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Hybrid quantum–classical multi-agent decision-making framework based on hierarchical Bayesian networks in the noisy intermediate-scale quantum era 认领 引用
1
作者 Hao Shi Chenghao Han +1 位作者 Peng Wang Ming Zhang 《Chinese Physics B》 SCIE EI CAS CSCD 2025年第12期61-74,共14页
Although quantum Bayesian networks provide a promising paradigm for multi-agent decision-making,their practical application faces two challenges in the noisy intermediate-scale quantum(NISQ)era.Limited qubit resources... Although quantum Bayesian networks provide a promising paradigm for multi-agent decision-making,their practical application faces two challenges in the noisy intermediate-scale quantum(NISQ)era.Limited qubit resources restrict direct application to large-scale inference tasks.Additionally,no quantum methods are currently available for multi-agent collaborative decision-making.To address these,we propose a hybrid quantum–classical multi-agent decision-making framework based on hierarchical Bayesian networks,comprising two novel methods.The first one is a hybrid quantum–classical inference method based on hierarchical Bayesian networks.It decomposes large-scale hierarchical Bayesian networks into modular subnetworks.The inference for each subnetwork can be performed on NISQ devices,and the intermediate results are converted into classical messages for cross-layer transmission.The second one is a multi-agent decision-making method using the variational quantum eigensolver(VQE)in the influence diagram.This method models the collaborative decision-making with the influence diagram and encodes the expected utility of diverse actions into a Hamiltonian and subsequently determines the intra-group optimal action efficiently.Experimental validation on the IonQ quantum simulator demonstrates that the hierarchical method outperforms the non-hierarchical method at the functional inference level,and the VQE method can obtain the optimal strategy exactly at the collaborative decision-making level.Our research not only extends the application of quantum computing to multi-agent decision-making but also provides a practical solution for the NISQ era. 展开更多
关键词 quantum Bayesian networks multi-agent decision-making hybrid quantum–classical algorithms hierarchical Bayesian networks
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Meteorological and traffic effects on air pollutants using Bayesian networks and deep learning 认领 引用
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作者 Yuan-Chien Lin Yu-Ting Lin +1 位作者 Cai-Rou Chen Chun-Yeh Lai 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2025年第6期54-70,共17页
Traffic emissions have become the major air pollution source in urban areas.Therefore,understanding the highly non-stational and complex impact of traffic factors on air quality is very important for building air qual... Traffic emissions have become the major air pollution source in urban areas.Therefore,understanding the highly non-stational and complex impact of traffic factors on air quality is very important for building air quality prediction models.Using real-world air pollutant data from Taipei City,this study integrates diverse factors,including traffic flow,speed,rainfall patterns,andmeteorological factors.We constructed a Bayesian network probabilitymodel based on rainfall events as a big data analysis framework to investigate understand traffic factor causality relationships and condition probabilities for meteorological factors and air pollutant concentrations.Generalized Additive Model(GAM)verified non-linear relationships between traffic factors and air pollutants.Consequently,we propose a long short term memory(LSTM)model to predict airborne pollutant concentrations.This study propose a new approach of air pollutants and meteorological variable analysis procedure by considering both rainfall amount and patterns.Results indicate improved air quality when controlling vehicle speed above 40 km/h and maintaining an average vehicle flow0.9 and 0.8 for first and second order air pollutants,i.e.,CO,NO,NO2,and NOx;and O3,PM10,and PM2.5,respectively. 展开更多
关键词 Air quality Rainfall pattern Traffic emissions Generalized additive model Bayesian networks LSTM model
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Dynamic Reliability Assessment Approach for Deepwater Subsea Wellhead Systems via Hybrid Bayesian Networks 认领 引用
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作者 LI Jia-yi CHANG Yuan-jiang +2 位作者 LIU Xiu-quan XU Liang-bin CHEN Guo-ming 《China Ocean Engineering》 SCIE EI CSCD 2025年第1期100-110,共11页
The deepwater subsea wellhead(SW)system is the foundation for the construction of oil and gas wells and the crucial channel for operation.During riser connection operation,the SW system is subjected to cyclic dynamic ... The deepwater subsea wellhead(SW)system is the foundation for the construction of oil and gas wells and the crucial channel for operation.During riser connection operation,the SW system is subjected to cyclic dynamic loads which cause fatigue damage to the SW system,and continuously accumulated fatigue damage leads to fatigue failure of the SW system,rupture,and even blowout accidents.This paper proposes a hybrid Bayesian network(HBN)-based dynamic reliability assessment approach for deepwater SW systems during their service life.In the proposed approach,the relationship between the accumulation of fatigue damage and the fatigue failure probability of the SW system is predicted,only considering normal conditions.The HBN model,which includes the accumulation of fatigue damage under normal conditions and the other factors affecting the fatigue of the SW system,is subsequently developed.When predictive and diagnostic analysis techniques are adopted,the dynamic reliability of the SW system is achieved,and the most influential factors are determined.Finally,corresponding safety control measures are proposed to improve the reliability of the SW system effectively.The results illustrate that the fatigue failure speed increases rapidly when the accumulation fatigue damage is larger than 0.45 under normal conditions and that the reliability of the SW system is larger than 94%within the design life. 展开更多
关键词 deepwater subsea wellhead system reliability accumulation fatigue damage hybrid Bayesian network
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Variational Inference Based Kernel Dynamic Bayesian Networks for Construction of Prediction Intervals for Industrial Time Series With Incomplete Input 认领 引用 被引量：5
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作者 Long Chen Linqing Wang +2 位作者 Zhongyang Han Jun Zhao Wei Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2020年第5期1437-1445,共9页
Prediction intervals(PIs)for industrial time series can provide useful guidance for workers.Given that the failure of industrial sensors may cause the missing point in inputs,the existing kernel dynamic Bayesian netwo... Prediction intervals(PIs)for industrial time series can provide useful guidance for workers.Given that the failure of industrial sensors may cause the missing point in inputs,the existing kernel dynamic Bayesian networks(KDBN),serving as an effective method for PIs construction,suffer from high computational load using the stochastic algorithm for inference.This study proposes a variational inference method for the KDBN for the purpose of fast inference,which avoids the timeconsuming stochastic sampling.The proposed algorithm contains two stages.The first stage involves the inference of the missing inputs by using a local linearization based variational inference,and based on the computed posterior distributions over the missing inputs the second stage sees a Gaussian approximation for probability over the nodes in future time slices.To verify the effectiveness of the proposed method,a synthetic dataset and a practical dataset of generation flow of blast furnace gas(BFG)are employed with different ratios of missing inputs.The experimental results indicate that the proposed method can provide reliable PIs for the generation flow of BFG and it exhibits shorter computing time than the stochastic based one. 展开更多
关键词 Industrial time series kernel dynamic Bayesian networks(KDBN) prediction intervals(PIs) variational inference
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Bayesian networks modeling for thermal error of numerical control machine tools 认领 引用 被引量：9
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作者 Xin-hua YAO Jian-zhong FU Zi-chen CHEN 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS 2008年第11期1524-1530,共7页
The interaction between the heat source location, its intensity, thermal expansion coefficient, the machine system configuration and the running environment creates complex thermal behavior of a machine tool, and also... The interaction between the heat source location, its intensity, thermal expansion coefficient, the machine system configuration and the running environment creates complex thermal behavior of a machine tool, and also makes thermal error prediction difficult. To address this issue, a novel prediction method for machine tool thermal error based on Bayesian networks (BNs) was presented. The method described causal relationships of factors inducing thermal deformation by graph theory and estimated the thermal error by Bayesian statistical techniques. Due to the effective combination of domain knowledge and sampled data, the BN method could adapt to the change of running state of machine, and obtain satisfactory prediction accuracy. Ex- periments on spindle thermal deformation were conducted to evaluate the modeling performance. Experimental results indicate that the BN method performs far better than the least squares (LS) analysis in terms of modeling estimation accuracy. 展开更多
关键词 Bayesian networks(BNs) Thermal error model Numerical control(NC)machine tool
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Prediction of risk of cardiovascular events in patients with mild to moderate coronary artery lesions using naive Bayesian networks 认领 引用 被引量：2
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作者 Wei WANG Xian-Tao SONG +7 位作者 Yun-Dai CHEN Xing-Sheng YANG Feng XU Min ZHANG Kai TAN Fei YUAN Dong LI Shu-Zheng LYU 《Journal of Geriatric Cardiology》 SCIE CAS CSCD 2016年第11期899-905,共7页
Background This prospective study integrated multiple clinical indexes and inflammatory markers associated with coronary atherosclerotic vulnerable plaque to establish a risk prediction model that can evaluate a patie... Background This prospective study integrated multiple clinical indexes and inflammatory markers associated with coronary atherosclerotic vulnerable plaque to establish a risk prediction model that can evaluate a patient with certain risk factors for the likelihood of the occurrence of a coronary heart disease event within one year. Methods This study enrolled in 2686 patients with mild to moderate coronary artery lesions. Eighty-five indexes were recorded, included baseline clinical data, laboratory studies, and procedural characteristics. During the 1-year follow-up, 233 events occurred, five patients died, four patients suffered a nonfatal myocardial infarction, four patients underwent revascularization, and 220 patients were readmitted for angina pectoris. The Risk Estimation Model and the Simplified Model were conducted using Bayesian networks and compared with the Single Factor Models. Results The area under the curve was 0.88 for the Bayesian Model and 0.85 for the Simplified Model, while the Single Factor Model had a maximum area under the curve of 0.65. Conclusion The new models can be used to assess the short-term risk of individual coronary heart disease events and may assist in guiding preventive care. 展开更多
关键词 Bayesian networks Cardiovascular events Prediction
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Learning Bayesian networks using genetic algorithm 认领 引用 被引量：4
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作者 Chen Fei Wang Xiufeng Rao Yimei 《Journal of Systems Engineering and Electronics》 SCIE EI 2007年第1期142-147,共6页
A new method to evaluate the fitness of the Bayesian networks according to the observed data is provided.The main advantage of this criterion is that it is suitable for both the complete and incomplete cases while the... A new method to evaluate the fitness of the Bayesian networks according to the observed data is provided.The main advantage of this criterion is that it is suitable for both the complete and incomplete cases while the others not.Moreover it facilitates the computation greatly.In order to reduce the search space,the notation of equivalent class proposed by David Chickering is adopted.Instead of using the method directly,the novel criterion,variable ordering,and equivalent class are combined,moreover the proposed mthod avoids some problems caused by the previous one.Later,the genetic algorithm which allows global convergence,lack in the most of the methods searching for Bayesian network is applied to search for a good model in thisspace.To speed up the convergence,the genetic algorithm is combined with the greedy algorithm.Finally,the simulation shows the validity of the proposed approach. 展开更多
关键词 Bayesian networks Genetic algorithm Structure learning Equivalent class
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Modeling of combined Bayesian networks and cognitive framework for decision-making in C2 认领 引用 被引量：8
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作者 Li Wang Mingzhe Wang 《Journal of Systems Engineering and Electronics》 SCIE EI 2010年第5期812-820,共9页
The command and control（C2） is a decision-making process based on human cognition,which contains operational,physical,and human characteristics,so it takes on uncertainty and complexity.As a decision support approac... The command and control（C2） is a decision-making process based on human cognition,which contains operational,physical,and human characteristics,so it takes on uncertainty and complexity.As a decision support approach,Bayesian networks（BNs） provide a framework in which a decision is made by combining the experts＇ knowledge and the specific data.In addition,an expert system represented by human cognitive framework is adopted to express the real-time decision-making process of the decision maker.The combination of the Bayesian decision support and human cognitive framework in the C2 of a specific application field is modeled and executed by colored Petri nets（CPNs）,and the consequences of execution manifest such combination can perfectly present the decision-making process in C2. 展开更多
关键词 Bayesian networks decision support cognitive framework command and control colored Petri nets.
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Optimization of High-Speed Railway Bridge Disaster Warning Systems Using Fuzzy Bayesian Networks and Embedded Runge-Kutta Pairs for Real-Time Risk Assessment 认领 引用
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作者 Yufei Zhang Hongyu Liang +5 位作者 Yuebing Liu Yuxin Fan Qiuyan Chen Wenjie Fu Genxi Wang Yucheng Shen 《Journal of World Architecture》 2025年第6期63-72,共10页
High-speed railway(HSR)bridges face multi-hazard risks from wind,earthquakes,and fires,necessitating optimized warning systems for safety and efficiency.This study proposes a framework integrating fuzzy Bayesian netwo... High-speed railway(HSR)bridges face multi-hazard risks from wind,earthquakes,and fires,necessitating optimized warning systems for safety and efficiency.This study proposes a framework integrating fuzzy Bayesian networks(FBNs)for probabilistic risk modeling with embedded Runge-Kutta pairs for dynamic simulations,enabling real-time assessments.FBNs handle uncertainties across 22 indicators from standards like GB 50352-2019,categorizing capabilities in prevention,extinguishing,evacuation,rescue,and management.Runge-Kutta(orders 6/5)solves ODEs for transient responses,approximating finite element outputs in surrogates like LSTM-RNNs.Alarm optimization uses objective functions balancing busyness and alarm frequency,tested on Chinese HSR lines.Results show wind alarm reductions up to 47.6%with minimal downtime increases,fire risks graded“good”(54.3%)with management as key improvement,and collision missed alarms<6%at<1ms.The system reduces missed warnings by 6%and delivers alerts in milliseconds,advancing HSR resilience and minimizing disruptions. 展开更多
关键词 Disaster warning Fuzzy Bayesian network High-speed railway Runge-Kutta Risk assessment
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Mean Opinion Score Estimation for Mobile Broadband Networks Using Bayesian Networks 认领 引用
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作者 Ayman A.El-Saleh Abdulraqeb Alhammadi +2 位作者 Ibraheem Shayea Azizul Azizan Wan Haslina Hassan 《Computers, Materials & Continua》 SCIE EI 2022年第9期4571-4587,共17页
Mobile broadband(MBB)networks are expanding rapidly to deliver higher data speeds.The fifth-generation cellular network promises enhanced-MBB with high-speed data rates,low power connectivity,and ultralow latency vide... Mobile broadband(MBB)networks are expanding rapidly to deliver higher data speeds.The fifth-generation cellular network promises enhanced-MBB with high-speed data rates,low power connectivity,and ultralow latency video streaming.However,existing cellular networks are unable to perform well due to high latency and low bandwidth,which degrades the performance of various applications.As a result,monitoring and evaluation of the performance of these network-supported services is critical.Mobile network providers optimize and monitor their network performance to ensure the highest quality of service to their end-users.This paper proposes a Bayesian model to estimate the minimum opinion score(MOS)of video streaming services for any particular cellular network.The MOS is the most commonly used metric to assess the quality of experience.The proposed Bayesian model consists of several input data,namely,round-trip time,stalling load,and bite rates.It was examined and evaluated using several test data sizes with various performance metrics.Simulation results show the proposed Bayesian network achieved higher accuracy overall test data sizes than a neural network.The proposed Bayesian network obtained a remarkable overall accuracy of 90.36%and outperformed the neural network. 展开更多
关键词 Quality of experience quality of service bayesian networks minimum opinion score artificial intelligence prediction mobile broadband
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Uncertain Knowledge Reasoning Based on the Fuzzy Multi Entity Bayesian Networks 认领 引用
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作者 Dun Li Hong Wu +3 位作者 Jinzhu Gao Zhuoyun Liu Lun Li Zhiyun Zheng 《Computers, Materials & Continua》 SCIE EI 2019年第7期301-321,共21页
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
关键词 Ontology language uncertainty representation uncertainty reasoning fuzzy multi entity Bayesian networks belief propagation algorithm fuzzy PR-OWL
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Reliability analysis of monotone coherent multi-state systems based on Bayesian networks 认领 引用 被引量：2
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作者 Binghua Song Zhongbao Zhou +2 位作者 Chaoqun Ma Jinglun Zhou Shaofeng Geng 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2016年第6期1326-1335,共10页
The Bayesian networks (BNs) provide a robust probabilistic method of reasoning under uncertainty and have been successfully applied to a variety of real-world tasks. Aiming to explore the capabilities of the BN formal... The Bayesian networks (BNs) provide a robust probabilistic method of reasoning under uncertainty and have been successfully applied to a variety of real-world tasks. Aiming to explore the capabilities of the BN formalism in reliability analysis of monotone coherent multi-state systems, the BNs are compared with a popular tool for reliability analysis of monotone coherent multi-state systems, namely the multi-state fault trees (MFTs). It is shown that any MFT can be directly mapped into BN and the basic inference techniques on the latter may be used to obtain classical parameters computed from the former (i.e. probability distribution of top variable, minimal upper vectors and maximum lower vectors for any performance level, importance measures of components). Furthermore, some additional information can be obtained by using BN, both at the modeling and analysis level. At the modeling level, several restrictive assumptions implicit in the MFT methodology can be removed and various kinds of dependencies among components can be accommodated. At the analysis level, a general diagnostic analysis can be performed. The comparison of these methods is illustrated by an example of the water supply system. © 2016 Beijing Institute of Aerospace Information. 展开更多
关键词 Bayesian networks Probability distributions Reliability Reliability theory Vectors Water supply Water supply systems
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Analysis of rockburst mechanism and warning based on microseismic moment tensors and dynamic Bayesian networks 认领 引用 被引量：20
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作者 Haoyu Mao Nuwen Xu +4 位作者 Xiang Li Biao Li Peiwei Xiao Yonghong Li Peng Li 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2023年第10期2521-2538,共18页
One of the major factors inhibiting the construction of deep underground projects is the risk posed by rockbursts.A study was conducted on the access tunnel of the Shuangjiangkou hydropower station to determine the ev... One of the major factors inhibiting the construction of deep underground projects is the risk posed by rockbursts.A study was conducted on the access tunnel of the Shuangjiangkou hydropower station to determine the evolutionary mechanism of microfractures within the surrounding rock mass during rockburst development and develop a rockburst warning model.The study area was chosen through the combination of field studies with an analysis of the spatial and temporal distribution of microseismic(MS)events.The moment tensor inversion method was adopted to study rockburst mechanism,and a dynamic Bayesian network(DBN)was applied to investigating the sensitivity of MS source parameters for rockburst warnings.A MS multivariable rockburst warning model was proposed and validated using two case studies.The results indicate that fractures in the surrounding rock mass during the development of strain-structure rockbursts initially show shear failure and are then followed by tensile failure.The effectiveness of the DBN-based rockburst warning model was demonstrated using self-validation and K-fold cross-validation.Moment magnitude and source radius are the most sensitive factors based on an investigation of the influence on the parent and child nodes in the model,which can serve as important standards for rockburst warnings.The proposed rockburst warning model was found to be effective when applied to two actual projects. 展开更多
关键词 Microseismic monitoring Moment tensor Dynamic Bayesian network(DBN) Rockburst warning Shuangjiangkou hydropower station
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Reliability Modeling and Evaluation of Complex Multi-State System Based on Bayesian Networks Considering Fuzzy Dynamic of Faults 认领 引用 被引量：5
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作者 Fangjun Zuo Meiwei Jia +2 位作者 Guang Wen Huijie Zhang Pingping Liu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2021年第11期993-1012,共20页
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
关键词 Bayesian network(BN) dynamics fuzzy multi-state
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Target threat estimation based on discrete dynamic Bayesian networks with small samples 认领 引用 被引量：7
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作者 YE Fang MAO Ying +1 位作者 LI Yibing LIU Xinrui 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2022年第5期1135-1142,共8页
The accuracy of target threat estimation has a great impact on command decision-making.The Bayesian network,as an effective way to deal with the problem of uncertainty,can be used to track the change of the target thr... The accuracy of target threat estimation has a great impact on command decision-making.The Bayesian network,as an effective way to deal with the problem of uncertainty,can be used to track the change of the target threat level.Unfortunately,the traditional discrete dynamic Bayesian network(DDBN)has the problems of poor parameter learning and poor reasoning accuracy in a small sample environment with partial prior information missing.Considering the finiteness and discreteness of DDBN parameters,a fuzzy k-nearest neighbor(KNN)algorithm based on correlation of feature quantities(CF-FKNN)is proposed for DDBN parameter learning.Firstly,the correlation between feature quantities is calculated,and then the KNN algorithm with fuzzy weight is introduced to fill the missing data.On this basis,a reasonable DDBN structure is constructed by using expert experience to complete DDBN parameter learning and reasoning.Simulation results show that the CF-FKNN algorithm can accurately fill in the data when the samples are seriously missing,and improve the effect of DDBN parameter learning in the case of serious sample missing.With the proposed method,the final target threat assessment results are reasonable,which meets the needs of engineering applications. 展开更多
关键词 discrete dynamic Bayesian network(DDBN) parameter learning missing data filling Bayesian estimation
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An evaluation method of contribution rate based on fuzzy Bayesian networks for equipment system-of-systems architecture 认领 引用 被引量：6
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作者 XU Renjie LIU Xin +2 位作者 CUI Donghao XIE Jian GONG Lin 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2023年第3期574-587,共14页
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
关键词 equipment system-of-systems architecture(ESoSA) contribution rate evaluation fuzzy Bayesian network(FBN) fuzzy set theory
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Structure learning on Bayesian networks by finding the optimal ordering with and without priors 认领 引用 被引量：5
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作者 HE Chuchao GAO Xiaoguang GUO Zhigao 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2018年第6期1209-1227,共19页
Ordering based search methods have advantages over graph based search methods for structure learning of Bayesian networks in terms on the efficiency. With the aim of further increasing the accuracy of ordering based s... Ordering based search methods have advantages over graph based search methods for structure learning of Bayesian networks in terms on the efficiency. With the aim of further increasing the accuracy of ordering based search methods, we first propose to increase the search space, which can facilitate escaping from the local optima. We present our search operators with majorizations, which are easy to implement. Experiments show that the proposed algorithm can obtain significantly more accurate results. With regard to the problem of the decrease on efficiency due to the increase of the search space, we then propose to add path priors as constraints into the swap process. We analyze the coefficient which may influence the performance of the proposed algorithm, the experiments show that the constraints can enhance the efficiency greatly, while has little effect on the accuracy. The final experiments show that, compared to other competitive methods, the proposed algorithm can find better solutions while holding high efficiency at the same time on both synthetic and real data sets. 展开更多
关键词 Bayesian network structure learning ordering search space graph search space prior constraint
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Reliability Analysis of Electrical System of Computer Numerical Control Machine Tool Based on Bayesian Networks 认领 引用 被引量：2
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作者 黄土地 晏晶 +2 位作者 姜梅 彭卫文 黄洪钟 《Journal of Shanghai Jiaotong university(Science)》 EI 2016年第5期635-640,共6页
The core of computer numerical control(CNC) machine tool is the electrical system which controls and coordinates every part of CNC machine tool to complete processing tasks, so it is of great significance to strengthe... The core of computer numerical control(CNC) machine tool is the electrical system which controls and coordinates every part of CNC machine tool to complete processing tasks, so it is of great significance to strengthen the reliability of the electrical system. However, the electrical system is very complex due to many uncertain factors and dynamic stochastic characteristics when failure occurs. Therefore, the traditional fault tree analysis(FTA) method is not applicable. Bayesian network(BN) not only has a unique advantage to analyze nodes with multiply states in reliability analysis for complex systems, but also can solve the state explosion problem properly caused by Markov model when dealing with dynamic fault tree(DFT). In addition, the forward causal reasoning of BN can get the conditional probability distribution of the system under considering the uncertainty;the backward diagnosis reasoning of BN can recognize the weak links in system, so it is valuable for improving the system reliability. 展开更多
关键词 dynamic fault tree(DFT) Bayesian network(BN) reliability computer numerical control(CNC) machine tool electrical system
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Using junction trees for structural learning of Bayesian networks 认领 引用 被引量：1
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作者 Mingmin Zhu Sanyang Liu +1 位作者 Youlong Yang Kui Liu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第2期286-292,共7页
The learning Bayesian network （BN） structure from data is an NP-hard problem and still one of the most exciting chal- lenges in the machine learning. In this work, a novel algorithm is presented which combines ideas... The learning Bayesian network （BN） structure from data is an NP-hard problem and still one of the most exciting chal- lenges in the machine learning. In this work, a novel algorithm is presented which combines ideas from local learning, constraint- based, and search-and-score techniques in a principled and ef- fective way. It first reconstructs the junction tree of a BN and then performs a K2-scoring greedy search to orientate the local edges in the cliques of junction tree. Theoretical and experimental results show the proposed algorithm is capable of handling networks with a large number of variables. Its comparison with the well-known K2 algorithm is also presented. 展开更多
关键词 Bayesian network （BN） junction tree scoring function structural learning conditional independence.
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Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study 认领 引用
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作者 Shahad Alzahrani Hatim Alsuwat Emad Alsuwat 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第5期1635-1654,共20页
Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables.However,the reliability and integrity of learned Bayesian network models are highly dependent ... Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables.However,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams.One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance.In this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms.Our framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over time.We use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian networks.With regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC algorithm.By doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky dangers.Additionally,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost relevance.Our results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning attacks.Additionally,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data. 展开更多
关键词 Bayesian networks data poisoning attacks latent variables structure learning algorithms adversarial attacks
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