Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Article . 2009
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Article . 2009
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Article . 2009
License: CC BY
Data sources: Datacite
versions View all 2 versions
addClaim

Scenario Recognition In Modern Building Automation

Authors: Lang, Roland; Bruckner, Dietmar; Velik, Rosemarie; Deutsch, Tobias;

Scenario Recognition In Modern Building Automation

Abstract

{"references": ["C. M. Bishop, Neural Networks for Pattern Recognition, New York NY:\nOxford University Press Inc., p. 20, 1995.", "D. Bruckner, Probabilistic Models in Building Automation - Recognizing\nScnearios with Statistical Methods, Ph.D. Thesis, Vienna University of\nTechnology, 2007.", "W. Burgstaller, Interpretation of Scenarios in Buildings, Ph.D. Thesis,\nVienna University of Technology, 2007.", "T. Deutsch, R. Lang, G. Pratl, E. Brainin, S. Teicher, Applying Psychoanalytical\nand Neuro-Scientific Models to Automation. Proc. International\nConference on Intelligent Environments, pp. 111-118, 2006.", "D. Dietrich, G. Russ, C. Tamarit, G. Koller, M. Ponweiser, M. Vincze,\nModellierung des technischen Wahrnehmungsbewusstseins fr den Bereich\nHome Automation, e&i, Vol. 11, pp. 454-455, 2001.", "M. Dornes, Der kompetente Sugling - Die prverbale Entwicklung des\nMenschen, Fischer Taschenbuch Verlag, 2001.", "R.W. Picard R. W, Affective Computing, The MIT Press, 1997.", "G. Pratl, P. Palensky, The Project ARS - The Next Step Towards an\nIntelligent Environment, Proc. International Conference on Intelligent\nEnvironments, pp. 55-62, 2005.", "G. Pratl, W. Penzhorn, D. Dietrich, W. Burgstaller, Perceptive Awareness\nin Building Automation. Proc. International Conference on Computational\nCybernetics, pp. 259-264, 2005.\n[10] G. Pratl, Processing and Symbolization of Ambient Sensor Data, Ph.D.\nThesis, Vienna University of Technology, 2006.\n[11] G. Pratl, D. Dietrich, G. Hancke, W. Penzhorn, A New Model for Autonomous,\nNetworked Control Systems, IEEE Transactions on Industrial\nInformatics, Vol. 1, Issue 3, pp. 21-32, 2007.\n[12] L. R. Rabiner, B. Juang, An Introduction to Hidden Markov Models,\nASSAP Magazine, Vol. 3, pp. 4-16, 1986.\n[13] R. Rakotomamonjy, R. Le Riche, D. Gualandris, and Z. Harchaoui,\nA Comparison of Statistical Learning Approaches for Engine Torque\nEstimation. Control Engineering Practice, Vol. 16, Issue 1, pp. 43-55,\n2007.\n[14] E. M. Tapia, S. S. Intille, K. Larson, Activity Recognition in the Home\nUsing Simple and Ubiquitous Sensors, Pervasive, pp. 158-175, 2004.\n[15] R. Velik, G. Pratl, R. Lang, Multi-Sensory, Symbolic, Knowledge-Base\nModel for Humanlike Perception, Proc. International Conference on\nFieldbuses and Networks in Industrial and Embedded Systems, pp. 273-\n278, 2007."]}

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, surveillance system., ubiquitous computing, scenariorecognition

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
    OpenAIRE UsageCounts
    Usage byUsageCounts
    visibility views 3
    download downloads 5
  • 3
    views
    5
    downloads
    Powered byOpenAIRE UsageCounts
Powered by OpenAIRE graph
Found an issue? Give us feedback
visibility
download
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
0
Average
Average
Average
3
5
Green