Machine learning method has been widely used in various geotechnical engineering risk analysis in recent years. However, the overfitting problem often occurs due to the small number of samples obtained in history. Thi...Machine learning method has been widely used in various geotechnical engineering risk analysis in recent years. However, the overfitting problem often occurs due to the small number of samples obtained in history. This paper proposes the FuzzySVM(support vector machine) geotechnical engineering risk analysis method based on the Bayesian network. The proposed method utilizes the fuzzy set theory to build a Bayesian network to reflect prior knowledge, and utilizes the SVM to build a Bayesian network to reflect historical samples. Then a Bayesian network for evaluation is built in Bayesian estimation method by combining prior knowledge with historical samples. Taking seismic damage evaluation of slopes as an example, the steps of the method are stated in detail. The proposed method is used to evaluate the seismic damage of 96 slopes along roads in the area affected by the Wenchuan earthquake. The evaluation results show that the method can solve the overfitting problem, which often occurs if the machine learning methods are used to evaluate risk of geotechnical engineering, and the performance of the method is much better than that of the previous machine learning methods. Moreover,the proposed method can also effectively evaluate various geotechnical engineering risks in the absence of some influencing factors.展开更多
The contribution rate of equipment system-of-systems architecture(ESoSA)is an important index to evaluate the equipment update,development,and architecture optimization.Since the traditional ESoSA contribution rate ev...The contribution rate of equipment system-of-systems architecture(ESoSA)is an important index to evaluate the equipment update,development,and architecture optimization.Since the traditional ESoSA contribution rate evaluation method does not make full use of the fuzzy information and uncertain information in the equipment system-of-systems(ESoS),and the Bayesian network is an effective tool to solve the uncertain information,a new ESoSA contribution rate evaluation method based on the fuzzy Bayesian network(FBN)is proposed.Firstly,based on the operation loop theory,an ESoSA is constructed considering three aspects:reconnaissance equipment,decision equipment,and strike equipment.Next,the fuzzy set theory is introduced to construct the FBN of ESoSA to deal with fuzzy information and uncertain information.Furthermore,the fuzzy importance index of the root node of the FBN is used to calculate the contribution rate of the ESoSA,and the ESoSA contribution rate evaluation model based on the root node fuzzy importance is established.Finally,the feasibility and rationality of this method are validated via an empirical case study of aviation ESoSA.Compared with traditional methods,the evaluation method based on FBN takes various failure states of equipment into consideration,is free of acquiring accurate probability of traditional equipment failure,and models the uncertainty of the relationship between equipment.The proposed method not only supplements and improves the ESoSA contribution rate assessment method,but also broadens the application scope of the Bayesian network.展开更多
In the traditional reliability evaluation based on the Bayesian method,the failure probability of nodes is usually expressed by the average failure rate within a period of time.Aiming at the shortcomings of traditiona...In the traditional reliability evaluation based on the Bayesian method,the failure probability of nodes is usually expressed by the average failure rate within a period of time.Aiming at the shortcomings of traditional Bayesian network reliability evaluation methods,this paper proposes a Bayesian network reliability evaluation method considering dynamics and fuzziness.The fuzzy theory and the dynamic of component failure probability are introduced to construct the dynamic fuzzy set function.Based on the solving characteristics of the dynamic fuzzy set and Bayesian network,the fuzzy dynamic probability and fuzzy dynamic importance degree of the fault state of leaf nodes are solved.Finally,through the dynamic fuzzy reliability analysis of CNC machine tool hydraulic system balance circuit,the application of this method in system reliability evaluation is verified,which provides support for fault diagnosis of CNC machine tools.展开更多
Bayesian network( BN) is a powerful tool of uncertainty reasoning. Considering the insufficient information,incorporating fuzzy probability into BN is an effective method. Fuzzy BN was used to solve this problem. In t...Bayesian network( BN) is a powerful tool of uncertainty reasoning. Considering the insufficient information,incorporating fuzzy probability into BN is an effective method. Fuzzy BN was used to solve this problem. In this paper,fuzzy BN was applied in wafer stage system,which was an important part of lithography. BN of wafer stage was transferred from fault tree( FT). The quantitative assessment based on fuzzy BN was carried out. The Birnbaum importance factors of basic events were calculated. Therefore,the system failure probability and the vulnerable components could be gotten.展开更多
With the rapid development of the semantic web and the ever-growing size of uncertain data,representing and reasoning uncertain information has become a great challenge for the semantic web application developers.In t...With the rapid development of the semantic web and the ever-growing size of uncertain data,representing and reasoning uncertain information has become a great challenge for the semantic web application developers.In this paper,we present a novel reasoning framework based on the representation of fuzzy PR-OWL.Firstly,the paper gives an overview of the previous research work on uncertainty knowledge representation and reasoning,incorporates Ontology into the fuzzy Multi Entity Bayesian Networks theory,and introduces fuzzy PR-OWL,an Ontology language based on OWL2.Fuzzy PROWL describes fuzzy semantics and uncertain relations and gives grammatical definition and semantic interpretation.Secondly,the paper explains the integration of the Fuzzy Probability theory and the Belief Propagation algorithm.The influencing factors of fuzzy rules are added to the belief that is propagated between the nodes to create a reasoning framework based on fuzzy PR-OWL.After that,the reasoning process,including the SSFBN structure algorithm,data fuzzification,reasoning of fuzzy rules,and fuzzy belief propagation,is scheduled.Finally,compared with the classical algorithm from the aspect of accuracy and time complexity,our uncertain data representation and reasoning method has higher accuracy without significantly increasing time complexity,which proves the feasibility and validity of our solution to represent and reason uncertain information.展开更多
This paper presents a new approach for offshore risk analysis that is capable of dealing with linguistic probabilities in Bayesian networks （ BNs）. In this paper, linguistic probabilities are used to describe occurr...This paper presents a new approach for offshore risk analysis that is capable of dealing with linguistic probabilities in Bayesian networks （ BNs）. In this paper, linguistic probabilities are used to describe occurrence likelihood of hazardous events that may cause possible accidents in offshore operations. In order to use fuzzy information, an f-weighted valuation function is proposed to transform linguistic judgements into crisp probability distributions which can be easily put into a BN to model causal relationships among risk factors. The use of linguistic variables makes it easier for human experts to express their knowledge, and the transformation of linguistic judgements into crisp probabilities can significantly save the cost of computation, modifying and maintaining a BN model. The flexibility of the method allows for multiple forms of information to be used to quantify model relationships, including formally assessed expert opinion when quantitative data are lacking, or when only qualitative or vague statements can be made. The model is a modular representation of uncertain knowledge caused due to randomness, vagueness and ignorance. This makes the risk analysis of offshore engineering systems more functional and easier in many assessment contexts. Specifically, the proposed f-weighted valuation function takes into account not only the dominating values, but also the a-level values that are ignored by conventional valuation methods. A case study of the collision risk between a Floating Production, Storage and Off-loading （FPSO） unit and the anthorised vessels due to human elements during operation is used to illustrate the application of the proposed model.展开更多
为改善载人密闭舱室整体舒适度,提升作业人员的生理、心理舒适性与工作效率。对载人密闭舱室舒适度评估方法展开深入探究,在完成舱室整体舒适度评估的基础上,能够进一步明确影响舱室综合舒适度的基本事件并进行重要度排序,从而更有针对...为改善载人密闭舱室整体舒适度,提升作业人员的生理、心理舒适性与工作效率。对载人密闭舱室舒适度评估方法展开深入探究,在完成舱室整体舒适度评估的基础上,能够进一步明确影响舱室综合舒适度的基本事件并进行重要度排序,从而更有针对性的改进和指导载人密闭舱室设计。构建了载人密闭舱室舒适度故障树分析(Fault tree analysis,FTA)模型,将载人密闭舱室舒适度影响因素划分生理环境、物理因素、主观感受3个中间等级,下行提取17个基本事件,综合模糊贝叶斯(Fuzzy bayesian networks,FBN)方法进行正向诊断,评估载人密闭舱室的整体舒适性;展开逆向因果推理,寻找造成舱室不舒适的主要原因。结果表明:实例探究中选取西北工业大学载人密闭实验室进行整体舒适度评估,并通过16位被试人员的主观评价验证了载人密闭舱室FTA-FBN舒适度评估方法的有效性与可靠性;逆向推理计算各基本事件对舱室整体舒适度的影响概率并进行排序,指出载人密闭舱室设计改进方向。展开更多
基金supported by the National Key Research and Development Program (Grant No. 2017YFC0504901)Sichuan Traffic Construction Science and Technology Project(Grant No. 2016B2–2)Doctoral Innovation Fund Program of Southwest Jiaotong University(Grant No. D-CX201804)
文摘Machine learning method has been widely used in various geotechnical engineering risk analysis in recent years. However, the overfitting problem often occurs due to the small number of samples obtained in history. This paper proposes the FuzzySVM(support vector machine) geotechnical engineering risk analysis method based on the Bayesian network. The proposed method utilizes the fuzzy set theory to build a Bayesian network to reflect prior knowledge, and utilizes the SVM to build a Bayesian network to reflect historical samples. Then a Bayesian network for evaluation is built in Bayesian estimation method by combining prior knowledge with historical samples. Taking seismic damage evaluation of slopes as an example, the steps of the method are stated in detail. The proposed method is used to evaluate the seismic damage of 96 slopes along roads in the area affected by the Wenchuan earthquake. The evaluation results show that the method can solve the overfitting problem, which often occurs if the machine learning methods are used to evaluate risk of geotechnical engineering, and the performance of the method is much better than that of the previous machine learning methods. Moreover,the proposed method can also effectively evaluate various geotechnical engineering risks in the absence of some influencing factors.
基金supported by the National Key Research and Development Project(2018YFB1700802)the National Natural Science Foundation of China(72071206)the Science and Technology Innovation Plan of Hunan Province(2020RC4046).
文摘The contribution rate of equipment system-of-systems architecture(ESoSA)is an important index to evaluate the equipment update,development,and architecture optimization.Since the traditional ESoSA contribution rate evaluation method does not make full use of the fuzzy information and uncertain information in the equipment system-of-systems(ESoS),and the Bayesian network is an effective tool to solve the uncertain information,a new ESoSA contribution rate evaluation method based on the fuzzy Bayesian network(FBN)is proposed.Firstly,based on the operation loop theory,an ESoSA is constructed considering three aspects:reconnaissance equipment,decision equipment,and strike equipment.Next,the fuzzy set theory is introduced to construct the FBN of ESoSA to deal with fuzzy information and uncertain information.Furthermore,the fuzzy importance index of the root node of the FBN is used to calculate the contribution rate of the ESoSA,and the ESoSA contribution rate evaluation model based on the root node fuzzy importance is established.Finally,the feasibility and rationality of this method are validated via an empirical case study of aviation ESoSA.Compared with traditional methods,the evaluation method based on FBN takes various failure states of equipment into consideration,is free of acquiring accurate probability of traditional equipment failure,and models the uncertainty of the relationship between equipment.The proposed method not only supplements and improves the ESoSA contribution rate assessment method,but also broadens the application scope of the Bayesian network.
基金This research was supported by the Sichuan Science and Technology Depart-ment under Contract Nos.2019YJ0396 and 2018JY0516the National Natural Science Foundation of China under the Contract No.51705041.
文摘In the traditional reliability evaluation based on the Bayesian method,the failure probability of nodes is usually expressed by the average failure rate within a period of time.Aiming at the shortcomings of traditional Bayesian network reliability evaluation methods,this paper proposes a Bayesian network reliability evaluation method considering dynamics and fuzziness.The fuzzy theory and the dynamic of component failure probability are introduced to construct the dynamic fuzzy set function.Based on the solving characteristics of the dynamic fuzzy set and Bayesian network,the fuzzy dynamic probability and fuzzy dynamic importance degree of the fault state of leaf nodes are solved.Finally,through the dynamic fuzzy reliability analysis of CNC machine tool hydraulic system balance circuit,the application of this method in system reliability evaluation is verified,which provides support for fault diagnosis of CNC machine tools.
基金the Fundamental Research Funds for the Central Universities,China(Nos.ZYGX2011J090,ZYGX2011J084)
文摘Bayesian network( BN) is a powerful tool of uncertainty reasoning. Considering the insufficient information,incorporating fuzzy probability into BN is an effective method. Fuzzy BN was used to solve this problem. In this paper,fuzzy BN was applied in wafer stage system,which was an important part of lithography. BN of wafer stage was transferred from fault tree( FT). The quantitative assessment based on fuzzy BN was carried out. The Birnbaum importance factors of basic events were calculated. Therefore,the system failure probability and the vulnerable components could be gotten.
基金The authors are grateful to the editors and reviewers for their suggestions and comments.This work was supported by National Key Research and Development Project(2018YFC0824400)National Social Science Foundation project(17BXW065)+1 种基金Science and Technology Research project of Henan(1521023110285)Higher Education Teaching Reform Research and Practice Projects of Henan(32180189).
文摘With the rapid development of the semantic web and the ever-growing size of uncertain data,representing and reasoning uncertain information has become a great challenge for the semantic web application developers.In this paper,we present a novel reasoning framework based on the representation of fuzzy PR-OWL.Firstly,the paper gives an overview of the previous research work on uncertainty knowledge representation and reasoning,incorporates Ontology into the fuzzy Multi Entity Bayesian Networks theory,and introduces fuzzy PR-OWL,an Ontology language based on OWL2.Fuzzy PROWL describes fuzzy semantics and uncertain relations and gives grammatical definition and semantic interpretation.Secondly,the paper explains the integration of the Fuzzy Probability theory and the Belief Propagation algorithm.The influencing factors of fuzzy rules are added to the belief that is propagated between the nodes to create a reasoning framework based on fuzzy PR-OWL.After that,the reasoning process,including the SSFBN structure algorithm,data fuzzification,reasoning of fuzzy rules,and fuzzy belief propagation,is scheduled.Finally,compared with the classical algorithm from the aspect of accuracy and time complexity,our uncertain data representation and reasoning method has higher accuracy without significantly increasing time complexity,which proves the feasibility and validity of our solution to represent and reason uncertain information.
基金This project is funded bythe UK Engineering and Physical Sciences Research Council (EPSRC) under Grant Refer-ences:GR/S85504 and GR/S85498
文摘This paper presents a new approach for offshore risk analysis that is capable of dealing with linguistic probabilities in Bayesian networks （ BNs）. In this paper, linguistic probabilities are used to describe occurrence likelihood of hazardous events that may cause possible accidents in offshore operations. In order to use fuzzy information, an f-weighted valuation function is proposed to transform linguistic judgements into crisp probability distributions which can be easily put into a BN to model causal relationships among risk factors. The use of linguistic variables makes it easier for human experts to express their knowledge, and the transformation of linguistic judgements into crisp probabilities can significantly save the cost of computation, modifying and maintaining a BN model. The flexibility of the method allows for multiple forms of information to be used to quantify model relationships, including formally assessed expert opinion when quantitative data are lacking, or when only qualitative or vague statements can be made. The model is a modular representation of uncertain knowledge caused due to randomness, vagueness and ignorance. This makes the risk analysis of offshore engineering systems more functional and easier in many assessment contexts. Specifically, the proposed f-weighted valuation function takes into account not only the dominating values, but also the a-level values that are ignored by conventional valuation methods. A case study of the collision risk between a Floating Production, Storage and Off-loading （FPSO） unit and the anthorised vessels due to human elements during operation is used to illustrate the application of the proposed model.
文摘为改善载人密闭舱室整体舒适度,提升作业人员的生理、心理舒适性与工作效率。对载人密闭舱室舒适度评估方法展开深入探究,在完成舱室整体舒适度评估的基础上,能够进一步明确影响舱室综合舒适度的基本事件并进行重要度排序,从而更有针对性的改进和指导载人密闭舱室设计。构建了载人密闭舱室舒适度故障树分析(Fault tree analysis,FTA)模型,将载人密闭舱室舒适度影响因素划分生理环境、物理因素、主观感受3个中间等级,下行提取17个基本事件,综合模糊贝叶斯(Fuzzy bayesian networks,FBN)方法进行正向诊断,评估载人密闭舱室的整体舒适性;展开逆向因果推理,寻找造成舱室不舒适的主要原因。结果表明:实例探究中选取西北工业大学载人密闭实验室进行整体舒适度评估,并通过16位被试人员的主观评价验证了载人密闭舱室FTA-FBN舒适度评估方法的有效性与可靠性;逆向推理计算各基本事件对舱室整体舒适度的影响概率并进行排序,指出载人密闭舱室设计改进方向。
