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Progressive Self-Supervised clustering With Novel Category Discovery

IEEE TRANSACTIONS ON CYBERNETICS 2022年第10期52卷 10393-10406页

作者： Wang, Jingyu Ma, Zhenyu Nie, Feiping Li, Xuelong Northwestern Polytech Univ Sch Astronaut Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China Northwestern Polytech Univ Sch Astronaut Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China

These days, clustering is one of the most classical themes to analyze data structures in machine learning and pattern recognition. Recently, the anchor-based graph has been widely adopted to promote the clustering accuracy of plentiful graph-based clustering techniques. In order to achieve more satisfying clustering performance, we propose a novel clustering approach referred to as the progressive self-supervised clustering method with novel category discovery (PSSCNCD), which consists of three separate procedures specifically. First, we propose a new semisupervised framework with novel category discovery to guide label propagation processing, which is reinforced by the parameter-insensitive anchor-based graph obtained from balanced K-means and hierarchical K-means (BKHK). Second, we design a novel representative point selected strategy based on our semisupervised framework to discover each representative point and endow pseudolabel progressively, where every pseudolabel hypothetically corresponds to a real category in each self-supervised label propagation. Third, when sufficient representative points have been found, the labels of all samples will be finally predicted to obtain terminal clustering results. In addition, the experimental results on several toy examples and benchmark data sets comprehensively demonstrate that our method outperforms other clustering approaches.

关键词： clustering methods Bipartite graph Optimization Data models clustering algorithms Matrix decomposition Laplace equations Anchor-based graph progressively selected strategy pseudolabel representative points self-supervised clustering semisupervised framework

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Multidimensional Feature Extraction Based Minutely Solar Irradiance Forecasting Method Using All-Sky Images

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2024年第3期60卷 4494-4504页

作者： Wu, Xinyue Zhen, Zhao Zhang, Jianmei Wang, Fei Xu, Fei Ren, Hui Su, Ying Sun, Yong Yang, Heng North China Elect Power Univ Dept Elect Engn Baoding 071003 Peoples R China State Grid Gansu Elect Power Co Elect Power Res Inst Lanzhou 730070 Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing 102206 Peoples R China North China Elect Power Univ Hebei Key Lab Distributed Energy Storage & Microgr Baoding 071003 Peoples R China Tsinghua Univ State Key Lab New Type Power Syst Operat & Control Beijing 100084 Peoples R China China Three Gorges Corp Inst Sci & Technol Beijing 100038 Peoples R China

With the increasing penetration of photovoltaic (PV) power generation in the grid, minutely irradiance prediction, which is the basis of minutely PV power prediction, has become very important. Aiming at the problems of extracting feature redundancy, weakening key area features, and high cloud-sky misidentification rate in the current minutely irradiance prediction, this paper proposes a minutely solar irradiance forecasting method based on multidimensional feature extraction using all-sky image, which is beneficial to achieve higher accuracy in solar power forecasting. First, the improved clustering-boundary correction algorithm is used to identify cloud and sky pixels and classify the all-sky images into four cloud-sky types. Then capture the subimages of the cloud domain that will cover the sun in the future dynamically according to the results of the cloud displacement vector calculation and extract local features and overall features as multidimensional features based on convolutional neural network (CNN) and image RGB matrix, respectively. Finally combined the multidimensional features with meteorological factors and historical irradiance to respectively construct irradiance mapping models for four cloud-sky types to achieve irradiance prediction on a ten-minute scale. Compared with the benchmarks, the mean absolute percentage error (MAPE) of the proposed method is reduced by 0.21%, 20.21%, 2.53%, and 5.30% for four cloud-sky types: clear sky, block clouds, thin clouds, and thick clouds, respectively. The proposed method can be widely used in PV plants equipped with all-sky imagers to provide data support for the optimal operation and maintenance of the plant.

关键词： Clouds Feature extraction Meteorology clustering algorithms Solar irradiance Power systems Forecasting All-sky image clustering-boundary correction algorithm minutely solar irradiance forecasting multidimensional features extraction

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Accelerating the k-Means plus plus Algorithm by Using Geometric Information

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IEEE ACCESS 2025年 13卷 67693-67717页

作者： Corominas, Guillem Rodriguez Blesa, Maria J. Blum, Christian Univ Politecn Catalunya UPC Dept Comp Sci Barcelona 08034 Spain Artificial Intelligence Res Inst IIIA CSIC Bellaterra 08193 Spain

clustering is a fundamental task in data analysis with applications across a wide range of fields, such as computer vision, pattern recognition, and data mining. Real-world use cases include social network analysis, medical imaging, market segmentation, and anomaly detection, to name a few. In this paper, we propose an acceleration of the exact k-means++ algorithm using geometric information, specifically the Triangle Inequality and additional norm filters, along with a two-step sampling procedure. Our experiments demonstrate that the accelerated version outperforms the standard k-means++ version in terms of the number of visited points and distance calculations, achieving greater speedup as the number of clusters increases. The version utilizing the Triangle Inequality is particularly effective for low-dimensional data, while the additional norm-based filter enhances performance in high-dimensional instances with greater norm variance among points. Additional experiments show the behavior of our algorithms when executed concurrently across multiple jobs and examine how memory performance impacts practical speedup.

关键词： clustering algorithms Approximation algorithms Standards Measurement Machine learning algorithms Vectors Filters Computational efficiency Scalability Prediction algorithms clustering D-2 sampling k-means plus plus norm triangle inequality

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Multimodal Renewable Energy Hybrid Supply Optimization Model Based on Heterogeneous Cloud Wireless Access

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IEEE ACCESS 2024年 12卷 78286-78303页

作者： Tian, Feng Wang, Hongjiang Jiang, He Zhao, Baida Shenyang Inst Engn Coll Informat Shenyang 110036 Peoples R China Shenyang Inst Engn Sch Renewable Energy Shenyang 110036 Peoples R China Shenyang Inst Engn Innovat & Entrepreneurship Educ Ctr Shenyang 110036 Peoples R China

With the increasing emphasis on environmental issues, the utilization of renewable energy has been recognized as a feasible solution to address the energy crisis and reduce environmental pollution. In view of this, this article proposes a multi-modal renewable energy hybrid power supply optimization model based on heterogeneous cloud wireless access. The model innovatively combines heterogeneous cloud wireless access technology and various intelligent optimization algorithms, including k-clustering algorithm, particle swarm optimization algorithm, and whale optimization algorithm, forming a hybrid optimization algorithm. In order to comprehensively evaluate the actual performance of the model, this study recruited 20 experts to provide detailed ratings on four core dimensions: cost-benefit ratio, reliability, robustness, and user satisfaction. The results showed that the model scored 95.1, 96.4, 95.6, and 96.2 in the four dimensions of cost-benefit ratio, reliability indicators, robustness, and user satisfaction, respectively. This series of significant data not only confirms the theoretical superiority of the model, but also demonstrates its strong potential and practical value in practical applications. In summary, this study provides a promising and innovative solution for the field of renewable energy supply.

关键词： Optimization clustering algorithms Heuristic algorithms Renewable energy sources Wireless communication Cloud computing Whale optimization algorithms K-clustering algorithm heterogeneous cloud radio access particle swarm optimization algorithm WOA algorithm energy supply optimization

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UCRTD: An Unequally Clustered Routing Protocol Based on Multihop Threshold Distance for Wireless Sensor Networks

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IEEE INTERNET OF THINGS JOURNAL 2024年第17期11卷 29001-29019页

作者： Wang, Zhendong Zeng, Weibing Yang, Shuxin He, Daojing Chan, Sammy Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou 341000 Peoples R China Harbin Inst Technol Shenzhen Sch Comp Sci & Technol Shenzhen 518055 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

Cluster head (CH) nodes near the base station (BS) die prematurely due to the need to perform more communication tasks, which can lead to disruption of network connectivity and makes it difficult to achieve the goal of load balancing in wireless sensor networks (WSNs), this problem is known as hotspot problem. To solve this problem, nonuniform clustering strategies have been proposed. However, all the current-related nonuniform clustering protocols have some drawbacks, such as the lack of a theoretical basis for the value of the multihop threshold distance between clusters, the limited attention to the data transmission process, and the insufficient load balancing of the protocols in the face of complex and variable networks. Based on the above problems, we propose an unequally clustered routing protocol based on multihop threshold distance (UCRTD) for WSNs. First, this article analyzes the energy-saving threshold distance for multihop communication in conjunction with the energy consumption model of WSNs, and based on the multihop energy-saving threshold distance, a strategy for selecting the best energy-saving relay node is proposed. In intracluster communication, considering that medium-sized networks form larger clusters, cluster members (CMs) within the cluster that are farther away from the CH take multihop communication. For intercluster communication, to maximize the network lifetime, the most energy-efficient CH node with the highest residual energy is selected in the routing phase for alternate multihop transmission, and this strategy effectively prolongs the network lifetime and also ensures the load balance of the network. Simulation results show that the proposed UCRTD effectively prolongs the network lifetime and maintains good load balancing under multiple network environments when compared with four existing EEUC, EBUC, EADUC, and EAUCA unequal clustering protocols as well as LEACH protocol.

关键词： Wireless sensor networks Spread spectrum communication Relays clustering algorithms Routing protocols Energy consumption Internet of Things Multihop threshold distance nonuniform cluster routing protocol wireless sensor networks (WSNs)

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Synthetic Data Generation for Residential Load Patterns via Recurrent GAN and Ensemble Method

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷

作者： Liang, Xinyu Wang, Ziheng Wang, Hao Monash Univ Fac Informat Technol Dept Data Sci & AI Melbourne Vic 3800 Australia Monash Univ Monash Energy Inst Melbourne Vic 3800 Australia

Generating synthetic residential load data that can accurately represent actual electricity consumption patterns is crucial for effective power system planning and operation. The necessity for synthetic data is underscored by the inherent challenges associated with using real-world load data, such as privacy considerations and logistical complexities in large-scale data collection. In this work, we tackle the abovementioned challenges by developing the ensemble recurrent generative adversarial network (ERGAN) framework to generate high-fidelity synthetic residential load data. ERGAN leverages an ensemble of recurrent generative adversarial networks (GANs), augmented by a loss function that concurrently takes into account adversarial loss and differences between statistical properties. Our developed ERGAN can capture diverse load patterns across various households, thereby enhancing the realism and diversity of the synthetic data generated. Comprehensive evaluations demonstrate that our method consistently outperforms established benchmarks in the synthetic generation of residential load data across various performance metrics, including diversity, similarity, and statistical measures. The findings confirm the potential of ERGAN as an effective tool for energy applications requiring synthetic yet realistic load data. We also make the generated synthetic residential load patterns publicly available at https://***/AdamLiang42/ERGAN-Dataset.

关键词： Load modeling Generative adversarial networks Data models Synthetic data Home appliances Electricity Data privacy Complexity theory Renewable energy sources clustering algorithms Generative adversarial network (GAN) residential electricity load synthetic load generation

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Consensus One-Step Multi-View Subspace clustering

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第10期34卷 4676-4689页

作者： Zhang, Pei Liu, Xinwang Xiong, Jian Zhou, Sihang Zhao, Wentao Zhu, En Cai, Zhiping Natl Univ Def Technol Sch Comp Changsha 410073 Hunan Peoples R China Southwestern Univ Finance & Econ Sch Business Adm Chengdu 611130 Sichuan Peoples R China Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Hunan Peoples R China

Multi-view clustering has attracted increasing attention in multimedia, machine learning and data mining communities. As one kind of the essential multi-view clustering algorithm, multi-view subspace clustering (MVSC) becomes more and more popular due to its strong ability to reveal the intrinsic low dimensional clustering structure hidden across views. Despite superior clustering performance in various applications, we observe that existing MVSC methods directly fuse multi-view information in the similarity level by merging noisy affinity matrices;and isolate the processes of affinity learning, multi-view information fusion and clustering. Both factors may cause insufficient utilization of multi-view information, leading to unsatisfying clustering performance. This paper proposes a novel consensus one-step multi-view subspace clustering (COMVSC) method to address these issues. Instead of directly fusing multiple affinity matrices, COMVSC optimally integrates discriminative partition-level information, which is helpful to eliminate noise among data. Moreover, the affinity matrices, consensus representation and final clustering labels matrix are learned simultaneously in a unified framework. By doing so, the three steps can negotiate with each other to best serve the clustering task, leading to improved performance. Accordingly, we propose an iterative algorithm to solve the resulting optimization problem. Extensive experiment results on benchmark datasets demonstrate the superiority of our method against other state-of-the-art approaches.

关键词： clustering methods Kernel Task analysis Redundancy Fuses clustering algorithms Sparse matrices Multi-view clustering subspace clustering data fusion

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Variable-Length Subsequence clustering in Time Series

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第2期34卷 983-995页

作者： Duan, Jiangyong Guo, Lili Chinese Acad Sci Technol & Engn Ctr Space Utilizat Key Lab Space Utilizat Beijing 100094 Peoples R China Tsinghua Univ Sch Software Beijing 100084 Peoples R China

Subsequence clustering is an important issue in time series data mining. Observing that most time series consist of various patterns with different unknown lengths, we propose an optimization framework to adaptively estimate the lengths and representations for different patterns. Our framework minimizes the inner subsequence cluster errors with respect to subsequence clusters and segmentation under time series cover constraint where the subsequence cluster lengths can be variable. To optimize our framework, we first generate abundant initial subsequence clusters with different lengths. Then, three cluster operations, i.e., cluster splitting, combination and removing, are used to iteratively refine the cluster lengths and representations by respectively splitting clusters consisting of different patterns, joining neighboring clusters belonging to the same pattern and removing clusters to the predefined cluster number. During each cluster refinement, we employ an efficient algorithm to alternatively optimize subsequence clusters and segmentation based on dynamic programming. Our method can automatically and efficiently extract the unknown variable-length subsequence clusters in the time series. Comparative results with the state-of-the-art are conducted on various synthetic and real time series, and quantitative and qualitative performances demonstrate the effectiveness of our method.

关键词： Time series analysis Data mining Optimization clustering algorithms clustering methods Adaptation models Feature extraction Time series data mining subsequence clustering variable-length patterns time series segmentation

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CTB-PKI: clustering and Trust Enabled Blockchain Based PKI System for Efficient Communication in P2P Network

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IEEE ACCESS 2022年 10卷 124277-124290页

作者： Panigrahi, Amrutanshu Nayak, Ajit Kumar Paul, Rourab Sahu, Bibhuprasad Kant, Shashi Siksha O Anusandhan Deemed Univ Dept Comp Sci & Engn Bhubaneswar 751030 Odisha India Siksha O Anusandhan Deemed Univ Dept Comp Sci & Informat Technol Bhubaneswar 751030 Odisha India Vardhaman Coll Engn Autonomous Dept AI & DS Hyderabad 501218 Telangana India Bule Hora Univ Coll Business & Econ Dept Management Oromia Ethiopia

The decentralization feature of public and private blockchain-based applications is achieved by selecting different nodes as validators or Certificate Authority (CA) for each transaction. Public blockchain uses Proof of Work (PoW) to search for the validator. PoW causes an enormous amount of energy. Therefore, Proof of Stake (PoS), and Proof of Authority (PoA) emerged as alternate solutions. Selection of a new CA using PoS or PoA algorithms for each transaction may improve transaction security. However, a network may have a large number of transactions and participants. Selecting a CA for each transaction using PoS or PoA may cause a significant amount of block propagation delay, which can reduce network efficiency drastically. This paper proposes a different approach to increase the efficiency of Blockchain-based Public Key Infrastructure (BC - PKI ). The proposed approach creates clusters of participant nodes based on their validation time, response time, and trust. This method selects a cluster based on the budget of response time and validation time given by the node that intends to start a transaction. Thereafter, the node which has the highest trust in that cluster is chosen as a CA for the next transaction. Instead of searching on all participant nodes, our approach searches on the nodes of the chosen cluster which reduces the searching space of the CA selection process. This research work adopts a trust evaluation approach where the trust factor is quantified based on its experience and reputation. The node trust is reevaluated after every successful and unsuccessful transaction. A node that performs more successful transactions has more trust value. The node that has a higher trust value has a higher probability to be selected as a CA for a transaction. The trust reevaluation process is followed by the clustering process. The result shows the proposed approach can reduce similar to 38.5% response time and similar to 2.2% validation time as compared to infra

关键词： Peer-to-peer computing Blockchains Protocols Time factors Servers Evidence theory clustering algorithms Smart contracts Blockchain certificate authority clustering public key infrastructure smart contract

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Enhancing the Understanding of Train Delays With Delay Evolution Pattern Discovery: A clustering and Bayesian Network Approach

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第9期23卷 15367-15381页

作者： Huang, Ping Spanninger, Thomas Corman, Francesco Swiss Fed Inst Technol Inst Transport Planning & Syst CH-8093 Zurich Switzerland Southwest Jiaotong Univ Natl Engn Lab Integrated Transportat Big Data App Chengdu 610031 Peoples R China

Train delay evolutions exhibit different patterns (i.e., increasing delays, decreasing delays, or unchanged delays), because of the effects of stochastic disturbances and pre-scheduled supplement/recovery times. The dynamics and uncertainty of the train delay evolution make train delay prediction a challenging task. This study presents a hybrid framework, called context-driven Bayesian network (CDBN), composed of a delay evolution pattern discovery model, i.e., a K-Means clustering approach, and a train delay prediction model, i.e., Bayesian network (BN), to address this problem. The clustering algorithm is used to uncover the delay evolution patterns, and classify the data into different categories, based on the delay jumps, i.e., the change of a delay from one station to a consequent station. The BN model, which considers the delays in previous stations to overcome the Markov property assumption, is used as the predictive model of train delays. The data in each category (classified by the clustering model) are used to train and test the BN model separately. We evaluated the BN model, the clustering algorithm, and the CDBN model, by comparing against their counterparts, respectively. The results show that: (1) the proposed BN structure has advantages over the common delay prediction models built on Markov property;(2) the clustering is effective, and it can extensively improve the accuracy of the predictive model;and (3) the CDBN outperforms the existing delay prediction models in wide usability, because of its more profound understanding of the delay evolution patterns.

关键词： Delays Predictive models Markov processes Data models Bayes methods Rail transportation clustering algorithms Train delays delay evolution pattern discovery K-Means clustering Bayesian network

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