Scenario Recognition in Modern Building Automation
Modern building automation needs to deal with very
different types of demands, depending on the use of a building and the
persons acting in it. To meet the requirements of situation awareness
in modern building automation, scenario recognition becomes more
and more important in order to detect sequences of events and to react
to them properly. We present two concepts of scenario recognition
and their implementation, one based on predefined templates and the
other applying an unsupervised learning algorithm using statistical
methods. Implemented applications will be described and their advantages
and disadvantages will be outlined.
[1] C. M. Bishop, Neural Networks for Pattern Recognition, New York NY:
Oxford University Press Inc., p. 20, 1995.
[2] D. Bruckner, Probabilistic Models in Building Automation - Recognizing
Scnearios with Statistical Methods, Ph.D. Thesis, Vienna University of
Technology, 2007.
[3] W. Burgstaller, Interpretation of Scenarios in Buildings, Ph.D. Thesis,
Vienna University of Technology, 2007.
[4] T. Deutsch, R. Lang, G. Pratl, E. Brainin, S. Teicher, Applying Psychoanalytical
and Neuro-Scientific Models to Automation. Proc. International
Conference on Intelligent Environments, pp. 111-118, 2006.
[5] D. Dietrich, G. Russ, C. Tamarit, G. Koller, M. Ponweiser, M. Vincze,
Modellierung des technischen Wahrnehmungsbewusstseins fr den Bereich
Home Automation, e&i, Vol. 11, pp. 454-455, 2001.
[6] M. Dornes, Der kompetente Sugling - Die prverbale Entwicklung des
Menschen, Fischer Taschenbuch Verlag, 2001.
[7] R.W. Picard R. W, Affective Computing, The MIT Press, 1997.
[8] G. Pratl, P. Palensky, The Project ARS - The Next Step Towards an
Intelligent Environment, Proc. International Conference on Intelligent
Environments, pp. 55-62, 2005.
[9] G. Pratl, W. Penzhorn, D. Dietrich, W. Burgstaller, Perceptive Awareness
in Building Automation. Proc. International Conference on Computational
Cybernetics, pp. 259-264, 2005.
[10] G. Pratl, Processing and Symbolization of Ambient Sensor Data, Ph.D.
Thesis, Vienna University of Technology, 2006.
[11] G. Pratl, D. Dietrich, G. Hancke, W. Penzhorn, A New Model for Autonomous,
Networked Control Systems, IEEE Transactions on Industrial
Informatics, Vol. 1, Issue 3, pp. 21-32, 2007.
[12] L. R. Rabiner, B. Juang, An Introduction to Hidden Markov Models,
ASSAP Magazine, Vol. 3, pp. 4-16, 1986.
[13] R. Rakotomamonjy, R. Le Riche, D. Gualandris, and Z. Harchaoui,
A Comparison of Statistical Learning Approaches for Engine Torque
Estimation. Control Engineering Practice, Vol. 16, Issue 1, pp. 43-55,
2007.
[14] E. M. Tapia, S. S. Intille, K. Larson, Activity Recognition in the Home
Using Simple and Ubiquitous Sensors, Pervasive, pp. 158-175, 2004.
[15] R. Velik, G. Pratl, R. Lang, Multi-Sensory, Symbolic, Knowledge-Base
Model for Humanlike Perception, Proc. International Conference on
Fieldbuses and Networks in Industrial and Embedded Systems, pp. 273-
278, 2007.
[1] C. M. Bishop, Neural Networks for Pattern Recognition, New York NY:
Oxford University Press Inc., p. 20, 1995.
[2] D. Bruckner, Probabilistic Models in Building Automation - Recognizing
Scnearios with Statistical Methods, Ph.D. Thesis, Vienna University of
Technology, 2007.
[3] W. Burgstaller, Interpretation of Scenarios in Buildings, Ph.D. Thesis,
Vienna University of Technology, 2007.
[4] T. Deutsch, R. Lang, G. Pratl, E. Brainin, S. Teicher, Applying Psychoanalytical
and Neuro-Scientific Models to Automation. Proc. International
Conference on Intelligent Environments, pp. 111-118, 2006.
[5] D. Dietrich, G. Russ, C. Tamarit, G. Koller, M. Ponweiser, M. Vincze,
Modellierung des technischen Wahrnehmungsbewusstseins fr den Bereich
Home Automation, e&i, Vol. 11, pp. 454-455, 2001.
[6] M. Dornes, Der kompetente Sugling - Die prverbale Entwicklung des
Menschen, Fischer Taschenbuch Verlag, 2001.
[7] R.W. Picard R. W, Affective Computing, The MIT Press, 1997.
[8] G. Pratl, P. Palensky, The Project ARS - The Next Step Towards an
Intelligent Environment, Proc. International Conference on Intelligent
Environments, pp. 55-62, 2005.
[9] G. Pratl, W. Penzhorn, D. Dietrich, W. Burgstaller, Perceptive Awareness
in Building Automation. Proc. International Conference on Computational
Cybernetics, pp. 259-264, 2005.
[10] G. Pratl, Processing and Symbolization of Ambient Sensor Data, Ph.D.
Thesis, Vienna University of Technology, 2006.
[11] G. Pratl, D. Dietrich, G. Hancke, W. Penzhorn, A New Model for Autonomous,
Networked Control Systems, IEEE Transactions on Industrial
Informatics, Vol. 1, Issue 3, pp. 21-32, 2007.
[12] L. R. Rabiner, B. Juang, An Introduction to Hidden Markov Models,
ASSAP Magazine, Vol. 3, pp. 4-16, 1986.
[13] R. Rakotomamonjy, R. Le Riche, D. Gualandris, and Z. Harchaoui,
A Comparison of Statistical Learning Approaches for Engine Torque
Estimation. Control Engineering Practice, Vol. 16, Issue 1, pp. 43-55,
2007.
[14] E. M. Tapia, S. S. Intille, K. Larson, Activity Recognition in the Home
Using Simple and Ubiquitous Sensors, Pervasive, pp. 158-175, 2004.
[15] R. Velik, G. Pratl, R. Lang, Multi-Sensory, Symbolic, Knowledge-Base
Model for Humanlike Perception, Proc. International Conference on
Fieldbuses and Networks in Industrial and Embedded Systems, pp. 273-
278, 2007.
@article{"International Journal of Information, Control and Computer Sciences:57352", author = "Roland Lang and Dietmar Bruckner and Rosemarie Velik and Tobias Deutsch", title = "Scenario Recognition in Modern Building Automation", abstract = "Modern building automation needs to deal with very
different types of demands, depending on the use of a building and the
persons acting in it. To meet the requirements of situation awareness
in modern building automation, scenario recognition becomes more
and more important in order to detect sequences of events and to react
to them properly. We present two concepts of scenario recognition
and their implementation, one based on predefined templates and the
other applying an unsupervised learning algorithm using statistical
methods. Implemented applications will be described and their advantages
and disadvantages will be outlined.", keywords = "Building automation, ubiquitous computing, scenariorecognition, surveillance system.", volume = "3", number = "4", pages = "1074-9", }