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A probabilistic graphical model for Predicting Cascade Failures of Electric Vehicle Charging Networks Caused by Hurricanes

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IEEE TRANSACTIONS ON SMART GRID 2025年第1期16卷 627-639页

作者： Zhang, Tianze Tang, Difei Fan, Pengyu Wang, Qi Wang, Peng Nanjing Normal Univ NARI Sch Elect & Automat Engn Nanjing 210046 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

Electric vehicle charging networks (EVCNs) are vulnerable to power outages. The resilience of critical charging infrastructures is vital for providing uninterrupted service. When a hurricane occurs, the offline charging stations (CSs) may cause the redistribution of EV traffic flow, resulting in the congestion at the healthy CSs and potentially leading to a cascade failure. This paper presents a probabilistic graphical modeling (PGM) framework for predicting the hurricane-induced cascading failures of EVCNs. A Bayesian network is proposed to capture the relationships between a hurricane, the power and traffic systems coupled by an EVCN. An AC-based Cascading Failure model (ACCF) is established to simulate the line overloading and load shedding. EV load redistribution is formulated as a traffic assignment problem considering the road impedance between the nearby CSs. The proposed PGM is trained by the Expectation-Maximization (EM) algorithm considering the traffic flow as the hidden variable. Clique tree method is used to infer the propagation probability of cascading failure. The case study demonstrates the causal relationship between hazards and cascading failures of EVCNs under different hurricane scenarios. Results show that the proposed method can predict the propagation of cascading failures in EVCNs by identifying and quantifying the correlation among the susceptible CSs.

关键词： Hurricanes Resilience probabilistic logic Power system protection Power system faults Power systems Power system reliability Cascade failure charging network electric vehicle probabilistic graphical model

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probabilistic graphical model BASED FAST HEVC INTER PREDICTION 24

PROBABILISTIC GRAPHICAL MODEL BASED FAST HEVC INTER PREDICTI...

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24th IEEE International Conference on Image Processing (ICIP)

作者： Fang, Meiyuan Wen, Jiangtao Han, Yuxing Tsinghua Univ Beijing 100084 Peoples R China South China Agr Univ Guangzhou 510642 Guangdong Peoples R China

ISBN: (纸本)9781509021758

The High Efficiency Video Coding (HEVC) standard achieves 50% improvement in coding efficiency compared with H.264/ AVC by introducing many more video encoding tools achieving different coding performance and complexity tradeoffs [1]. Various techniques have been proposed to reduce the complexity of HEVC encoding. In this paper, an effective Bayesian network based complexity reduction framework for HEVC encoding is proposed. The proposed framework is able to calculate the conditional probabilities of different status in the encoding process, which can be used to speedup the encoders by neglecting small probability events. Experimental results show that the proposed algorithm can save 52.5% of computational complexity with a loss of 1.41% in compression performance on average.

关键词： probabilistic graphical model HEVC video coding complexity reduction

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probabilistic graphical model Based Residential Energy Behavioral Analysis on Hybrid Computing Platform

Probabilistic Graphical Model Based Residential Energy Behav...

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China International Conference on Electricity Distribution (CICED)

作者： Dai, Jiangpeng Chai, Bo Qiu, Hongbin Zhang, Bo Jiang, Wei Global Energy Interconnect Res Inst Inst Comp Technol & Applicat Shanghai Peoples R China Southeast Univ Dept Elect Engn Dhaka Bangladesh

ISBN: (纸本)9781467390682

The accurate customers energy behavioral can be analyzed from large amount of data acquired by smart meters. Practically, an ideal analysis system should meet two kinds of data demand: fast with low accuracy and slow with high accuracy. In this paper, a hybrid computing platform to process large amount of real-time data is proposed to analyze customer's energy behavioral. The structure and function of the platform is introduced. A graphical model based power consumption characteristics analysis algorithm is proposed. The implementation of the algorithm on hybrid computing platform is introduced. With energy usage data, four characteristics of resident energy consumption models are clustered. The experimental results show that the proposed algorithm can improve the efficiency of mass data clustering analysis and also proves the feasibility of the model.

关键词： hybrid computing energy consumption behavior big data probabilistic graphical model mapreduce

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probabilistic graphical model FOR FLASH MEMORY PROGRAMMING

PROBABILISTIC GRAPHICAL MODEL FOR FLASH MEMORY PROGRAMMING

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IEEE Statistical Signal Processing Workshop (SSP)

作者： Peleato, Borja Agarwal, Rajiv Cioffi, John Stanford Univ Dept Elect Engn Stanford CA 94305 USA

ISBN: (纸本)9781467301831

Flash memory presents significant advantages over hard drives in terms of read speed and power efficiency;however its lifetime can be several orders of magnitude smaller. Thus increasing lifetime of flash memory via signal processing techniques is an important research area. The first half of the paper presents a statistical method for estimating the health of the cells in a Flash memory, based on which a variable error correction coding scheme can be used to increase lifetime. The second half of the paper proposes a statistical approach to increase lifetime when the flash controller can dynamically vary the program and erase operation strategy. This approach uses Markov Decision Processes (MDP) to choose the optimal program or erase strategy at any given point in the life of a Flash memory based on its current state or health. From a bigger picture stand-point, the paper presents a novel way of flash management using a Markov model for health of the memory at any given point in its lifetime.

关键词： Flash memories Markov decision process hidden Markov model probabilistic graphical model

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Learning Deep Patch representation for probabilistic graphical model-Based Face Sketch Synthesis

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2021年第6期129卷 1820-1836页

作者： Zhu, Mingrui Li, Jie Wang, Nannan Gao, Xinbo Xidian Univ State Key Lab Integrated Serv Networks Sch Telecommun Engn Xian 710071 Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing 400065 Peoples R China

Face sketch synthesis has a wide range of applications in both digital entertainment and law enforcement. State-of-the-art examplar-based methods typically exploit a probabilistic graphical model (PGM) to represent the joint probability distribution over all of the patches selected from a set of training data. However, these methods suffer from two main shortcomings: (1) most of these methods capture the evidence between patches in pixel-level, which lead to inaccurate parameter estimation under bad environment conditions such as light variations and clutter backgrounds;(2) the assumption that a photo patch and its corresponding sketch patch share similar geometric manifold structure is not rigorous. It has shown that deep convolutional neural network (CNN) has outstanding performance in learning to extract high-level feature representation. Therefore, we extract uniform deep patch representations of test photo patches and training sketch patches from a specially designed CNN model to replace pixel intensity, and directly match between them, which can help select better candidate patches from training data as well as improve parameter learning process. In this way, we investigate a novel face sketch synthesis method called DPGM that combines generative PGM and discriminative deep patch representation, which can jointly model the distribution over the parameters for deep patch representation and the distribution over the parameters for sketch patch reconstruction. Then, we apply an alternating iterative optimization strategy to simultaneously optimize two kinds of parameters. Therefore, both the representation capability of deep patch representation and the reconstruction ability of sketch patches can be boosted. Eventually, high quality reconstructed sketches which is robust against light variations and clutter backgrounds can be obtained. Extensive experiments on several benchmark datasets demonstrate that our method can achieve superior performance than other stat

关键词： Face sketch synthesis probabilistic graphical model Deep patch representation Generative model

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A probabilistic graphical model for the classification of mobile LiDAR point clouds

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2018年 143卷 108-123页

作者： Kang, Zhizhong Yang, Juntao China Univ Geosci Sch Land Sci & Technol Dept Remote Sensing & Geoinformat Engn Xueyuan Rd 29 Beijing 100083 Peoples R China

Mobile Light Detection And Ranging (LiDAR) point clouds have the characteristics of complex and incomplete scenes, uneven point density and noises, which raises great challenges for automatically interpreting 3D scene. Aiming at the problem of 3D point cloud classification, we propose a probabilistic graphical model for automatic classification of mobile LiDAR point clouds in this paper. First, the super-voxels are generated as primitives based on the similar geometric and radiometric properties. Second, we construct point-based multi-scale visual features that are used to describe the texture information at various scales. Third, the topic model is used to analyze the semantic correlations among points within super-voxels to establish the semantic representation, which is finally fed into the proposed probabilistic graphical model. The proposed model combines Bayesian network and Markov random fields to obtain locally continuous and globally optimal classification results. To evaluate the effectiveness and the robustness of the proposed method, experiments were conducted using mobile LiDAR point clouds for three types of street scenes. Experimental results demonstrate that our proposed model is efficient and robust for extracting vehicles, buildings, street trees and pole-like objects, with overall accuracies of 98.17%, 97.41% and 96.81% respectively. Moreover, compared with other existing methods, our proposed model can provide higher classification correctness, specifically for small objects such as cars and pole-like objects.

关键词： Mobile LiDAR probabilistic graphical model Classification Super-voxelization Latent Dirichlet allocation

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Uncertainty quantification for multiscale disk forging of polycrystal materials using probabilistic graphical model techniques

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COMPUTATIONAL MATERIALS SCIENCE 2014年 84卷 278-292页

作者： Chen, Peng Zabaras, Nicholas Cornell Univ Sibley Sch Mech & Aerosp Engn Mat Proc Design & Control Lab Ithaca NY 14853 USA Cornell Univ Ctr Appl Math Ithaca NY 14853 USA

The mechanical properties of a deformed workpiece are sensitive to the initial microstructure before processing commences. Generally, the initial microstructure is random in nature and location specific. The location-dependence of the microstructure dramatically increases the dimensionality of the stochastic input and thus leads to the "curse of dimensionality" in stochastic deformation problems. In this work, a graph-theoretic approach is used to address the stochastic multiscale deformation problem and compute the propagation of the initial microstructure uncertainty to the forged disk properties. Following the finite element representation of the multiscale deformation problem, a graphical representation is introduced with nodes in the graph representing the macro-and meso-scale random variables and links between nodes modeling the dependence relations between variables at each scale and across scales. model reduction techniques are employed locally in the graph to represent the initial random microstructure. Then the conditional distribution of the multi-output mechanical responses on the low-dimensional representation of the initial microstructure is factorized into a product of local potential functions. An expectation-maximization algorithm is used to learn the non-parametric representation of these potentials using a set of training data. A non-parametric loopy belief propagation method is applied to perform uncertainty quantification tasks. The non-parametric nature of the model is able to capture non-Gaussian features of the responses. The developed framework can be used as a surrogate model to predict the mechanical response fields for any input microstructure realization as well as our confidence on such predictions. A multiscale disk forging example of FCC nickel is presented to demonstrate the accuracy and efficiency of the constructed framework for addressing uncertainty quantification problems in multiscale deformation processes. (C) 2013 Elsevier B.

关键词： Stochastic multiscale simulation Location-dependent microstructure Disk forging Mechanical properties probabilistic graphical model Non-parametric belief propagation Loopy belief propagation

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Constructing Service Semantic Link Network Based on the probabilistic graphical model

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2012年第6期5卷 1040-1051页

作者： Zhao, Anping Ma, Yan Chongqing Normal Univ Coll Comp & Informat Sci Chongqing 401331 Peoples R China

Automatic services collaboration calls for the development of semantically structured service network to maximize the utility of Web services. Service Semantic Link Network (S-SLN) is the semantic model for effectively managing Web service resources by the dependency relationship between services. We provided an effective method for constructing S-SLN based on the graphical structure representation of the dependencies embedded in a probabilistic model. A Markov network is an undirected graph whose links represents probability dependencies. We first learned Markov network structure from Web services data, and then transformed the undirected Markov network structure into a directed graph structure of S-SLN based on the same joint probability distribution. Finally, experimental results show the effectiveness of the method.

关键词： Web service probabilistic graphical model S-SLN Service relationship

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Salient and non-salient fiducial detection using a probabilistic graphical model

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PATTERN RECOGNITION 2014年第1期47卷 208-215页

作者： Benitez-Quiroz, C. F. Rivera, Samuel Gotardo, Paulo F. U. Martinez, Aleix M. Ohio State Univ Computat Biol & Cognit Sci Lab Columbus OH 43210 USA

Deformable shape detection is an important problem in computer vision and pattern recognition. However, standard detectors are typically limited to locating only a few salient landmarks such as landmarks near edges or areas of high contrast, often conveying insufficient shape information. This paper presents a novel statistical pattern recognition approach to locate a dense set of salient and non-salient landmarks in images of a deformable object. We explore the fact that several object classes exhibit a homogeneous structure such that each landmark position provides some information about the position of the other landmarks. In our model, the relationship between all pairs of landmarks is naturally encoded as a probabilistic graph. Dense landmark detections are then obtained with a new sampling algorithm that, given a set of candidate detections, selects the most likely positions as to maximize the probability of the graph. Our experimental results demonstrate accurate, dense landmark detections within and across different databases. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Shape modeling Detailed face shape detection Face detection probabilistic graphical model Landmark detection

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Parallel probabilistic graphical model approach for nonparametric Bayesian inference

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JOURNAL OF COMPUTATIONAL PHYSICS 2018年 372卷 546-563页

作者： Lee, Wonjung Zabaras, Nicholas City Univ Hong Kong Dept Math Hong Kong Hong Kong Peoples R China Univ Notre Dame Dept Aeronaut & Mech Engn Notre Dame IN 46556 USA Univ Warwick Sch Engn Ctr Predict Modelling Warwick England

We propose an efficient uncertainty quantification framework that makes use of multiple probabilistic graphical models to yield a nonparametric Gaussian mixture description of the target probability distribution. The methodology is indeed generic, but this work focuses on its application to the particular class of the inference problems arising from the hidden Markov process and the associated observations in a sequence. The implementation procedure is demonstrated with the dynamical system models in both low and high dimension. In case of the low dimension, it is shown that the usual factor graph for the sequential data can be used to produce a very accurate approximate solution. However, for high dimensional systems, a new family of the factor graphs are developed in order to achieve an effective dimension reduction and to facilitate a synergetic application together with multiple graphs in addressing the Bayesian data assimilation. As a result, a new paradigm for the probabilistic filtering and smoothing emerges, and the applicability of the graphical model approach has been broadened. (C) 2018 Elsevier Inc. All rights reserved.

关键词： probabilistic graphical model Bayesian inference Data assimilation Gaussian mixture

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