scholarly journals A Driving Behavior Awareness Model based on a Dynamic Bayesian Network and Distributed Genetic Algorithm

2018 ◽  
Vol 11 (1) ◽  
pp. 469 ◽  
Author(s):  
Guotao Xie ◽  
Hongbo Gao ◽  
Bin Huang ◽  
Lijun Qian ◽  
Jianqiang Wang
Keyword(s):  
Genetic Algorithm ◽  
Bayesian Network ◽  
Dynamic Bayesian Network ◽  
Driving Behavior ◽  
Model Based ◽  
Distributed Genetic Algorithm

MRF model based image segmentation using hierarchical distributed genetic algorithm

1998 ◽  
Vol 34 (25) ◽  
pp. 2394 ◽  
Author(s):  
Hang Joon Kim ◽  
Eun Yi Kim ◽  
Jin Wook Kim ◽  
Se Hyun Park
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Model Based ◽  
Distributed Genetic Algorithm

A driver fatigue recognition model based on information fusion and dynamic Bayesian network

2010 ◽  
Vol 180 (10) ◽  
pp. 1942-1954 ◽  
Author(s):  
Guosheng Yang ◽  
Yingzi Lin ◽  
Prabir Bhattacharya
Keyword(s):  
Bayesian Network ◽  
Information Fusion ◽  
Dynamic Bayesian Network ◽  
Driver Fatigue ◽  
Recognition Model ◽  
Model Based

scholarly journals INCREASING EFFECTIVENESS OF MODEL-BASED FAULT DIAGNOSIS: A DYNAMIC BAYESIAN NETWORK DESIGN FOR DECISION MAKING

2006 ◽  
Vol 39 (13) ◽  
pp. 90-95 ◽  
Author(s):  
Philippe WEBER° ◽  
Didier THEILLIOL° ◽  
Christophe AUBRUN° ◽  
Alexandre EVSUKOFF
Keyword(s):  
Decision Making ◽  
Fault Diagnosis ◽  
Bayesian Network ◽  
Network Design ◽  
Dynamic Bayesian Network ◽  
Model Based

scholarly journals Decision Support Model for Ecological Operation of Reservoirs Based on Dynamic Bayesian Network

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1658
Author(s):  
Tao Zhou ◽  
Zengchuan Dong ◽  
Xiuxiu Chen ◽  
Qihua Ran
Keyword(s):  
Decision Support ◽  
Bayesian Network ◽  
Reservoir Operation ◽  
Dynamic Bayesian Network ◽  
Operation Model ◽  
Decision Support Model ◽  
Model Based ◽  
Proposed Model ◽  
Ecological Targets ◽  
Support Model

In this study, a model was proposed based on the sustainable boundary approach, to provide decision support for reservoir ecological operation with the dynamic Bayesian network. The proposed model was developed in four steps: (1) calculating and verifying the sustainable boundaries in combination with the ecological objectives of the study area, (2) generating the learning samples by establishing an optimal operation model and a Monte Carlo simulation model, (3) establishing and training a dynamic Bayesian network by learning the examples and (4) calculating the probability of the economic and ecological targets exceeding the set threshold from time to time with the trained dynamic Bayesian network model. Using the proposed model, the water drawing of the reservoir can be adjusted dynamically according to the probability of the economic and ecological targets exceeding the set threshold during reservoir operation. In this study, the proposed model was applied to the middle reaches of Heihe River, the effect of water supply proportion on the probability of the economic target exceeding the set threshold was analyzed, and the response of the reservoir water storage in each period to the probability of the target exceeding the set threshold was calculated. The results show that the risks can be analyzed with the proposed model. Compared with the existing studies, the proposed model provides guidance for the ecological operation of the reservoir from time to time and technical support for the formulation of reservoir operation chart. Compared with the operation model based on the designed guaranteed rate, the reservoir operation model based on uncertainty reduces the variation range of ecological flow shortage or the overflow rate and the economic loss rate by 5% and 6%, respectively. Thus, it can be seen that the decision support model based on the dynamic Bayesian network can effectively reduce the influence of water inflow and rainfall uncertainties on reservoir operation.

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