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A modeling and probabilistic Reasoning Method of Dynamic Uncertain Causality Graph for Industrial Fault Diagnosis

International Journal of Automation and computing 2014年第3期11卷 288-298页

作者： Chun-Ling Dong Qin Zhang Shi-Chao Geng Department of Geographical Information System Shandong Normal University School of Computer Science and Engineering Beihang University Institute of Nuclear and New Energy Technology Tsinghua University

Online automatic fault diagnosis in industrial systems is essential for guaranteeing safe, reliable and efficient ***, difficulties associated with computational overload, ubiquitous uncertainties and insufficient fault samples hamper the engineering application of intelligent fault diagnosis technology. Geared towards the settlement of these problems, this paper introduces the method of dynamic uncertain causality graph, which is a new attempt to model complex behaviors of real-world systems under uncertainties. The visual representation to causality pathways and self-relied "chaining" inference mechanisms are analyzed. In particular, some solutions are investigated for the diagnostic reasoning algorithm to aim at reducing its computational complexity and improving the robustness to potential losses and imprecisions in observations. To evaluate the effectiveness and performance of this method, experiments are conducted using both synthetic calculation cases and generator faults of a nuclear power plant. The results manifest the high diagnostic accuracy and efficiency, suggesting its practical significance in large-scale industrial applications.

关键词： Fault diagnosis causality model probabilistic graphical model uncertain knowledge representation weighted logic inference.

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Fraud Detection in Dynamic Interaction Network

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2020年第10期32卷 1936-1950页

作者： Lin, Hao Liu, Guannan Wu, Junjie Zuo, Yuan Wan, Xin Li, Hong Beihang Univ Sch Econ & Management Beijing 100191 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing 100191 Peoples R China Beihang Univ Beijing Key Lab Emergency Support Simulat Technol Beijing 100191 Peoples R China Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing 100191 Peoples R China

Fraud detection from massive user behaviors is often regarded as trying to find a needle in a haystack. In this paper, we suggest abnormal behavioral patterns can be better revealed if both sequential and interaction behaviors of users can be modeled simultaneously, which however has rarely been addressed in prior work. Along this line, we propose a COllective Sequence and INteraction (COSIN) model, in which the behavioral sequences and interactions between source and target users in a dynamic interaction network are modeled uniformly in a probabilistic graphical model. More specifically, the sequential schema is modeled with a hierarchical Hidden Markov model, and meanwhile it is shifted to the interaction schema to generate the interaction counts through Poisson factorization. A hybrid Gibbs-Variational algorithm is then proposed for efficient parameter estimation of the COSIN model. We conduct extensive experiments on both synthetic and real-world telecom datasets in different scales, and the results show that the proposed model outperforms some competitive baseline methods and is scalable. A case is further presented to show the precious explainability of the model.

关键词： Hidden Markov models Anomaly detection Data models probabilistic logic Correlation Telecommunications fraud detection sequential schema interaction schema telecommunication network probabilistic graphical model

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A novel probabilistic clustering model for heterogeneous networks

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MACHINE LEARNING 2016年第1期104卷 1-24页

作者： Deng, Zhi-Hong Xu, Xiaoran Peking Univ Sch Elect Engn & Comp Sci Key Lab Machine Percept Minist Educ Beijing Peoples R China Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90024 USA

Heterogeneous networks, consisting of multi-type objects coupled with various relations, are ubiquitous in the real world. Most previous work on clustering heterogeneous networks either converts them into homogeneous networks or simplifies the modeling of the heterogeneity in terms of specific objects, structures or assumptions. However, few studies consider all relevant objects and relations, and trade-off between integrating relevant objects and reducing the noises caused by relations across objects. In this paper, we propose a general probabilistic graphical model for clustering heterogeneous networks. First, we present a novel graphical representation based on our basic assumptions: different relation types produce different weight distributions to specify intra-cluster probability between two objects, and clusters are formed around cluster cores. Then, we derive an efficient algorithm called PROCESS, standing for probabilistic Clustering model for heterogeneouS networkS. PROCESS employs a balance-controlled message passing algorithm and mathematical programming for inference and estimation. Experimental results show that our approach is effective and significantly outperforms the state-of-the-art algorithms on both synthetic and real data from heterogeneous networks.

关键词： Clustering Heterogeneous networks probabilistic graphical model Algorithm

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Data-driven prediction of change propagation using Dependency Network

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2018年 70卷 149-158页

作者： Lee, Jihwan Hong, Yoo S. Pukyong Natl Univ Div Syst Management & Engn 45 Yongso Ro Busan South Korea Seoul Natl Univ Dept Ind Engn 1 Gwanak Ro Seoul South Korea

Change propagation is a central aspect of complex system developments. The prediction of change propagation is necessary to prevent further changes and to perform an assessment of the cost of planned changes. Bayesian Network has been applied to extract co-change patterns from the historical change log and to predict the probability of further changes caused by the change of other components. Due to the complexity of the Bayesian Network, however, its application to large scaled system can be limited. Also, Bayesian Network cannot represent the bi-directional relationship between system components. To address these limitations, this article proposes an alternative method using Dependency Network, which is an approximated version of the Bayesian Network. Detailed procedure for learning the DN from the data, as well as probabilistic inference algorithm using DN is explained. To show the feasibility of the model, a case study is conducted with empirical data obtained from the open-sourced software, Azureus. To validate the effectiveness of the proposed method, several experiments incorporating different parameters were conducted. The result confirms that our model can produce reliable and accurate estimation of change propagation probabilities.

关键词： Change propagation Change management Change prediction probabilistic graphical model Dependency Network Stochastic coupling

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A Method for Predicting Wikipedia Editors' Editing Interest Based on a Factor Graph model

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INTERNATIONAL JOURNAL OF WEB SERVICES RESEARCH 2016年第3期13卷 1-25页

作者： Zhang, Haisu Zhang, Sheng Wu, Zhaolin Huang, Liwei Ma, Yutao Acad Natl Def Informat Wuhan Peoples R China Beijing Inst Remote Sensing Beijing Peoples R China Wuhan Univ Sch Comp Wuhan Peoples R China WISET Automat Co Ltd Wuhan Peoples R China

Recruiting or recommending appropriate potential Wikipedia editors to edit a specific Wikipedia entry (or article) can play an important role in improving the quality and credibility of Wikipedia. According to empirical observations based on a small-scale dataset collected from Wikipedia, this paper proposes an Interest Prediction Factor Graph (IPFG) model, which is characterized by editor's social properties, hyperlinks between Wikipedia entries, the categories of an entry and other important features, to predict an editor's editing interest in types of Wikipedia entries. Furthermore, the paper suggests a parameter learning algorithm based on the gradient descent algorithm and the Loopy Sum-Product algorithm for factor graphs. An experiment on a Wikipedia dataset (with different frequencies of data collection) shows that the average prediction accuracy (F1 score) of the IPFG model for data collected quarterly could be up to 0.875, which is approximately 0.49 higher than that of a collaborative filtering approach. In addition, the paper analyzes how incomplete social properties and editing bursts affect the prediction accuracy of the IPFG model. The authors' results can provide insight into effective Wikipedia article tossing and can improve the quality of special entries that belong to specific categories by means of collective collaboration.

关键词： Factor Graph Interest Prediction probabilistic graphical model Social Network Mining Wikipedia

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Joint Video Object Discovery and Segmentation by Coupled Dynamic Markov Networks

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2018年第12期27卷 5840-5853页

作者： Liu, Ziyi Wang, Le Hua, Gang Zhang, Qilin Niu, Zhenxing Wu, Ying Zheng, Nanning Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Shaanxi Peoples R China Microsoft Res Redmond WA 98052 USA HERE Technol Chicago IL 60606 USA Alibaba Grp Hangzhou 311121 Zhejiang Peoples R China Northwestern Univ Dept Elect Engn & Comp Sci Evanston IL 60208 USA

It is a challenging task to extract segmentation mask of a target from a single noisy video, which involves object discovery coupled with segmentation. To solve this challenge, we present a method to jointly discover and segment an object from a noisy video, where the target disappears intermittently throughout the video. Previous methods either only fulfill video object discovery, or video object segmentation presuming the existence of the object in each frame. We argue that jointly conducting the two tasks in a unified way will be beneficial. In other words, video object discovery and video object segmentation tasks can facilitate each other. To validate this hypothesis, we propose a principled probabilistic model, where two dynamic Markov networks are coupled-one for discovery and the other for segmentation. When conducting the Bayesian inference on this model using belief propagation, the bi-directional message passing reveals a clear collaboration between these two inference tasks. We validated our proposed method in five data sets. The first three video data sets, i.e., the SegTrack data set, the YouTube-objects data set, and the Davis data set, are not noisy, where all video frames contain the objects. The two noisy data sets, i.e., the XJTU-Stevens data set, and the Noisy-ViDiSeg data set, newly introduced in this paper, both have many frames that do not contain the objects. When compared with state of the art, it is shown that although our method produces inferior results on video data sets without noisy frames, we are able to obtain better results on video data sets with noisy frames.

关键词： Object segmentation object discovery dynamic Markov networks probabilistic graphical model

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Causal discovery in the geosciences-Using synthetic data to learn how to interpret results

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COMPUTERS & GEOSCIENCES 2017年 99卷 50-60页

作者： Ebert-Uphoff, Imme Deng, Yi Colorado State Univ Dept Elect & Comp Engn Ft Collins CO 80523 USA Georgia Inst Technol Sch Earth & Atmospher Sci Atlanta GA 30332 USA

Causal discovery algorithms based on probabilistic graphical models have recently emerged in geoscience applications for the identification and visualization of dynamical processes. The key idea is to learn the structure of a graphical model from observed spatio-temporal data, thus finding pathways of interactions in the observed physical system. Studying those pathways allows geoscientists to learn subtle details about the underlying dynamical mechanisms-governing our planet. Initial studies using this approach on real-world atmospheric data have shown great potential for scientific discovery. However, in these initial studies no ground truth was available, so that the resulting graphs have been evaluated only by whether a domain expert thinks they seemed physically plausible. The lack of ground truth is a typical problem when using causal discovery in the geosciences. Furthermore, while most of the connections found by this method match domain knowledge, we encountered one type of connection for which no explanation was found. To address both of these issues we developed a simulation framework that generates synthetic data of typical atmospheric processes (advection and diffusion). Applying the causal discovery algorithm to the synthetic data allowed us (1) to develop a better understanding of how these physical processes appear in the resulting connectivity graphs, and thus how to better interpret such connectivity graphs when obtained from real-world data;(2) to solve the mystery of the previously unexplained connections.

关键词： probabilistic graphical model Structure learning Causal discovery Information flow Geoscience Climate Atmospheric science

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Identifying Physical Interactions from Climate Data: Challenges and Opportunities

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COMPUTING IN SCIENCE & ENGINEERING 2015年第6期17卷 27-34页

作者： Ebert-Uphoff, Imme Deng, Yi Colorado State Univ Dept Elect & Comp Engn Ft Collins CO 80523 USA Georgia Inst Technol Sch Earth & Atmospher Sci Atlanta GA 30332 USA

Recent research has shown the potential of using probabilistic graphical models to identify and visualize interactions in the Earth's climate system. Studying the resulting pathways is of great interest to scientists because it helps them learn subtle details about the underlying dynamical mechanisms governing our planet's climate.

关键词： climatology data analysis geophysics computing atmosphere climate data physical interaction identification Atmospheric modeling Computational modeling Data models graphical models Mathematical model Meteorology probabilistic logic atmosphere causal discovery climate science information flow pathways probabilistic graphical model scientific computing

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A two-level approximate model driven framework for characterizing Multi-Cell Upsets impacts on processors

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MICROELECTRONICS JOURNAL 2016年 48卷 7-17页

作者： Jiao, Jiajia Marculescu, Diana Juan, Da-Cheng Fu, Yuzhuo Shanghai Maritime Univ Shanghai Peoples R China Carnegie Mellon Univ Pittsburgh PA 15213 USA Google Mountain View CA 94043 USA Shanghai Jiao Tong Univ Shanghai Peoples R China

Soft error analysis is very significant for a good tradeoff between processor design cost (e.g. area and power) and reliability. In this paper, we propose an approximate model driven framework for efficient soft error analysis in processors. The proposed framework includes: 1) an approximate probabilistic graphical model (PGM) for the Single Bit Upset (SBU) estimation, uses average-and-max policy to handle the mapped PGM structure, node parameter and inference fast;2) an approximate boundary model for the more complex Multi-Cell Upsets (MCU) case, adopts relax-and-strict way to reuse the approximate PGM model and characterize MCU patterns completely. The comprehensive results confirm that, compared with the state-of-the-art, the proposed two-level methodology based on approximate models achieves fast estimation up to more 15.37 x speedup while only 8.14% accuracy loss on average. Furthermore, the complex MCU impacts are also estimated by the proposed method at the same order of magnitude as the runtime of the simple SBU case. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Soft error analysis Multi-Cell Upsets probabilistic graphical model Boundary model

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probabilistic graph-based valuation model for measuring the relative patent value in a valuation scenario

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PATTERN RECOGNITION LETTERS 2020年 138卷 204-210页

作者： Liu, Weidong Liu, Xin Qiao, Wenbo Inner Mongolia Univ Coll Comp Sci Hohhot 010021 Peoples R China Inner Mongolia Key Lab Social Comp & Data Proc Hohhot 010021 Peoples R China

With the intense competition of global intellectual property, the increasing number of patents promotes the potential of patent transactions. However,an unknown patent value degrades the patent transaction rate. Automatic patent valuation faces some challenges, including the following: (1) how to represent a valuation object, (2) how to construct the valuation scenario, and (3) how to generate and measure the patent value. To solve the above issues, we propose a probabilistic graph-based patent valuation model. In the model, the textual parts are combined with some structured parts of patents to represent a valuation object. A heterogeneous association network is constructed as the valuation scenario. Thereafter, a patent valuation model is formed by a generative process, which is represented by a probabilistic graphical *** patent value distribution is learned by making inference using the valuation model. We evaluate our model by comparing it with state-of-the-art models on patent data sets. The results show that our model outperforms other models in the evaluation measurements. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Patent value probabilistic graphical model

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