wilamowski-b-m-irwin-j-d-industrial-communication-systems-2011

68-10 Industrial Communication Systems
The authors of this text fully agree with this statement. It is of great importance to form teams with
members from both sciences working together on a daily basis rather than meeting regularly presenting
own theories about the other’s profession. An indication that David’s statement still remains true in
many areas is that there are still no examples of human-like intelligence in automation.
The development in machine perception has taken two ways: one related with industrial process
control, where machines are designed and built in order to increase productivity, reduce costs, as well
as enhance quality and flexibility in the production process. These machines mostly need to perceive
a well-known environment and therefore consist of a selected number of dedicated sensors. The sum
of sensor views composes the machines’ view of the world. Numerous publications have been written,
which explain mathematical ways to cope with sensor data to fulfill the needs of automation in these
respects [Ise84].
The second development path was and is concerned with the perception of humans [VLS+08] and
human activities, on the one hand, and with implementing perception systems imitating human perception
for broader application areas, on the other. Involved research fields are, among others, cognitive
sciences, AI, image processing, audio data processing, natural language processing, user interfaces, and
human machine interfaces.
Although the goal of perception systems always was to create human-like perception including all
sensory modalities of humans [KBS01], from the early beginning of computer science until now, the
user interface—the front end of the computer, through which computers can “perceive” humans—normally
does not offer very human-like communication channels. User interfaces up to now, including
today’s so-called tangible interfaces are still unintuitive (batch interfaces in the 1960s, command-line
interfaces in the 1970s, graphical user interfaces from the 1980s until today*).
The research field concerned with perceiving information about human users is called context aware
systems. Context-aware systems are used to build devices in the fields of intelligent environments or
ubiquitous computing. The common view in these communities is that computers will not only become
cheaper, smaller, and more powerful; they will also more or less disappear and hide by becoming integrated
in normal, everyday objects [Hai06,Mat04]. Technology will dissolve embedded into our surroundings.
Smart objects will communicate, cooperate, and virtually amalgamate without explicit user
interaction or commands to form consortia in order to offer or even fulfill tasks on behalf of a user.
They will be capable of not only sensing values, but also of deriving context information about the reasons,
intentions, desires, and beliefs of the user. This information may be shared over networks like the
Internet and used to compare and classify activities, find connections to other people and/or devices,
look up semantic databases, and much more.
One of the key issues in contemporary research toward this vision is scenario recognition [GJ07].
Scenario recognition tries to find sequences of particular behaviors in time, and groups it in a way
humans would. This can range from very simple examples like “a person walking along a corridor” up
to “there is a football match” in a stadium.
References
[Bis95] Bishop, C.M., Neural Networks for Pattern Recognition, New York, Oxford University Press, 1995.
[BKV+08] Bruckner, D., J. Kasbi, R. Velik, and W. Herzner, High-level hierarchical semantic processing
framework for smart sensor networks, In: Proceedings of the HSI, Krakow, Poland, 2008.
[BLS06] Beyerer, J., F. Puente Leon, and K.-D. Sommer (eds.), Information fusion in der Mess- und
Sensortechnik, Universitaetsverlag Karlsruhe, Karlsruhe, Germany, 2006.
[Bre02] Breazeal, C., Designing Sociable Robots, Cambridge, MA, MIT Press, 2002.
[CR04] Costello, M.C. and E.D. Reichle, LSDNet: A neural network for multisensory perception, In Sixth
International Conference on Cognitive Modeling, Pittsburgh, PA, 2004, pp. 341–341.
* http://en. wikipedia. org/wiki/User_interface
© 2011 by Taylor and Francis Group, LLC