Application of Bayesian for the Situation Assessment of Sea-Battlefield

2013 ◽  
Vol 346 ◽  
pp. 135-139 ◽  
Author(s):  
Yong Tao Yu ◽  
Ying Ding ◽  
Zheng Xi Ding
Keyword(s):  
Decision Support ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Conditional Probability ◽  
Assessment Model ◽  
Situation Assessment ◽  
Network Characteristics ◽  
Specific Assessment

The sea-battlefield situation is dynamic and how efficient sea-battlefield situation assessment is a major problem facing operational decision support. According to research based on Bayesian networks Sea-battlefield situation assessment, first constructed sea-battlefield situation assessment Bayesian network; followed by specific assessment objectives, to simplify creating sub Bayesian assessment model; once again based on Bayesian network characteristics to determine each node probability formula; finally, according to the formula for solving the edge of the probability and the conditional probability of each node, sea-battlefield situation assessment.

Improved Dynamic Bayesian Networks in Sea-Battlefield Situation Assessment

2014 ◽  
Vol 936 ◽  
pp. 2149-2154 ◽  
Author(s):  
Yong Tao Yu ◽  
Ying Ding
Keyword(s):  
Decision Support ◽  
Bayesian Networks ◽  
Hidden Variables ◽  
Dynamic Bayesian Networks ◽  
Situation Assessment ◽  
Assessment Task ◽  
Specific Assessment

How to efficiently evaluate the dynamic and complex the sea-battlefield situation facing the reality of the problem is the operational decision support. According to research sea-battlefield situation assessment based on improved dynamic Bayesian networks. First constructed the sea-battlefield situation assessment Bayesian networks model; second specific assessment task to establish the corresponding dynamic Bayesian networks; again reintroduced extended hidden variables, supplemental situation information to construct improved dynamic Bayesian networks; finally, according to the battlefield event reasoning, complete sea-battlefield situation assessment.

Improved Dynamic Bayesian Networks in Sea-Battlefield Situation Assessment

2014 ◽  
Vol 875-877 ◽  
pp. 2190-2195
Author(s):  
Yong Tao Yu ◽  
Ying Ding
Keyword(s):  
Decision Support ◽  
Bayesian Networks ◽  
Hidden Variables ◽  
Dynamic Bayesian Networks ◽  
Situation Assessment ◽  
Assessment Task ◽  
Specific Assessment

How to efficiently evaluate the dynamic and complex the sea-battlefield situation facing the reality of the problem is the operational decision support. According to research sea-battlefield situation assessment based on improved dynamic Bayesian networks. First constructed the sea-battlefield situation assessment Bayesian networks model; second specific assessment task to establish the corresponding dynamic Bayesian networks; again reintroduced extended hidden variables, supplemental situation information to construct improved dynamic Bayesian networks; finally, according to the battlefield event reasoning, complete sea-battlefield situation assessment.

scholarly journals Modelling fatigue assessment at the vehicle driver’s station

2018 ◽  
Vol 10 ◽  
pp. 02018
Author(s):  
Piotr Maksym ◽  
Halina Pawlak
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Health Condition ◽  
Assessment Model ◽  
Network Technology ◽  
Fatigue Assessment ◽  
Professional Drivers

The article presents the principles of fatigue assessment modeling in a driver's station using Bayesian networks. One of the causes of road collisions and accidents is fatigue. The factors determining fatigue are age of driver, psychophysical and health condition, time and length of the route being taken. At present, there is no clear criteria for assessing fatigue among professional drivers, so the objective of assessment is to attempt to design and construct a fatigue assessment model using Bayesian network technology.

scholarly journals NEW PRODUCT DEVELOPMENT WITH DYNAMIC DECISION SUPPORT

2009 ◽  
Vol 06 (02) ◽  
pp. 135-153 ◽  
Author(s):  
JACOBUS PETRUS VENTER ◽  
CORNELIS CRISTO VAN WAVEREN
Keyword(s):  
Decision Support ◽  
Product Development ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
New Product Development ◽  
New Product ◽  
Essential Elements ◽  
Research Approach ◽  
Decision Support Model ◽  
Development Management

The development of new and improved management methods for new product development is important. Existing methods suffer from a number of shortcomings, especially their inability to deal with a mixture of quantitative and qualitative data. The objective of this study is to apply decision support techniques (especially Bayesian networks) to the area of new product development management in order to address some of the shortcomings. The research approach is one of decision structuring and modeling. The literature shows the criteria that are important during the management of new product development. These criteria are used in a three-step decision structuring framework to develop a conceptual model based on a Bayesian network, in support of new product development management. The result is a Bayesian network that incorporates the knowledge of experts into a decision support model. The model is shown to be requisite because it contains all the essential elements of the problem on which decision-makers can base their action. The model can be used to perform 'what-if' analyses in various ways, thereby supporting the management of risk in new product development. This research not only contributes a model to support new product development management, but also provides insight into how decision support — especially Bayesian networks — can enhance technology management methods.

Application of Bayesian Network in Safety Evaluation of Metro Construction

2013 ◽  
Vol 838-841 ◽  
pp. 1463-1468
Author(s):  
Xiang Ke Liu ◽  
Zhi Shen Wang ◽  
Hai Liang Wang ◽  
Jun Tao Wang
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Posterior Probability ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Safety Evaluation ◽  
Tree Analysis ◽  
Tunnel Blasting ◽  
Better Than

The paper introduced the Bayesian networks briefly and discussed the algorithm of transforming fault tree into Bayesian networks at first, then regarded the structures impaired caused by tunnel blasting construction as a example, introduced the built and calculated method of the Bayesian networks by matlab. Then assumed the probabilities of essential events, calculated the probability of top event and the posterior probability of each essential events by the Bayesian networks. After that the paper contrast the characteristics of fault tree analysis and the Bayesian networks, Identified that the Bayesian networks is better than fault tree analysis in safety evaluation in some case, and provided a valid way to assess risk in metro construction.

scholarly journals Modeling the City Distribution System Reliability with Bayesian Networks to Identify Influence Factors

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Hao Zhang ◽  
Liyu Zhu ◽  
Shensi Xu
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
System Reliability ◽  
Distribution System ◽  
Complex Problem ◽  
Practical Significance ◽  
Correlation Degree ◽  
Urban Distribution ◽  
Distribution System Reliability ◽  
The City

Under the increasingly uncertain economic environment, the research on the reliability of urban distribution system has great practical significance for the integration of logistics and supply chain resources. This paper summarizes the factors that affect the city logistics distribution system. Starting from the research of factors that influence the reliability of city distribution system, further construction of city distribution system reliability influence model is built based on Bayesian networks. The complex problem is simplified by using the sub-Bayesian network, and an example is analyzed. In the calculation process, we combined the traditional Bayesian algorithm and the Expectation Maximization (EM) algorithm, which made the Bayesian model able to lay a more accurate foundation. The results show that the Bayesian network can accurately reflect the dynamic relationship among the factors affecting the reliability of urban distribution system. Moreover, by changing the prior probability of the node of the cause, the correlation degree between the variables that affect the successful distribution can be calculated. The results have significant practical significance on improving the quality of distribution, the level of distribution, and the efficiency of enterprises.

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