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Enhanced Visualization and Characterization of Low Frequency Oscillations in Power System

IEEE SYSTEMS JOURNAL 2023年第1期17卷 828-839页

作者： Anshuman, Arup Panigrahi, Bijaya Ketan Jena, Manas Kumar Indian Inst Technol Dept Elect Engn New Delhi 110016 India Indian Inst Technol Dept Elect Engn Palakkad 678557 India

Real-time monitoring of oscillation events provides enhanced wide-area situational awareness to the system operator. This article focuses on visualizing low frequency oscillatory modes, their detection, and classification following an event. The significant oscillatory modes present in the signal are extracted through a novel energy preserving, nonredundant "frequency partition based mode extraction (FPBME)" algorithm. These modes are analytically deduced and classified as governor mode, interarea mode, and local mode through a novel "energy based mode detection and classification (EBMDC)" algorithm. This article proposed method introduces the concept of "visualization" that is highly operator-friendly through the time domain plots of extracted modes. Time-domain plots of mode's instantaneous frequency and instantaneous energy, which are considered fundamental characterization of low-frequency oscillations, are also portrayed through the developed method. The proposed method is validated through various scenarios of Kundur's two-area system and IEEE-39 bus system. A practical test case on the ISO-NE system is also verified through the suggested scheme. Comparison of the proposed method in the context of visualization with other methods such as the Hilbert-Huang technique and continuous wavelet transform is also discussed in this article. The comparative assessment demonstrates the superior performance of the proposed method.

关键词： partitioning algorithms Oscillators Visualization Power system stability Heuristic algorithms Time-frequency analysis Time-domain analysis Frequency partitioning Hilbert spectral analysis (HSA) modes classification phasor measurement unit (PMU)

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Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surfaces-Empowered NOMA Networks

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IEEE SYSTEMS JOURNAL 2023年第4期17卷 5441-5451页

作者： Aldababsa, Mahmoud Khaleel, Aymen Basar, Ertugrul Nisantasi Univ Dept Elect & Elect Engn TR-34481742 Istanbul Turkiye Koc Univ Dept Elect & Elect Engn Commun Res & Innovat Lab CoreLab TR-34450 Istanbul Turkiye

In this article, we propose a novel simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted nonorthogonal multiple access (NOMA) system. Unlike most of the STAR-RIS-assisted NOMA works, we target scalable phase shift design for our proposed system that requires a reduced channel estimation overhead. Within this perspective, we propose novel algorithms to partition the STAR-RIS surface among the available users. These algorithms aim to determine the proper number of transmitting/reflecting elements needs to be assigned to each user in order to maximize the system sum rate while guaranteeing the quality-of-service requirements for individual users. For the proposed system, we derive closed-form analytical expressions for the outage probability (OP) and its corresponding asymptotic behavior under different user deployments. Finally, Monte Carlo simulations are performed in order to verify the correctness of the theoretical analysis. It is shown that the proposed system outperforms the classical NOMA and orthogonal multiple access systems in terms of OP and sum rate, under spatial correlation and phase errors.

关键词： NOMA partitioning algorithms Array signal processing Wireless communication Switches Resource management Quality of service Mode switching (MS) protocol nonorthogonal multiple access (NOMA) outage probability (OP) simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)

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A Novel Multicriteria Optimization Technique for VLSI Floorplanning Based on Hybridized Firefly and Ant Colony Systems

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IEEE ACCESS 2023年 11卷 14677-14692页

作者： Srinivasan, B. Venkatesan, R. Aljafari, Belqasem Kotecha, Ketan Indragandhi, V. Vairavasundaram, Subramaniyaswamy SASTRA Deemed Univ Sch Comp Thanjavur 613401 India Najran Univ Dept Elect Engn Najran 11001 Saudi Arabia Symbiosis Int Deemed Univ Symbiosis Ctr Appl Artificial Intelligence Pune 412115 India Vellore Inst Technol Sch Elect Engn Vellore 632014 India

In VLSI circuit design, physical design is one of the main steps in placing the circuit into the chip area. Floorplanning is a crucial step in the physical design of IC design, which generates a blueprint for the placement of circuit modules into the chip. A floorplanning step accepts a netlist as its input, given by the circuit-partitioning step of physical design. The floorplanning step generates optimal placements for the circuit modules. The netlist contains the modules' dimensions, size, and interconnect information. During the floorplan generation, the chip area, wire length required for connecting modules and the heat generated by the chips can be estimated. A good floorplan makes placements and routing simple. In order to improve the circuit performance by minimizing chip area, wire length, and peak temperature, it is essential to generate an optimized floorplan by developing metaheuristic optimization algorithms. A novel Hybridized Multicriteria Ant Colony and Firefly Optimization (HMAC-FO) technique is introduced to generate an optimized floorplan. The primary focus of HMAC-FO technique is to design a model for generating efficient floorplanning. The standard MCNC benchmark dataset has a number of modules with their connections. Firefly optimization is the main algorithm in HMAC-FO, which has been used in generating efficient floorplan with the optimized area, wire length and thermal. The firefly algorithm initially requires some number of solutions as the fireflies' population. In the firefly algorithm, usually, the populations are generated randomly. But, to improve the firefly optimization algorithm's performance and obtain better optimum results, the proposed technique uses ACO to generate the initial population, which are all optimal solutions. The firefly algorithm uses the set of optimal solutions as the initial population and generates the globally optimal result. The proposed technique has experimented with the standard MCNC benchmark circuits, an

关键词： Ant colony optimization Classification algorithms Very large scale integration Metaheuristics partitioning algorithms Heating systems VLSI floorplanning firefly optimization ant colony optimization multi-objective optimization hybrid optimization method

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A Sparse Framework for Robust Possibilistic K-Subspace Clustering

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2023年第4期31卷 1124-1138页

作者： Zeng, Shan Duan, Xiangjun Li, Hao Bai, Jun Tang, Yuanyan Wang, Zhiyong Wuhan Polytech Univ Coll Math & Comp Sci Wuhan Peoples R China Deakin Univ Sch Informat Technol Melbourne Vic Australia Univ Macau Fac Sci & Technol Macau Peoples R China Univ Sydney Sch Comp Sci Sydney NSW Australia

Clustering noisy, high-dimensional, and structurally complex data have always been a challenging task. As most existing clustering methods are not able to deal with both the adverse impact of noisy samples and the complex structures of data, in this article, we propose a novel robust and sparse possibilistic K-subspace (RSPKS) clustering algorithm to integrate subspace recovery and possibilistic clustering algorithms under a unified sparse framework. First, the proposed method sparsifies the membership matrix and the subspace projection vector under a dual-sparse framework to handle high-dimensional noisy data. This unifies dimensionality reduction and clustering using one objective function for which the optimization can be realized through synchronous iteration. Second, the reconstruction error of each sample in the local subspace is used as the distance metric for classification. That is, each sample itself is treated as a clustering prototype so as not to be affected by the structure of the overall data distribution. Therefore, the clustering prototype construction problem of the data with complex structures can be better addressed. Finally, to deal with nonlinear regions, our RSPKS method is further extended into a kernelized version, namely the kernelized RSPKS clustering algorithm. The experimental results on both synthetic and real-world datasets demonstrate that our proposed method outperforms state-of-the-art algorithms in terms of clustering accuracy.

关键词： Clustering algorithms partitioning algorithms Prototypes Clustering methods Sparse matrices Noise measurement Power capacitors Dual-sparse framework local subspace possi- bilistic K-subspace (KSS) clustering

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Consistency-Induced Multiview Subspace Clustering

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第2期53卷 832-844页

作者： Qin, Yalan Feng, Guorui Ren, Yanli Zhang, Xinpeng Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China

Multiview clustering has received great attention and numerous subspace clustering algorithms for multiview data have been presented. However, most of these algorithms do not effectively handle high-dimensional data and fail to exploit consistency for the number of the connected components in similarity matrices for different views. In this article, we propose a novel consistency-induced multiview subspace clustering (CiMSC) to tackle these issues, which is mainly composed of structural consistency (SC) and sample assignment consistency (SAC). To be specific, SC aims to learn a similarity matrix for each single view wherein the number of connected components equals to the cluster number of the dataset. SAC aims to minimize the discrepancy for the number of connected components in similarity matrices from different views based on the SAC assumption, that is, different views should produce the same number of connected components in similarity matrices. CiMSC also formulates cluster indicator matrices for different views, and shared similarity matrices simultaneously in an optimization framework. Since each column of similarity matrix can be used as a new representation of the data point, CiMSC can learn an effective subspace representation for the high-dimensional data, which is encoded into the latent representation by reconstruction in a nonlinear manner. We employ an alternating optimization scheme to solve the optimization problem. Experiments validate the advantage of CiMSC over 12 state-of-the-art multiview clustering approaches, for example, the accuracy of CiMSC is 98.06% on the BBCSport dataset.

关键词： Optimization Clustering algorithms Correlation Kernel Clustering methods partitioning algorithms Neural networks High-dimensional data multiview clustering sample assignment consistency (SAC) structural consistency (SC)

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Fast CU Decision Algorithm Based on Texture Complexity and CNN for VVC

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IEEE ACCESS 2023年 11卷 35808-35817页

作者： Li, Hongchan Zhang, Peng Jin, Baohua Zhang, Qiuwen Zhengzhou Univ Light Ind Coll Comp & Commun Engn Zhengzhou 450002 Peoples R China

Versatile Video Coding (VVC) introduces many advanced video coding techniques. These advanced video coding techniques not only improve video compression efficiency and video quality, but also greatly increase coding time and computational complexity. Therefore, this paper uses a fast CU partitioning decision algorithm based on texture complexity and convolutional neural networks (CNN). First, we performed statistics and analysis of CU segmentation patterns for videos in the standard training set, then processed large blocks of CU based on texture complexity. To realize the purpose of fast partitioning, we design different CNN models for CU with different segmentation modes. In the design of CNN model, we use the symmetric convolutional kernel and asymmetric convolutional kernel to extract features in different directions effectively. In the loss function, we used the cross-entropy function to train the CNN model to improve the accuracy of the model. Finally, a double threshold is set in the candidate list to achieve a compromise between coding performance and coding complexity. Experimental results show that, compared to the VTM10.0 anchoring algorithm, our fast scheme, in terms of encoding time, decreases by 55.90% and BDBR increases by 1.79%;our moderate scheme, in terms of encoding time, decreases by 47.90%. BDBR increases by only 1.29%.

关键词： Encoding Prediction algorithms Convolutional neural networks partitioning algorithms Copper Feature extraction Predictive models CNN asymmetric convolutional kernel symmetric convolutional kernel VVC

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Reinforcement Learning for Delay Tolerance and Energy Saving in Mobile Wireless Sensor Networks

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IEEE ACCESS 2023年 11卷 19819-19835页

作者： Al-Jerew, Oday Bassam, Nizar Al Alsadoon, Abeer Asia Pacific Int Coll Sydney NSW 2150 Australia Middle East Coll Muscat 124 Oman Charles Sturt Univ CSU Sch Comp Math & Engn Wagga Wagga NSW 2678 Australia Western Sydney Univ WSU Sch Comp Data & Math Sci Sydney NSW 2560 Australia

Reinforcement Learning (RL) has emerged as a promising approach for improving the performance of Wireless Sensor Networks (WSNs). The Q-learning technique is one approach of RL in which the algorithm continuously learns by interacting with the environment, gathering information to take certain actions. It maximizes performance by determining the optimal result from that environment. In this paper, we propose a data gathering algorithm based on a Q-learning approach named Bounded Hop Count - Reinforcement Learning Algorithm (BHC-RLA). The proposed algorithm uses a reward function to select a set of Cluster Heads (CHs) to balance between the energy-saving and data-gathering latency of a mobile Base Station (BS). In particular, the proposed algorithm selects groups of CHs to receive sensing data of cluster nodes within a bounded hop count and forward the data to the mobile BS when it arrives. In addition, the CHs are selected to minimize the BS tour length. Extensive experiments by simulation were conducted to evaluate the performance of the proposed algorithm against another traditional heuristic algorithm. We demonstrate that the proposed algorithm outperforms the existing work in the mean of the length of a mobile BS tour and a network's lifetime.

关键词： Clustering algorithms Sensors Wireless sensor networks partitioning algorithms Relays Heuristic algorithms Delays mobile data gathering delay tolerance relay hop count mobile base station tour

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SDP: Scalable Real-Time Dynamic Graph Partitioner

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2023年第1期16卷 564-574页

作者： Patwary, Md Anwarul Kaium Garg, Saurabh Battula, Sudheer Kumar Kang, Byeong Univ Western Australia Dept Comp Sci & Software Engn Crawley WA 6009 Australia Univ Tasmania Hobart Tas 7005 Australia SRM Univ AP Amaravati Comp Sci & Engn Amaravati 522502 Andhra Pradesh India Univ Tasmania Sch Comp Hobart Tas 7005 Australia

The time-evolving large graph has received attention due to it's participation in real-world applications such as social networks and PageRank calculation. It is necessary to partition a large-scale dynamic graph in a streaming manner in order to overcome the memory bottleneck while partitioning the computational load. Reducing network communication and balancing the load between the partitions are the criteria for achieving effective run-time performance in graph partitioning. Moreover, an optimal resource allocation is needed to utilise the resources while storing the graph streams into the partitions. A number of existing partitioning algorithms have been proposed to address the above problem. However, these partitioning methods are incapable of scaling the resources and handling the stream of data in real-time. In this study, we propose a dynamic graph partitioning method called Scalable Dynamic Graph Partitioner(SDP) using the streaming partitioning technique. The SDP contributes a novel vertex assigning method, communication-aware balancing method, and a scaling technique in order to produce an efficient dynamic graph partitioner. Experiment results show that the proposed method achieves up to 90% reduction of communication cost and 60%-70% balancing the load dynamically, compared with previous algorithms. Moreover, the proposed algorithm significantly reduces the execution time during partitioning.

关键词： Heuristic algorithms partitioning algorithms Resource management Dynamic scheduling Costs Social networking (online) Generators Dynamic graph streaming partitioning scalable

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Community Detection in Multiplex Networks Based on Evolutionary Multitask Optimization and Evolutionary Clustering Ensemble

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2023年第3期27卷 728-742页

作者： Lyu, Chao Shi, Yuhui Sun, Lijun Lin, Chin-Teng Harbin Inst Technol Dept Comp Sci & Engn Harbin 150001 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain inspired Intelligent Shenzhen 518055 Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Ctr Artificial Intelligence CIBCI Lab Sydney NSW 2007 Australia

Community detection in multiplex networks is an emerging research topic in the field of network science. Existing methods usually ignore the similarities among component layers of a multiplex network when detecting its community structures, which decreases the detection efficiency. In this article, we decompose the community detection in multiplex networks into two problems and propose a novel algorithm that can detect both the specific community partition for each component layer (layer-level community structure) and the composite community structure shared by all layers. First, by specifying the modularity optimization on a network layer as an optimization task, we model the layer-level community detection as a multitask optimization (MTO) problem and employ an evolutionary MTO algorithm to solve it. In this way, the topology correlations among different layers can be utilized to facilitate the community detection. Second, we propose an evolutionary clustering ensemble method to find the composite community structure based on the layer-level community partitions and the multiplex network. The proposed method is tested on both synthetic and real-world benchmark networks and compared with classical and state-of-the-art algorithms. Experimental results show that the proposed algorithm has superior community detection performances on multiplex networks.

关键词： Multiplexing Optimization Task analysis partitioning algorithms Multitasking Search problems Topology Community detection evolutionary multitask optimization (EMTO) multiplex networks

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Multiclass Sparse Centroids With Application to Fast Time Series Classification

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第8期34卷 5206-5211页

作者： Bradde, Tommaso Fracastoro, Giulia Calafiore, Giuseppe C. Politecn Torino Dept Elect & Telecommun I-10129 Turin Italy

In this article, we propose an efficient multiclass classification scheme based on sparse centroids classifiers. The proposed strategy exhibits linear complexity with respect to both the number of classes and the cardinality of the feature space. The classifier we introduce is based on binary space partitioning, performed by a decision tree where the assignation law at each node is defined via a sparse centroid classifier. We apply the presented strategy to the time series classification problem, showing by experimental evidence that it achieves performance comparable to that of state-of-the-art methods, but with a significantly lower classification time. The proposed technique can be an effective option in resource-constrained environments where the classification time and the computational cost are critical or, in scenarios, where real-time classification is necessary.

关键词： Time series analysis Training partitioning algorithms Feature extraction Computational efficiency Binary trees Task analysis Features selection nearest-centroid classifiers time-series classification

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