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pattern clustering of Hysteresis Time Series With Multivalued Mapping Using Tensor Decomposition

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2018年第6期48卷 993-1004页

作者： He, Hong Tan, Yonghong Shanghai Normal Univ Coll Informat Mech & Elect Engn Shanghai 200234 Peoples R China

Many actuators and sensors made by smart materials have hysteresis feature which is a complex nonlinear phenomenon with multivalued mapping. Accurate identification of hysteresis pattern in those sensors and actuators is helpful for improving the modeling and control strategies of the systems. In this paper, a general framework of the pattern clustering of hysteretic time series is developed on the basis of tensor decomposition. First, high-dimensional multivariate data of hysteresis objects are transformed into three-order hysteresis tensors. Then the multilinear principal component analysis (MPCA) is utilized to reduce the dimensionality of hysteresis tensors. Afterward, a novel tensor k-means clustering (CTKmeans) based on tensor distance and cycle variation feature initialization is developed for the clustering of tensor objects. In order to evaluate the performance of the proposed approach, the experiment of hysteresis feature test using polyvinylidene fluoride (PVDF)-based pressure sensors is implemented. The measured high-dimensional hysteresis time series of PVDF successively undergoes the procedures of filtering, segmentation, MPCA dimensionality reduction and CTKmeans clustering. The experimental results show that the MPCA can capture more significant inherent features of hysteresis objects than the principal component analysis (PCA) or the kernel PCA in the dimensionality reduction. In terms of tensor distance and cycle variation feature initialization, the CTKmeans outperforms the random-initialization-based tensor k-means and standard k-means in the clustering of hysteresis tensors. With the tensor decomposition approach of multivariate time series, the hysteresis objects with nonlinear multivalued mapping features can be effectively identified by the combination of MPCA and CTKmeans.

关键词： Dimensionality reduction hysteresis pattern clustering tensor time series analysis

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pattern clustering With Statistical Methods Using a DNA-Based Algorithm

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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IEEE TRANSACTIONS ON NANOBIOSCIENCE 2012年第2期11卷 100-110页

作者： Kim, Ikno Watada, Junzo Pedrycz, Witold Wu, Jui-Yu Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka 8080135 Japan Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2G7 Canada Taipei Med Univ Dept Biochem Sch Med Taipei Taiwan

clustering is commonly exploited in engineering, management, and science fields with the objective of revealing structure in pattern data sets. In this article, through clustering we construct meaningful collections of information granules (clusters). Although the underlying goal is obvious, its realization is fully challenging. Given their nature, clustering is a well-known NP-complete problem. The existing algorithms commonly produce some suboptimal solutions. As a vehicle of pattern clustering, we discuss in this article how to use a DNA-based algorithm. We also discuss the details of encoding being used here with statistical methods combined with the DNA-based algorithm for pattern clustering.

关键词： DNA-based algorithm ordering method pattern clustering splicing operation statistical method

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A VLSI SYSTOLIC ARCHITECTURE FOR pattern clustering

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IEEE TRANSACTIONS ON pattern ANALYSIS AND MACHINE INTELLIGENCE 1985年第1期7卷 80-89页

作者： NI, LM JAIN, AK MICHIGAN STATE UNIV DEPT COMP SCIE LANSINGMI 48824 USA

Cluster analysis is a valuable tool in exploratory pattern analysis, especially when very little prior information about the data is available. In unsupervised pattern recognition and image segmentation applications, clustering techniques play an important role. The squared-error clustering technique is the most popular one among different clustering techniques. Due to the iterative nature of the squared-error clustering, it demands substantial CPU time, even for modest numbers of patterns. Recent advances in VLSI microelectronic technology triggered the idea of implementing the squared-error clustering directly in hardware. A two-level pipelined systolic pattern clustering array is proposed in this paper. The memory storage and access schemes are designed to enable a rhythmic data flow between processing units. Each processing unit is pipelined to further enhance the system performance. The total processing time for each pass of pattern labeling and cluster center updating is essentially dominated by the time required to fetch the pattern matrix once. Detailed architectural configuration, system performance evaluation, and simulation experiments are presented. The modularity and the regularity of the system architecture make it suited for VLSI implementations.

关键词： Very large scale integration pattern clustering pattern analysis System performance Information analysis pattern recognition Image segmentation Central Processing Unit Microelectronics Hardware Author Keywords VLSI architecture Euclidean distance exploratory data analysis pattern clustering pattern recognition squared error systolic array two-level pipelining vector reduction

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Metagraph-Based Life pattern clustering With Big Human Mobility Data

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IEEE TRANSACTIONS ON BIG DATA 2023年第1期9卷 227-240页

作者： Li, Wenjing Zhang, Haoran Chen, Jinyu Li, Peiran Yao, Yuhao Shi, Xiaodan Shibasaki, Mariko Kobayashi, Hill Hiroki Song, Xuan Shibasaki, Ryosuke Univ Tokyo Ctr Spatial Informat Sci Kashiwa Chiba 2778568 Japan Locat Mind Inc Chiyoda Ku Tokyo 1010032 Japan Southern Univ Sci & Technol Southern Univ Sci & Technol Univ Tokyo Joint Res C Dept Comp & Engn Shenzhen 518055 Guangdong Peoples R China

Life pattern clustering is essential for abstracting the groups' characteristics of daily life patterns and activity regularity. Based on millions of GPS records, this research proposes a framework on the life pattern clustering which can efficiently identify the groups that have similar life patterns. The proposed method can retain original features of individual life pattern data without aggregation. Metagraph-based data structure is proposed for presenting the diverse life pattern. Spatial-temporal similarity includes significant places semantics, time-sequential properties and frequency are integrated into this data structure, which captures the uncertainty of an individual and the diversities between individuals. Non-negative-factorization-based method is utilized for reducing the dimension. The results show that our proposed method can effectively identify the groups that have similar life pattern in long term and takes advantage in computation efficiency and representational capacity compared with the traditional methods. We reveal the representative life pattern groups and analyze the group characteristics of human life patterns during different periods and different regions. We believe our work helps in future infrastructure planning, services improvement and policy making related to urban and transportation, thus promoting a humanized and sustainable city.

关键词： pattern clustering Global Positioning System Big Data Measurement Data structures Time-frequency analysis Semantics Metagraph life pattern clustering big data human mobility

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A Novel pattern clustering Algorithm Based on Particle Swarm Optimization Joint Adaptive Wavelet Neural Network Model

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MOBILE NETWORKS & APPLICATIONS 2017年第4期22卷 692-701页

作者： Su, Shoubao Guo, Haifeng Tian, Haimei Wu, Gang Jinling Inst Technol Jiangsu Key Lab Data Sci & Smart Software Nanjing 211169 Jiangsu Peoples R China Hohai Univ Sch Comp & Informat Nanjing 210098 Jiangsu Peoples R China

Cluster analysis as an important function of the data mining has made great progress in recent years a series of methods for analysis of accuracy provides a solid foundation. This paper proposes the novel pattern clustering algorithm based on particle swarm optimization joint adaptive wavelet neural network model. Nonlinear adaptive filter has the stronger ability of that signal processing. However, due to complicated calculation of the nonlinear adaptive filter, the actual use of linear adaptive filter is still. To deal with this challenge, we integrate the wavelet neural network for modification. Usually, the wavelet neural network learning process using the gradient descent method, even if the error function that along the negative gradient direction, and which requires the step length direction along the negative gradient direction. As well, we revise the PSO to enhance the wavelet NN in the process of search, the equilibrium relation between the global search ability and local search ability for the success of the algorithm plays an important role. With this modification, our method obtains the better performance. The experimental result shows the feasibility and the effectiveness of the method compared with the other latest and state-of-the-art methods.

关键词： pattern clustering Algorithm Particle swarm optimization (PSO) Wavelet neural network Adaptive model Machine learning

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Two-Stage Load pattern clustering Using Fast Wavelet Transformation

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IEEE TRANSACTIONS ON SMART GRID 2016年第5期7卷 2250-2259页

作者： Mets, Kevin Depuydt, Frederick Develder, Chris Univ Ghent iMinds Internet Based Commun Networks & Serv IBCN Dept Informat Technol B-9050 Ghent Belgium

Smart grids collect large volumes of smart meter data in the form of time series, or so-called load patterns. We outline the applications that benefit from analyzing this data (ranging from customer segmentation to operational system planning), and propose two-stage load pattern clustering. The first stage is performed per individual user and identifies the various typical daily power usage patterns (s) he exhibits. The second stage takes those typical user patterns as input to group users that are similar. To improve scalability, we use fast wavelet transformation (FWT) of the time series data, which reduces the dimensionality of the feature space where the clustering algorithm operates (i.e., from N data points in the time domain to log N). Another qualitative benefit of FWT is that patterns that are identical in shape, but just differ in a (typically small) time shift still end up in the same cluster. Furthermore, we use g-means instead of k-means as the clustering algorithm. Our comprehensive set of experiments analyzes the impact of using FWT versus time-domain features, and g-versus k-means, to conclude that in terms of cluster quality metrics our system is comparable to state-of-the-art methods, while being more scalable (because of the dimensionality reduction).

关键词： clustering methods meter reading pattern clustering smart grids

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Modeling energy consumption in residential buildings: A bottom-up analysis based on occupant behavior pattern clustering and stochastic simulation

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ENERGY AND BUILDINGS 2017年第Jul.期147卷 47-66页

作者： Diao, Longquan Sun, Yongjun Chen, Zejun Chen, Jiayu City Univ Hong Kong Dept Architecture & Civil Engn AC1Y6621Tat Chee Ave Kowloon Hong Kong Peoples R China City Univ Hong Kong Div Bldg Sci & Technol MMW 5424Tat Chee Ave Kowloon Hong Kong Peoples R China Tung Wah Coll Sch Business 21-F98 Shantung St Kowloon Hong Kong Peoples R China

In residential buildings, occupant behavior and occupancy status have a significant impact on energy consumption variation. Although the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) recommends a uniform occupancy schedule for building energy assessment, occupant behavior patterns and schedules could be different for each building due to occupants' lifestyles, preferences, occupations, and other differences. Existing occupant behavior models focus on analyzing occupants' sociodemographic characteristics to predict their energy consumption with statistical approaches. This paper proposes to identify and classify occupants' behavior with direct energy consumption outcomes and energy time use data through unsupervised clustering. Based on the American Time Use Survey (ATUS), the proposed approach integrates k-modes clustering and demographic-based probability neural networks and identifies 10 distinctive behavior patterns. With the results of the behavior classification and simulation, a bottom-up engineering model reveals that the proposed behavior model offers a more accurate and reliable prediction than the ASHRAE standard schedule. With qualified and sufficient time use data, the model is capable of automatically estimating energy consumption on even larger geographic scales. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Occupant behavior pattern clustering Bottom-up model Building energy simulation

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A Novel Sequence Discriminative Feature Extraction Network and Its Application in Offline Industrial Fault pattern clustering

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第2期20卷 3025-3033页

作者： Qian, Jinchuan Wei, Chihang Song, Zhihuan Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou 311121 Peoples R China

A valid fault pattern clustering method for stored fault data can effectively help offline fault diagnosis, and the clustering results can provide solid data support for the training of the fault diagnosis model. To achieve it, a novel sequence discriminative feature extraction network (SDFEN) is developed for extracting the discriminative features underlying the industrial time series. The proposed SDFEN composes a prediction network and an extraction network, which are trained successively. The prediction network is designed for preliminary extraction of the discriminative features, and a set of contribution rate sequences are calculated for the supervised training. Besides, to extract local information and contract the features from the same class, a local information extraction network is further connected, trained by reconstruction and neighbor prediction. Dynamic time warping helps to select neighbors among time series, while a parallel strategy is designed to reduce computing load. The final clustering result is given by the Gaussian mixture model. The feasibility and effectiveness of the proposed method are verified by experiments on the Tennessee Eastman benchmark process and the multiphase flow facility.

关键词： Feature extraction Time series analysis Indexes Task analysis Data mining pattern clustering Training Deep learning discriminative feature extraction fault clustering neighbor searching

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User Activity Recognition in Smart Homes Using pattern clustering Applied to Temporal ANN Algorithm

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SENSORS 2015年第5期15卷 11953-11971页

作者： Bourobou, Serge Thomas Mickala Yoo, Younghwan Pusan Natl Univ Dept Elect & Comp Engn Busan 609735 South Korea

This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen's temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home.

关键词： activity recognition Allen's temporal relations anomaly prediction neural network pattern clustering smart home

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Meta-Heuristic Optimization-Based Two-Stage Residential Load pattern clustering Approach Considering Intra-Cluster Compactness and Inter-Cluster Separation

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2020年第4期56卷 3375-3384页

作者： Li, Kangping Cao, Xin Ge, Xinxin Wang, Fei Lu, Xiaoxing Shi, Min Yin, Rui Mi, Zengqiang Chang, Shengqiang North China Elect Power Univ Dept Elect Engn Baoding 071003 Peoples R China Hebei Prov Construct & Investment Grp Co Ltd Shijiazhuang 050000 Hebei Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China North China Elect Power Univ Hebei Key Lab Distributed Energy Storage & Microg Baoding 071003 Peoples R China State Grid Hebei Elect Power Co Ltd Dispatch & Control Ctr Shijiazhuang 050021 Hebei Peoples R China Shijiazhuang Kelin Elect Co Ltd Shijiazhuang 050222 Hebei Peoples R China

This article proposes a meta-heuristic optimization-based two-stage residential load pattern clustering (LPC) approach to address two main issues that exist in the most current LPC methods: 1) unreasonable typical load pattern (TLP) extraction;2) a good clustering should achieve a good balance between the compactness and separation of the formed clusters. However, few clustering algorithms integrate both of these two aspects into the objective function of clustering for consideration. In the first stage, an adaptive density-based spatial clustering of applications with noise (DBSCAN) is proposed to automatically detect the uncommon load curves and obtain the TLP of each individual customer. In the second stage, LPC is formulated as an optimization problem in which clustering validity index (CVI) considering both compactness and separation is used as the objective function. Gravitational search algorithm (GSA) is adopted to solve this optimization problem. Four different CVIs are investigated to find the most appropriate one for LPC. A comparative case study using the real load data from 208 households from the U.K. verified the effectiveness of the proposed approach.

关键词： clustering algorithms Optimization Linear programming Power systems pattern clustering Indexes Gravitational search algorithm (GSA) incentive-based demand response intercluster separation intracluster compactness load pattern clustering (LPC) typical load pattern

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