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Distribution Network Topology Identification Using Smart Meter Data and Considering the Same-Bus-Different-Feeder Condition

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第8期19卷 8600-8613页

作者： Xu, Zhiqi Jiang, Wei Xu, Junjun Wu, Lei Wang, Minghua Chen, Jinming Chen, Ye Zhao, Junbo Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China State Grid Anhui Elect Power Co Ltd Power Supply Co Luan 237006 Peoples R China State Grid Jiangsu Elect Power Co Ltd Nanjing 210017 Peoples R China Univ Connecticut Elect & Comp Engn Storrs CT 06269 USA

Due to the rapid growth of distribution systems in urban areas, the increasing complexity of these distribution systems brings challenges to accurate topology identification. The collected voltage data from smart meters have been proven effective in topology identification applications. However, when multiple feeders are connected to the same bus, the accuracy of existing voltage-correlation-based topology identification can be degraded significantly. To address this challenge, a comprehensive inference method is proposed in this article to identify the on/off switch state in the distribution system considering the same-bus-different-feeder condition. The voltage-power-dependence principle among connected nodes is revealed. Based on this theory, a physical probabilistic network model is proposed to represent the causal relationships between the switch states and the voltage-power dependence in a distribution network. The belief propagation algorithm is introduced to deduce the topology identification model, which can reduce the time consumption of the inference. The performance of the proposed method and its advantage over the existing methods are verified in case studies.

关键词： Index Terms-Belief propagation (BP) algorithm distri-bution network probabilistic graphical model topology identification

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Representing and processing lineages over uncertain data based on the Bayesian network

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APPLIED SOFT COMPUTING 2015年 37卷 345-362页

作者： Yue, Kun Wu, Hao Liu, Weiyi Zhu, Yunlei Yunnan Univ Sch Informat Sci & Engn Dept Comp Sci & Engn Kunming 650504 Peoples R China

Processing lineages (also called provenances) over uncertain data consists in tracing the origin of uncertainty based on the process of data production and evolution. In this paper, we focus on the representation and processing of lineages over uncertain data, where we adopt Bayesian network (BN), one of the popular and important probabilistic graphical models (PGMs), as the framework of uncertainty representation and inferences. Starting from the lineage expressed as Boolean formulae for SPJ (Selection-Projection-Join) queries over uncertain data, we propose a method to transform the lineage expression into directed acyclic graphs (DAGs) equivalently. Specifically, we discuss the corresponding probabilistic semantics and properties to guarantee that the graphical model can support effective probabilistic inferences in lineage processing theoretically. Then, we propose the function-based method to compute the conditional probability table (CPT) for each node in the DAG. The BN for representing lineage expressions over uncertain data, called lineage BN and abbreviated as LBN, can be constructed while generally suitable for both safe and unsafe query plans. Therefore, we give the variable-elimination-based algorithm for LBN's exact inferences to obtain the probabilities of query results, called LBN-based query processing. Then, we focus on obtaining the probabilities of inputs or intermediate tuples conditioned on query results, called LBN-based inference query processing, and give the Gibbs-sampling-based algorithm for LBN's approximate inferences. Experimental results show the efficiency and effectiveness of our methods. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Uncertain data Lineage Inference query probabilistic graphical model Bayesian network Approximate inference

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modeling With Prejudice: Small-Sample Learning via Adversary for Semantic Segmentation

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IEEE ACCESS 2018年 6卷 77965-77974页

作者： Jiang, Zhiyu Wang, Qi Yuan, Yuan Northwestern Polytech Univ Ctr Opt Imagery Anal & Learning OPTIMAL Sch Comp Sci Xian 710072 Shaanxi Peoples R China

Semantic segmentation has become one of the core tasks for scene understanding and many high-level works heavily rely on its performance. In the past decades, much progress has been achieved. However, some problems still need to be settled. One problem is about the challenging classification of various objects, which are with diverse viewpoints, illumination, appearance, and cluttered backgrounds, in a unified framework. The other one is focusing on the unbalanced distribution of semantic labels, where long-tail phenomenon exists and the trained model tends to be biased toward the majority classes when testing. And this problem can be regarded as the small-sample learning problem in semantic segmentation for the number of training samples upon the minority classes are small. For tackling these problems, a small-sample learning method via adversary is proposed and three contributions are claimed: 1) discriminatory modeling for semantic segmentation: two submodels are simultaneously built based on the attribute of semantic classs;2) hierarchical contextual information consideration: both local and global contextual relationships are equally modeled under a hierarchical probabilistic graphical method and neighborhood relationship in label space are also considered;and 3) adversary learning for small-sample modeling: according to the structural relationships between small samples and the others, semantic classes are adversely modeled through computing the weighted costs. Experimental results on three benchmarks have verified the superiority of the proposed method compared with the state-of-the-arts.

关键词： Adversary learning CRF probabilistic graphical model semantic segmentation small-sample learning

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Inferring spatial-temporal attributes of vehicle itinerary with Automatic Vehicle Identification data: Methodology and application☆

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TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE 2024年 190卷

作者： Cao, Qi Liu, Yang Ren, Gang Wang, Shunchao Li, Dawei Deng, Yue Qu, Xiaobao Southeast Univ Sch Transportat Nanjing Peoples R China Tsinghua Univ Sch Vehicle & Mobil Beijing Peoples R China

Daily itinerary, consisting of an individual's trips and activities on a day, is usually fundamental input for many travel demand models. However, current research lacks effective methods to extract daily itineraries of large-scale samples for a long period. To this end, this study presents a methodology to Infer Daily Itineraries (IDI) of vehicles with Automatic Vehicle Identification (AVI) data. A problem-specific probabilistic graphical model is constructed to define how possible one itinerary is true given its observed AVI data. To seek the most possible itinerary among vast feasible states, a candidate movement state generation algorithm and optimal itinerary searching algorithm are developed. Empirical studies have been conducted based on field-test data. Compared with two benchmarks, the proposed IDI improved the inference accuracy significantly even for activities with missing observations. Sensitivity analyses on the size of traffic area zone and data collection have also been performed, which can provide guidance for administrations and researchers on the partition of the study region and placement of the sensors. As the AVI system captures almost entire samples, vehicle movements inferred by IDI can provide a better representation of traffic patterns. This enables a series of applications related to transportation policy and practice. Traffic congestion tracking and parking demand estimation are introduced as two application examples.

关键词： Itinerary inference probabilistic graphical model Spatial and temporal analysis Automatic Vehicle Identification data Travel demand model

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A Physical probabilistic Network model for Distribution Network Topology Recognition Using Smart Meter Data

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IEEE TRANSACTIONS ON SMART GRID 2019年第6期10卷 6965-6973页

作者： Jiang, Wei Chen, Jinming Tang, Haibo Cheng, Shu Hu, Qinran Cai, Mengmeng Rahman, Saifur Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Virginia Tech Adv Res Inst Arlington VA 22203 USA

Given the considerable scale of distribution networks in urban and rural areas, as well as the lack of management records, adjustments of switches during the distribution system operation are poorly documented. Such deficiency results in the inaccuracy of models stored in the distribution network automation system, and thus misleads the state estimation. With the emergence of information and communication technology, a large number of the feeder and residential smart meter data are accumulated. Such data can help recognize the operation modes of distribution networks by analyzing the relationships between the on/off states of switches and the voltage correlations among buses. However, the limited quantity and quality of the sampling data restrict the implementation of data-driven recognition. In this paper, a physical-probabilistic-network (PPN) model applied for inferring overall operation mode of distribution networks is proposed. Based on which, a belief propagation-based algorithm is proposed for the inference even under situations when there are only partial bus voltages data available. Meanwhile, the required variable for inference can be reduced from the active trail analysis. Experiment results are used to compare its performance with classic methods and to prove its effectiveness and advantages.

关键词： Distribution network topology recognition probabilistic graphical model belief propagation algorithm

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A knowledge based system for the management of a time stamped uncertain observation set with application on preserving mobility

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2021年 134卷 53-71页

作者： Delcroix, Veronique Strugeon, Emmanuelle Grislin-Le Puisieux, Francois Univ Polytech Hauts De France CNRS UMR 8201 LAMIH F-59313 Valenciennes France INSA Hauts De France F-59313 Valenciennes France CHU Lille Pole Gerontol Hop Geriatr Bateliers Lille France

The aim of this study is to maintain up-to-date information about the current state of elderly people that are medically followed for risks of fall. Our proposal consists of an individual information database management system that can provide information on demand on various variables. Such a system has to deal with several sources of uncertainty: lack of information, evolving information and reliability of the information sources. We consider that the features of the person may evolve with time causing uncertainty due to obsolete information. Our context includes new information received bit by bit, with no possibility to collect all required information at once. This paper establishes a first proposal to manage a set of uncertain observations, in order to reduce erroneous and obsolete information while keeping the benefit of previously collected information. We propose an architecture of the system based on a probabilistic knowledge model about the characteristics of interest, a set of decay functions that help to evaluate the confidence degree in previous observations, and a reasoning module to manage new observations, maintain the compatibility and the quality of the observation set. We detail the algorithms of the reasoning module, and the algorithm to update the confidence degree of the observations. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Information aging Uncertain information Information quality probabilistic graphical model Knowledge based system Reasoning with uncertainty

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Belief Propagation Based Cooperative Compressed Spectrum Sensing in Wideband Cognitive Radio Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2011年第9期10卷 3020-3031页

作者： Zhang, Zhenghao Han, Zhu Li, Husheng Yang, Depeng Pei, Changxing Xidian Univ ISN Lab Xian Shaanxi Peoples R China Univ Houston Dept Elect & Comp Engn Houston TX USA Kyung Hee Univ Dept Elect & Radio Engn Seoul South Korea Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN USA

Wideband spectrum sensing in heterogenous cognitive radio networks has two significant challenges to tackle. One is the spectrum acquisition in the wideband scenario due to the limited sampling capability;the other is how to collaborate among the secondary users. Compressed spectrum sensing provides a powerful approach to acquire wideband signal. Moreover, most cooperative spectrum sensing methods assume that all the secondary users experience the same occupancy of primary users, which may be infeasible in a heterogenous spectrum environment where secondary users at different locations may be affected by different primary users. In this paper, we propose a probabilistic graphical model to represent and fuse multi-prior information from one hop neighboring secondary users. Belief propagation (BP) is used for the statistical inference of the spectrum occupancy. Numerical simulation results demonstrate that the proposed BP based cooperative compressed spectrum sensing can effectively achieve cooperation in heterogenous environments and improve performance of compressed spectrum sensing under a low sampling rate and low signal-to-noise ratio (SNR), compared with the other distributed cooperative compressed sensing methods.

关键词： Heterogenous cognitive radio networks compressed spectrum sensing belief propagation probabilistic graphical model

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modeling Data Correlations in Recommendation

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IEEE ACCESS 2017年 5卷 11030-11042页

作者： He, Yuan Wang, Cheng Jiang, Changjun Tongji Univ Dept Comp Sci & Engn Shanghai 200092 Peoples R China Minist Educ Key Lab Embedded Syst & Serv Comp Shanghai 201804 Peoples R China Shanghai Elect Transact & Informat Serv Collabora Shanghai 201804 Peoples R China

In the field of recommender systems, the Beer & Nappies is a famous story, which reveals the latent relationships between different categories of items. Though matrix factorization (MF) has demonstrated its great effectiveness in most previous work, it neglects the co-occurrences of items selected by individuals. In most MF-based models, the latent preferences of users (or the latent categories of items) are assumed independent, which thereby leads to the weak correlation between the Beer and the Nappies. It also greatly limits the models' ability in recommendation. In this paper, we propose a pure probabilistic generative model, which applies a Gaussian prior to capture the semantic correlations between the latent factors. We also show that our model theoretically achieves better expressive power than traditional MF-based models. We derive efficient inference and learning algorithms based on variational EM methods. The effectiveness of our proposed model is comprehensively verified on three different public data sets. Experimental results show that our approach achieves significant improvements on prediction quality compared with the current state of the art.

关键词： Matrix factorization probabilistic graphical model recommender systems

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An example-based approach to 3D man-made object reconstruction from line drawings

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PATTERN RECOGNITION 2016年 60卷 543-553页

作者： Zou, Changqing Xue, Tianfan Peng, Xiaojiang Li, Honghua Zhang, Baochang Tan, Ping Liu, Jianzhuang Hengyang Normal Univ Hengyang Hunan Peoples R China Simon Fraser Univ Burnaby BC V5A 1S6 Canada MIT Cambridge MA 02139 USA Shandong Univ Jinan Lixia Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China

3D reconstruction from a single 2D line drawing is an important but challenging problem" in computer vision. Existed methods usually fail when line drawings contain large degree of noise named sketch errors. In this paper, we present an example-based approach to reconstructing 3D object, either planar or curved, from a single-view line drawing with sketch errors. Our method is to first decompose the input line drawing into primitive components and cluster them into local groups, then turn each group into 3D shapes via a novel example-based algorithm, and lastly integrate those recovered 3D shapes from all groups to build a final complete 3D model. Comprehensive experiments on a wide range of line drawings depicting man-made objects show that the proposed approach outperform previous work, especially for line drawings containing large degree of sketch errors. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： 3D reconstruction Example-based Line drawing probabilistic graphical model

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Color to Gray: Visual Cue Preservation

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2010年第9期32卷 1537-1552页

作者： Song, Mingli Tao, Dacheng Chen, Chun Li, Xuelong Chen, Chang Wen Zhejiang Univ Coll Comp Sci Hangzhou 310027 Zhejiang Peoples R China Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Chinese Acad Sci State Key Lab Transient Opt & Photon Xian Inst Opt & Precis Mech Xian 710119 Peoples R China SUNY Buffalo Dept Comp Sci & Engn Buffalo NY 14260 USA

Both commercial and scientific applications often need to transform color images into gray-scale images, e. g., to reduce the publication cost in printing color images or to help color blind people see visual cues of color images. However, conventional color to gray algorithms are not ready for practical applications because they encounter the following problems: 1) Visual cues are not well defined so it is unclear how to preserve important cues in the transformed gray-scale images;2) some algorithms have extremely high time cost for computation;and 3) some require human-computer interactions to have a reasonable transformation. To solve or at least reduce these problems, we propose a new algorithm based on a probabilistic graphical model with the assumption that the image is defined over a Markov random field. Thus, color to gray procedure can be regarded as a labeling process to preserve the newly well-defined visual cues of a color image in the transformed gray-scale image. Visual cues are measurements that can be extracted from a color image by a perceiver. They indicate the state of some properties of the image that the perceiver is interested in perceiving. Different people may perceive different cues from the same color image and three cues are defined in this paper, namely, color spatial consistency, image structure information, and color channel perception priority. We cast color to gray as a visual cue preservation procedure based on a probabilistic graphical model and optimize the model based on an integral minimization problem. We apply the new algorithm to both natural color images and artificial pictures, and demonstrate that the proposed approach outperforms representative conventional algorithms in terms of effectiveness and efficiency. In addition, it requires no human-computer interactions.

关键词： Color to gray probabilistic graphical model visual cue

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