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Détail de l'auteur
Auteur Velik, Rosemarie
Documents disponibles écrits par cet auteur
Affiner la rechercheAutonomous perception and decision making in building automation / Velik, Rosemarie in IEEE transactions on industrial electronics, Vol. 57 N° 11 (Novembre 2010)
[article]
in IEEE transactions on industrial electronics > Vol. 57 N° 11 (Novembre 2010) . - pp. 3645 - 3652
Titre : Autonomous perception and decision making in building automation Type de document : texte imprimé Auteurs : Velik, Rosemarie, Auteur ; Zucker, Gerhard, Auteur Année de publication : 2011 Article en page(s) : pp. 3645 - 3652 Note générale : Génie électrique Langues : Anglais (eng) Mots-clés : Autonomous decision making Autonomous perception Bionics Building automation Cognitive automation Index. décimale : 621.38 Dispositifs électroniques. Tubes à électrons. Photocellules. Accélérateurs de particules. Tubes à rayons X Résumé : System complexity has reached a level where it is hard to apply existing information analysis methods to automatically derive appropriate decisions. Building automation is on the verge of being unable to extract relevant information and control a building accordingly. Many different industries in today's automation could provide information by means of different sensors, but the ability to integrate this information is missing. This paper describes approaches on how to cope with increased complexity by introducing models for perception and decision making that are based on findings in neuroscience and psychoanalysis, scientific disciplines that are far-off from engineering but nevertheless promise valuable contributions to intelligent automation. DEWEY : 621.38 ISSN : 0278-0046 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5373925 [article] Autonomous perception and decision making in building automation [texte imprimé] / Velik, Rosemarie, Auteur ; Zucker, Gerhard, Auteur . - 2011 . - pp. 3645 - 3652.
Génie électrique
Langues : Anglais (eng)
in IEEE transactions on industrial electronics > Vol. 57 N° 11 (Novembre 2010) . - pp. 3645 - 3652
Mots-clés : Autonomous decision making Autonomous perception Bionics Building automation Cognitive automation Index. décimale : 621.38 Dispositifs électroniques. Tubes à électrons. Photocellules. Accélérateurs de particules. Tubes à rayons X Résumé : System complexity has reached a level where it is hard to apply existing information analysis methods to automatically derive appropriate decisions. Building automation is on the verge of being unable to extract relevant information and control a building accordingly. Many different industries in today's automation could provide information by means of different sensors, but the ability to integrate this information is missing. This paper describes approaches on how to cope with increased complexity by introducing models for perception and decision making that are based on findings in neuroscience and psychoanalysis, scientific disciplines that are far-off from engineering but nevertheless promise valuable contributions to intelligent automation. DEWEY : 621.38 ISSN : 0278-0046 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5373925 Behavior learning in dwelling environments with hidden Markov models / Bruckner, Dietmar in IEEE transactions on industrial electronics, Vol. 57 N° 11 (Novembre 2010)
[article]
in IEEE transactions on industrial electronics > Vol. 57 N° 11 (Novembre 2010) . - pp. 3653 - 3660
Titre : Behavior learning in dwelling environments with hidden Markov models Type de document : texte imprimé Auteurs : Bruckner, Dietmar, Auteur ; Velik, Rosemarie, Auteur Année de publication : 2011 Article en page(s) : pp. 3653 - 3660 Note générale : Génie électrique Langues : Anglais (eng) Mots-clés : Semantic networks Surveillance Index. décimale : 621.38 Dispositifs électroniques. Tubes à électrons. Photocellules. Accélérateurs de particules. Tubes à rayons X Résumé : Building automation systems (BASs) have seen widespread distribution also in private residences over the past few years. The ongoing technological developments in the fields of sensors, actuators, as well as embedded systems lead to more and more complex and larger systems. These systems allow ever-better observations of activities in buildings with a rapidly growing number of possible applications. Unfortunately, control systems with lots of parameters, which would be normally utilized, are hard to describe and-from a context-deriving view-hard to understand with standard control engineering techniques. This paper presents an approach to how statistical methods can be applied to (future) BASs to extract semantic and context information from sensor data. A hierarchical model structure based on hidden Markov models is proposed to establish a framework. The lower levels of the model structure are used to observe the sensor values themselves, whereas the higher levels provide a basis for the semantic interpretation of what is happening in the building. Ultimately, the system should be able to give a condensed overview of the daily routine of a sensor or the process that the sensor observes. While knowing the context of the sensor, a human operator can easily interpret the result. DEWEY : 621.38 ISSN : 0278-0046 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5437257 [article] Behavior learning in dwelling environments with hidden Markov models [texte imprimé] / Bruckner, Dietmar, Auteur ; Velik, Rosemarie, Auteur . - 2011 . - pp. 3653 - 3660.
Génie électrique
Langues : Anglais (eng)
in IEEE transactions on industrial electronics > Vol. 57 N° 11 (Novembre 2010) . - pp. 3653 - 3660
Mots-clés : Semantic networks Surveillance Index. décimale : 621.38 Dispositifs électroniques. Tubes à électrons. Photocellules. Accélérateurs de particules. Tubes à rayons X Résumé : Building automation systems (BASs) have seen widespread distribution also in private residences over the past few years. The ongoing technological developments in the fields of sensors, actuators, as well as embedded systems lead to more and more complex and larger systems. These systems allow ever-better observations of activities in buildings with a rapidly growing number of possible applications. Unfortunately, control systems with lots of parameters, which would be normally utilized, are hard to describe and-from a context-deriving view-hard to understand with standard control engineering techniques. This paper presents an approach to how statistical methods can be applied to (future) BASs to extract semantic and context information from sensor data. A hierarchical model structure based on hidden Markov models is proposed to establish a framework. The lower levels of the model structure are used to observe the sensor values themselves, whereas the higher levels provide a basis for the semantic interpretation of what is happening in the building. Ultimately, the system should be able to give a condensed overview of the daily routine of a sensor or the process that the sensor observes. While knowing the context of the sensor, a human operator can easily interpret the result. DEWEY : 621.38 ISSN : 0278-0046 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5437257 Neurosymbolic alerting rules / Velik, Rosemarie in IEEE transactions on industrial electronics, Vol. 57 N° 11 (Novembre 2010)
[article]
in IEEE transactions on industrial electronics > Vol. 57 N° 11 (Novembre 2010) . - pp. 3661 - 3668
Titre : Neurosymbolic alerting rules Type de document : texte imprimé Auteurs : Velik, Rosemarie, Auteur ; Boley, Harold, Auteur Année de publication : 2011 Article en page(s) : pp. 3661 - 3668 Note générale : Génie électrique Langues : Anglais (eng) Mots-clés : Building automation Decision making Neurosymbolic networks Perception Rule markup language (RuleML) Index. décimale : 621.38 Dispositifs électroniques. Tubes à électrons. Photocellules. Accélérateurs de particules. Tubes à rayons X Résumé : Future building automation will require complex (humanlike) perception and decision-making processes not being feasible with classical approaches. In this paper, we address both the perception and the decision-making process and present an alerting model that reacts to perceived situations in a building with decisions about possible alerts. Perception is based on the neurosymbolic information-processing model, which detects candidate alerts. Integrated with perception, decision making is based on the rule model of the Rule Markup Language, which computes alerts to relevant building occupants about current opportunities and risks. A general model of neurosymbolic alerting rules is developed and exemplified with a use case of building alerts. DEWEY : 621.38 ISSN : 0278-0046 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5422660 [article] Neurosymbolic alerting rules [texte imprimé] / Velik, Rosemarie, Auteur ; Boley, Harold, Auteur . - 2011 . - pp. 3661 - 3668.
Génie électrique
Langues : Anglais (eng)
in IEEE transactions on industrial electronics > Vol. 57 N° 11 (Novembre 2010) . - pp. 3661 - 3668
Mots-clés : Building automation Decision making Neurosymbolic networks Perception Rule markup language (RuleML) Index. décimale : 621.38 Dispositifs électroniques. Tubes à électrons. Photocellules. Accélérateurs de particules. Tubes à rayons X Résumé : Future building automation will require complex (humanlike) perception and decision-making processes not being feasible with classical approaches. In this paper, we address both the perception and the decision-making process and present an alerting model that reacts to perceived situations in a building with decisions about possible alerts. Perception is based on the neurosymbolic information-processing model, which detects candidate alerts. Integrated with perception, decision making is based on the rule model of the Rule Markup Language, which computes alerts to relevant building occupants about current opportunities and risks. A general model of neurosymbolic alerting rules is developed and exemplified with a use case of building alerts. DEWEY : 621.38 ISSN : 0278-0046 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5422660