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43rd Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Zhang, Yuping You, Liyu Jia, Chunhua Univ Elect Sci & Technol China Sch Automat Engn Chengdu Sichuan Peoples R China

ISBN: (纸本)9781538611272

This paper aims to develop a novel fault detection and diagnosis (FDD) method based on Bayesian network (BN). Firstly, the structure of BN is constructed according to the fault feature, which can be obtained from k2 algorithm and expertise. Then, corresponding parameters of BN are derived from dataset and expertise. Secondly, junction tree algorithm reasoning is provided. Finally, a framework of BN for vehicle start system fault diagnosis is proposed. There are three cases to verify the method. The maximum probability fault can be found according to the way of BN reference results, which demonstrates the feasibility of the developed method.

关键词： Bayesian network fault detection and diagnosis k2 algorithm junction tree reference

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Research on the Influencing Factors of Residents' Travel Based on Bayesian Network

Research on the Influencing Factors of Residents' Travel Bas...

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International Conference on Cyber Security Intelligence and Analytics (CSIA)

作者： Wang, Fengying Du, Liming Li, Gui Dong, Jie Shenyang Jianzhu Univ 9 Hunnan East Rd Shenyang Liaoning Peoples R China

ISBN: (纸本)9783030152352;9783030152345

Traffic has become an important factor affecting the development of a city and the choice of mode of transport is an important and indispensable component of traffic planning and policy development. In this paper, the urban residents of Shenyang City as the object of study, the application of Bayesian network method, the use of Shenyang city residents travel mode survey data to calculate the influencing factors of the relevant factors and behavioral economics theory of urban residents Travel mode selection to do a whole analysis and research. On the basis of reading a lot of literature, this paper briefly introduces the influencing factors of urban residents 'travel mode, and makes a detailed analysis of residents' travel behavior by using the results of previous literature research. The contents of the analysis mainly include the relative deterministic factors and the relative uncertainty factors that affect the residents' travel mode. Then, the related knowledge and learning methods and parameter estimation of the Bayesian network are introduced. The Bayesian network method and the economic theory Combined to further analyze and study the influencing factors of residents' travel mode. Draw conclusions and provide some suggestions and reference for the improvement of traffic policy.

关键词： Influencing factors Bayesian network k2 algorithm

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Analysis and Forecast of Subway Construction Cost Based on Bayesian Network 12

Analysis and Forecast of Subway Construction Cost Based on B...

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12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2023

作者： Guo, Feng Niu, Donghui Zhang, Guoling Hou, Na Hu, Zhengbing China Railway Seventh Bureau Group Electrical Engineering Co. Ltd Zhengzhou450008 China National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine

ISBN: (纸本)9798350358056

In the process of subway construction, there is a complex nonlinear relationship among the engineering quantity, material cost, labor cost and total cost, which cannot be accurately calculated by manpower alone. Therefore, the reasonable deployment of human, material, capital and other resources in the future requires a high-precision and highly reliable analysis and prediction model. Aiming at this problem, this paper proposes a Bayesian network model of subway construction project cost prediction based on principal component analysis and k2 algorithm on the basis of analyzing the financial data, contract data and the existing engineering quantity calculation data in the information data system. Firstly, the principal component analysis method is used to screen the influencing factors of subway mechanical and electrical engineering and extract the core data features. Based on the extracted features, a Bayesian network model was built according to the principle of k2 algorithm to dynamically analyze the engineering cost, and the actual subway engineering cost data was used to train, verify and predict the model. The experimental results show that the accuracy of the prediction model proposed in this paper is 87.5%, and the prediction results are completely within the controllable range, thus proving the effectiveness of the model and realizing the dynamic tracking of the construction cost of electromechanical engineering. © 2023 IEEE.

关键词： Bayesian network k2 algorithm principal component analysis Subway cost prediction

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A Bayesian network model for fault diagnosis of a lock mechanism based on degradation data

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ENGINEERING FAILURE ANALYSIS 2021年 122卷 105225-105225页

作者： Pang, Tianyang Yu, Tianxiang Song, Bifeng Northwestern Polytech Univ Sch Aeronaut Xian 710072 Shaanxi Peoples R China

This paper aims to build a diagnosis model of a lock mechanism system considering multiple joints wear. The lock mechanism is a complex mechanical system. Diagnosis is difficult because fault modes are not easy to identify. The diagnosis result is affected by the strong dependence of wear between each component. Besides, some lock mechanism?s wear detection data is challenging to acquire because of fewer sensors in some particular situations. For these problems, an improved Bayesian network-based fault diagnosis methodology considering component degradation is proposed to distinguish the fault types. An experiment of congeneric lock mechanisms is conducted, and wear data of the joints is obtained. The Bayesian networks model in which the dependence of components is not considered is established based on experimental data. Because the Bayesian network structure is affected by the strong dependence between components, the k2 algorithm is used to build the Bayesian network structure based on acquired wear data to obtain causality between components. The entire fault model is built by combining two established Bayesian networks. Three fault diagnosis cases are used to validate the accuracy and efficiency of the proposed model. A comparison is made between the improved diagnosis model and the model without considering dependence. Finally, the revolute joints are ranked by the established diagnostic model so that the weakest component can be identified.

关键词： Lock mechanism Bayesian network Fault diagnosis Wear k2 algorithm

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Risk Assessment and Prediction of Underground Utility Tunnels Based on Bayesian Network: A Case Study in Beijing, China

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2023年第6期32卷 2350096-2350096页

作者： Chen, Yongjun Li, Xiaojian Wang, Wenjuan Wu, Guangye Wang, Lulin Beijing Univ Civil Engn & Architecture Sch Urban Econ & Management Beijing 100044 Peoples R China Capital Univ Econ & Business Coll Business Adm 121 Zhangjialukou Beijing 10070 Peoples R China

The underground utility tunnels accommodate various types of urban lifelines, which are of great significance for improving the living standards of the citizens. With the rapid development of underground utility tunnels, the large-scale underground utility tunnel systems are gradually becoming the operational lifeblood of China's large cities. Currently, most of the underground utility tunnels' risks are estimated and analyzed from a static perspective, and the analysis results are one-sided. This study proposes a dynamic risk evaluation framework. A risk assessment and sensitivity analysis framework based on Bayesian network is established in this study. Combined with the groundwater and electric tunnel risk accident case study, the operation and maintenance data of Beijing Future Science and Technology City from 2010 to 2018 are collected for learning to obtain the conditional probability of the Bayesian network node by using the k2 algorithm. The overall evolution process from the beginning to the end of groundwater tunnel accidents is clearly described and displayed. Through sensitivity analysis and critical path analysis, the critical points of an accident and the probabilities of risk occurrence are identified and predicted. This proposed framework could facilitate the underground utility tunnel management for controlling risk resources, mitigating risk damage and reducing risk losses.

关键词： Underground utility tunnels risk factor identification Bayesian Network structure and parameter learning EM k2 algorithm

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Identifying vital nodes for yeast network by dynamic network entropy

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BMC BIOINFORMATICS 2024年第1期25卷 242页

作者： Liu, Jingchen Wang, Yan Men, Jiali Wang, Haohua Hainan Univ Sch Math & Stat Haikou 570228 Hainan Peoples R China Univ South China Affiliated Hosp 1 Dept Neurol Hengyang 421001 Hunan Peoples R China Hainan Univ Sch Life Sci Haikou 570228 Hainan Peoples R China Hainan Univ Key Lab Engn Modeling & Stat Computat Hainan Prov Haikou 570228 Hainan Peoples R China Shandong Univ Sch Math Jinan 250100 Shandong Peoples R China

BackgroundThe progress of the cell cycle of yeast involves the regulatory relationships between genes and the interactions proteins. However, it is still obscure which type of protein plays a decisive role in regulation and how to identify the vital nodes in the regulatory network. To elucidate the sensitive node or gene in the progression of yeast, here, we select 8 crucial regulatory factors from the yeast cell cycle to decipher a specific network and propose a simple mixed k2 algorithm to identify effectively the sensitive nodes and genes in the evolution of *** the multivariate of cell cycle data, we first utilize the k2 algorithm limited to the stationary interval for the time series segmentation to measure the scores for refining the specific network. After that, we employ the network entropy to effectively screen the obtained specific network, and simulate the gene expression data by a normal distribution approximation and the screened specific network by the partial least squares method. We can conclude that the robustness of the specific network screened by network entropy is better than that of the specific network with the determined relationship by comparing the obtained specific network with the determined relationship. Finally, we can determine that the node CDH1 has the highest score in the specific network through a sensitivity score calculated by network entropy implying the gene CDH1 is the most sensitive regulatory *** is clearly of great potential value to reconstruct and visualize gene regulatory networks according to gene databases for life activities. Here, we present an available algorithm to achieve the network reconstruction by measuring the network entropy and identifying the vital nodes in the specific nodes. The results indicate that inhibiting or enhancing the expression of CDH1 can maximize the inhibition or enhancement of the yeast cell cycle. Although our algorithm is simple, it is also the first

关键词： Network entropy Gene regulatory network k2 algorithm Partial least squares Network simulation Time series plateau interval

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Intrusion Detection based on Graph oriented Big Data Analytics

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Procedia Computer Science 2020年 176卷 572-581页

作者： Ahlem Abid Farah Jemili Universite de Sousse ISITCom 4011 Hammam Sousse Tunisia Universite de Sousse ISITCom MARS Research Laboratory LR17ES05 4011 Hammam Sousse Tunisia

Intrusion detection has been the subject of numerous studies in industry and academia. Still, Cybersecurity analysts always want greater precision and global threat analysis to secure their systems in cyberspace. To improve the intrusion detection system, the visualization of the security events in the form of graphs and diagrams is significant to improve the accuracy of alerts. In this paper, we propose an approach of an IDS based on cloud computing, big data technique and, using a machine learning graph algorithm which can detect in real-time different attacks as early as possible. We use the MAWILab intrusion detection dataset. We choose Microsoft Azure as a unified cloud environment to load our dataset on Azure blob storage. We implement the k2 algorithm, which is a graphical machine learning algorithm to classify attacks. Our system showed a great performance due to the graphical machine learning algorithm and Apache Spark structured streaming engine.

关键词： Intrusion detection MAWILab Apache Spark Structured Streaming Big Data Microsoft Azure Cloud Graph Machine Learning k2 algorithm

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Vehicle Lane Change Model Based on Improved Bayesian Network Structure Learning

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International Journal of Intelligent Technologies and Applied Statistics 2018年第4期11卷 255-270页

作者： Tong Dang Fang Dai

In order to reduce traffic safety issues caused by vehicle lane change, a lane change model based on improved Bayesian network structure learning is established. The assumption of the traditional k2 algorithm that the prior probability of the network structure follows the uniform distribution is too arbitrary. Based on the Cooper-Herskovits (CH) scoring function, a new I-CH scoring function, which is constructed by using the connection probability information, is proposed to describe the network structure prior. After selecting experiments samples from the dataset Next Generation Simulation (NGSIM), the samples are discretized by the ChiMerge algorithm and then, the connection probability matrix with initial Bayesian network is obtained by computing the mutual information (MI) between variables. For the problem of node sequence in k2 algorithm, the principal component analysis is applied to optimize the node order and the combining I-CH score was proposed to train and verify this model. The experimental results show that the recognition rate of this model for the lane change behavior is up to 94.9% and the lane change model can be used in real-time situation.

关键词： Traffic safety Vehicle lane change model Bayesian network k2 algorithm I-CH scoring function

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A Bayesian Network for Symptom-diagnosis Data

A Bayesian Network for Symptom-diagnosis Data

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2018 International Conference on Computational, Modeling, Simulation and Mathematical Statistics(CMSMS 2018)

作者： Ying YU Jian-ye LU Zheng YANG Wen-wen YANG Institute of Electromechanical Engineering and Automation Shanghai University

Bayesian network is an important expression tool of uncertain knowledge by integrating probability table and graph. This paper applies Bayesian network into clinical data for knowledge discovery and constructs a Bayesian network for symptom-diagnosis data with optimal structure. First of all, a Markov chain-Monte Carlo based Metropolis-Hastings sampling method is introduced to fill in the missing data;Then, a k2 algorithm is used to search for all possible Bayesian networks among the relationship between symptoms and diagnoses;Further, the BDE scoring function is used to determine the optimal network structure illustrating the relationship between symptoms and diagnoses. The results show that the network structure accuracy has greatly improved after filling the missing data with the method of Metropolis-Hastings sampling.

关键词： Bayesian Markov chain-Monte Carlo Metropolis-Hastings sampling k2 algorithm BDE scoring function

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Reconstruction of Gene Regulatory Network Based on Bayesian Network

Reconstruction of Gene Regulatory Network Based on Bayesian ...

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2015 International Conference on Advanced Educational Technology and Information Engineering(AETIE 2015)

作者： B.Yu M.H.Wang L.k.Zhao College of Mathematics and Physics Qingdao University of Science and Technology

Gene regulatory network, which focuses on the complex interactions of genes in 1ife, is an important part in the study of the functiona1 genomics and the frontier of bioinformatics and systems biology research. Bayesian network is powerfu1 tools to genetic networks research. As for the empirical analysis, a subset consisting of 15 genes is chosen from yeast gene expressing data as training data, we first use greedy search(GS) algorithm to get the priori information for the k2 algorithm, and then apply the k2 method to the dataset of gene expression profile to get the topological structure of the Bayesian networks, thus inferring the interaction between genes. We verify the effectiveness of our approach by consulting the corresponding experiment articles, and reduce the computational time in learning network, finally get more stable structures.

关键词： Bayesian network gene regulatory network greedy search algorithm k2 algorithm

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