scholarly journals Dynamic Bayesian Network for Time-Dependent Classification Problems in Robotics

2017 ◽  
Author(s):  
Cristiano Premebida ◽  
Francisco A. A. Souza ◽  
Diego R. Faria
Keyword(s):  
Bayesian Network ◽  
Dynamic Bayesian Network ◽  
Time Dependent ◽  
Classification Problems

A Study of Time-Dependent Variations in Personal Thermal Comfort via a Dynamic Bayesian Network

2015 ◽  
Author(s):  
Ali Ghahramani ◽  
Chao Tang ◽  
Zheng Yang ◽  
Burcin Becerik-Gerber
Keyword(s):  
Bayesian Network ◽  
Thermal Comfort ◽  
Dynamic Bayesian Network ◽  
Time Dependent

Corrosion Growth Modeling by Learning a Dynamic Bayesian Network From Multiple In-Line Inspection Data

2018 ◽  
Author(s):  
Wei Xiang ◽  
Wenxing Zhou
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Failure Probability ◽  
Dynamic Bayesian Network ◽  
Time Dependent ◽  
Parameter Learning ◽  
Random Errors ◽  
Defect Depth ◽  
Corrosion Defects ◽  
Dependent Failure

This paper establishes a dynamic Bayesian network to model the growth of corrosion defects on energy pipelines. The integrated model characterizes the growth of defect depth by a homogeneous gamma process and considers the biases and random errors associated with the in-line inspection (ILI) tools. The distributions of the mean value and coefficient of variation of the annual growth of defect depth are learned from multiple ILI data using the parameter learning technique of Bayesian networks. With the same technique, the distributions of the biases and standard deviation of random errors associated with ILI tools are learned from ILI data and their corresponding field measurements. An example with real corrosion management data is used to illustrate the process of developing the model structure, learning model parameters and predicting the corrosion growth and time-dependent failure probability. The results indicate that the model can in general predict the growth of corrosion defects with reasonable accuracy and the ILI-reported and field-measured depth can be used to update the time-dependent failure probability in a near-real-time manner. In comparison with existing growth models, the graphical feature of Bayesian networks makes it more intuitive and transparent to users. The employment of parameter learning provides a semi-automated and convenient approach to elicit the probabilistic information from ILI and field measurement data. The above advantages will facilitate the application of the model in the practice of corrosion management in pipeline industry.

Time and Spatial Modeling of Wear Mechanisms in Nuclear Steam Turbine Blades Using Dynamic Bayesian Networks

2018 ◽  
Author(s):  
David A. Quintanar-Gago ◽  
Pamela F. Nelson ◽  
Ángeles Díaz-Sánchez
Keyword(s):  
Bayesian Network ◽  
Failure Mode ◽  
Wear Mechanisms ◽  
Failure Modes ◽  
Turbine Blades ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
Time Dependent ◽  
Prototype Model ◽  
Quantitative Methodology

This paper describes a quantitative methodology to estimate the probability of blade failure modes resulting from typical wear mechanisms in nuclear turbines, which can be used to optimize maintenance. The approach used to model time and spatial dependence of wear mechanisms that affect blades involves the coupling of a Static Bayesian Network to a Dynamic Bayesian Network. This prototype model has been designed to use conditional and time dependent Weibull-like failure rates that can be computed from reliability data bases (failure times and modes, associated causes, row and blade part that failed) to quantify Markov matrixes contained within dynamic nodes. The model can be used to make inferences such as the most probable causes of failure in a row and blade part, and visualize the probability as a function of time. It can be also used to determine the riskier location given evidence such as failure mode or the wear mechanisms involved. Also, maintenance tasks acting over time dependent failure functions have been implemented to exemplify the effect of perfect and three kinds of imperfect actions and how they affect the mechanisms and failure mode evolution, given the conditional dependences among them.

Audio-visual Speaker Tracking Based on Dynamic Bayesian Network

2009 ◽  
Vol 34 (9) ◽  
pp. 1083-1089
Author(s):  
Nai-Gao JIN
Keyword(s):  
Bayesian Network ◽  
Dynamic Bayesian Network ◽  
Speaker Tracking

Current Development and Review of Dynamic Bayesian Network-Based Methods for Inferring Gene Regulatory Networks from Gene Expression Data

2014 ◽  
Vol 9 (5) ◽  
pp. 531-539 ◽  
Author(s):  
Lian Chai ◽  
Mohd Mohamad ◽  
Safaai Deris ◽  
Chuii Chong ◽  
Yee Choon ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bayesian Network ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Dynamic Bayesian Network ◽  
Expression Data ◽  
Current Development ◽  
Gene Regulatory

Multi-target Tracking based on Dynamic Bayesian Network with Reparameterized Approximate Variational Inference

2021 ◽  
pp. 1-1
Author(s):  
Wenqiong Zhang ◽  
Jun Zhang ◽  
Ming Bao ◽  
Xiao-Ping Zhang ◽  
Xiaodong Li
Keyword(s):  
Bayesian Network ◽  
Target Tracking ◽  
Dynamic Bayesian Network ◽  
Variational Inference ◽  
Multi Target Tracking

A robust target intention recognition method based on dynamic bayesian network

2021 ◽  
Author(s):  
Qunli Xiao ◽  
Yuanna Liu ◽  
Xinyang Deng ◽  
Wen Jiang
Keyword(s):  
Bayesian Network ◽  
Dynamic Bayesian Network ◽  
Recognition Method ◽  
Intention Recognition
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