scholarly journals Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network

2021 ◽  
Vol 11 (8) ◽  
pp. 3619
Author(s):  
Weiliang Qiao ◽  
Xiaoxue Ma ◽  
Yang Liu ◽  
He Lan
Keyword(s):  
Bayesian Network ◽  
Absorptive Capacity ◽  
Fuzzy Theory ◽  
Back Propagation ◽  
Expert Elicitation ◽  
Safety Level ◽  
Prior Probabilities ◽  
Safety Issues ◽  
Northern Sea Route ◽  
Probability Table

The safety level of the northern sea route (NSR) is a common concern for the related stakeholders. To address the risks triggered by disruptions initiating from the harsh environment and human factors, a comprehensive framework is proposed based on the perspective of resilience. Notably, the resilience of NSR is decomposed into three capacities, namely, the absorptive capacity, adaptive capacity, and restorative capacity. Moreover, the disruptions to the resilience are identified. Then, a Bayesian network (BN) model is established to quantify resilience, and the prior probabilities of parent nodes and conditional probability table for the network are obtained by fuzzy theory and expert elicitation. Finally, the developed Bayesian networkBN model is simulated to analyze the resilience level of the NSR by back propagation, sensitivity analysis, and information entropy analysis. The general interpretation of these analyses indicates that the emergency response, ice-breaking capacity, and rescue and anti-pollution facilities are the critical factors that contribute to the resilience of the NSR. Good knowledge of the absorptive capacity is the most effective way to reduce the uncertainty of NSR resilience. The present study provides a resilience perspective to understand the safety issues associated with the NSR, which can be seen as the main innovation of this work.

scholarly journals Human Factor Risk Modeling for Shipyard Operation by Mapping Fuzzy Fault Tree into Bayesian Network

2021 ◽  
Vol 19 (1) ◽  
pp. 297
Author(s):  
Yang Liu ◽  
Xiaoxue Ma ◽  
Weiliang Qiao ◽  
Huiwen Luo ◽  
Peilong He
Keyword(s):  
Human Factors ◽  
Bayesian Network ◽  
Human Factor ◽  
Fuzzy Theory ◽  
Fault Tree ◽  
Expert Elicitation ◽  
Risk Modeling ◽  
Nested Model ◽  
Prior Probabilities ◽  
Operational Activities

The operational activities conducted in a shipyard are exposed to high risk associated with human factors. To investigate human factors involved in shipyard operational accidents, a double-nested model was proposed in the present study. The modified human factor analysis classification system (HFACS) was applied to identify the human factors involved in the accidents, the results of which were then converted into diverse components of a fault tree and, as a result, a single-level nested model was established. For the development of a double-nested model, the structured fault tree was mapped into a Bayesian network (BN), which can be simulated with the obtained prior probabilities of parent nodes and the conditional probability table by fuzzy theory and expert elicitation. Finally, the developed BN model is simulated for various scenarios to analyze the identified human factors by means of structural analysis, path dependencies and sensitivity analysis. The general interpretation of these analysis verify the effectiveness of the proposed methodology to evaluate the human factor risks involved in operational accidents in a shipyard.

HFACS Analysis of U.S. General Aviation Accidents Using Bayesian Network

2020 ◽  
Vol 64 (1) ◽  
pp. 1655-1659
Author(s):  
Chuyang Yang ◽  
John H. Mott
Keyword(s):  
Human Factors ◽  
Bayesian Network ◽  
General Aviation ◽  
Transportation Safety ◽  
Aviation Industry ◽  
Contributing Factors ◽  
The Sustainable Development ◽  
Aviation Accidents ◽  
Prior Probabilities ◽  
Probability Table

Safety is one of the most important factors that affects the sustainable development of the aviation industry. With the increasing robustness of technologies, humans have played a progressively more important causal role in aviation accidents. This paper applies an HFACS-BN model (HFACS: Human Factors Analysis and Classification System; BN: Bayesian Network) to analyze the root causes of aviation accidents. General aviation (GA) accident reports were collected from the U.S. National Transportation Safety Board (NTSB) accident database. The authors encoded the human factors of sample cases based on the HFACS framework and constructed a corresponding BN. From this work, parameter estimation associated with a conditional probability table (CPT) was conducted to determine prior probabilities of contributing factors, and a sensitivity test was conducted to determine the most significant factors. This study provides guidance to the federal government to facilitate risk management in order to reduce fatal general aviation accidents.

scholarly journals Reliability Analysis of Metro Door System Based on Fuzzy Multi-State Bayesian Network

2021 ◽  
Vol 53 (5) ◽  
pp. 210509
Author(s):  
Zhenliang Fu ◽  
Na Li ◽  
Xueyan Tian ◽  
Yonghua Li ◽  
Ziqiang Sheng
Keyword(s):  
Missing Data ◽  
Bayesian Network ◽  
Reliability Analysis ◽  
Fuzzy Theory ◽  
Probability Model ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Practical Experience ◽  
Prior Probabilities ◽  
System A

Considering the shortcomings of the fault tree analysis (FTA) method in the reliability analysis of metro door systems, Bayesian network (BN) and fuzzy theory were introduced to establish the failure probability model of a metro door system. A fault tree of the metro door system was established based on the structure of the metro door, the operation data record and the practical experience of relevant engineers. The BN of the metro door system was constructed based on the fault tree. For the problem that the prior probabilities of root nodes with missing data were unavailable, fuzzy theory was introduced to convert the expert language values on these missing data nodes to corresponding prior probabilities, which were substituted into the BN along with the root nodes whose prior probabilities were obtained from the operation fault data to calculate the leaf node probability. Cause analysis of the metro door system was performed with bi-directional reasoning of BN, which provided a way to find the key factors that caused door faults and the metro door system fault probabilities.

Transformer Fault Diagnosis Based on Improved Quantum Genetic Algorithm and BP Network

2010 ◽  
Vol 29-32 ◽  
pp. 1543-1549 ◽  
Author(s):  
Jie Wei ◽  
Hong Yu ◽  
Jin Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fuzzy Theory ◽  
Back Propagation ◽  
Gas Analysis ◽  
Bp Network ◽  
Quantum Genetic Algorithm ◽  
Transformer Fault

Three-ratio of the IEC is a convenient and effective approach for transformer fault diagnosis in the dissolved gas analysis (DGA). Fuzzy theory is used to preprocess the three-ratio for its boundary that is too absolute. As the same time, an improved quantum genetic algorithm IQGA (QGASAC) is used to optimize the weight and threshold of the back propagation (BP). The local and global searching ability of the QGASAC approach is utilized to find the BP optimization solution. It can overcome the slower convergence velocity and hardly getting the optimization of the BP neural network. So, aiming at the shortcoming of BP neural network and three-ratio, blurring the boundary of the gas ratio and the QGASAC algorithm is introduced to optimize the BP network. Then the QGASAC-IECBP method is proposed in this paper. Experimental results indicate that the proposed algorithm in this paper that both convergence velocity and veracity are all improved to some extent. And in this paper, the proposed algorithm is robust and practical.

Air Combat Situation Assessment by Gray Fuzzy Bayesian Network

2011 ◽  
Vol 69 ◽  
pp. 114-119 ◽  
Author(s):  
Yong Bo Xuan ◽  
Chang Qiang Huang ◽  
Wang Xi Li
Keyword(s):  
Bayesian Network ◽  
Domain Knowledge ◽  
Fuzzy Theory ◽  
Fuzzy Probability ◽  
Fuzzy Information ◽  
Bayesian Network Model ◽  
Uncertainty Reasoning ◽  
Situation Assessment ◽  
Air Combat ◽  
Operational Decision Making

Automatic and accurate situation assessment is essential for aircraft to conduct and maintain operations autonomously and effectively. There are many uncertainties during the process of air combat situation assessment which have a significant influence on operational decision making. For the uncertainty of advanced aircraft in air combat Situation assessment, with the uncertainty knowledge representation of gray fuzzy theory and uncertainty reasoning of Bayesian network, the fuzzy information can change into the probability of domain knowledge through fuzzy probability conversion formula, A gray fuzzy Bayesian network model for situation assessment of air combat is established, the simulation results shows that the model is reasonable and feasible.

scholarly journals Non-dimensional method of capacity limit states for the assessment of the safety level in the structures of large panel buildings.

2021 ◽  
pp. 42-47
Author(s):  
Piotr BIERANOWSKI ◽  
Adam BARYŁKA
Keyword(s):  
Assessment Method ◽  
Point Of View ◽  
Limit States ◽  
Safety Level ◽  
Safety Issues ◽  
Vertical Reinforcement ◽  
Capacity Limit ◽  
Large Panel ◽  
Computer Research ◽  
Construction Model

In the article deals with the European safety issues of large-panel buildings from the point of view of exceeding the ULS. The proprietary assessment method was proposed under the name: Dimensional Limit States Method, which is dedicated to the assessment of the safety level in the structures of large-panel buildings. The work was based on many years of computer research conducted by the author. In the paper uses the proprietary  construction model of the Wk-70 system building, presents the results for vertical reinforcement inserts.

scholarly journals Determination of the Type of Heart Syndrome in Traditional Chinese Medicine with the Bayesian Network Method

2020 ◽  
Vol 12 (2) ◽  
pp. 32-38
Author(s):  
Asto Buditjahjanto
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Expert Knowledge ◽  
Prior Probabilities ◽  
Network Method

The determination of a disease syndrome in the TCM is difficult enough to determine because it requires a lot of experience in observing patients' symptoms that appear in disease syndrome and their disease syndrome history. Symptoms that appear in one disease syndrome are varied and can also appear in other disease syndromes. This research limits the determination of the type of syndrome only in the heart organ. The purpose of this study is to determine the type of heart syndrome in TCM by using Bayesian Networks. Bayesian Networks is used because it has the advantage of adapting expert knowledge toward the preferences of symptoms that arise at a type of heart syndrome. The expert's preference is in the weights that act as prior probabilities that are used as the basis for calculations on the Bayesian Networks. The results showed that the Bayesian Networks can be used to determine the type of heart syndrome well. The results of trials on 7 patients yield the same diagnosis between the doctor's diagnosis and the Bayesian Networks calculation

scholarly journals Questionnaire Investigation on Tourists’ Behavior and its Sensibility Analysis Utilizing Bayesian Network

2018 ◽  
Vol 7 (1) ◽  
pp. 11
Author(s):  
Akane Okubo ◽  
Tsuyosi Aburai ◽  
Kazuhiro Takeyasu
Keyword(s):  
Sensitivity Analysis ◽  
Local Government ◽  
Bayesian Network ◽  
Causal Relationship ◽  
Back Propagation ◽  
Izu Peninsula ◽  
Regional Collaboration ◽  
Cherry Blossom ◽  
Sensibility Analysis ◽  
Tourism Service

Tourists from abroad are increasing rapidly in Japan. Kawazu town in Izu Peninsula is famous for its cherry trees. In the cherry blossom season, many tourists visit this town. In order to get much more visitors, tourists’ behavior should be investigated much further. The Kawazu Cherry Blossom Festival was carried out in February 2015. Our research investigation was performed during that period. In this paper, a questionnaire investigation is executed in order to clarify tourists’ behavior, and to seek the possibility of developing regional collaboration among local government, tourism related industry and visitors. In this research, we construct the model utilizing Bayesian Network and causal relationship is sequentially chained by the characteristics of travelers, an objective to visit Izu Peninsula in Japan and the main occasion to visit them. We analyzed them by sensitivity analysis and some useful results were obtained. Sensitivity analysis is performed by back propagation method. We have presented the paper concerning this. But the volume becomes too large, therefore we have split them and this paper shows the latter half of the investigation result by setting evidence to Bayesian Network items. These are utilized for constructing a much more effective and useful tourism service. We have obtained fruitful results. To confirm the findings by utilizing the new consecutive visiting records would be the future works to be investigated.

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