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Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study
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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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Rock mass quality prediction on tunnel faces with incomplete multi-source dataset via tree-augmented naive Bayesian network
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作者 Hongwei Huang Chen Wu +3 位作者 Mingliang Zhou Jiayao Chen Tianze Han Le Zhang 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2024年第3期323-337,共15页
Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantita... Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality. 展开更多
关键词 Rock mass quality Tunnel faces Incomplete multi-source dataset Improved Swin Transformer bayesian networks
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Bayesian network-based survival prediction model for patients having undergone post-transjugular intrahepatic portosystemic shunt for portal hypertension
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作者 Rong Chen Ling Luo +3 位作者 Yun-Zhi Zhang Zhen Liu An-Lin Liu Yi-Wen Zhang 《World Journal of Gastroenterology》 SCIE CAS 2024年第13期1859-1870,共12页
BACKGROUND Portal hypertension(PHT),primarily induced by cirrhosis,manifests severe symptoms impacting patient survival.Although transjugular intrahepatic portosystemic shunt(TIPS)is a critical intervention for managi... BACKGROUND Portal hypertension(PHT),primarily induced by cirrhosis,manifests severe symptoms impacting patient survival.Although transjugular intrahepatic portosystemic shunt(TIPS)is a critical intervention for managing PHT,it carries risks like hepatic encephalopathy,thus affecting patient survival prognosis.To our knowledge,existing prognostic models for post-TIPS survival in patients with PHT fail to account for the interplay among and collective impact of various prognostic factors on outcomes.Consequently,the development of an innovative modeling approach is essential to address this limitation.AIM To develop and validate a Bayesian network(BN)-based survival prediction model for patients with cirrhosis-induced PHT having undergone TIPS.METHODS The clinical data of 393 patients with cirrhosis-induced PHT who underwent TIPS surgery at the Second Affiliated Hospital of Chongqing Medical University between January 2015 and May 2022 were retrospectively analyzed.Variables were selected using Cox and least absolute shrinkage and selection operator regression methods,and a BN-based model was established and evaluated to predict survival in patients having undergone TIPS surgery for PHT.RESULTS Variable selection revealed the following as key factors impacting survival:age,ascites,hypertension,indications for TIPS,postoperative portal vein pressure(post-PVP),aspartate aminotransferase,alkaline phosphatase,total bilirubin,prealbumin,the Child-Pugh grade,and the model for end-stage liver disease(MELD)score.Based on the above-mentioned variables,a BN-based 2-year survival prognostic prediction model was constructed,which identified the following factors to be directly linked to the survival time:age,ascites,indications for TIPS,concurrent hypertension,post-PVP,the Child-Pugh grade,and the MELD score.The Bayesian information criterion was 3589.04,and 10-fold cross-validation indicated an average log-likelihood loss of 5.55 with a standard deviation of 0.16.The model’s accuracy,precision,recall,and F1 score were 0.90,0.92,0.97,and 0.95 respectively,with the area under the receiver operating characteristic curve being 0.72.CONCLUSION This study successfully developed a BN-based survival prediction model with good predictive capabilities.It offers valuable insights for treatment strategies and prognostic evaluations in patients having undergone TIPS surgery for PHT. 展开更多
关键词 bayesian network CIRRHOSIS Portal hypertension Transjugular intrahepatic portosystemic shunt Survival prediction model
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Analysis of rockburst mechanism and warning based on microseismic moment tensors and dynamic Bayesian networks 被引量:3
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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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Application of Bayesian Analysis Based on Neural Network and Deep Learning in Data Visualization
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作者 Jiying Yang Qi Long +1 位作者 Xiaoyun Zhu Yuan Yang 《Journal of Electronic Research and Application》 2024年第4期88-93,共6页
This study aims to explore the application of Bayesian analysis based on neural networks and deep learning in data visualization.The research background is that with the increasing amount and complexity of data,tradit... This study aims to explore the application of Bayesian analysis based on neural networks and deep learning in data visualization.The research background is that with the increasing amount and complexity of data,traditional data analysis methods have been unable to meet the needs.Research methods include building neural networks and deep learning models,optimizing and improving them through Bayesian analysis,and applying them to the visualization of large-scale data sets.The results show that the neural network combined with Bayesian analysis and deep learning method can effectively improve the accuracy and efficiency of data visualization,and enhance the intuitiveness and depth of data interpretation.The significance of the research is that it provides a new solution for data visualization in the big data environment and helps to further promote the development and application of data science. 展开更多
关键词 Neural network Deep learning bayesian analysis Data visualization Big data environment
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Reliability analysis for wireless communication networks via dynamic Bayesian network
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作者 YANG Shunqi ZENG Ying +2 位作者 LI Xiang LI Yanfeng HUANG Hongzhong 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2023年第5期1368-1374,共7页
The dynamic wireless communication network is a complex network that needs to consider various influence factors including communication devices,radio propagation,network topology,and dynamic behaviors.Existing works ... The dynamic wireless communication network is a complex network that needs to consider various influence factors including communication devices,radio propagation,network topology,and dynamic behaviors.Existing works focus on suggesting simplified reliability analysis methods for these dynamic networks.As one of the most popular modeling methodologies,the dynamic Bayesian network(DBN)is proposed.However,it is insufficient for the wireless communication network which contains temporal and non-temporal events.To this end,we present a modeling methodology for a generalized continuous time Bayesian network(CTBN)with a 2-state conditional probability table(CPT).Moreover,a comprehensive reliability analysis method for communication devices and radio propagation is suggested.The proposed methodology is verified by a reliability analysis of a real wireless communication network. 展开更多
关键词 dynamic bayesian network(Dbn) wireless commu-nication network continuous time bayesian network(CTbn) network reliability
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Uncertainty quantification of predicting stable structures for high-entropy alloys using Bayesian neural networks
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作者 Yonghui Zhou Bo Yang 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2023年第6期118-124,I0005,共8页
High entropy alloys(HEAs)have excellent application prospects in catalysis because of their rich components and configuration space.In this work,we develop a Bayesian neural network(BNN)based on energies calculated wi... High entropy alloys(HEAs)have excellent application prospects in catalysis because of their rich components and configuration space.In this work,we develop a Bayesian neural network(BNN)based on energies calculated with density functional theory to search the configuration space of the CoNiRhRu HEA system.The BNN model was developed by considering six independent features of Co-Ni,Co-Rh,CoRu,Ni-Rh,Ni-Ru,and Rh-Ru in different shells and energies of structures as the labels.The root mean squared error of the energy predicted by BNN is 1.37 me V/atom.Moreover,the influence of feature periodicity on the energy of HEA in theoretical calculations is discussed.We found that when the neural network is optimized to a certain extent,only using the accuracy indicator of root mean square error to evaluate model performance is no longer accurate in some scenarios.More importantly,we reveal the importance of uncertainty quantification for neural networks to predict new structures of HEAs with proper confidence based on BNN. 展开更多
关键词 Uncertainty quantification High-entropy alloys bayesian neural networks Energy prediction Structure screening
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Bottom hole pressure prediction based on hybrid neural networks and Bayesian optimization
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作者 Chengkai Zhang Rui Zhang +4 位作者 Zhaopeng Zhu Xianzhi Song Yinao Su Gensheng Li Liang Han 《Petroleum Science》 SCIE EI CAS CSCD 2023年第6期3712-3722,共11页
Many scholars have focused on applying machine learning models in bottom hole pressure (BHP) prediction. However, the complex and uncertain conditions in deep wells make it difficult to capture spatial and temporal co... Many scholars have focused on applying machine learning models in bottom hole pressure (BHP) prediction. However, the complex and uncertain conditions in deep wells make it difficult to capture spatial and temporal correlations of measurement while drilling (MWD) data with traditional intelligent models. In this work, we develop a novel hybrid neural network, which integrates the Convolution Neural Network (CNN) and the Gate Recurrent Unit (GRU) for predicting BHP fluctuations more accurately. The CNN structure is used to analyze spatial local dependency patterns and the GRU structure is used to discover depth variation trends of MWD data. To further improve the prediction accuracy, we explore two types of GRU-based structure: skip-GRU and attention-GRU, which can capture more long-term potential periodic correlation in drilling data. Then, the different model structures tuned by the Bayesian optimization (BO) algorithm are compared and analyzed. Results indicate that the hybrid models can extract spatial-temporal information of data effectively and predict more accurately than random forests, extreme gradient boosting, back propagation neural network, CNN and GRU. The CNN-attention-GRU model with BO algorithm shows great superiority in prediction accuracy and robustness due to the hybrid network structure and attention mechanism, having the lowest mean absolute percentage error of 0.025%. This study provides a reference for solving the problem of extracting spatial and temporal characteristics and guidance for managed pressure drilling in complex formations. 展开更多
关键词 Bottom hole pressure Spatial-temporal information Improved GRU Hybrid neural networks bayesian optimization
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BN-GEPSO:Learning Bayesian Network Structure Using Generalized Particle Swarm Optimization
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作者 Muhammad Saad Salman Ibrahim M.Almanjahie +1 位作者 AmanUllah Yasin Ammara Nawaz Cheema 《Computers, Materials & Continua》 SCIE EI 2023年第5期4217-4229,共13页
At present Bayesian Networks(BN)are being used widely for demonstrating uncertain knowledge in many disciplines,including biology,computer science,risk analysis,service quality analysis,and business.But they suffer fr... At present Bayesian Networks(BN)are being used widely for demonstrating uncertain knowledge in many disciplines,including biology,computer science,risk analysis,service quality analysis,and business.But they suffer from the problem that when the nodes and edges increase,the structure learning difficulty increases and algorithms become inefficient.To solve this problem,heuristic optimization algorithms are used,which tend to find a near-optimal answer rather than an exact one,with particle swarm optimization(PSO)being one of them.PSO is a swarm intelligence-based algorithm having basic inspiration from flocks of birds(how they search for food).PSO is employed widely because it is easier to code,converges quickly,and can be parallelized easily.We use a recently proposed version of PSO called generalized particle swarm optimization(GEPSO)to learn bayesian network structure.We construct an initial directed acyclic graph(DAG)by using the max-min parent’s children(MMPC)algorithm and cross relative average entropy.ThisDAGis used to create a population for theGEPSO optimization procedure.Moreover,we propose a velocity update procedure to increase the efficiency of the algorithmic search process.Results of the experiments show that as the complexity of the dataset increases,our algorithm Bayesian network generalized particle swarm optimization(BN-GEPSO)outperforms the PSO algorithm in terms of the Bayesian information criterion(BIC)score. 展开更多
关键词 bayesian network structure learning particle swarm optimization
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An evaluation method of contribution rate based on fuzzy Bayesian networks for equipment system-of-systems architecture
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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 EI 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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产生“Tuned”模板的Bayesian Networks方法 被引量:8
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作者 郑肇葆 潘励 虞欣 《武汉大学学报(信息科学版)》 EI CSCD 北大核心 2006年第4期304-307,共4页
介绍了Bayesian Networks(简称BNs)产生“Tuned”模板新方法的基本原理以及BNs法与蚁群行为仿真技术和单纯形法组合的方法。通过实际航空影像的实验结果表明,新方法对纹理影像的识别率是令人满意的,同时还将新方法与遗传算法的结果作了... 介绍了Bayesian Networks(简称BNs)产生“Tuned”模板新方法的基本原理以及BNs法与蚁群行为仿真技术和单纯形法组合的方法。通过实际航空影像的实验结果表明,新方法对纹理影像的识别率是令人满意的,同时还将新方法与遗传算法的结果作了对比,结果表明新方法是很有应用前景的。 展开更多
关键词 bayesian networks Tuned模板 影像纹理分类 单纯形法
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少量样本下基于PCA-BNs的多故障诊断
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作者 王进花 马雪花 +2 位作者 岳亮辉 安永胜 曹洁 《振动与冲击》 EI CSCD 北大核心 2024年第4期288-296,共9页
针对一些工业设备因有标签故障样本数据少而导致诊断准确率低的问题,提出了一种PCA-BNs主成分分析和斯网络(principal component analysis-Bayesian networks, PCA-BNs)结合的多故障网络模型的建模方法。通过PCA对时序信号进行降维,得... 针对一些工业设备因有标签故障样本数据少而导致诊断准确率低的问题,提出了一种PCA-BNs主成分分析和斯网络(principal component analysis-Bayesian networks, PCA-BNs)结合的多故障网络模型的建模方法。通过PCA对时序信号进行降维,得到相互独立的故障特征,提高提取故障关键信息的能力;利用融合单故障贝叶斯网络构建多故障贝叶斯网络结构的方法,解决BN建模过程耗时的问题;通过高斯分布与极大似然估计结合的方法确定网络参数,提高少量数据BN建模的精度,实现在少量样本下的故障诊断。试验结果表明,基于PCA-BNs的故障诊断方法在少量样本条件下,能实现高精度的故障诊断,并且有效缩减了算法运行时间。 展开更多
关键词 工业设备 故障诊断 时序信号 贝叶斯网络
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基于AESL-GA的BN球磨机滚动轴承故障诊断方法
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作者 王进花 汤国栋 +1 位作者 曹洁 李亚洁 《北京航空航天大学学报》 EI CAS CSCD 北大核心 2024年第4期1138-1146,共9页
针对基于知识的贝叶斯网络(BN)构建方法存在不完全和不精确的缺点,提出一种基于知识引导和数据挖掘的BN结构构建方法。针对单一信号故障诊断结果不精确的问题和故障信息中存在的不确定性问题,将电流信号与振动信号融合建立BN的特征节点... 针对基于知识的贝叶斯网络(BN)构建方法存在不完全和不精确的缺点,提出一种基于知识引导和数据挖掘的BN结构构建方法。针对单一信号故障诊断结果不精确的问题和故障信息中存在的不确定性问题,将电流信号与振动信号融合建立BN的特征节点,分别提取2种信号的故障特征参数,利用区分度指标法进行特征筛选,将其作为BN结构特征层的节点。将专家知识构建的初始BN结构结合自适应精英结构遗传算法(AESL-GA)进行结构优化,通过自适应限制进化过程中的搜索空间,减少自由参数的数量,提高其全局搜索能力,得到最优BN结构。通过MQY5585溢流型球磨机滚动轴承实测数据和Paderborn University轴承数据集对所提方法进行验证,结果证明了所提方法的有效性。 展开更多
关键词 贝叶斯网络 故障诊断 自适应精英结构遗传算法 滚动轴承 信号融合
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基于改进BNN-LSTM的风电功率概率预测
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作者 李昱 《微型电脑应用》 2024年第3期206-209,共4页
针对确定性的风电功率预测难以提供预测结果的波动区间和支撑风险决策的问题,以贝叶斯网络为基础,通过将先验分布置于LSTM网络层权重参数之上,构建了贝叶斯LSTM神经网络(BNN-LSTM)。以时间卷积神经网络(TCNN)处理风电功率预测的历史时... 针对确定性的风电功率预测难以提供预测结果的波动区间和支撑风险决策的问题,以贝叶斯网络为基础,通过将先验分布置于LSTM网络层权重参数之上,构建了贝叶斯LSTM神经网络(BNN-LSTM)。以时间卷积神经网络(TCNN)处理风电功率预测的历史时序数据,提取时序数据的关联特征。使用互信息熵方法分析了风电功率的气象数据集,剔除关联性小的变量,对气象数据集进行降维处理。并采用嵌入(embedding)结构学习风电功率时间分类特征。随后将TCNN处理后的时序数据、降维后的气象数据以及时间分类特征数据一起送入BNN-LSTM预测模型,通过在某风电数据集不同算法的概率预测指标pinball损失和Winkler评分的对比验证,可知,本文所提方法能从可对风电功率波动做出较为准确的响应,预测效果更好。 展开更多
关键词 贝叶斯神经网络 bnN-LSTM 时间卷积神经网络 风电功率 互信息熵 概率预测
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基于CIA-ISM-BN煤矿瓦斯爆炸事故分析
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作者 盛武 张琪 《华北理工大学学报(自然科学版)》 CAS 2024年第3期32-43,共12页
为识别动态情景下煤矿瓦斯爆炸事故的影响因素,推断因素之间的因果关系,采用CIA(Cross Impact Analysis)、ISM(Interpretive Structural Model)和BN(Bayesian Network)结合的情景建模方法来构建煤矿井下瓦斯爆炸事故风险评价模型。利用C... 为识别动态情景下煤矿瓦斯爆炸事故的影响因素,推断因素之间的因果关系,采用CIA(Cross Impact Analysis)、ISM(Interpretive Structural Model)和BN(Bayesian Network)结合的情景建模方法来构建煤矿井下瓦斯爆炸事故风险评价模型。利用CIA-ISM组合生成不同影响级别下瓦斯爆炸事故影响因素的因果层次网络,并进行情景推断和分析。将层次网络映射到BN模型中,通过概率推理量化复杂依赖关系的层次网络,确定瓦斯爆炸的主要致因和造成的损害。结果表明:(1)重视监管机制、违规生产、管理技术、员工安全教育培训、违章作业和通风环境之间的微循环,可以有效阻断事故演化路径;(2)瓦斯爆炸事故发生下,造成人员伤亡、经济损失和社会影响以及有毒有害等次灾害的概率分别为94.7%,95.0%,24.0%;(3)不健全的监管机制是导致瓦斯爆炸事故发生的根本原因。 展开更多
关键词 矿业工程 瓦斯爆炸 交叉影响分析-解释结构模型-贝叶斯网络 情景分析 次生灾害
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基于FDHHFLTS-BN的海底管道泄漏失效风险定量分析 被引量:1
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作者 刘富鹏 杨九 +1 位作者 吴世博 徐立新 《中国安全科学学报》 CAS CSCD 北大核心 2024年第1期166-170,共5页
为预防海底油气管道泄漏失效事故,提出基于自由双层次犹豫模糊语言术语集(FDHHFLTS)和贝叶斯网络(BN)的FDHHFLTS-BN风险分析方法,用于分析海底油气管道泄漏失效事故概率及事故的关键风险因素。将故障树模型转换为BN结构,由专家根据FDHHF... 为预防海底油气管道泄漏失效事故,提出基于自由双层次犹豫模糊语言术语集(FDHHFLTS)和贝叶斯网络(BN)的FDHHFLTS-BN风险分析方法,用于分析海底油气管道泄漏失效事故概率及事故的关键风险因素。将故障树模型转换为BN结构,由专家根据FDHHFLTS评估基本事件发生可能性;采用最佳最差法(BWM)确定专家权重,结合相似性聚合方法(SAM)聚合专家意见;依据构建的BN模型,正向推理得到事故发生概率,反向推理得到后验概率,并进行敏感性分析。将该方法应用于实例分析,结果表明:分析段海底管道泄漏事故的概率值为P=6.20×10^(-3);焊缝施工缺陷、材料施工缺陷和渔具作用等为事故发生的关键因素;与传统方法对比分析结果证明,所提方法在确定海底管道风险方面具有一定的优势。 展开更多
关键词 自由双层次犹豫模糊语言术语集(FDHHFLTS) 贝叶斯网络(bn) 海底管道泄漏 风险分析 相似性聚合方法(SAM)
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Prediction of TBM jamming risk in squeezing grounds using Bayesian and artificial neural networks 被引量:13
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作者 Rohola Hasanpour Jamal Rostami +2 位作者 Jürgen Schmitt Yilmaz Ozcelik Babak Sohrabian 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2020年第1期21-31,共11页
This study presents an application of artificial neural network(ANN)and Bayesian network(BN)for evaluation of jamming risk of the shielded tunnel boring machines(TBMs)in adverse ground conditions such as squeezing gro... This study presents an application of artificial neural network(ANN)and Bayesian network(BN)for evaluation of jamming risk of the shielded tunnel boring machines(TBMs)in adverse ground conditions such as squeezing grounds.The analysis is based on database of tunneling cases by numerical modeling to evaluate the ground convergence and possibility of machine entrapment.The results of initial numerical analysis were verified in comparison with some case studies.A dataset was established by performing additional numerical modeling of various scenarios based on variation of the most critical parameters affecting shield jamming.This includes compressive strength and deformation modulus of rock mass,tunnel radius,shield length,shield thickness,in situ stresses,depth of over-excavation,and skin friction between shield and rock.Using the dataset,an ANN was trained to predict the contact pressures from a series of ground properties and machine parameters.Furthermore,the continuous and discretized BNs were used to analyze the risk of shield jamming.The results of these two different BN methods are compared to the field observations and summarized in this paper.The developed risk models can estimate the required thrust force in both cases.The BN models can also be used in the cases with incomplete geological and geomechanical properties. 展开更多
关键词 bayesian network(bn) Artificial neural network(ANN) Shielded tunnel BORING machine(TBM) Jamming RISK Numerical simulation SQUEEZING ground
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Efficient Bayesian networks for slope safety evaluation with large quantity monitoring information 被引量:8
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作者 Xueyou Li Limin Zhang Shuai Zhang 《Geoscience Frontiers》 SCIE CAS CSCD 2018年第6期1679-1687,共9页
New sensing and wireless technologies generate massive data. This paper proposes an efficient Bayesian network to evaluate the slope safety using large-quantity field monitoring information with underlying physical me... New sensing and wireless technologies generate massive data. This paper proposes an efficient Bayesian network to evaluate the slope safety using large-quantity field monitoring information with underlying physical mechanisms. A Bayesian network for a slope involving correlated material properties and dozens of observational points is constructed. 展开更多
关键词 SLOPE reliability Monitoring INFORMATION bayesian networks RISK management VALUE of INFORMATION BIG data
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Learning Bayesian networks by constrained Bayesian estimation 被引量:3
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作者 GAO Xiaoguang YANG Yu GUO Zhigao 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2019年第3期511-524,共14页
Bayesian networks (BNs) have become increasingly popular in recent years due to their wide-ranging applications in modeling uncertain knowledge. An essential problem about discrete BNs is learning conditional probabil... Bayesian networks (BNs) have become increasingly popular in recent years due to their wide-ranging applications in modeling uncertain knowledge. An essential problem about discrete BNs is learning conditional probability table (CPT) parameters. If training data are sparse, purely data-driven methods often fail to learn accurate parameters. Then, expert judgments can be introduced to overcome this challenge. Parameter constraints deduced from expert judgments can cause parameter estimates to be consistent with domain knowledge. In addition, Dirichlet priors contain information that helps improve learning accuracy. This paper proposes a constrained Bayesian estimation approach to learn CPTs by incorporating constraints and Dirichlet priors. First, a posterior distribution of BN parameters is developed over a restricted parameter space based on training data and Dirichlet priors. Then, the expectation of the posterior distribution is taken as a parameter estimation. As it is difficult to directly compute the expectation for a continuous distribution with an irregular feasible domain, we apply the Monte Carlo method to approximate it. In the experiments on learning standard BNs, the proposed method outperforms competing methods. It suggests that the proposed method can facilitate solving real-world problems. Additionally, a case study of Wine data demonstrates that the proposed method achieves the highest classification accuracy. 展开更多
关键词 bayesian networks (bns) PARAMETER LEARNING CONSTRAINTS SPARSE data
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Finding optimal Bayesian networks by a layered learning method 被引量:3
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作者 YANG Yu GAO Xiaoguang GUO Zhigao 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2019年第5期946-958,共13页
It is unpractical to learn the optimal structure of a big Bayesian network(BN)by exhausting the feasible structures,since the number of feasible structures is super exponential on the number of nodes.This paper propos... It is unpractical to learn the optimal structure of a big Bayesian network(BN)by exhausting the feasible structures,since the number of feasible structures is super exponential on the number of nodes.This paper proposes an approach to layer nodes of a BN by using the conditional independence testing.The parents of a node layer only belong to the layer,or layers who have priority over the layer.When a set of nodes has been layered,the number of feasible structures over the nodes can be remarkably reduced,which makes it possible to learn optimal BN structures for bigger sizes of nodes by accurate algorithms.Integrating the dynamic programming(DP)algorithm with the layering approach,we propose a hybrid algorithm—layered optimal learning(LOL)to learn BN structures.Benefitted by the layering approach,the complexity of the DP algorithm reduces to O(ρ2^n?1)from O(n2^n?1),whereρ<n.Meanwhile,the memory requirements for storing intermediate results are limited to O(C k#/k#^2 )from O(Cn/n^2 ),where k#<n.A case study on learning a standard BN with 50 nodes is conducted.The results demonstrate the superiority of the LOL algorithm,with respect to the Bayesian information criterion(BIC)score criterion,over the hill-climbing,max-min hill-climbing,PC,and three-phrase dependency analysis algorithms. 展开更多
关键词 bayesian network (bn) structure LEARNING layeredoptimal LEARNING (LOL)
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