rather than
displacing them entirely.

The futurologist Jeremy Rifkin claimed that 3D printing signals the
beginning of a third industrial revolution, succeeding the production
line assembly that dominated manufacturing starting in the late 19th
century.

Since the 1950s, a number of writers and social commentators have
speculated in some depth about the social and cultural changes that
might result from the advent of commercially affordable additive
manufacturing technology. Amongst the more notable ideas to have emerged
from these inquiries has been the suggestion that, as more and more 3D
printers start to enter people's homes, the conventional relationship
between the home and the workplace might get further eroded. Likewise,
it has also been suggested that, as it becomes easier for businesses to
transmit designs for new objects around the globe, so the need for
high-speed freight services might also become less. Finally, given the
ease with which certain objects can now be replicated, it remains to be
seen whether changes will be made to current copyright legislation so as
to protect intellectual property rights with the new technology widely
available.

As 3D printers became more accessible to consumers, online social
platforms have developed to support the community. This includes
websites that allow users to access information such as how to build a
3D printer, as well as social forums that discuss how to improve 3D
print quality and discuss 3D printing news, as well as social media
websites that are dedicated to share 3D models. RepRap is a wiki based
website that was created to hold all information on 3d printing, and has
developed into a community that aims to bring 3D printing to everyone.
Furthermore, there are other sites such as Pinshape, Thingiverse and
MyMiniFactory, which were created initially to allow users to post 3D
files for anyone to print, allowing for decreased transaction cost of
sharing 3D files. These websites have allowed greater social interaction
between users, creating communities dedicated to 3D printing.

Some call attention to the conjunction of Commons-based peer production
with 3D printing and other low-cost manufacturing techniques. The
self-reinforced fantasy of a system of eternal growth can be overcome
with the development of economies of scope, and here, society can play
an important role contributing to the raising of the whole productive
structure to a higher plateau of more sustainable and customized
productivity. Further, it is true that many issues, problems, and
threats arise due to the democratization of the means of production, and
especially regarding the physical ones. For instance, the recyclability
of advanced nanomaterials is still questioned; weapons manufacturing
could become easier; not to mention the implications for counterfeiting
and on IP. It might be maintained that in contrast to the industrial
paradigm whose competitive dynamics were about economies of scale,
Commons-based peer production 3D printing could develop economies of
scope. While the advantages of scale rest on cheap global
transportation, the economies of scope share infrastructure costs
(intangible and tangible productive resources), taking advantage of the
capabilities of the fabrication tools. And following Neil Gershenfeld in
that ``some of the least developed parts of the world need some of the
most advanced technologies,'' Commons-based peer production and 3D
printing may offer the necessary tools for thinking globally but acting
locally in response to certain needs.

Larry Summers wrote about the ``devastating consequences'' of 3D
printing and other technologies (robots, artificial intelligence, etc.)
for those who perform routine tasks. In his view, ``already there are
more American men on disability insurance than doing production work in
manufacturing. And the trends are all in the wrong direction,
particularly for the less skilled, as the capacity of capital embodying
artificial intelligence to replace white-collar as well as blue-collar
work will increase rapidly in the years ahead.'' Summers recommends more
vigorous cooperative efforts to address the ``myriad devices'' (e.g.,
tax havens, bank secrecy, money laundering, and regulatory arbitrage)
enabling the holders of great wealth to ``avoid paying'' income and
estate taxes, and to make it more difficult to accumulate great fortunes
without requiring ``great social contributions'' in return, including:
more vigorous enforcement of anti-monopoly laws, reductions in
``excessive'' protection for intellectual property, greater
encouragement of profit-sharing schemes that may benefit workers and
give them a stake in wealth accumulation, strengthening of collective
bargaining

arrangements, improvements in corporate governance, strengthening of
financial regulation to eliminate subsidies to financial activity,
easing of land-use restrictions that may cause the real estate of the
rich to keep rising in value, better training for young people and
retraining for displaced workers, and increased public and private
investment in infrastructure development---e.g., in energy production
and transportation.

Michael Spence wrote that ``Now comes a \ldots{} powerful, wave of
digital technology that is replacing labor in increasingly complex
tasks. This process of labor substitution and disintermediation has been
underway for some time in service sectors---think of ATMs, online
banking, enterprise resource planning, customer relationship management,
mobile payment systems, and much more. This revolution is spreading to
the production of goods, where robots and 3D printing are displacing
labor.'' In his view, the vast majority of the cost of digital
technologies comes at the start, in the design of hardware (e.g.~3D
printers) and, more important, in creating the software that enables
machines to carry out various tasks. ``Once this is achieved, the
marginal cost of the hardware is relatively low (and declines as scale
rises), and the marginal cost of replicating the software is essentially
zero. With a huge potential global market to amortize the upfront fixed
costs of design and testing, the incentives to invest {[}in digital
technologies{]} are compelling.''

Spence believes that, unlike prior digital technologies, which drove
firms to deploy underutilized pools of valuable labor around the world,
the motivating force in the current wave of digital technologies ``is
cost reduction via the replacement of labor.'' For example, as the cost
of 3D printing technology declines, it is ``easy to imagine'' that
production may become ``extremely'' local and customized. Moreover,
production may occur in response to actual demand, not anticipated or
forecast demand. Spence believes that labor, no matter how inexpensive,
will become a less important asset for growth and employment expansion,
with labor-intensive, process-oriented manufacturing becoming less
effective, and that re-localization will appear in both developed and
developing countries. In his view, production will not disappear, but it
will be less labor-intensive, and all countries will eventually need to
rebuild their growth models around digital technologies and the human
capital supporting their deployment and expansion. Spence writes that
``the world we are entering is one in which the most powerful global
flows will be ideas and digital capital, not goods, services, and
traditional capital. Adapting to this will require shifts in mindsets,
policies, investments (especially in human capital), and quite possibly
models of employment and distribution.''

Naomi Wu regards the usage of 3D printing in the Chinese classroom
(where rote memorization is standard) to teach design principles and
creativity as the most exciting recent development of the technology,
and more generally regards 3D printing as being the next desktop
publishing revolution.

\subsection*{}

{\label{808198}}

\subsection*{Self-driving car}

{\label{808198}}

\subsection*{Outline}

{\label{667524}}

A self-driving car (also known as an autonomous car or a driverless car)
is a vehicle that is capable of sensing its environment and moving with
little or no human input.

Autonomous cars combine a variety of sensors to perceive their
surroundings, such as radar, computer vision, Lidar, sonar, GPS,
odometry and inertial measurement units. Advanced control systems
interpret sensory information to identify appropriate navigation paths,
as well as obstacles and relevant signage.

Potential benefits include reduced costs, increased safety, increased
mobility, increased customer satisfaction and reduced crime. Safety
benefits include a reduction in traffic collisions, resulting injuries
and related costs, including for insurance. Automated cars are predicted
to increase traffic flow; provide enhanced mobility for children, the
elderly, disabled, and the poor; relieve travelers from driving and
navigation chores; lower fuel consumption; significantly reduce needs
for parking space; reduce crime; and facilitate business models for
transportation as a service, especially via the sharing economy.

Problems include safety, technology, liability, desire by individuals to
control their cars, legal framework

and government regulations; risk of loss of privacy and security
concerns, such as hackers or terrorism; concern about the resulting loss
of driving-related jobs in the road transport industry; and risk of
increased suburbanization as travel becomes more convenient.

\subsection*{History}

{\label{588086}}

General Motors' Firebird II of the 1950s was described as having an
``electronic brain'' that allowed it to move into a lane with a metal
conductor and follow it along.

\par\null\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure7/Figure7}
\caption{{Waymo Chrysler Pacifica Hybrid undergoing testing in the San Francisco
Bay Area.
{\label{848240}}%
}}
\end{center}
\end{figure}

Experiments have been conducted on automating driving since at least the
1920s; trials began in the 1950s. The first truly automated car was
developed in 1977, by Japan's Tsukuba Mechanical Engineering Laboratory.
The vehicle tracked white street markers, which were interpreted by two
cameras on the vehicle, using an analog computer for signal processing.
The vehicle reached speeds up to 30 kilometres per hour (19 mph), with
the support of an elevated rail.

Autonomous prototype cars appeared in the 1980s, with Carnegie Mellon
University's Navlab and ALV projects funded by DARPA starting in 1984
and Mercedes-Benz and Bundeswehr University Munich's EUREKA Prometheus
Project in 1987. By 1985, the ALV had demonstrated self-driving speeds
on two-lane roads of 31 kilometers per hour (19 mph) with obstacle
avoidance added in 1986 and off-road driving in day and nighttime
conditions by 1987. From the 1960s through the second DARPA Grand
Challenge in 2005, automated vehicle research in the U.S. was primarily
funded by DARPA, the US Army and the U.S. Navy yielding incremental
advances in speeds, driving competence in more complex conditions,
controls and sensor systems. Companies and research organizations have
developed prototypes.

The U.S. allocated \$650 million in 1991 for research on the National
Automated Highway System, which demonstrated automated driving through a
combination of automation, embedded in the highway with automated
technology in vehicles and cooperative networking between the vehicles
and with the highway infrastructure. The program concluded with a
successful demonstration in 1997 but without clear direction or funding
to implement the system on a larger scale. Partly funded by the National
Automated Highway System and DARPA, the Carnegie Mellon University
Navlab drove 4,584 kilometers (2,848 mi) across America in 1995, 4,501
kilometers (2,797 mi) or 98\% of it autonomously. Navlab's record
achievement stood unmatched for two decades until 2015 when Delphi
improved it by piloting an Audi, augmented with Delphi technology, over
5,472 kilometers (3,400 mi) through 15 states while remaining in
self-driving mode 99\%

of the time. In 2015, the US states of Nevada, Florida, California,
Virginia, and Michigan, together with Washington, D.C., allowed the
testing of automated cars on public roads.

In 2017, Audi stated that its latest A8 would be automated at speeds of
up to 60 kilometres per hour (37 mph) using its ``Audi AI.'' The driver
would not have to do safety checks such as frequently gripping the
steering wheel. The Audi A8 was claimed to be the first production car
to reach level 3 automated driving, and Audi would be the first
manufacturer to use laser scanners in addition to cameras and ultrasonic
sensors for their system.

In November 2017, Waymo announced that it had begun testing driverless
cars without a safety driver in the driver position; however, there is
still an employee in the car. In July 2018, Waymo announced that its
test vehicles had traveled in automated mode for over 8,000,000 miles
(13,000,000 km), increasing by 1,000,000 miles (1,600,000 kilometers)
per month (Fig. {\ref{848240}}).

\subsection*{~Terminology}

{\label{321496}}

There is some inconsistency in terminology used in the self-driving car
industry. Various organizations have proposed to define an accurate and
consistent vocabulary. Such confusion has been documented in SAE J3016
which states that ``Some vernacular usages associate autonomous
specifically with full driving automation (level 5), while other usages
apply it to all levels of driving automation, and some state legislation
has defined it to correspond approximately to any ADS at or above level
3 (or to any vehicle equipped with such an ADS).''

\subsubsection*{Words definition and safety
considerations}

{\label{234108}}

Modern vehicles provide partly automated features such as keeping the
car within its lane, speed controls or emergency braking. Nonetheless,
differences remain between a fully autonomous self-driving car on one
hand and driver assistance technologies on the other hand. According to
the BBC, confusion between those concepts leads to deaths.

Association of British Insurers considers the usage of the word
autonomous in marketing for modern cars to be dangerous, because car ads
make motorists think `autonomous' and `autopilot' means a vehicle can
drive itself, when they still rely on the driver to ensure safety.
Technology alone still is not able to drive the car.

When some car makers suggest or claim vehicles are self-driving, when
they are only partly automated, drivers risk becoming excessively
confident, leading to crashes, while fully self-driving cars are still a
long way off in the UK.

\subsubsection*{Autonomous vs.~automated}

{\label{562729}}

Autonomous means self-governing. Many historical projects related to
vehicle automation have been automated (made automatic) subject to a
heavy reliance on artificial aids in their environment, such as magnetic
strips. Autonomous control implies satisfactory performance under
significant uncertainties in the environment and the ability to
compensate for system failures without external intervention.

One approach is to implement communication networks both in the
immediate vicinity (for collision avoidance) and farther away (for
congestion management). Such outside influences in the decision process
reduce an individual vehicle's autonomy, while still not requiring human
intervention.

Wood et al. (2012) wrote, ``This Article generally uses the term
`autonomous,' instead of the term `automated.' '' The term
``autonomous'' was chosen ``because it is the term that is currently in
more widespread use (and thus is more familiar to the general public).
However, the latter term is arguably more accurate. `Automated' connotes
control or operation by a machine, while `autonomous' connotes acting
alone or independently. Most of the vehicle concepts (that we are
currently aware of) have a person in the driver's seat, utilize a
communication connection to the Cloud or other vehicles, and do not
independently select

either destinations or routes for reaching them. Thus, the term
`automated' would more accurately describe these vehicle concepts.'' As
of 2017, most commercial projects focused on automated vehicles that did
not communicate with other vehicles or with an enveloping management
regime.

Put in the words of one Nissan engineer, ``A truly autonomous car would
be one where you request it to take you to work and it decides to go to
the beach instead.''

EuroNCAP defines autonomous in ``Autonomous Emergency Braking'' as:
``the system acts independently of the driver to avoid or mitigate the
accident.'' which implies the autonomous system is not the driver.

\subsubsection*{Autonomous versus
cooperative}

{\label{136597}}

To make a car travel without any driver embedded within the vehicle some
system makers used a remote driver.

But according to SAE J3016, some driving automation systems may indeed
be autonomous if they perform all of their functions independently and
self-sufficiently, but if they depend on communication and/or
cooperation with outside entities, they should be considered cooperative
rather than autonomous.

\subsubsection*{Self-driving car}

{\label{608571}}

Techemergence says.

~~~~~~~~``Self-driving'' is a rather vague term with a vague meaning

~~~~~~~~---?Techemergence

PC mag definition is: A computer-controlled car that drives itself. Also
called an ``autonomous vehicle'' and ``driverless car,'' self-driving
cars date back to the 1939 New York World's Fair when General Motors
predicted the development of self-driving, radio-controlled electric
cars.

UCSUSA definition is: Self-driving vehicles are cars or trucks in which
human drivers are never required to take control to safely operate the
vehicle. Also known as autonomous or ``driverless'' cars, they combine
sensors and software to control, navigate, and drive the vehicle.
Currently, there are no legally operating, fully-autonomous vehicles in
the United States.

NHTSA definition is: These self-driving vehicles ultimately will
integrate onto U.S. roadways by progressing through six levels of driver
assistance technology advancements in the coming years. This includes
everything from no automation (where a fully engaged driver is required
at all times), to full autonomy (where an automated vehicle operates
independently, without a human driver).

\subsection*{Classification}

{\label{273227}}

A classification system based on six different levels (ranging from
fully manual to fully automated systems) was published in 2014 by SAE
International, an automotive standardization body, as J3016, Taxonomy
and Definitions for Terms Related to On-Road Motor Vehicle Automated
Driving Systems. This classification system is based on the amount of
driver intervention and attentiveness required, rather than the vehicle
capabilities, although these are very loosely related. In the United
States in 2013, the National Highway Traffic Safety Administration
(NHTSA) released a formal classification system, but abandoned this
system in favor of the SAE standard in 2016. Also in 2016, SAE updated
its classification, called J3016\_201609.

\subsubsection*{Levels of driving
automation}

{\label{332866}}

In SAE's automation level definitions, ``driving mode'' means ``a type
of driving scenario with characteristic dynamic driving task
requirements (e.g., expressway merging, high speed cruising, low speed
traffic jam, closed-campus operations, etc.)''

Level 0: Automated system issues warnings and may momentarily intervene
but has no sustained vehicle control.

Level 1 (``hands on''): The driver and the automated system share
control of the vehicle. Examples are Adaptive Cruise Control (ACC),
where the driver controls steering and the automated system controls
speed; and Parking Assistance, where steering is automated while speed
is under manual control. The driver must be ready to retake full control
at any time. Lane Keeping Assistance (LKA) Type II is a further example
of level 1 self-driving.

Level 2 (``hands off''): The automated system takes full control of the
vehicle (accelerating, braking, and steering). The driver must monitor
the driving and be prepared to intervene immediately at any time if the
automated system fails to respond properly. The shorthand ``hands off''
is not meant to be taken literally. In fact, contact between hand and
wheel is often mandatory during SAE 2 driving, to confirm that the
driver is ready to intervene.

Level 3 (``eyes off''): The driver can safely turn their attention away
from the driving tasks, e.g.~the driver can text or watch a movie. The
vehicle will handle situations that call for an immediate response, like
emergency braking. The driver must still be prepared to intervene within
some limited time, specified by the manufacturer, when called upon by
the vehicle to do so. As an example, the 2018 Audi A8 Luxury Sedan was
the first commercial car to claim to be capable of level 3 self-driving.
This particular car has a so-called Traffic Jam Pilot. When activated by
the human driver, the car takes full control of all aspects of driving
in slow-moving traffic at up to 60 kilometers per hour (37 mph). The
function works only on highways with a physical barrier separating one
stream of traffic from oncoming traffic.

Level 4 (``mind off''): As level 3, but no driver attention is ever
required for safety, i.e.~the driver may safely go to sleep or leave the
driver's seat. Self-driving is supported only in limited spatial areas
(geofenced) or under special circumstances, like traffic jams. Outside
of these areas or circumstances, the vehicle must be able to safely
abort the trip, i.e.~park the car, if the driver does not retake
control.

Level 5 (``steering wheel optional''): No human intervention is required
at all. An example would be a robotic taxi.

In the formal SAE definition below, note in particular what happens in
the shift from SAE 2 to SAE 3: the human driver no longer has to monitor
the environment. This is the final aspect of the ``dynamic driving
task'' that is now passed over from the human to the automated system.
At SAE 3, the human driver still has the responsibility to intervene
when asked to do so by the automated system. At SAE 4 the human driver
is relieved of that responsibility and at SAE 5 the automated system
will never need to ask for an intervention.

\subsection*{Legal definition}

{\label{869213}}

In the district of Columbia (DC) code, ``Autonomous vehicle'' means a
vehicle capable of navigating District roadways and interpreting
traffic-control devices without a driver actively operating any of the
vehicle's control systems. The term ``autonomous vehicle'' excludes a
motor vehicle enabled with active safety systems or driver- assistance
systems, including systems to provide electronic blind-spot assistance,
crash avoidance, emergency braking, parking assistance, adaptive cruise
control, lane-keep assistance, lane-departure warning, or traffic-jam
and queuing assistance, unless the system alone or in combination with
other systems enables the vehicle on which the technology is installed
to drive without active control or monitoring by a human operator.

In the same district code, it is considered that:

An autonomous vehicle may operate on a public roadway; provided, that
the vehicle:

(1) Has a manual override feature that allows a driver to assume control
of the autonomous vehicle at any time;

(2) Has a driver seated in the control seat of the vehicle while in
operation who is prepared to take control of the autonomous vehicle at
any moment; and

(3) Is capable of operating in compliance with the District's applicable
traffic laws and motor vehicle laws and traffic control devices.

\subsection*{Technical challenges}

{\label{669265}}

The challenge for driverless car designers is to produce control systems
capable of analyzing sensory data in order to provide accurate detection
of other vehicles and the road ahead. Modern self-driving cars generally
use Bayesian simultaneous localization and mapping (SLAM) algorithms,
which fuse data from multiple sensors and an off-line map into current
location estimates and map updates. Waymo has developed a variant of
SLAM with detection and tracking of other moving objects (DATMO), which
also handles obstacles such as cars and pedestrians. Simpler systems may
use roadside real-time locating system (RTLS) technologies to aid
localization. Typical sensors include Lidar, stereo vision, GPS and IMU.
Udacity is developing an open-source software stack. Control systems on
automated cars may use Sensor Fusion, which is an approach that
integrates information from a variety of sensors on the car to produce a
more consistent, accurate, and useful view of the environment.

Driverless vehicles require some form of machine vision for the purpose
of visual object recognition. Automated cars are being developed with
deep neural networks, a type of deep learning architecture with many
computational stages, or levels, in which neurons are simulated from the
environment that activate the network. The neural network depends on an
extensive amount of data extracted from real-life driving scenarios,
enabling the neural network to ``learn'' how to execute the best course
of action.

In May 2018, researchers from MIT announced that they had built an
automated car that can navigate unmapped roads. Researchers at their
Computer Science and Artificial Intelligence Laboratory (CSAIL) have
developed a new system, called MapLite, which allows self-driving cars
to drive on roads that they have never been on before, without using 3D
maps. The system combines the GPS position of the vehicle, a ``sparse
topological map'' such as OpenStreetMap, (i.e.~having 2D features of the
roads only), and a series of sensors that observe the road conditions.

\subsection*{Human factor challenges}

{\label{440752}}

Alongside the many technical challenges that autonomous cars face, there
exist many human and social factors that may impede upon the wider
uptake of the technology. As things become more automated, the human
users need to have trust in the automation, which can be a challenge in
itself.

\subsection*{Testing}

{\label{278164}}

Testing vehicles with varying degrees of automation can be done
physically, in closed environments, on public roads (where permitted,
typically with a license or permit or adhering to a specific set of
operating principles) or virtually, i.e.~in computer simulations. When
driven on public roads, automated vehicles require a person to monitor
their proper operation and ``take over'' when needed. Apple is currently
testing self-driven cars, and has increased the number of test vehicles
from 3 to 27 in January 2018, and to 45 in March 2018.

One way to assess the progress of automated vehicles is to compute the
average distance driven between ``disengagements'', when the automated
system is turned off, typically by a human driver. In 2017, Waymo
reported 63 disengagements over 352,545 miles (567,366 km) of testing,
or 5,596 miles (9,006 km) on average, the highest among companies
reporting such figures. Waymo also traveled more distance in total than
any other. Their 2017 rate of 0.18 disengagements per 1,000 miles (1,600
km) was an improvement from 0.2 disengagements per 1,000 miles (1,600
km) in 2016 and 0.8 in 2015. In March, 2017, Uber reported an average of
0.67 miles (1.08 km) per disengagement. In the final three months of
2017, Cruise Automation (now owned by GM) averaged 5,224 miles (8,407
km) per disruption over 62,689 miles (100,888 km). In July

\par\null\par\null

2018, the first electric driverless racing car ``Robocar'' completed 1.8
kilometers track, using its navigation system and artificial
intelligence (Table {\ref{312180}}).

\par\null\selectlanguage{english}
\begin{table}[h!]
\centering
\normalsize\begin{tabulary}{1.0\textwidth}{CCC}
Maker & Distance between disengagements & Distance \\
BMW & 638 miles (1027\selectlanguage{ngerman} km) & 638 miles (1027\selectlanguage{ngerman} km) \\
Bosch & 0.68 miles (1.09\selectlanguage{ngerman} km) & 983 miles (1582\selectlanguage{ngerman} km) \\
Delphi Automotive Systems & 14.9 miles (24.0\selectlanguage{ngerman} km) & 2658 miles (4278\selectlanguage{ngerman} km) \\
Ford & 196.6 miles (316.4\selectlanguage{ngerman} km) & 590 miles (950\selectlanguage{ngerman} km) \\
General Motors & 54.7 miles (88.0\selectlanguage{ngerman} km) & 8156 miles (13126\selectlanguage{ngerman} km) \\
Mercedes Benz & 2 miles (3.2\selectlanguage{ngerman} km) & 673 miles (1083\selectlanguage{ngerman} km) \\
Nissan & 263.3 miles (423.7\selectlanguage{ngerman} km) & 6056 miles (9746\selectlanguage{ngerman} km) \\
Tesla & 2.9 miles (4.7\selectlanguage{ngerman} km) & 550 miles (890\selectlanguage{ngerman} km) \\
Waymo & 5127.9 miles (8252.6\selectlanguage{ngerman} km) & 635868 miles (1023330\selectlanguage{ngerman} km) \\
\end{tabulary}
\caption{{Self-driving car testing results.
{\label{312180}}%
}}
\end{table}\subsection*{Fields of application}

{\label{931868}}

\subsubsection*{Automated trucks}

{\label{667332}}

Several companies are said to be testing automated technology in semi
trucks. Otto, a self-driving trucking company that was acquired by Uber
in August 2016, demonstrated their trucks on the highway before being
acquired. In May 2017, San Francisco-based startup Embark announced a
partnership with truck manufacturer Peterbilt to test and deploy
automated technology in Peterbilt's vehicles. Waymo has also said to be
testing automated technology in trucks, however no timeline has been
given for the project.

In March 2018, Starsky Robotics, the San Francisco-based automated truck
company, completed a 7-mile (11 km) fully driverless trip in Florida
without a single human in the truck. Starsky Robotics became the first
player in the self-driving truck game to drive in fully automated mode
on a public road without a person in the cab. In Europe, the truck
Platooning is considered with the Safe Road Trains for the Environment
approach.Vehicular automation also covers other kinds of vehicles such
as Buses, Trains, Trucks. Lockheed Martin with funding from the U.S.
Army developed an automated truck convoying system that uses a lead
truck operated by a human driver with a number of trucks following
autonomously. Developed as part of the Army's Autonomous Mobility
Applique System (AMAS), the system consists of an automated driving
package that has been installed on more than nine types of vehicles and
has completed more than 55,000 hours of driving at speeds up to 64
kilometres per hour (40 mph) as of 2014. As of 2017 the Army was
planning to field 100-200 trucks as part of a rapid-fielding program.

\subsubsection*{Transport systems}

{\label{626238}}

In Europe, cities in Belgium, France, Italy and the UK are planning to
operate transport systems for automated cars, and Germany, the
Netherlands, and Spain have allowed public testing in traffic. In 2015,
the UK launched public trials of the LUTZ Pathfinder automated pod in
Milton Keynes. Beginning in summer 2015, the French government allowed
PSA Peugeot-Citroen to make trials in real conditions in the Paris area.
The experiments were planned to be extended to other cities such as
Bordeaux and Strasbourg by 2016. The alliance between French companies
THALES and Valeo (provider of the first self-parking car system that
equips Audi and Mercedes premi) is testing its own system. New Zealand
is planning to use automated vehicles for public transport in Tauranga
and Christchurch.

In China, Baidu and King Long produce automated minibus, a vehicle with
14 seats, but without driving seat. With 100 vehicles produced, 2018
will be the first year with commercial automated service in China. Those
minibuses should be at level 4, that is driverless in closed roads.

\subsubsection*{}

{\label{307157}}

\subsubsection*{Potential advantages}

{\label{307157}}

\subsubsection*{Safety}

{\label{497584}}

Driving safety experts predict that once driverless technology has been
fully developed, traffic collisions (and resulting deaths and injuries
and costs), caused by human error, such as delayed reaction time,
tailgating, rubbernecking, and other forms of distracted or aggressive
driving should be substantially reduced. Consulting firm McKinsey \&
Company estimated that widespread use of autonomous vehicles could
``eliminate 90\% of all auto accidents in the United States, prevent up
to US\$190 billion in damages and health-costs annually and save
thousands of lives.''

According to motorist website ``TheDrive.com'' operated by Time
magazine, none of the driving safety experts they were able to contact
were able to rank driving under an autopilot system at the time (2017)
as having achieved a greater level of safety than traditional fully
hands-on driving, so the degree to which these benefits asserted by
proponents will manifest in practice cannot be assessed. Confounding
factors that could reduce the net effect on safety may include
unexpected interactions between humans and partly or fully automated
vehicles, or between different types of vehicle system; complications at
the boundaries of functionality at each automation level (such as
handover when the vehicle reaches the limit of its capacity); the effect
of the bugs and flaws that inevitably occur in complex interdependent
software systems; sensor or data shortcomings; and successful compromise
by malicious interveners.

\subsubsection*{Welfare}

{\label{169411}}

Automated cars could reduce labor costs; relieve travelers from driving
and navigation chores, thereby replacing behind-the-wheel commuting
hours with more time for leisure or work; and also would lift
constraints on occupant ability to drive, distracted and texting while
driving, intoxicated, prone to seizures, or otherwise impaired. For the
young, the elderly, people with disabilities, and low-income citizens,
automated cars could provide enhanced mobility. The removal of the
steering wheel---along with the remaining driver interface and the
requirement for any occupant to assume a forward-facing position---would
give the interior of the cabin greater ergonomic flexibility. Large
vehicles, such as motorhomes, would attain appreciably enhanced ease of
use.

\subsubsection*{Traffic}

{\label{661032}}

Additional advantages could include higher speed limits; smoother rides;
and increased roadway capacity; and minimized traffic congestion, due to
decreased need for safety gaps and higher speeds. Currently, maximum
controlled-access highway throughput or capacity according to the U.S.
Highway Capacity Manual is about 2,200 passenger vehicles per hour per
lane, with about 5\% of the available road space is taken up by cars.
One study estimated that automated cars could increase capacity by 273\%
(\textasciitilde{}8,200 cars per hour per lane). The study also
estimated that with 100\% connected vehicles using vehicle-to-vehicle
communication, capacity could reach 12,000 passenger vehicles per hour
(up 445\% from 2,200 pc/h per lane) traveling safely at 120 km/h (75
mph) with a following gap of about 6 m (20 ft) of each other. Currently,
at highway speeds drivers keep between 40 to 50 m (130 to 160 ft) away
from the car in front. These increases in highway capacity could have a
significant impact in traffic congestion, particularly in urban areas,
and even effectively end highway congestion in some places. The ability
for authorities to manage traffic flow would increase, given the extra
data and driving behavior predictability combined with less need for
traffic police and even road signage.

\subsubsection*{Lower costs}

{\label{874664}}

Safer driving is expected to reduce the costs of vehicle insurance.
Reduced traffic congestion and the improvements in traffic flow due to
widespread use of automated cars will also translate into better fuel
efficiency. Additionally, self-driving cars will be able to accelerate
and brake more efficiently, meaning higher

fuel economy from reducing wasted energy typically associated with
inefficient changes to speed (energy typically lost due to friction, in
the form of heat and sound).

\subsubsection*{Parking space}

{\label{137994}}

Manually driven vehicles are reported to be used only 4-5\% time, and
being parked and unused for the remaining 95-96\% of the time.
Autonomous vehicles could, on the other hand, be used continuously after
it has reached its destination. This could dramatically reduce the need
for parking space. For example, in Los Angeles, 14\% of the land is used
for parking alone, equivalent to some 17,020,594 square meters. This
combined with the potential reduced need for road space due to improved
traffic flow, could free up tremendous amounts of land in urban areas,
which could then be used for parks, recreational areas, buildings, among
other uses; making cities more livable.

\subsubsection*{Related effects}

{\label{820070}}

By reducing the (labor and other) cost of mobility as a service,
automated cars could reduce the number of cars that are individually
owned, replaced by taxi/pooling and other car sharing services. This
would also dramatically reduce the size of the automotive production
industry, with corresponding environmental and economic effects.
Assuming the increased efficiency is not fully offset by increases in
demand, more efficient traffic flow could free roadway space for other
uses such as better support for pedestrians and cyclists.

The vehicles' increased awareness could aid the police by reporting on
illegal passenger behavior, while possibly enabling other crimes, such
as deliberately crashing into another vehicle or a pedestrian. However,
this may also lead to much expanded mass surveillance if there is wide
access granted to third parties to the large data sets generated. The
future of passenger rail transport in the era of automated cars is not
clear.

\subsubsection*{Potential limits or
obstacles}

{\label{355334}}

The sort of hoped-for potential benefits from increased vehicle
automation described may be limited by foreseeable challenges, such as
disputes over liability (will each company operating a vehicle accept
that they are its ``driver'' and thus responsible for what their car
does, or will some try to project this liability onto others who are not
in control?), the time needed to turn over the existing stock of
vehicles from non-automated to automated, and thus a long period of
humans and autonomous vehicles sharing the roads, resistance by
individuals to having to forfeit control of their cars, concerns about
the safety of driverless in practice, and the implementation of a legal
framework and consistent global government regulations for self-driving
cars. Other obstacles could include de-skilling and lower levels of
driver experience for dealing with potentially dangerous situations and
anomalies, ethical problems where an automated vehicle's software is
forced during an unavoidable crash to choose between multiple harmful
courses of action (`the trolley problem'), concerns about making large
numbers of people currently employed as drivers unemployed (at the same
time as many other alternate blue collar occupations may be undermined
by automation), the potential for more intrusive mass surveillance of
location, association and travel as a result of police and intelligence
agency access to large data sets generated by sensors and
pattern-recognition AI (making anonymous travel difficult), and possibly
insufficient understanding of verbal sounds, gestures and non-verbal
cues by police, other drivers or pedestrians.

Possible technological obstacles for automated cars are:

\begin{itemize}
\tightlist
\item
  artificial Intelligence is still not able to function properly in
  chaotic inner-city environments,
\item
  a car's computer could potentially be compromised, as could a
  communication system between cars,
\item
  susceptibility of the car's sensing and navigation systems to
  different types of weather (such as snow) or deliberate interference,
  including jamming and spoofing,
\item
  avoidance of large animals requires recognition and tracking, and
  Volvo found that software suited to caribou, deer, and elk was
  ineffective with kangaroos,
\item
  autonomous cars may require very high-quality specialized maps to
  operate properly. Where these maps may be out of date, they would need
  to be able to fall back to reasonable behaviors,
\item
  competition for the radio spectrum desired for the car's
  communication,
\item
  field programmability for the systems will require careful evaluation
  of product development and the component supply chain,
\item
  current road infrastructure may need changes for automated cars to
  function optimally,
\item
  discrepancy between people's beliefs of the necessary government
  intervention may cause a delay in accepting automated cars on the
  road. Whether the public desires no change in existing laws, federal
  regulation, or another solution; the framework of regulation will
  likely result in differences of opinion,
\item
  employment - Companies working on the technology have an increasing
  recruitment problem in that the available talent pool has not grown
  with demand. As such, education and training by third party
  organizations such as providers of online courses and self-taught
  community-driven projects such as DIY Robocars and Formula Pi have
  quickly grown in popularity, while university level extra-curricular
  programmers such as Formula Student Driverless have bolstered graduate
  experience. Industry is steadily increasing freely available
  information sources, such as code, datasets and glossaries to widen
  the recruitment pool.
\end{itemize}

\subsubsection*{Potential disadvantages}

{\label{574833}}

A direct impact of widespread adoption of automated vehicles is the loss
of driving-related jobs in the road transport industry. There could be
resistance from professional drivers and unions who are threatened by
job losses. In addition, there could be job losses in public transit
services and crash repair shops. The automobile insurance industry might
suffer as the technology makes certain aspects of these occupations
obsolete. A frequently cited paper by Michael Osborne and Carl Benedikt
Frey found that automated cars would make many jobs redundant.

Privacy could be an issue when having the vehicle's location and
position integrated into an interface in which other people have access
to. In addition, there is the risk of automotive hacking through the
sharing of information through V2V (Vehicle to Vehicle) and V2I (Vehicle
to Infrastructure) protocols. There is also the risk of terrorist
attacks. Self-driving cars could potentially be loaded with explosives
and used as bombs.

The lack of stressful driving, more productive time during the trip, and
the potential savings in travel time and cost could become an incentive
to live far away from cities, where land is cheaper, and work in the
city's core, thus increasing travel distances and inducing more urban
sprawl, more fuel consumption and an increase in the carbon footprint of
urban travel. There is also the risk that traffic congestion might
increase, rather than decrease. Appropriate public policies and
regulations, such as zoning, pricing, and urban design are required to
avoid the negative impacts of increased suburbanization and longer
distance travel.

Some believe that once automation in vehicles reaches higher levels and
becomes reliable, drivers will pay less attention to the road. Research
shows that drivers in automated cars react later when they have to
intervene in a critical situation, compared to if they were driving
manually. Depending on the capabilities of automated vehicles and the
frequency with which human intervention is needed, this may counteract
any increase in safety, as compared to all-human driving, that may be
delivered by other factors.

Ethical and moral reasoning come into consideration when programming the
software that decides what action the car takes in an unavoidable crash;
whether the automated car will crash into a bus, potentially killing
people inside; or swerve elsewhere, potentially killing its own
passengers or nearby pedestrians. A question that programmers of AI
systems find difficult to answer (as do ordinary people, and ethicists)
is ``what decision should the car make that causes the `smallest' damage
to people's lives?''

The ethics of automated vehicles are still being articulated, and may
lead to controversy. They may also require closer consideration of the
variability, context-dependency, complexity and non-deterministic nature
of human ethics. Different human drivers make various ethical decisions
when driving, such as avoiding harm to themselves, or putting themselves
at risk to protect others. These decisions range from rare extremes

such as self-sacrifice or criminal negligence, to routine decisions good
enough to keep the traffic flowing but bad enough to cause accidents,
road rage and stress.

Human thought and reaction time may sometimes be too slow to detect the
risk of an upcoming fatal crash, think through the ethical implications
of the available options, or take an action to implement an ethical
choice. Whether a particular automated vehicle's capacity to correctly
detect an upcoming risk, analyze the options or choose a `good' option
from among bad choices would be as good or better than a particular
human's may be difficult to predict or assess. This difficulty may be in
part because the level of automated vehicle system understanding of the
ethical issues at play in a given road scenario, sensed for an instant
from out of a continuous stream of synthetic physical predictions of the
near future, and dependent on layers of pattern recognition and
situational intelligence, may be opaque to human inspection because of
its origins in probabilistic machine learning rather than a simple,
plain English `human values' logic of parsable rules. The depth of
understanding, predictive power and ethical sophistication needed will
be hard to implement, and even harder to test or assess.

The scale of this challenge may have other effects. There may be few
entities able to marshal the resources and AI capacity necessary to meet
it, as well as the capital necessary to take an automated vehicle system
to market and sustain it operationally for the life of a vehicle, and
the legal and `government affairs' capacity to deal with the potential
for liability for a significant proportion of traffic accidents. This
may have the effect of narrowing the number of different system
operators, and eroding the presently quite diverse global vehicle market
down to a small number of system suppliers.

\subsection*{}

{\label{318490}}

\subsection*{}

{\label{890756}}\par\null\par\null\par\null\par\null



\(Fw=pc\cdot g\cdot Vc\)

\(a^2+b^2=c^2\)

\selectlanguage{polish}\section*{Rozdział 1. Smartdom -- geneza powstania systemu Smart
Home}\selectlanguage{english}\label{rozdziaux142-1.-smartdom-geneza-powstania-systemu-smart-home}



\subsection*{Tendencja ostatnich lat}\label{tendencja-ostatnich-lat}

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A. Szewczyk, Inteligentne domy -- marzeniem mieszka\selectlanguage{polish}ń\selectlanguage{english}c\selectlanguage{ngerman}ów miast? Zeszyty
Naukowe Uniwesrytetu Szczeci\selectlanguage{polish}ń\selectlanguage{english}skiego, Studia Informatica nr 29(721),
2012, s. 138.

K. P\selectlanguage{polish}ł\selectlanguage{english}achta, Mikroprocesorowy sterownik mocy w domowych sieciach
elektrycznych. Projekt In\selectlanguage{polish}ż\selectlanguage{english}ynierski, Politechnika Wroc\selectlanguage{polish}ł\selectlanguage{english}awska, Wroc\selectlanguage{polish}ł\selectlanguage{english}aw
2010, s. 56.

~

E. Niezabitowska, Budynek inteligentny - Podstawowe systemy
bezpiecze\selectlanguage{polish}ń\selectlanguage{english}stwa w budynkach inteligentnych. T.2. \selectlanguage{polish}Ś\selectlanguage{english}l\selectlanguage{polish}ą\selectlanguage{english}sk, Wydawnictwo WP\selectlanguage{polish}Ś
\selectlanguage{english}2010, s. 75.



\selectlanguage{polish}\section*{Rozdział 2. System EIB -- podstawa systemu Smart
Home}\selectlanguage{english}\label{rozdziaux142-2.-system-eib-podstawa-systemu-smart-home}



\subsection*{System EIB}\label{system-eib}

System EIB okre\selectlanguage{polish}ś\selectlanguage{english}lany tak\selectlanguage{polish}ż\selectlanguage{english}e mianem instabus jest inteligentnym,
zdecentralizowanym systemem automatyki budynku, opartym na po\selectlanguage{polish}łą\selectlanguage{english}czeniu
instalacji elektrycznej ze specyficzn\selectlanguage{polish}ą \selectlanguage{english}sieci\selectlanguage{polish}ą \selectlanguage{english}komputerow\selectlanguage{polish}ą. \selectlanguage{english}S\selectlanguage{polish}ł\selectlanguage{english}u\selectlanguage{polish}ż\selectlanguage{english}y do
za\selectlanguage{polish}łą\selectlanguage{english}czania, sterowania, nadzoru jak r\selectlanguage{ngerman}ównie\selectlanguage{polish}ż \selectlanguage{english}regulowania urz\selectlanguage{polish}ą\selectlanguage{english}dze\selectlanguage{polish}ń
\selectlanguage{english}technicznych b\selectlanguage{polish}ę\selectlanguage{english}d\selectlanguage{polish}ą\selectlanguage{english}cych w obszarze budynku. Ka\selectlanguage{polish}ż\selectlanguage{english}dy element systemu, zwany
elementem magistralnym ma w\selectlanguage{polish}ł\selectlanguage{english}asny system, kt\selectlanguage{ngerman}óry odpowiada za obieg
informacji mi\selectlanguage{polish}ę\selectlanguage{english}dzy nim a systemem. Obieg danych realizowany jest przy
u\selectlanguage{polish}ż\selectlanguage{english}yciu elektronicznych telegram\selectlanguage{ngerman}ów przesy\selectlanguage{polish}ł\selectlanguage{english}anych pomi\selectlanguage{polish}ę\selectlanguage{english}dzy urz\selectlanguage{polish}ą\selectlanguage{english}dzeniami.
Zatem rozkaz w\selectlanguage{polish}łą\selectlanguage{english}czenia oprawy o\selectlanguage{polish}ś\selectlanguage{english}wietleniowej przesy\selectlanguage{polish}ł\selectlanguage{english}any jest w formie
telegramu od przycisku do oprawy, za pomoc\selectlanguage{polish}ą \selectlanguage{english}magistrali. W\selectlanguage{polish}ł\selectlanguage{english}a\selectlanguage{polish}ś\selectlanguage{english}ciwa wymiana
informacji zachodzi przez dwie \selectlanguage{polish}ż\selectlanguage{english}y\selectlanguage{polish}ł\selectlanguage{english}y przewodu steruj\selectlanguage{polish}ą\selectlanguage{english}cego, kt\selectlanguage{ngerman}óre s\selectlanguage{polish}ł\selectlanguage{english}u\selectlanguage{polish}żą \selectlanguage{english}do
zasilania komponent\selectlanguage{ngerman}ów magistralnych. Standardowym magistralnym przewodem
jest ekranowana dwuparowa skr\selectlanguage{polish}ę\selectlanguage{english}tka 0,8 mm2. Obieg informacji nie jest
odseparowany od zasilaj\selectlanguage{polish}ą\selectlanguage{english}cego napi\selectlanguage{polish}ę\selectlanguage{english}cia 24 V pr\selectlanguage{polish}ą\selectlanguage{english}du sta\selectlanguage{polish}ł\selectlanguage{english}ego (DC), kt\selectlanguage{ngerman}óre
jest dopasowywane podczas nadawania. Przyk\selectlanguage{polish}ł\selectlanguage{english}ad za\selectlanguage{polish}łą\selectlanguage{english}czania o\selectlanguage{polish}ś\selectlanguage{english}wietlenia w
wykonaniu EIB jest zapewne w poj\selectlanguage{polish}ę\selectlanguage{english}ciu elektryka zbyt skomplikowany i
drogi dla tak prostej funkcji. Jednak nie zapominajmy, \selectlanguage{polish}ż\selectlanguage{english}e przy pomocy
tego samego przewodu magistralnego (tylko 2 \selectlanguage{polish}ż\selectlanguage{english}y\selectlanguage{polish}ł\selectlanguage{english}y) opr\selectlanguage{ngerman}ócz informacji
w\selectlanguage{polish}łą\selectlanguage{english}cz/wy\selectlanguage{polish}łą\selectlanguage{english}cz opraw\selectlanguage{polish}ę \selectlanguage{english}o\selectlanguage{polish}ś\selectlanguage{english}wietleniow\selectlanguage{polish}ą, \selectlanguage{english}przesy\selectlanguage{polish}ł\selectlanguage{english}ane s\selectlanguage{polish}ą \selectlanguage{english}tysi\selectlanguage{polish}ą\selectlanguage{english}ce innych
informacji (warto\selectlanguage{polish}ś\selectlanguage{english}ci temperatur i nat\selectlanguage{polish}ęż\selectlanguage{english}enia o\selectlanguage{polish}ś\selectlanguage{english}wietlenia w poszczeg\selectlanguage{ngerman}ólnych
pomieszczeniach, rozkazy regulatorów temperatury, sygna\selectlanguage{polish}ł\selectlanguage{english}y obecno\selectlanguage{polish}ś\selectlanguage{english}ci,
warto\selectlanguage{polish}ść \selectlanguage{english}aktualnego czasu i inne).

System otwarty w odr\selectlanguage{ngerman}ó\selectlanguage{polish}ż\selectlanguage{english}nieniu od systemu zamkni\selectlanguage{polish}ę\selectlanguage{english}tego (systemu firmowego)
nie jest ograniczony tylko do jednej firmy. G\selectlanguage{polish}ł\selectlanguage{ngerman}ówn\selectlanguage{polish}ą \selectlanguage{english}zasad\selectlanguage{polish}ą \selectlanguage{english}takiego
systemu jest umo\selectlanguage{polish}ż\selectlanguage{english}liwienie produkowania urz\selectlanguage{polish}ą\selectlanguage{english}dze\selectlanguage{polish}ń \selectlanguage{english}zgodnych ze standardem
systemu przez wielu producent\selectlanguage{ngerman}ów.

Jednym z zasadniczych zada\selectlanguage{polish}ń \selectlanguage{english}Europejskiej Magistrali Instalacyjnej jest
zespolenie instalacji, kt\selectlanguage{ngerman}óre w typowym wykonaniu pracuj\selectlanguage{polish}ą \selectlanguage{english}osobno. Obni\selectlanguage{polish}ż\selectlanguage{english}a
to ilo\selectlanguage{polish}ść \selectlanguage{english}przewod\selectlanguage{ngerman}ów oraz kabli umieszczonych w budynku, przy jednoczesnym
zachowaniu uprzednich funkcji osprz\selectlanguage{polish}ę\selectlanguage{english}tu, a niekiedy rozszerzaj\selectlanguage{polish}ą\selectlanguage{english}c zakres
ich wdra\selectlanguage{polish}ż\selectlanguage{english}ania. Kooperacja ta jest bezpo\selectlanguage{polish}ś\selectlanguage{english}rednio powi\selectlanguage{polish}ą\selectlanguage{english}zana ze wzrostem
intrat z oszcz\selectlanguage{polish}ę\selectlanguage{english}dzania na energii. Dla przyk\selectlanguage{polish}ł\selectlanguage{english}adu czujnik otwarcia okna
wysy\selectlanguage{polish}ł\selectlanguage{english}a informacj\selectlanguage{polish}ę, \selectlanguage{english}kt\selectlanguage{ngerman}óra mo\selectlanguage{polish}ż\selectlanguage{english}e by\selectlanguage{polish}ć \selectlanguage{english}wykorzystana zar\selectlanguage{ngerman}ówno przez system
antyw\selectlanguage{polish}ł\selectlanguage{english}amaniowy, jak te\selectlanguage{polish}ż \selectlanguage{english}poprzez instalacj\selectlanguage{polish}ę \selectlanguage{english}grzewcz\selectlanguage{polish}ą. \selectlanguage{english}W kolejnym
przypadku, dzi\selectlanguage{polish}ę\selectlanguage{english}ki zamykaniu grzejnikowych zawor\selectlanguage{ngerman}ów przy otwarciu okien,
otrzymuje si\selectlanguage{polish}ę \selectlanguage{english}oszcz\selectlanguage{polish}ę\selectlanguage{english}dno\selectlanguage{polish}ść \selectlanguage{english}energii.

System zosta\selectlanguage{polish}ł \selectlanguage{english}przyj\selectlanguage{polish}ę\selectlanguage{english}ty jako europejski standard w zakresie kompleksowej
automatyzacji budynku i dzi\selectlanguage{polish}ę\selectlanguage{english}ki swojej otwarto\selectlanguage{polish}ś\selectlanguage{english}ci umo\selectlanguage{polish}ż\selectlanguage{english}liwi\selectlanguage{polish}ł \selectlanguage{english}wielu
producentom z r\selectlanguage{ngerman}ó\selectlanguage{polish}ż\selectlanguage{english}nych bran\selectlanguage{polish}ż \selectlanguage{english}po\selectlanguage{polish}łą\selectlanguage{english}czenia swoich urz\selectlanguage{polish}ą\selectlanguage{english}dze\selectlanguage{polish}ń \selectlanguage{english}w jeden system z
innymi. Problem integracji zostaje rozwi\selectlanguage{polish}ą\selectlanguage{english}zany ju\selectlanguage{polish}ż \selectlanguage{english}na poziomie projektu.
Cyfrowy przesy\selectlanguage{polish}ł \selectlanguage{english}informacji steruj\selectlanguage{polish}ą\selectlanguage{english}cej umo\selectlanguage{polish}ż\selectlanguage{english}liwia wykorzystanie tylko
jednego przewodu steruj\selectlanguage{polish}ą\selectlanguage{english}cego. Skr\selectlanguage{ngerman}ót DC (ang. \emph{direct current})
oznacza pr\selectlanguage{polish}ą\selectlanguage{english}d sta\selectlanguage{polish}ł\selectlanguage{english}y. Ze wzgl\selectlanguage{polish}ę\selectlanguage{english}du na unifikacj\selectlanguage{polish}ę \selectlanguage{english}wg norm europejskich na
wszystkich urz\selectlanguage{polish}ą\selectlanguage{english}dzeniach elektrycznych wyprodukowanych na terenie Unii
Europejskiej nanoszone s\selectlanguage{polish}ą \selectlanguage{english}oznaczenia okre\selectlanguage{polish}ś\selectlanguage{english}laj\selectlanguage{polish}ą\selectlanguage{english}ce rodzaj pr\selectlanguage{polish}ą\selectlanguage{english}du DC, pr\selectlanguage{polish}ą\selectlanguage{english}d
sta\selectlanguage{polish}ł\selectlanguage{english}y lub AC (ang. \emph{altemating current}) pr\selectlanguage{polish}ą\selectlanguage{english}d przemienny.

Upraszcza to w znaczny spos\selectlanguage{ngerman}ób struktur\selectlanguage{polish}ą \selectlanguage{english}instalacji oraz zwi\selectlanguage{polish}ę\selectlanguage{english}ksza jej
przejrzysto\selectlanguage{polish}ść. \selectlanguage{english}Technika systemowa budynku przyj\selectlanguage{polish}ęł\selectlanguage{english}a si\selectlanguage{polish}ę \selectlanguage{english}w pa\selectlanguage{polish}ń\selectlanguage{english}stwach
Europy Zachodniej, szczeg\selectlanguage{ngerman}ólnie w budownictwie~ publicznej u\selectlanguage{polish}ż\selectlanguage{english}yteczno\selectlanguage{polish}ś\selectlanguage{english}ci.
Zasadniczym powodem stosowania uk\selectlanguage{polish}ł\selectlanguage{english}adu s\selectlanguage{polish}ą \selectlanguage{english}najwi\selectlanguage{polish}ę\selectlanguage{english}ksze sposobno\selectlanguage{polish}ś\selectlanguage{english}ci
obni\selectlanguage{polish}ż\selectlanguage{english}enia zu\selectlanguage{polish}ż\selectlanguage{english}ycia pr\selectlanguage{polish}ą\selectlanguage{english}du (do 40\%). Jest on tak\selectlanguage{polish}ż\selectlanguage{english}e coraz ch\selectlanguage{polish}ę\selectlanguage{english}tniej wdra\selectlanguage{polish}ż\selectlanguage{english}any
w jednorodzinnym budownictwie r\selectlanguage{ngerman}ównie\selectlanguage{polish}ż \selectlanguage{english}z uwagi na prostot\selectlanguage{polish}ę \selectlanguage{english}oraz komfort
obs\selectlanguage{polish}ł\selectlanguage{english}ugi. Polska, d\selectlanguage{polish}ążą\selectlanguage{english}ca do europejskich standard\selectlanguage{ngerman}ów, jest krajem, który
rozpoczyna w\selectlanguage{polish}ł\selectlanguage{english}asn\selectlanguage{polish}ą \selectlanguage{english}drog\selectlanguage{polish}ę \selectlanguage{english}ku innowacyjnym instalacjom automatyki budynku,
mimo \selectlanguage{polish}ż\selectlanguage{english}e od 1995 r. powsta\selectlanguage{polish}ł \selectlanguage{english}u nas szereg instalacji czerpi\selectlanguage{polish}ą\selectlanguage{english}cych ze
standardu EIB.

Nowoczesne technologie wykorzystywane w budynkach z systemem EBI
pozwalaj\selectlanguage{polish}ą \selectlanguage{english}na oszcz\selectlanguage{polish}ę\selectlanguage{english}dno\selectlanguage{polish}ść \selectlanguage{english}energii i wody w mieszkalnictwie w nast\selectlanguage{polish}ę\selectlanguage{english}puj\selectlanguage{polish}ą\selectlanguage{english}cym
zakresie:

\selectlanguage{ngerman}§~ 10-50 \% w ogrzewaniu i klimatyzowaniu mieszka\selectlanguage{polish}ń,

\selectlanguage{ngerman}§~ 10-50 \% w zu\selectlanguage{polish}ż\selectlanguage{english}yciu wody,

\selectlanguage{ngerman}§~ 30-50 \% w ch\selectlanguage{polish}ł\selectlanguage{english}odzeniu,

30-50 \% w o\selectlanguage{polish}ś\selectlanguage{english}wietleniu.

\par\null

\cite{BONINI_2008}

\selectlanguage{polish}\section*{Rozdział 3. Budowa i zasady działania systemu
EIB}\selectlanguage{english}\label{rozdziaux142-3.-budowa-i-zasady-dziaux142ania-systemu-eib}



\selectlanguage{polish}\subsection*{Sygnały polecenia}\selectlanguage{english}\label{sygnaux142y-polecenia}

Aby elementy systemu mog\selectlanguage{polish}ł\selectlanguage{english}y wymienia\selectlanguage{polish}ć \selectlanguage{english}pomi\selectlanguage{polish}ę\selectlanguage{english}dzy sob\selectlanguage{polish}ą \selectlanguage{english}informacje (sygna\selectlanguage{polish}ł\selectlanguage{english}y
polecenia) musz\selectlanguage{polish}ą \selectlanguage{english}by\selectlanguage{polish}ć \selectlanguage{english}odpowiednio po\selectlanguage{polish}łą\selectlanguage{english}czone. Po\selectlanguage{polish}łą\selectlanguage{english}czona struktura
po\selectlanguage{polish}łą\selectlanguage{english}cze\selectlanguage{polish}ń, \selectlanguage{english}s\selectlanguage{polish}ł\selectlanguage{english}u\selectlanguage{polish}żą\selectlanguage{english}cych do obiegu informacji w uk\selectlanguage{polish}ł\selectlanguage{english}adzie, nazywana jest
topologi\selectlanguage{polish}ą. \selectlanguage{english}W zale\selectlanguage{polish}ż\selectlanguage{english}no\selectlanguage{polish}ś\selectlanguage{english}ci od rodzaju po\selectlanguage{polish}łą\selectlanguage{english}cze\selectlanguage{polish}ń \selectlanguage{english}mo\selectlanguage{polish}ż\selectlanguage{english}na wyr\selectlanguage{ngerman}ó\selectlanguage{polish}ż\selectlanguage{english}ni\selectlanguage{polish}ć \selectlanguage{english}kilka typ\selectlanguage{ngerman}ów
topologii sieci. Poniewa\selectlanguage{polish}ż \selectlanguage{english}system insta-bus EIB w du\selectlanguage{polish}ż\selectlanguage{english}ej mierze korzysta z
rozwi\selectlanguage{polish}ą\selectlanguage{english}za\selectlanguage{polish}ń \selectlanguage{english}wdra\selectlanguage{polish}ż\selectlanguage{english}anych w sieciach komputerowych oraz przemys\selectlanguage{polish}ł\selectlanguage{english}owych
sieciach automatyki, topologia oraz spos\selectlanguage{ngerman}ób komunikowania si\selectlanguage{polish}ę \selectlanguage{english}pomi\selectlanguage{polish}ę\selectlanguage{english}dzy
sob\selectlanguage{polish}ą \selectlanguage{english}komponent\selectlanguage{ngerman}ów systemu, zwanych u\selectlanguage{polish}ż\selectlanguage{english}ytkownikami, bazuje na strukturze
okre\selectlanguage{polish}ś\selectlanguage{english}lanej mianem magistrali. Pot\selectlanguage{polish}ę\selectlanguage{english}guje ona obieg mi\selectlanguage{polish}ę\selectlanguage{english}dzy ogromn\selectlanguage{polish}ą \selectlanguage{english}ilo\selectlanguage{polish}ś\selectlanguage{english}ci\selectlanguage{polish}ą
\selectlanguage{english}uczestnik\selectlanguage{ngerman}ów, którzy po\selectlanguage{polish}łą\selectlanguage{english}czeni s\selectlanguage{polish}ą \selectlanguage{english}mi\selectlanguage{polish}ę\selectlanguage{english}dzy sob\selectlanguage{polish}ą \selectlanguage{english}za przy u\selectlanguage{polish}ż\selectlanguage{english}yciu jednego
przewodu.\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/magistrala/magistrala}
\caption{{Magistrala
{\label{778438}}%
}}
\end{center}
\end{figure}



\selectlanguage{polish}\section*{Rozdział 4. Urządzenia stosowane w ramach systemu
EIB}\selectlanguage{english}\label{rozdziaux142-4.-urzux105dzenia-stosowane-w-ramach-systemu-eib}

Klasyfikacja urz\selectlanguage{polish}ą\selectlanguage{english}dze\selectlanguage{polish}ń \selectlanguage{english}systemu EIB mo\selectlanguage{polish}ż\selectlanguage{english}e odbywa\selectlanguage{polish}ć \selectlanguage{english}si\selectlanguage{polish}ę \selectlanguage{english}na podstawie dw\selectlanguage{ngerman}óch
kryteriów. Pierwszym z nich jest rodzaj budowy urz\selectlanguage{polish}ą\selectlanguage{english}dzenia. Na jego
podstawie urz\selectlanguage{polish}ą\selectlanguage{english}dzenia mo\selectlanguage{polish}ż\selectlanguage{english}emy podzieli\selectlanguage{polish}ć \selectlanguage{english}na:

\selectlanguage{ngerman}§~ podtynkowe,

§~ do monta\selectlanguage{polish}ż\selectlanguage{english}u na szynie profilowej 35 mm (tzw. euroszynie lub szynie
kapeluszowej),

\selectlanguage{ngerman}§~ do wbudowania w urz\selectlanguage{polish}ą\selectlanguage{english}dzenia,

\selectlanguage{ngerman}§~ natynkowe,

§~ do szybkiego monta\selectlanguage{polish}ż\selectlanguage{english}u,

\selectlanguage{ngerman}§~ do monta\selectlanguage{polish}ż\selectlanguage{english}u w kana\selectlanguage{polish}ł\selectlanguage{english}ach elektroinstalacyjnych.

Drugim kryterium podzia\selectlanguage{polish}ł\selectlanguage{english}u urz\selectlanguage{polish}ą\selectlanguage{english}dze\selectlanguage{polish}ń, \selectlanguage{english}wa\selectlanguage{polish}ż\selectlanguage{english}niejszym z punktu widzenia
systemu, jest funkcja, jak\selectlanguage{polish}ą \selectlanguage{english}spe\selectlanguage{polish}ł\selectlanguage{english}nia dane urz\selectlanguage{polish}ą\selectlanguage{english}dzenie w systemie. Wed\selectlanguage{polish}ł\selectlanguage{english}ug
tego kryterium mo\selectlanguage{polish}ż\selectlanguage{english}emy wyr\selectlanguage{ngerman}ó\selectlanguage{polish}ż\selectlanguage{english}ni\selectlanguage{polish}ć \selectlanguage{english}du\selectlanguage{polish}ż\selectlanguage{english}o wi\selectlanguage{polish}ę\selectlanguage{english}cej grup produkt\selectlanguage{ngerman}ów:

§~ wy\selectlanguage{polish}ś\selectlanguage{english}wietlacze,

\selectlanguage{ngerman}§~ wyj\selectlanguage{polish}ś\selectlanguage{english}cia,

\selectlanguage{ngerman}§~ wej\selectlanguage{polish}ś\selectlanguage{english}cia,

\selectlanguage{ngerman}§~ wej\selectlanguage{polish}ś\selectlanguage{english}cia/wyj\selectlanguage{polish}ś\selectlanguage{english}cia,

\selectlanguage{ngerman}§~ urz\selectlanguage{polish}ą\selectlanguage{english}dzenia do sterowania o\selectlanguage{polish}ś\selectlanguage{english}wietleniem,

\selectlanguage{ngerman}§~ urz\selectlanguage{polish}ą\selectlanguage{english}dzenia do sterowania ogrzewaniem, wentylacj\selectlanguage{polish}ą \selectlanguage{english}i klimatyzacj\selectlanguage{polish}ą,

\selectlanguage{ngerman}§~ urz\selectlanguage{polish}ą\selectlanguage{english}dzenia zdalnego zarz\selectlanguage{polish}ą\selectlanguage{english}dzania (falami podczerwieni lub radiowymi),

\selectlanguage{ngerman}§~ urz\selectlanguage{polish}ą\selectlanguage{english}dzenia do nadzoru i powiadamiania,

\selectlanguage{ngerman}§~ urz\selectlanguage{polish}ą\selectlanguage{english}dzenia komunikacyjne,

\selectlanguage{ngerman}§~ kontrolery,

§~ czujniki fizyczne,

§~ przyciski,

§~ urz\selectlanguage{polish}ą\selectlanguage{english}dzenia czasowe;

\selectlanguage{ngerman}§~ urz\selectlanguage{polish}ą\selectlanguage{english}dzenia systemowe,

\selectlanguage{ngerman}§~ elementy dodatkowe.

\cite{Kastner_2002}

Tak jak napisano, system jest uk\selectlanguage{polish}ł\selectlanguage{english}adem zdecentralizowanym, zatem nie
posiada wyseparowanego sterownika, kt\selectlanguage{ngerman}óry gospodaruje instalacj\selectlanguage{polish}ą. \selectlanguage{english}Dlatego
ka\selectlanguage{polish}ż\selectlanguage{english}de urz\selectlanguage{polish}ą\selectlanguage{english}dzenie aktywne musi posiada\selectlanguage{polish}ć \selectlanguage{english}odpowiednie modu\selectlanguage{polish}ł\selectlanguage{english}y, kt\selectlanguage{ngerman}óre
umo\selectlanguage{polish}ż\selectlanguage{english}liwiaj\selectlanguage{polish}ą \selectlanguage{english}wymian\selectlanguage{polish}ę \selectlanguage{english}informacji pomi\selectlanguage{polish}ę\selectlanguage{english}dzy nim, a innymi urz\selectlanguage{polish}ą\selectlanguage{english}dzeniami.
Elementy te, posiadaj\selectlanguage{polish}ą \selectlanguage{english}tzw. porty magistralne, tj. systemy umo\selectlanguage{polish}ż\selectlanguage{english}liwiaj\selectlanguage{polish}ą\selectlanguage{english}ce
po\selectlanguage{polish}łą\selectlanguage{english}czenie z magistral\selectlanguage{polish}ą. \selectlanguage{english}Urz\selectlanguage{polish}ą\selectlanguage{english}dzenia takie nazywa si\selectlanguage{polish}ę \selectlanguage{english}elementami
magistralnymi, czyli aparatami, kt\selectlanguage{ngerman}óre pod\selectlanguage{polish}łą\selectlanguage{english}czone do magistrali powinny
bezpo\selectlanguage{polish}ś\selectlanguage{english}rednio wdra\selectlanguage{polish}ż\selectlanguage{english}a\selectlanguage{polish}ć \selectlanguage{english}komunikacj\selectlanguage{polish}ę \selectlanguage{english}(kreowa\selectlanguage{polish}ć \selectlanguage{english}telegramy, wysy\selectlanguage{polish}ł\selectlanguage{english}a\selectlanguage{polish}ć, \selectlanguage{english}odbiera\selectlanguage{polish}ć
\selectlanguage{english}je oraz realizowa\selectlanguage{polish}ć \selectlanguage{english}zakodowane komendy). Ka\selectlanguage{polish}ż\selectlanguage{english}dy element magistralny sk\selectlanguage{polish}ł\selectlanguage{english}ada
si\selectlanguage{polish}ę \selectlanguage{english}z portu magistralnego i z elementu ko\selectlanguage{polish}ń\selectlanguage{english}cowego, kt\selectlanguage{ngerman}óre wymieniaj\selectlanguage{polish}ą
\selectlanguage{english}informacj\selectlanguage{polish}ę \selectlanguage{english}pomi\selectlanguage{polish}ę\selectlanguage{english}dzy sob\selectlanguage{polish}ą \selectlanguage{english}poprzez dziesi\selectlanguage{polish}ę\selectlanguage{english}ciopinowe z\selectlanguage{polish}łą\selectlanguage{english}cze adaptacyjne.
Port magistralny rejestruje telegramy nap\selectlanguage{polish}ł\selectlanguage{english}ywaj\selectlanguage{polish}ą\selectlanguage{english}ce z magistrali, dekoduje
je oraz steruje komponentem ko\selectlanguage{polish}ń\selectlanguage{english}cowym, a w odwrotnym kierunku uzyskuje od
niego dane, koduje je oraz wysy\selectlanguage{polish}ł\selectlanguage{english}a w formie telegramu. Zawiera modu\selectlanguage{polish}ł
\selectlanguage{english}komunikacyjny, umo\selectlanguage{polish}ż\selectlanguage{english}liwiaj\selectlanguage{polish}ą\selectlanguage{english}cy oddzielenie napi\selectlanguage{polish}ę\selectlanguage{english}cia zasilaj\selectlanguage{polish}ą\selectlanguage{english}cego od danych
oraz kontrolny modu\selectlanguage{polish}ł \selectlanguage{english}z mikroprocesorem, zarz\selectlanguage{polish}ą\selectlanguage{english}dzaj\selectlanguage{polish}ą\selectlanguage{english}cy przesy\selectlanguage{polish}ł\selectlanguage{english}aniem oraz
odbieraniem telegram\selectlanguage{ngerman}ów i sterowaniem komunikacj\selectlanguage{polish}ą \selectlanguage{english}z komponentem ko\selectlanguage{polish}ń\selectlanguage{english}cowym.

\selectlanguage{polish}Łą\selectlanguage{english}cze adaptacyjne posiada styki zasilaj\selectlanguage{polish}ą\selectlanguage{english}ce element ko\selectlanguage{polish}ń\selectlanguage{english}cowy oraz styki
danych, realizuj\selectlanguage{polish}ą\selectlanguage{english}ce, w zale\selectlanguage{polish}ż\selectlanguage{english}no\selectlanguage{polish}ś\selectlanguage{english}ci od rodzaju komponentu ko\selectlanguage{polish}ń\selectlanguage{english}cowego,
komunikacj\selectlanguage{polish}ę \selectlanguage{english}mi\selectlanguage{polish}ę\selectlanguage{english}dzy portem magistralnym a ko\selectlanguage{polish}ń\selectlanguage{english}cowym elementem. Przez jeden
ze styk\selectlanguage{ngerman}ów z\selectlanguage{polish}łą\selectlanguage{english}cza, port magistralny rozpoznaje typ pod\selectlanguage{polish}łą\selectlanguage{english}czonego do niego
komponentu ko\selectlanguage{polish}ń\selectlanguage{english}cowego. Urz\selectlanguage{polish}ą\selectlanguage{english}dzenia wykonane w tej samej technice, np. do
monta\selectlanguage{polish}ż\selectlanguage{english}u podtynkowego, mog\selectlanguage{polish}ą \selectlanguage{english}pe\selectlanguage{polish}ł\selectlanguage{english}ni\selectlanguage{polish}ć \selectlanguage{english}w systemie r\selectlanguage{ngerman}ó\selectlanguage{polish}ż\selectlanguage{english}ne funkcje. R\selectlanguage{ngerman}ównie\selectlanguage{polish}ż
\selectlanguage{english}urz\selectlanguage{polish}ą\selectlanguage{english}dzenie pe\selectlanguage{polish}ł\selectlanguage{english}ni\selectlanguage{polish}ą\selectlanguage{english}ce konkretn\selectlanguage{polish}ą \selectlanguage{english}funkcj\selectlanguage{polish}ę \selectlanguage{english}w systemie mo\selectlanguage{polish}ż\selectlanguage{english}e wyst\selectlanguage{polish}ę\selectlanguage{english}powa\selectlanguage{polish}ć \selectlanguage{english}w
r\selectlanguage{ngerman}ó\selectlanguage{polish}ż\selectlanguage{english}nych obudowach. Podstawowym podzia\selectlanguage{polish}ł\selectlanguage{english}em urz\selectlanguage{polish}ą\selectlanguage{english}dze\selectlanguage{polish}ń \selectlanguage{english}pod wzgl\selectlanguage{polish}ę\selectlanguage{english}dem
pe\selectlanguage{polish}ł\selectlanguage{english}nionych funkcji w systemie, jest podzia\selectlanguage{polish}ł \selectlanguage{english}na aktory i sensory.
Sensory, czyli urz\selectlanguage{polish}ą\selectlanguage{english}dzenia zadaj\selectlanguage{polish}ą\selectlanguage{english}ce, s\selectlanguage{polish}ą \selectlanguage{english}to elementy wydaj\selectlanguage{polish}ą\selectlanguage{english}ce polecenia i
wysy\selectlanguage{polish}ł\selectlanguage{english}aj\selectlanguage{polish}ą\selectlanguage{english}ce je na magistral\selectlanguage{polish}ę. \selectlanguage{english}Aktory, czyli urz\selectlanguage{polish}ą\selectlanguage{english}dzenia wykonawcze,
odbieraj\selectlanguage{polish}ą \selectlanguage{english}polecenia wydane przez sensory, nades\selectlanguage{polish}ł\selectlanguage{english}ane przy pomocy
telegram\selectlanguage{ngerman}ów przez magistral\selectlanguage{polish}ę, \selectlanguage{english}i wykonuj\selectlanguage{polish}ą \selectlanguage{english}te polecenia. Nie s\selectlanguage{polish}ą \selectlanguage{english}to jednak
odbiorniki takie, jak np. oprawa o\selectlanguage{polish}ś\selectlanguage{english}wietleniowa, lecz aparaty steruj\selectlanguage{polish}ą\selectlanguage{english}ce
nimi.

\par\null



\selectlanguage{polish}\section*{Rozdział 5. Funkcje systemu
Smartdom}\selectlanguage{english}\label{rozdziaux142-5.-funkcje-systemu-smartdom}



\subsection*{Funkcje Smart Dom}\label{funkcje-smart-dom}

Przygotowuj\selectlanguage{polish}ą\selectlanguage{english}c si\selectlanguage{polish}ę \selectlanguage{english}do instalacji systemu, nale\selectlanguage{polish}ż\selectlanguage{english}y przede wszystkim
okre\selectlanguage{polish}ś\selectlanguage{english}li\selectlanguage{polish}ć, \selectlanguage{english}jakie s\selectlanguage{polish}ą \selectlanguage{english}oczekiwania u\selectlanguage{polish}ż\selectlanguage{english}ytkownik\selectlanguage{ngerman}ów lub inwestorów w zakresie
wykonywanych przez niego funkcji.~\cite{1} W szczególno\selectlanguage{polish}ś\selectlanguage{english}ci
nale\selectlanguage{polish}ż\selectlanguage{english}y zwr\selectlanguage{ngerman}óci\selectlanguage{polish}ć \selectlanguage{english}uwag\selectlanguage{polish}ę \selectlanguage{english}na nast\selectlanguage{polish}ę\selectlanguage{english}puj\selectlanguage{polish}ą\selectlanguage{english}ce obszary zastosowa\selectlanguage{polish}ń \selectlanguage{english}systemu
automatyki:

\begin{itemize}
\tightlist
\item
  ~ sterowanie o\selectlanguage{polish}ś\selectlanguage{english}wietleniem,
\item
  ~ strefowa regulacja temperatury,
\item
  ~ sterowanie \selectlanguage{polish}ż\selectlanguage{english}aluzjami, roletami, markizami,
\item
  ~ funkcje nadzoru,
\item
  ~ telekomunikacja,
\item
  zarz\selectlanguage{polish}ą\selectlanguage{english}dzanie urz\selectlanguage{polish}ą\selectlanguage{english}dzeniami gospodarstwa domowego
\end{itemize}

\par\null\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/smart-dom/smart-dom}
\caption{{Smart Dom
{\label{414584}}%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{table}[h!]
\centering
\normalsize\begin{tabulary}{1.0\textwidth}{CCCCCCCCC}
 &  &  &  &  &  &  &  &  \\
 &  &  &  &  &  &  &  &  \\
 &  &  &  &  &  &  &  &  \\
 &  &  &  &  &  &  &  &  \\
 &  &  &  &  &  &  &  &  \\
 &  &  &  &  &  &  &  &  \\
 &  &  &  &  &  &  &  &  \\
 &  &  &  &  &  &  &  &  \\
 &  &  &  &  &  &  &  &  \\
 &  &  &  &  &  &  &  &  \\
 &  &  &  &  &  &  &  &  \\
 &  &  &  & Nazwisko: & Pr\selectlanguage{polish}ą\selectlanguage{english}d & Woda & \selectlanguage{polish}Ś\selectlanguage{english}mieci & \selectlanguage{polish}Łą\selectlanguage{english}cznie \\
 &  &  &  & Kowalski & 350 & 250 & 230 & 830 \\
 &  &  &  & Zelek & 370 & 270 & 200 & 840 \\
 &  &  &  & Klimczak & 430 & 400 & 350 & 1180 \\
 &  &  &  & Nowak & 390 & 350 & 370 & 1110 \\
 &  &  &  & Bulanda & 270 & 300 & 200 & 770 \\
 &  &  &  & Czerwi\selectlanguage{polish}ń\selectlanguage{english}ski & 150 & 100 & 150 & 400 \\
 &  &  &  & Su\selectlanguage{polish}ł\selectlanguage{english}kowski & 400 & 250 & 200 & 850 \\
 &  &  &  & Golonka & 330 & 310 & 240 & 880 \\
\end{tabulary}
\caption{\selectlanguage{polish}{Wydatki mieszkańców%
}\selectlanguage{ngerman}}
\end{table}\selectlanguage{ngerman}

\selectlanguage{english}
\FloatBarrier
\bibliographystyle{plainnat}
\bibliography{bibliography/converted_to_latex.bib%
}

\end{document}

