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20th World Congress of the International-Federation-of-Automatic-Control (IFAC)

作者： Zeng, Jiusun Luo, Shihua Cai, Jinhui Xie, Lei Kruger, Uwe Xiong, Weili China Jiliang Univ Coll Metrol & Measurement Engn Hangzhou 310018 Zhejiang Peoples R China Jiangxi Univ Finance & Econ Sch Stat Nanchang 330001 Jiangxi Peoples R China Zhejiang Univ Inst Cyber Syst & Control Hangzhou 310027 Zhejiang Peoples R China Rensselaer Polytech Inst Dept Biomed Engn Troy NY 12180 USA Jiangnan Univ Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Peoples R China

probabilistic graphical models like Bayesian networks have been widely used in process monitoring and fault diagnosis, however, their application is mostly limited to discrete variables or continuous Gaussian variables due to the difficult in estimation of multivariate joint density. In order to deal with the estimation problem of multivariate joint density for continuous variables, this paper decomposes the graphical model into hierarchical structure so that the problem of joint density estimation can be transferred to estimation of several conditional probability densities and low-dimensional probability densities. The conditional densities can be effectively estimated from data by a nonparametric kernel method and the low-dimensional densities can be estimated using the kernel density estimation (KDE). Based on the estimated densities, process faults can be detected by examining which probability is lower than the cutoff value. Application to the blast furnace ironmaking process is used to illustrate the advantages of the proposed method. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： probabilistic graphical model process monitoring nonparametric density estimation conditional density estimation kernel density estimation

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Comparison of Different probabilistic graphical models as Causal models in Alarm Flood Reduction 17

Comparison of Different Probabilistic Graphical Models as Ca...

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17th IEEE International Conference on Industrial Informatics (INDIN)

作者： Wunderlich, Paul Hranisavljevic, Nana OWL Univ Appl Sci & Arts InT Inst Ind IT Lemgo Germany Fraunhofer IOSB INA Fraunhofer Ctr Machine Learning Lemgo Germany

ISBN: (纸本)9781728129273

The increasing amount of alarms and information for an operator in a modern plant becomes a significant safety risk. Although the notifications are a valuable support, they also lead to the curse of overloading with information for the operator. Due to the huge amount of alarms it is almost impossible to separate the crucial information from the insignificant ones. Therefore, new procedures are required to reduce these alarm floods and support the operator to minimize the safety risk. One approach is based on learning a causal model that represents the relationships between the alarms. This allows alarm sequences that are causally implied to be reduced to the root cause alarm. Fundamental element of this approach is the causal model. Therefore in this work, different probabilistic graphical models are considered and evaluated on the basis of appropriate criteria. A real use case of a bottle filling module serves as a benchmark for how well they are suitable as a causal model for the application in alarm flood reduction.

关键词： probabilistic graphical model causal model alarm flood reduction Bayesian network Markov chain restricted boltzmann machine automata

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GENERATIVE modelING OF TEMPORAL SIGNAL FEATURES USING HIERARCHICAL probabilistic graphical modelS

GENERATIVE MODELING OF TEMPORAL SIGNAL FEATURES USING HIERAR...

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IEEE Digital Signal Processing Workshop/IEEE Signal Processing Education Workshop (DSP/SPE)

作者： Gang, Ren Bocko, Gregory Lundberg, Justin Headlam, Dave Bocko, Mark F. Univ Rochester Edmund A Hajim Sch Engn & Appl Sci Dept Elect & Comp Engn Rochester NY 14627 USA Univ Rochester Eastman Sch Music Dept Music Theory Rochester NY 14627 USA

ISBN: (纸本)9781612842271

We propose generative modeling algorithms that analyze the temporal features of non-stationary signals and represent their temporal structural dependencies using hierarchical probabilistic graphical models. First, several template sampling methods are introduced to embed the temporal signal features into multiple instantiations of statistical variables. Then the learning schemes that obtain hierarchical probabilistic graphical models from data instantiations are detailed. Based on the sampled temporal instantiations, multiple probabilistic graphical models are discovered and fit to the signal support regions. The evolution structure of these graphical models is depicted using a higher-level structural model. Finally, performance evaluations based on both simulated datasets and audio feature dataset are presented.

关键词： feature analysis signal modeling generative modeling non-stationary signal probabilistic graphical model clustering combinational optimization

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Use of Bayesian probabilistic graphical models for Simultaneous Variable Rate / Variable Pressure-Drop Deconvolution of Single Well and Multi-Well Cases

Use of Bayesian Probabilistic Graphical Models for Simultane...

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作者： Fulford, David Steven Texas A&M University

学位级别：硕士

Deconvolution is a useful tool for removing of the effects of non-constant production rate on the observed flowing pressures, or the effects of non-constant flowing pressure on the observed producing rates. The caveat of deconvolution is that it is non-unique and numerically unstable. To maximize the chance of success, an operator can run a controlled experiment in order to minimize these properties of the deconvolution. Examples of such experiments are a stepped production rate test (constant rate steps) or stepped flowing pressure test (constant flowing pressure steps) where the value of a step is constant over a specified time interval. These experiments are costly and therefore frequently not performed, thereby limiting the use of deconvolution as a practical tool for reservoir performance analysis. In this work we deconvolve both cases simultaneously—constant rate and constant flowing pressure—by correlation of the deconvolved responses. A probabilistic graphic model (PGM) captures the relationships of influence (or probability) between the observed time, rate, and pressure values, and the unobserved latent constant-rate pressure function and constant-pressure rate function. A stochastic algorithm using Hamiltonian Monte Carlo simulation iteratively solves and directly samples the solution space to illustrate the inherent non-uniqueness of the solution. Acceptance or rejection of a specific iteration's proposed solution after evaluation of all functions ensures that acceptance only occurs if all variables and functions are probabilistically likely given the posterior joint distribution of the entire PGM. Numerical stability is achieved by solving the forward convolution problem–as opposed to the inverse deconvolution problem–as well as utilization of cost functions that are less biased by noise than the squared error cost function. The structure of the PGM is not altered no matter whether we choose to evaluate just the variable rate case or the variab

关键词： deconvolution bayesian probabilistic graphical model reservoir analysis

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Knowledge-Aided Semantic Communication Leveraging probabilistic graphical modeling 100

Knowledge-Aided Semantic Communication Leveraging Probabilis...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Wan, Haowen Yang, Qianqian Tang, Jiancheng Shi, Zhiguo Zhejiang University Polytechnic Institute Hangzhou China Zhejiang University College of Information Science and Electronic Engineering Hangzhou China Jinhua Institute of Zhejiang University Jinhua China

ISBN: (纸本)9798331517786

In this paper, we propose a semantic communication approach based on probabilistic graphical model (PGM). The proposed approach involves constructing a PGM from a training dataset, which is then shared as common knowledge between the transmitter and receiver. We evaluate the importance of various semantic features and present a PGM-based compression algorithm designed to eliminate predictable portions of semantic information. Furthermore, we introduce a technique to reconstruct the discarded semantic information at the receiver end, generating approximate results based on the PGM. Simulation results indicate a significant improvement in transmission efficiency over existing methods, while maintaining the quality of the transmitted images. © 2024 IEEE.

关键词： Image transmission probabilistic graphical model Semantic communication

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Strengthening probabilistic graphical models: The Purge-and-Merge Algorithm

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IEEE ACCESS 2021年 9卷 149423-149432页

作者： Streicher, Simon Du Preez, Johan A. Stellenbosch Univ Dept Elect & Elect Engn ZA-7600 Stellenbosch South Africa

probabilistic graphical models (PGMs) are powerful tools for solving systems of complex relationships over a variety of probability distributions. However, while tree-structured PGMs always result in efficient and exact solutions, inference on graph (or loopy) structured PGMs is not guaranteed to discover the optimal solutions. It is in principle possible to convert loopy PGMs to an equivalent tree structure, but this is usually impractical for interesting problems due to exponential blow-up. To address this, we developed the "purge-and-merge" algorithm. This algorithm iteratively nudges a malleable graph structure towards a tree structure by selectively merging factors. The merging process is designed to avoid exponential blow-up by way of sparse structures from which redundancy is purged as the algorithm progresses. We set up tasks to test the algorithm on constraint-satisfaction puzzles such as Sudoku, Fill-a-pix, and Kakuro, and it outperformed other PGM-based approaches reported in the literature. While the tasks we set focussed on the binary logic of CSP, we believe the purge-and-merge algorithm could be extended to general PGM inference.

关键词： probabilistic logic Belief propagation Merging Tools Licenses Task analysis Speech recognition probabilistic graphical model probabilistic reasoning belief propagation cluster graph sudoku constraint-satisfaction problem

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A probabilistic generative model for tracking multi-knowledge concept mastery probability

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Frontiers of Computer Science 2024年第3期18卷 155-170页

作者： Hengyu LIU Tiancheng ZHANG Fan LI Minghe YU Ge YU School of Computer Science and Engineering Northeastern UniversityShenyang 110169China Software College Northeastern UniversityShenyang 110169China

Knowledge tracing aims to track students’knowledge status over time to predict students’future performance *** a real environment,teachers expect knowledge tracing models to provide the interpretable result of knowledge *** chain-based knowledge tracing(MCKT)models,such as Bayesian Knowledge Tracing,can track knowledge concept mastery probability over ***,as the number of tracked knowledge concepts increases,the time complexity of MCKT predicting student performance increases exponentially(also called explaining away problem).When the number of tracked knowledge concepts is large,we cannot utilize MCKT to track knowledge concept mastery probability over *** addition,the existing MCKT models only consider the relationship between students’knowledge status and problems when modeling students’responses but ignore the relationship between knowledge concepts in the same *** address these challenges,we propose an inTerpretable probabilistic gEnerative model(TRACED),which can track students’numerous knowledge concepts mastery probabilities over *** solve explain away problem,we design long and short-term memory(LSTM)-based networks to approximate the posterior distribution,predict students’future performance,and propose a heuristic algorithm to train LSTMs and probabilistic graphical model *** better model students’exercise responses,we proposed a logarithmic linear model with three interactive strategies,which models students’exercise responses by considering the relationship among students’knowledge status,knowledge concept,and *** conduct experiments with four real-world datasets in three knowledge-driven *** experimental results show that TRACED outperforms existing knowledge tracing methods in predicting students’future performance and can learn the relationship among students,knowledge concepts,and problems from students’exercise *** also conduct several case *** case studies show that

关键词： probabilistic graphical model deep learning knowledge tracing learner modeling

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Tag Correspondence model for User Tag Suggestion

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Journal of Computer Science & Technology 2015年第5期30卷 1063-1072页

作者：涂存超刘知远孙茂松 Department of Computer Science and Technology Tsinghua University Beijing 100084 China State Key Laboratory on Intelligent Technology and Systems Tsinghua University Beijing 100084 China National Laboratory for Information Science and Technology Tsinghua University Beijing 100084 China Jiangsu Collaborative Innovation Center for Language Ability Jiangsu Normal University Xuzhou 221009 China

Some microblog services encourage users to annotate themselves with multiple tags, indicating their attributes and interests. User tags play an important role for personalized recommendation and information retrieval. In order to better understand the semantics of user tags, we propose Tag Correspondence model （TCM） to identify complex correspondences of tags from the rich context of microblog users. The correspondence of a tag is referred to as a unique element in the context which is semantically correlated with this tag. In TCM, we divide the context of a microblog user into various sources （such as short messages, user profile, and neighbors）. With a collection of users with annotated tags, TCM can automatically learn the correspondences of user tags from multiple sources. With the learned correspondences, we are able to interpret implicit semantics of tags. Moreover, for the users who have not annotated any tags, TCM can suggest tags according to users＇ context information. Extensive experiments on a real-world dataset demonstrate that our method can efficiently identify correspondences of tags, which may eventually represent semantic meanings of tags.

关键词： microblog user tag suggestion tag correspondence model probabilistic graphical model context

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A probabilistic Framework for Temporal Cognitive Diagnosis in Online Learning Systems

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Journal of Computer Science & Technology 2023年第6期38卷 1203-1222页

作者：刘嘉聿汪飞马海平黄振亚刘淇陈恩红苏喻 School of Data Science University of Science and Technology of ChinaHefei 230026China State Key Laboratory of Cognitive Intelligence Hefei 230088China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei 230026China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China Institutes of Physical Science and Information Technology Anhui UniversityHefei 230601China School of Computer Science and Artificial Intelligence Hefei Normal UniversityHefei 230061China

Cognitive diagnosis is an important issue of intelligent education systems,which aims to estimate students'proficiency on specific knowledge *** existing studies rely on the assumption of static student states and ig-nore the dynamics of proficiency in the learning process,which makes them unsuitable for online learning *** this paper,we propose a unified temporal item response theory(UTIRT)framework,incorporating temporality and random-ness of proficiency evolving to get both accurate and interpretable diagnosis ***,we hypothesize that stu-dents'proficiency varies as a Wiener process and describe a probabilistic graphical model in UTIRT to consider temporali-ty and randomness ***,based on the relationship between student states and exercising answers,we hy-pothesize that the answering result at time k contributes most to inferring a student's proficiency at time k,which also re-flects the temporality aspect and enables us to get analytical maximization(M-step)in the expectation maximization(EM)algorithm when estimating model *** UTIRT is a framework containing unified training and inferenc-ing methods,and is general to cover several typical traditional models such as Item Response Theory(IRT),multidimen-sional IRT(MIRT),and temporal IRT(TIRT).Extensive experimental results on real-world datasets show the effective-ness of UTIRT and prove its superiority in leveraging temporality theoretically and practically over TIRT.

关键词： cognitive diagnosis probabilistic graphical model item response theory(IRT) stochastic process expectation maximization(EM)algorithm

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Image Segmentation with a Unified graphical model

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2010年第8期32卷 1406-1425页

作者： Zhang, Lei Ji, Qiang Rensselaer Polytech Inst Troy NY 12180 USA

We propose a unified graphical model that can represent both the causal and noncausal relationships among random variables and apply it to the image segmentation problem. Specifically, we first propose to employ Conditional Random Field (CRF) to model the spatial relationships among image superpixel regions and their measurements. We then introduce a multilayer Bayesian Network (BN) to model the causal dependencies that naturally exist among different image entities, including image regions, edges, and vertices. The CRF model and the BN model are then systematically and seamlessly combined through the theories of Factor Graph to form a unified probabilistic graphical model that captures the complex relationships among different image entities. Using the unified graphical model, image segmentation can be performed through a principled probabilistic inference. Experimental results on the Weizmann horse data set, on the VOC2006 cow data set, and on the MSRC2 multiclass data set demonstrate that our approach achieves favorable results compared to state-of-the-art approaches as well as those that use either the BN model or CRF model alone.

关键词： Image segmentation probabilistic graphical model Conditional Random Field Bayesian Network factor graph

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