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A Two-Layer Hybrid Optimization Approach for Large-Scale Offshore Wind Farm Collector System Planning

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第11期17卷 7433-7444页

作者： Zuo, Tengjun Zhang, Yuchen Meng, Ke Tong, Ziyuan Dong, Zhao Yang Fu, Yang Nanjing Inst Technol Nanjing 211112 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Shanghai Univ Elect Power Shanghai 200090 Peoples R China

Constructing large-scale offshore wind farms (OWFs) has become the main direction of utilizing wind power to help realize the energy transformation. Traditionally, the planning of the OWF collector system would rely on either heuristic or deterministic optimization algorithms, which, respectively, suffer from unstable outputs and a lack of freedom in searching for a globally optimal solution. This article innovatively designs a hybrid optimization approach combining algorithms in these two categories to achieve a balance between improved economic efficiency and stable outputs. The whole design consists of two layers of hybrid optimizations. The outer layer is to partition wind turbines (WTs) into groups, where each group is allocated with an offshore substation with the optimized location for power collection and transmission. This partitioning and locating optimization is solved through a combination of the deterministic fuzzy C-means clustering method and the genetic algorithm (GA). The inner layer is to arrange optimal connections using proper cable ratings among WTs within each group, and GA is properly integrated into the deterministic two-phase Clark and Wright's saving algorithm to solve the problem. The collector system planning, in this article, concerns both the investment cost and the long-term power-loss cost. The former consists of the networks of internal medium voltage and the external high voltage, which collect the power from WTs and transmit it to the onshore grid. The proposed design is tested on a benchmark OWF collector system, and the test result verifies its achievements in higher economic efficiency with stable outputs.

关键词： Planning Optimization Heuristic algorithms Topology Genetic algorithms partitioning algorithms Investment Clark and Wright's saving algorithm collector system planning double-sided ring topology offshore wind farm (OWF) two-layer hybrid optimization

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System Level-Based Voltage-Sag Mitigation Using Distributed Energy Resources

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IEEE SYSTEMS JOURNAL 2021年第4期15卷 5023-5033页

作者： Mustafa, Ahmed M. Nassar, Mohammed E. Salama, M. M. A. Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada Alexandria Univ Fac Engn Alexandria 21544 Egypt

As the growing demand for battery energy storage systems (BESSs) generally follows the renewable energy sources (RESs) trend, the active management of BESS- and RES-based distributed energy resources (DERs) could effectively solve voltage-sag problems in active distribution systems (ADSs). DERs are typically interfaced with the grid through power-electronic inverters. They are considered power resources with adjustable active/reactive power injections that could contribute to the enhancement of the grid's performance. This article proposes a new framework for active/reactive power management of inverter-based DERs to mitigate voltage-sag problems in ADSs. The new framework utilizes a novel zonal-based voltage control algorithm for mitigating voltage-sag problems in ADSs. The algorithm identifies mitigation zones' virtual boundaries based on a proposed electrical influence intensity index that reflects the electrical connectivity of the network. A novel zone capability index (ZCI) ranks the mitigation zones according to DER capacity, zonal loading conditions, and the number of nodes within each mitigation zone. The voltage mitigation algorithm minimizes the energy injected from DERs' inverters to achieve maximum voltage sag alleviation in each mitigation zone. This zonal-based mitigation algorithm is applied to the IEEE 33-bus distribution system under voltage-sag conditions to verify the proposed algorithm's feasibility and effectiveness.

关键词： partitioning algorithms Voltage control Indexes Sensitivity Tools Reactive power Power quality Active distribution systems (ADSs) electrical influence intensity (EII) mitigation zone modularity power quality voltage-sag

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PSubCLUS: A Parallel Subspace Clustering Algorithm Based On Spark

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IEEE ACCESS 2021年 9卷 2535-2544页

作者： Wen, Xiao Juan, Hu Anhui Normal Univ Sch Educ Sci Wuhu 241002 Peoples R China Wanjiang Univ Technol Anhui Higher Educ Inst Key Lab Unmanned Aerial Vehicle Dev & Data Applic Maanshan 243000 Peoples R China Wanjiang Univ Technol Maanshan Engn Technol Res Ctr Wireless Sensor Net Maanshan 243000 Peoples R China

Clustering is one of the most important unsupervised machine learning tasks. It is widely used to solve problems of intrusion detection, text analysis, image segmentation etc. Subspace clustering is the most important method for high-dimensional data clustering. In order to solve the problem of parallel subspace clustering for high-dimensional big data, this paper proposes a parallel subspace clustering algorithm based on spark named PSubCLUS which is inspired by SubCLU, a classical subspace clustering algorithm. While Spark is the most popular big data parallel processing platform currently, PSubCLUS uses the Resilient Distributed Datasets (RDD) provided by Spark to store data points in a distributed way. The two main performing stages of this algorithm, one-dimensional subspace clustering and iterative clustering, can be executed in parallel on each worker node of cluster. PSubCLUS also uses a repartition method based on the number of data points to achieve load balancing. Experimental results show that PSubCLUS has good parallel speedup and ideal load balancing effect, which is suitable for solving the parallel subspace clustering of high-dimensional big data.

关键词： Clustering algorithms Sparks partitioning algorithms Machine learning algorithms Distributed databases Classification algorithms Software algorithms Big data applications clustering algorithms parallel

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Trust: Triangle Counting Reloaded on GPUs

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2021年第11期32卷 2646-2660页

作者： Pandey, Santosh Wang, Zhibin Zhong, Sheng Tian, Chen Zheng, Bolong Li, Xiaoye Li, Lingda Hoisie, Adolfy Ding, Caiwen Li, Dong Liu, Hang Stevens Inst Technol Elect & Comp Engn Hoboken NJ 07030 USA Nanjing Univ State Key Lab Novel Software Technol Nanjing 210008 Jiangsu Peoples R China Huazhong Univ Sci & Technol Wuhan 430074 Hubei Peoples R China Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA 94720 USA Brookhaven Natl Lab Computat Sci Initiat Upton NY 11973 USA Brookhaven Natl Lab Upton NY 11973 USA Univ Connecticut Dept Comp Sci & Engn Storrs CT 06269 USA Univ Calif Merced Dept Elect Engn & Comp Sci Merced CA 95343 USA

Triangle counting is a building block for a wide range of graph applications. Traditional wisdom suggests that i) hashing is not suitable for triangle counting, ii) edge-centric triangle counting beats vertex-centric design, and iii) communication-free and workload balanced graph partitioning is a grand challenge for triangle counting. On the contrary, we advocate that i) hashing can help the key operations for scalable triangle counting on Graphics Processing Units (GPUs), i.e., list intersection and graph partitioning, ii) vertex-centric design reduces both hash table construction cost and memory consumption, which is limited on GPUs. In addition, iii) we exploit graph and workload collaborative, and hashing-based 2D partitioning to scale vertex-centric triangle counting over 1000 GPUs with sustained scalability. In this article, we present Trust which performs triangle counting with the hash operation and vertex-centric mechanism at the core. To the best of our knowledge, Trust is the first work that achieves over one trillion Traversed Edges Per Second (TEPS) rate for triangle counting.

关键词： Graphics processing units Two dimensional displays partitioning algorithms Message systems Instruction sets Arrays Time complexity GPGPU triangle counting graph algorithms parallel processing

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Analysis of Partial Modification Problems Using Integral Equation Discontinuous Galerkin With Mesh Modification Algorithm

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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2021年第10期20卷 1948-1952页

作者： Li, Xiaojie Shen, Ziang Chen, Xinlei Gu, Changqing Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Key Lab Radar Imaging & Microwave Photon Nanjing 210016 Peoples R China Southeast Univ State Key Lab Millimeter Waves Nanjing 210096 Peoples R China Nanjing Univ Informat Sci & Technol KLME & CIC FEMD Nanjing 210044 Peoples R China

The method based on the partitioned inverse matrix formula and the Sherman-Morrison-Woodbury formula has been developed to solve the partial modification problems efficiently. However, it requires the meshes of the mother-structure, the add-structure, and the subtract-structure are separated or conformal. It is inconvenient and inflexible for users. In this letter, the integral equation discontinuous Galerkin (IEDG) with a mesh modification algorithm (MMA) is proposed to mitigate this problem. With the help of the IEDG and the MMA, the mother-structure and the add-structure can be meshed independently, and the mesh errors caused by removing the subtract-structure from the mother-structure are reduced. Numerical results are given to demonstrate the advantages of the improved method.

关键词： Method of moments Junctions Integral equations Impedance Radar imaging partitioning algorithms Microwave photonics Integral equation discontinuous Galerkin (IEDG) mesh modification algorithm (MMA) method of moments (MoM) partial modification method (PMM)

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Signal Control Period Division Method Based on Locally Linear Embedding and Particle Swarm Optimization Combined With K-Means Clustering

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IEEE ACCESS 2021年 9卷 147613-147625页

作者： Tian, Xiujuan Liang, Chunyan Feng, Tianjun Chen, Chun Jilin Jianzhu Univ Sch Transportat Sci & Engn Changchun 130118 Jilin Peoples R China

In order to optimize the existing signal control period division method and improve signal control effect, a new period division method based on Locally Linear Embedding and Particle Swarm Optimization combined with K-means clustering (LLE-PSO-K) algorithm is proposed in this paper. Firstly, traffic flow characteristics of signal-controlled intersections are fully considered, and a multi-dimensional flow matrix is constructed based on the phase traffic flow. In order to reduce the computational complexity of the model and improve the operating efficiency of the method, manifold learning Locally Linear Embedding (LLE) algorithm is brought in to reduce the dimension of the multidimensional phase flow matrix. Then, the dimensionality reduction matrix is used as input data, and signal control period is divided by using Particle Swarm Optimization combined with K-means clustering (PSO-K) algorithm. Finally, an actual intersection in a city is selected to verify the performance of the proposed method. For comparative analysis, control periods are divided based on the phase traffic flow data with 15min, 30min and 1h interval respectively. Results show that for different time intervals, the division of the proposed method is better than other methods, of which the invalid control periods are less. Besides, the optimal clustering number can be obtained, which proves the effectiveness of the new proposed method.

关键词： Clustering algorithms Control systems Switches partitioning algorithms Heuristic algorithms Delays Particle swarm optimization Traffic engineering signal control control period division PSO-K clustering LLE dimension reduction

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A Local-Neighborhood Information Based Overlapping Community Detection Algorithm for Large-Scale Complex Networks

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IEEE-ACM TRANSACTIONS ON NETWORKING 2021年第2期29卷 543-556页

作者： Cheng, Fan Wang, Congtao Zhang, Xingyi Yang, Yun Anhui Univ Sch Comp Sci & Technol Key Lab Intelligent Comp Signal Proc Minist Educ Hefei 230039 Peoples R China Yunnan Univ Natl Pilot Sch Software Kunming 650500 Yunnan Peoples R China

As the size of available networks is continuously increasing (even with millions of nodes), large-scale complex networks are receiving significant attention. While existing overlapping-community detection algorithms are quite effective in analyzing complex networks, most of these algorithms suffer from scalability issues when applied to large-scale complex networks, which can have more than 1,000,000 nodes. To address this problem, we propose an efficient local-expansion-based overlapping-community detection algorithm using local-neighborhood information (OCLN). During the iterative expansion process, only neighbors of nodes added in the last iteration (rather than all neighbors) are considered to determine whether they can join the community. This significantly reduces the computational cost and enhances the scalability for community detection in large-scale networks. A belonging coefficient is also proposed in OCLN to filter out incorrectly identified nodes. Theoretical analysis demonstrates that the computational complexity of the proposed OCLN is linear with respect to the size of the network to be detected. Experiments on large-scale LFR benchmark and real-world networks indicate the effectiveness of OCLN for overlapping-community detection in large-scale networks, in terms of both computational efficiency and detected-community quality.

关键词： Detection algorithms Complex networks Scalability partitioning algorithms IEEE merchandise Signal processing algorithms IEEE transactions Overlapping-community detection local neighborhood large-scale network linear complexity

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Scene Segmentation of Multi-Band ISAR Fusion Imaging Based on MB-PCSBL

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IEEE SENSORS JOURNAL 2021年第3期21卷 3520-3532页

作者： Zhu, Xiaoxiu Guo, Baofeng Hu, Wenhua Shi, Lin Ma, Juntao Xue, Dongfang Army Engn Univ Dept Elect & Opt Engn Shijiazhuang Campus Shijiazhuang 050003 Hebei Peoples R China

We consider the problem of achieving multi-band inverse synthetic aperture radar (ISAR) fusion imaging of block structure targets with unknown block partition and develop a block-sparse recovering method based on matrix block pattern-coupled sparse Bayesian learning algorithm. Based on the sparse representation of multi-band ISAR fusion imaging model, a pattern-coupled hierarchical Gaussian prior is proposed to characterize the pattern relevance of scattering coefficients. The sparsity of each coefficient is controlled not only by its own hyperparameter, but also by the hyperparameters corresponding to its eight neighboring coefficients in the data matrix. The correlations between the coefficients in rows and columns are determined by different parameters, respectively. The proposed prior model can increase the model flexibility and promote the generation of block structures. Moreover, the whole observation scene is segmented into multiple sub-scenes to reduce the memory storage space and the computational complexity. Parameters and the fusion image result of each sub-scene are derived by the expectation-maximization method. The multi-band ISAR fusion image result of the whole scene is obtained through the stitching of the sub-scenes imaging results. Experimental results demonstrate the effectiveness and superiority of the proposed algorithm.

关键词： Imaging Radar imaging partitioning algorithms Bayes methods Correlation Sparse matrices Inverse synthetic aperture radar (ISAR) multi-band fusion sparse Bayesian learning block-sparse signal expectation-maximization

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SPACE: Structured Compression and Sharing of Representational Space for Continual Learning

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IEEE ACCESS 2021年 9卷 150480-150494页

作者： Saha, Gobinda Garg, Isha Ankit, Aayush Roy, Kaushik Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

Humans learn incrementally from sequential experiences throughout their lives, which has proven hard to emulate in artificial neural networks. Incrementally learning tasks causes neural networks to overwrite relevant information learned about older tasks, resulting in 'Catastrophic Forgetting'. Efforts to overcome this phenomenon often utilize resources poorly, for instance, by growing the network architecture or needing to save parametric importance scores, or violate data privacy between tasks. To tackle this, we propose SPACE, an algorithm that enables a network to learn continually and efficiently by partitioning the learnt space into a Core space, that serves as the condensed knowledge base over previously learned tasks, and a Residual space, which is akin to a scratch space for learning the current task. After learning each task, the Residual is analyzed for redundancy, both within itself and with the learnt Core space. A minimal number of extra dimensions required to explain the current task are added to the Core space and the remaining Residual is freed up for learning the next task. We evaluate our algorithm on P-MNIST, CIFAR and a sequence of 8 different datasets, and achieve comparable accuracy to the state-of-the-art methods while overcoming catastrophic forgetting. Additionally, our algorithm is well suited for practical use. The partitioning algorithm analyzes all layers in one shot, ensuring scalability to deeper networks. Moreover, the analysis of dimensions translates to filter-level sparsity, and the structured nature of the resulting architecture gives us up to 5x improvement in energy efficiency during task inference over the current state-of-the-art.

关键词： Task analysis Principal component analysis Redundancy Data privacy Complexity theory Training partitioning algorithms Continual learning catastrophic forgetting deep learning algorithm principal component analysis

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Adaptive Feature Calculation and Diagonal Mapping for Successive Recovery of Tampered Regions

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2021年第7期31卷 2617-2630页

作者： Liu, Tong Yuan, Xiaochen Macau Univ Sci & Technol Fac Informat Technol Macau Peoples R China Zhuhai MUST Sci & Technol Res Inst Zhuhai 519000 Peoples R China

This article proposes an adaptive scheme for image tampered region localization and content recovery. To generate the watermark information comprised of the authentication data and recovery data, we firstly propose the Adaptive Authentication Feature Calculation algorithm to obtain the authentication data, which includes the information of block location and block feature. The DWT-based Block Feature Calculation method is then proposed to calculate the block feature, and the quantization method is employed to calculate the block location. The recovery data is composed of self-recovery bits and mapped-recovery bits. The self-recovery bits are obtained by the Set partitioning in Hierarchical Trees encoding algorithm. For retrieving the damaged codes caused by tampering, we propose the Diagonal Mapping algorithm and apply it to the self-recovery bits, thus generating the mapped-recovery bits, to provide a guarantee of recovery data. Experimental results show the superior performance of the proposed scheme in terms of tamper detection and image recovery, by comparing with the state-of-the-art works. The results demonstrate that the proposed method shows efficiency in the adaptiveness, well localization, strong capability for image recovery, and the effectiveness of attack resistance.

关键词： Watermarking Authentication Discrete wavelet transforms Image coding partitioning algorithms Media Adaptive authentication feature calculation DWT-based block feature calculation diagonal mapping tamper detection successive content self-recovery

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