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Soft Subspace Based Ensemble Clustering for Multivariate Time Series Data

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第10期34卷 7761-7774页

作者： He, Guoliang Jiang, Wenjun Peng, Rong Yin, Ming Han, Min Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Dalian Univ Technol Sch Elect Informat & Elect Engn Dalian 116024 Peoples R China

Recently, multivariate time series (MTS) clustering has gained lots of attention. However, state-of-the-art algorithms suffer from two major issues. First, few existing studies consider correlations and redundancies between variables of MTS data. Second, since different clusters usually exist in different intrinsic variables, how to efficiently enhance the performance by mining the intrinsic variables of a cluster is challenging work. To deal with these issues, we first propose a variable-weighted K-medoids clustering algorithm (VWKM) based on the importance of a variable for a cluster. In VWKM, the proposed variable weighting scheme could identify the important variables for a cluster, which can also provide knowledge and experience to related experts. Then, a Reverse nearest neighborhood-based density Peaks approach (RP) is proposed to handle the problem of initialization sensitivity of VWKM. Next, based on VWKM and the density peaks approach, an ensemble Clustering framework (SSEC) is advanced to further enhance the clustering performance. Experimental results on ten MTS datasets show that our method works well on MTS datasets and outperforms the state-of-the-art clustering ensemble approaches.

关键词： Clustering algorithms Clustering methods Time series analysis Principal component analysis partitioning algorithms Linear programming Weight measurement Ensemble clustering hard subspace clustering multivariate time series (MTS) soft subspace clustering

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Diffusion Partitioned-Block Frequency-Domain Adaptive Filter Over Multitask Networks

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第4期70卷 1640-1644页

作者： Han, Hongyu Peng, Yishu Zhang, Fenglian Le, Dinh Cong Zhang, Sheng Sichuan Normal Univ Sch Comp Sci Chengdu 610068 Peoples R China Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Sichuan Peoples R China Vinh Univ Sch Engn & Technol Vinh 83518 Nghe An Vietnam

As a generalization of the frequency-domain adaptive filter (FDAF) algorithm, partitioned-block frequency-domain adaptive filter (PBFDAF) results in minimal signal path delay. In this brief, we propose the diffusion normalized PBFDAF algorithm based on an unsupervised clustering strategy to address the low complexity implementation issues in multitask networks. Each node adaptively adjusts the combination weight coefficients by minimizing the instantaneous mean square deviation (MSD) in frequency-domain. The simulation results demonstrate that the proposed algorithm achieves superior performance.

关键词： Frequency-domain analysis Clustering algorithms partitioning algorithms Adaptive systems Indexes Delays Transforms Adaptive networks frequency-domain diffusion strategy adaptive combination weight

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Optimal Low-Complexity Orthogonal Block Based Detection of OTFS for Low-Dispersion Channels

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第4期72卷 5419-5423页

作者： Liu, Wei Ding, Yajie Li, Chuan Hanzo, Lajos Xidian Univ State Key Labs Integrated Serv Networks Xian 710071 Peoples R China Xian Univ Posts & Telecommun Sch Comp & Technol Xian 710061 Peoples R China Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ England

Orthogonal time frequency space (OTFS) modulation constitutes a promising technology for high-mobility scenarios. However, the detection of OTFS systems imposes substantial complexity. Hence, we propose a novel orthogonal block (OB) based detection scheme for significantly reducing the OTFS detection complexity without any performance loss with integer Doppler shifts. This is achieved by recognizing that the received signal can be partitioned into multiple parallel orthogonal blocks. Therefore, the detection of data symbols within an orthogonal block only depends on the signals received within this orthogonal block with reduced dimension. Explicitly, we propose a graph theory based orthogonal block identification algorithm, which models the relationship between the received signal and the original information symbols as a bipartite graph, where a depth first search (DFS) algorithm is invoked for partitioning the received signals into orthogonal blocks. For each orthogonal block, the existing detection algorithms can be used. Since the size of orthogonal blocks may be much lower than that of the original received signals, the detection complexity can be significantly reduced. For example, the complexity of the OB based MMSE detector is approximately a factor 4096 lower than that of the traditional MMSE detector for a channel having two paths.

关键词： Symbols Detectors Complexity theory Detection algorithms Delays partitioning algorithms Approximation algorithms Depth first search low complexity orthogonal block OTFS

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Transformational Supervisor Localization

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IEEE CONTROL SYSTEMS LETTERS 2023年 7卷 1682-1687页

作者： Thuijsman, Sander Cai, Kai Reniers, Michel Eindhoven Univ Technol Mech Engn Dept NL-5600 MB Eindhoven Netherlands Osaka Metropolitan Univ Dept Core Informat Osaka 5588585 Japan

Supervisor localization can be applied to distribute a monolithic supervisor into local supervisors. Performing supervisor localization can be computationally costly. In this letter, we consider systems that evolve over time. We study how to reuse the results from a previous supervisor localization, to more efficiently compute local supervisors when the system is adapted. We call this approach transformational supervisor localization, and present algorithms for the procedure. The efficiency of the procedure is experimentally evaluated.

关键词： Location awareness Automata Computational modeling Adaptation models Behavioral sciences partitioning algorithms Computational efficiency Discrete-event systems supervisory control model controller reduction computational methods automata

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Planar Array Subarray Division Method in Microwave Wireless Power Transmission Based on PSO&K-Means Algorithm

IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION

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IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION 2023年 4卷 520-527页

作者： Zhang, Qian He Zhang, Qing He Shen, Zhao Yang China Three Gorges Univ Hubei Key Lab Intelligent Vis Based Monitoring Hyd Yichang 443002 Hubei Peoples R China China Three Gorges Univ Coll Comp & Informat Technol Yichang 443002 Hubei Peoples R China

For the problem of maximizing beam collection efficiency in microwave wireless power transmission, a clustering algorithm based on a combination of the particle swarm algorithm and the K-means algorithm is proposed for the subarray partitioning of planar arrays. By minimizing the difference between the subarray beam collection efficiency and the reference beam collection efficiency, the subarray partitioning problem is transformed into an excitation matching problem. Then, the optimal subarray configuration is obtained by using a clustering algorithm that combines the particle swarm algorithm and the K-means algorithm. The effectiveness of the proposed method is evaluated by integrating the beam collection efficiency with parameters such as the receiving area and the size of the planar array. The superiority of the proposed method is demonstrated by a comparison with other advanced methods.

关键词： Clustering algorithms Microwave antenna arrays Transmitting antennas partitioning algorithms Receiving antennas Planar arrays Wireless power transmission Microwave wireless power transmission (MWPT) planar array antenna INDEX TERMS particle swarm optimization algorithm (PSO) K-means clustering algorithm beam collection efficiency (BCE)

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Real-time DNN Model partitioning for QoE Enhancement in Mobile Vision Applications 19

Real-time DNN Model Partitioning for QoE Enhancement in Mobi...

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19th Annual IEEE International Conference on Sensing, Communication, and Networking (IEEE SECON)

作者： Lim, Jeong-A Kim, Yeongjin Inha Univ Dept Elect Engn Incheon South Korea

ISBN: (数字)9781665486439

ISBN: (纸本)9781665486439

As deep learning technology advances, mobile vision applications such as augmented reality or autonomous vehicles are widespread. The quality of experience (QoE) of such applications highly depends on hardware specification of mobile device, dynamic service requests, stochastic network status and characteristics of DNN model. In this paper, we propose an algorithm called RT-DMP that jointly optimizes DNN model partitioning and process/network resources adapting to system dynamics by leveraging virtual queue-based Lyapunov optimization framework. The RT-DMP jointly makes decisions on (i) partition point between a mobile device and an MEC server, (ii) mobile GPU clock frequency, and (iii) transmission rate through the wireless network every time slot. We theoretically show that RT-DMP optimally strikes the balance among three QoE metrics that are energy consumption, throughput and end-to-end latency, which has not been addressed in existing studies. Finally, we demonstrate the performance and feasibility of RT-DMP via trace-driven simulations and real testbed based on Nvidia Jetson TX2 and a high-end MEC server.

关键词： Energy consumption Adaptation models Computational modeling Heuristic algorithms Mobile handsets partitioning algorithms Quality of experience

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PAW: Data partitioning Meets Workload Variance 38

PAW: Data Partitioning Meets Workload Variance

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38th IEEE International Conference on Data Engineering (ICDE)

作者： Li, Zhe Yiu, Man Lung Chan, Tsz Nam Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

In distributed storage systems (e.g., HDFS, Amazon S3, Databricks), partitioning is applied on a dataset in order to enhance performance and availability. Recently, partitioning methods have been designed to optimize the query performance of partitions with respect to the historical query workload. Nevertheless, in practice, future query workloads may deviate from the historical query workload, thus deteriorating the performance of existing partitioning methods. To fill this research gap, we model the variance of future query workloads from the historical query workload, then exploit this characteristic to produce partitions that perform well for future query workloads. In addition, we explore the space of irregular shaped partition regions to further optimize the query performance. Experimental results on TPC-H and real datasets show that our proposal is up to 70x more efficient than the state-of-the-art method.

关键词： Costs Shape Conferences Data engineering Data models Space exploration partitioning algorithms

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A Novel partitioning Approach in Active Distribution Networks for Voltage Sag Mitigation

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IEEE ACCESS 2024年 12卷 149206-149220页

作者： Mahmoodi, Saman Tarimoradi, Hadi Univ Kurdistan Dept Elect Engn Sanandaj *** Iran

The growing emphasis on power quality has posed significant challenges for distribution system operators (DSOs). Among these challenges, short-term voltage fluctuations, specifically voltage sag, have drawn considerable attention. In this study, three concepts of average edge (AE), lower average edge (LAE), and upper average edge (UAE) based on the electrical connection matrix and voltage-magnitude sensitivity matrix are defined and used as the partitioning first level. At the second level, a kernel smoothing function is employed to refine the zoning process. Subsequently, strategic locations within each zone are identified: the vertex and middle buses. These carefully selected buses serve as installation points for dynamic voltage restorers (DVRs). In response, this study proposes a novel solution by partitioning the distribution network into distinct zones. The focus lies in developing a two-level offline partitioning approach for active distribution networks (ADNs) that incorporate photovoltaic (PV) systems. To evaluate the effectiveness of the proposed method, numerical studies were conducted on modified IEEE 33-bus, IEEE 69-bus, and Iranian 95-bus systems, with simulations performed using MATLAB/Simulink. The proposed method provides good performance and fast calculation speed for distribution network partitioning, as confirmed by the results. Test results show improved bus voltage with PV unit integration. Additionally, power loss in the IEEE 33-bus, IEEE 69-bus, and Iranian 95-bus networks decreased by 47.73 kW, 56.87 kW, and 69.63 kW, respectively. Furthermore, the voltage profile improved from 0.75 p.u. to 0.928 p.u. during a voltage sag in the IEEE 33-bus system, and in steady state, the voltage increased from 0.933 to 0.959 p.u.

关键词： Power quality Distribution networks Active distribution networks partitioning algorithms Clustering algorithms Voltage control Prevention and mitigation Optimization models Microgrids Indexes Smoothing methods DERs DVRs distribution network partitioning Kernel smoothing function short-term voltage fluctuations voltage sag

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A Novel Approach to Quantum Circuit partitioning

A Novel Approach to Quantum Circuit Partitioning

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IEEE-Computer-Society Annual Symposium on VLSI (ISVLSI)

作者： Clark, Joseph Thapliyal, Himanshu Humble, Travis S. Univ Tennessee Knoxville TN 37996 USA Oak Ridge Natl Lab Oak Ridge TN USA

ISBN: (数字)9781665466059

ISBN: (纸本)9781665466059

Quantum synthesis presents an effective method of circuit optimization, but scales exponentially with the number of qubits in the circuit. This problem can be addressed by partitioning the circuit into blocks with a limited number of qubits. Existing partitioning algorithms make large trade-offs to achieve either high speed or quality. We propose a method of circuit partitioning which is competitive with existing algorithms for both metrics. The proposed method is compared with two existing methods across common circuit architectures, matching an exhaustive solution in performance and a fast solution on time.

关键词： Measurement Circuit optimization Qubit Computer architecture Very large scale integration partitioning algorithms Quantum circuit

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The Random Number partitioning Problem: Overlap Gap Property and Algorithmic Barriers

The Random Number Partitioning Problem: Overlap Gap Property...

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IEEE International Symposium on Information Theory (ISIT)

作者： Gamarnik, David Kizildag, Eren C. MIT Cambridge MA 02139 USA

ISBN: (纸本)9781665421607;9781665421591

We focus on the problem of algorithmically finding a near-optimal solution for the (random) number partitioning problem (NPP), a problem that is of great practical and theoretical significance. The NPP possesses a striking gap between the existential and best algorithmic guarantee: when its input has i.i.d. standard Gaussian entries, the optimal value of NPP is Theta(root n2(-n)) (w.h.p.);whereas the best polynomial-time algorithm achieves an exponentially worse value of only 2(-Theta(log2n)) (w.h.p.). In this paper, we inquire into the origin of this gap by studying the landscape of the NPP through the lens of statistical physics and establish the presence of the Overlap Gap Property (OGP), a topological barrier for large classes of algorithms. We then leverage the OGP to establish that (a) sufficiently stable algorithms fail to find a near-optimal solution with value below 2(-omega(nlog-1/5n));and (b) a very natural Monte Carlo Markov Chain dynamics mixes slowly. A technical innovation of our paper is that we consider the overlap structure of mtuples of near- optimal solutions where m itself grows in n. Our hardness result for stable algorithms is based on a Ramsey-theoretic argument from extremal combinatorics. To the best of our knowledge, this is the first usage of Ramsey Theory to show algorithmic hardness.

关键词： Technological innovation Monte Carlo methods Heuristic algorithms Markov processes partitioning algorithms Standards Physics

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