A Hybrid Context-Aware Wearable System with Evolutionary Optimization and Selective Inference of Dynamic Bayesian Networks

2011 ◽  
pp. 444-451 ◽  
Author(s):  
Jun-Ki Min ◽  
Sung-Bae Cho
Keyword(s):  
Bayesian Networks ◽  
Evolutionary Optimization ◽  
Dynamic Bayesian Networks ◽  
Context Aware ◽  
Wearable System ◽  
Selective Inference

scholarly journals Dynamic Bayesian Networks for Context-Aware Fall Risk Assessment

Sensors ◽  
2014 ◽  
Vol 14 (5) ◽  
pp. 9330-9348 ◽  
Author(s):  
Gregory Koshmak ◽  
Maria Linden ◽  
Amy Loutfi
Keyword(s):  
Risk Assessment ◽  
Bayesian Networks ◽  
Fall Risk ◽  
Dynamic Bayesian Networks ◽  
Context Aware ◽  
Fall Risk Assessment

ConaMSN: A context-aware messenger using dynamic Bayesian networks with wearable sensors

2010 ◽  
Vol 37 (6) ◽  
pp. 4680-4686 ◽  
Author(s):  
Jin-Hyuk Hong ◽  
Sung-Ihk Yang ◽  
Sung-Bae Cho
Keyword(s):  
Bayesian Networks ◽  
Wearable Sensors ◽  
Dynamic Bayesian Networks ◽  
Context Aware

Integrated modular Bayesian networks with selective inference for context-aware decision making

2015 ◽  
Vol 163 ◽  
pp. 38-46 ◽  
Author(s):  
Seung-Hyun Lee ◽  
Kyon-Mo Yang ◽  
Sung-Bae Cho
Keyword(s):  
Decision Making ◽  
Bayesian Networks ◽  
Context Aware ◽  
Selective Inference

Multiple small objects tracking based on dynamic Bayesian networks with spatial prior

Optik ◽  
2014 ◽  
Vol 125 (10) ◽  
pp. 2243-2247 ◽  
Author(s):  
Rui Yao ◽  
Yanning Zhang ◽  
Yong Zhou ◽  
Shixiong Xia
Keyword(s):  
Bayesian Networks ◽  
Dynamic Bayesian Networks ◽  
Objects Tracking

Multimodal Sensor Fusion for Indoor Occupancy Determination

2015 ◽  
Vol 764-765 ◽  
pp. 1319-1323
Author(s):  
Rong Shue Hsiao ◽  
Ding Bing Lin ◽  
Hsin Piao Lin ◽  
Jin Wang Zhou
Keyword(s):  
Bayesian Networks ◽  
Sensor Fusion ◽  
Dynamic Bayesian Networks ◽  
Sensor Nodes ◽  
Detection Accuracy ◽  
Multisensor Fusion ◽  
Fusion Algorithm ◽  
Improve Energy Efficiency ◽  
Occupancy Detection ◽  
Automation Systems

Pyroelectric infrared (PIR) sensors can detect the presence of human without the need to carry any device, which are widely used for human presence detection in home/office automation systems in order to improve energy efficiency. However, PIR detection is based on the movement of occupants. For occupancy detection, PIR sensors have inherent limitation when occupants remain relatively still. Multisensor fusion technology takes advantage of redundant, complementary, or more timely information from different modal sensors, which is considered an effective approach for solving the uncertainty and unreliability problems of sensing. In this paper, we proposed a simple multimodal sensor fusion algorithm, which is very suitable to be manipulated by the sensor nodes of wireless sensor networks. The inference algorithm was evaluated for the sensor detection accuracy and compared to the multisensor fusion using dynamic Bayesian networks. The experimental results showed that a detection accuracy of 97% in room occupancy can be achieved. The accuracy of occupancy detection is very close to that of the dynamic Bayesian networks.

scholarly journals A dynamic Bayesian network approach for prognosis computations on discrete state systems

2017 ◽  
Vol 231 (5) ◽  
pp. 516-533 ◽  
Author(s):  
Josquin Foulliaron ◽  
Laurent Bouillaut ◽  
Patrice Aknin ◽  
Anne Barros
Keyword(s):  
Complex Systems ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Multicomponent System ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Discrete State ◽  
Virtual Maintenance

The maintenance optimization of complex systems is a key question. One important objective is to be able to anticipate future maintenance actions required to optimize the logistic and future investments. That is why, over the past few years, the predictive maintenance approaches have been an expanding area of research. They rely on the concept of prognosis. Many papers have shown how dynamic Bayesian networks can be relevant to represent multicomponent complex systems and carry out reliability studies. The diagnosis and maintenance group from French institute of science and technology for transport, development and networks (IFSTTAR) developed a model (VirMaLab: Virtual Maintenance Laboratory) based on dynamic Bayesian networks in order to model a multicomponent system with its degradation dynamic and its diagnosis and maintenance processes. Its main purpose is to model a maintenance policy to be able to optimize the maintenance parameters due to the use of dynamic Bayesian networks. A discrete state-space system is considered, periodically observable through a diagnosis process. Such systems are common in railway or road infrastructure fields. This article presents a prognosis algorithm whose purpose is to compute the remaining useful life of the system and update this estimation each time a new diagnosis is available. Then, a representation of this algorithm is given as a dynamic Bayesian network in order to be next integrated into the Virtual Maintenance Laboratory model to include the set of predictive maintenance policies. Inference computation questions on the considered dynamic Bayesian networks will be discussed. Finally, an application on simulated data will be presented.

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