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Consensus-Based Distributed Intentional Controlled Islanding of Power Grids

IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2024年第1期11卷 233-243页

作者： Lo Iudice, Francesco Cardona-Rivera, Ricardo Grotta, Antonio Coraggio, Marco Bernardo, Mario di Univ Naples Federico II Dept Elect Engn & Informat Technol I-80138 Naples Italy Italian Inst Technol Ctr Translat Neurophysiol I-44121 Ferrara Italy Scuola Super Merid Sch Adv Studies I-80138 Naples Italy

The problem of partitioning a power grid into a set of islands can be a solution to restore power dispatchment in sections of a grid affected by an extreme failure. Current solutions to this problem usually involve finding the partition of the grid into islands that minimize the sum of their absolute power imbalances. This combinatorial problem is often solved through heuristic centralized methods. In this article, we propose instead a distributed online algorithm through which nodes can migrate among islands, self-organizing the network into a suitable partition. We prove that under a set of appropriate assumptions, the proposed solution yields a partition, whose absolute power imbalance falls within a given bound of the optimal solution. We validate our analytical results by testing our partitioning strategy on the IEEE 118 and 300 benchmark problems.

关键词： Power grids Estimation partitioning algorithms Power system dynamics Network systems Islanding Generators Decentralized control islanding smart grids

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Eigenvalue Ratio Inspired Partition Learning and Fusion for Multiple Kernel Clustering

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第12期36卷 8135-8147页

作者： Yang, Wenqi Tang, Chang Zheng, Xiao Zhu, Xinzhong Liu, Xinwang China Univ Geosci Sch Comp Sci Wuhan 430079 Peoples R China Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321004 Peoples R China Beijing Geekplus Technol Co Ltd AI Res Inst Beijing 100101 Peoples R China

Multiple kernel clustering (MKC) aims to extract and integrate the clustering information from a set of pre-defined kernels for handling data which cannot be linearly separated well. More precisely, existing MKC methods generally devote to learn the complementary information from a set of kernel partitions, whose feature dimensions are commonly fixed as the upper bound n or lower bound c , where n and c represents the number of samples and clusters, respectively. However, the adopting of the lower bound or upper bound generally leads to poor clustering performance caused by the lack or redundancy of clustering information carried by kernel partitions. To tackle this issue, we propose a novel late fusion multiple kernel clustering method, termed as Eigenvalue Ratio Inspired Partition Learning and Fusion for Multiple Kernel Clustering (ERMKC), in this paper. Specifically, we propose an eigenvalue ratio based criterion to guide the kernel partition learning for each single kernel matrix, which ensures more suitable feature dimensions for the learnt kernel partitions. In addition, we also propose a novel late fusion model for fusing the learnt kernel partitions optimally. Furthermore, we conduct extensive experiments on numerous benchmark datasets to evaluate the proposed ERMKC method, whose results verify the effectiveness and advantage of the proposed method compared to the other state-of-the-art methods.

关键词： Kernel Eigenvalues and eigenfunctions Clustering algorithms partitioning algorithms Clustering methods Measurement Mathematical models Clustering multiple kernel clustering eigenvalue ratio late fusion strategy

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TDEC: Evidential Clustering Based on Transfer Learning and Deep Autoencoder

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第10期32卷 5585-5597页

作者： Jiao, Lianmeng Wang, Feng Liu, Zhun-Ga Pan, Quan Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China

Evidential clustering is a promising clustering framework using Dempster-Shafer belief function theory to model uncertain data. However, evidential clustering needs to estimate more parameters compared with other clustering algorithms, and thus the clustering performance of evidential clustering will be greatly affected if data is insufficient or contaminated. In addition, the existing evidential clustering algorithms can not well deal with high-dimensional data such as texts and images. To solve the above problems, an evidential clustering algorithm based on transfer learning and deep autoencoder (TDEC) is proposed. The TDEC utilizes deep autoencoder to obtain evidential clustering-friendly representations of the original data, and applies the maximum mean discrepancy (MMD) constraint between the source network and the target network, so that the network can learn domain-invariant features. The algorithm jointly trains the deep evidential clustering networks in the source domain and the target domain, and realizes the deep feature representations of high-dimensional data in the target domain for evidential clustering by minimizing reconstruction loss, entropy-based evidential clustering loss, MMD loss and the regular penalty term of the network parameters. In addition, an iterative optimization method to solve the TDEC objective function is proposed. Extensive experiments were conducted to evaluate the clustering performance of the proposed TDEC algorithm compared with the existing shallow transfer clustering algorithms and deep clustering algorithms. For both image and text clustering tasks, the proposed TDEC achieved approximately 5% performance improvement over the comparison algorithms on average. In addition, the practical application value of the proposed TDEC algorithm was demonstrated in unsupervised remote sensing image scene classification.

关键词： Clustering algorithms partitioning algorithms Transfer learning Evidence theory Feature extraction Fuzzy systems Approximation algorithms Deep autoencoder evidential clustering unsupervised transfer learning

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Piecewise-Constant Representation and Sampling of Bandlimited Signals on Graphs

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2024年 10卷 332-346页

作者： Yang, Guangrui Zhang, Qing Yang, Lihua South China Agr Univ Coll Math & Informat Dept Math Guangzhou 510642 Peoples R China Jiangsu Univ Sch Math Sci Zhenjiang 212013 Peoples R China Sun Yat Sen Univ Sch Math Guangzhou 510275 Peoples R China Sun Yat Sen Univ Guangdong Prov Key Lab Computat Sci Guangzhou 510275 Peoples R China

Signal representations on graphs are at the heart of most graph signal processing techniques, allowing for targeted signal models for tasks such as denoising, compression, sampling, reconstruction and detection. This paper studies the piecewise-constant representation of bandlimited graph signals, thereby establishing the relationship between the bandlimited graph signal and the piecewise-constant graph signal. For this purpose, we first introduce the concept of epsilon-level piecewise-constant representation for a general signal space. Then, using a distance matrix, a single-layer piecewise-constant representation algorithm is proposed to find an epsilon-level piecewise-constant representation for bandlimited graph signals. On this basis, we further propose a multi-layer piecewise-constant representation algorithm, which can find a node partition with as few pieces as possible to represent bandlimited graph signals piecewise within a preset error bound. Finally, as an application, we apply the node partition obtained by the multi-layer algorithm to establish a sampling theory for bandlimited signals, which does not need to compute the eigendecomposition of a variation operator in both sampling and signal reconstruction. Numerical experiments show that the proposed algorithms have good performance.

关键词： partitioning algorithms Vectors Signal processing Information processing Signal processing algorithms Approximation algorithms Laplace equations Bandlimited graph signal graph signal processing piecewise-constant graph signal piecewise-constant representation sampling

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A Machine Learning-Powered Tier partitioning Methodology for Monolithic 3-D ICs

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2022年第11期41卷 4575-4586页

作者： Lu, Yi-Chen Pentapati, Sai Zhu, Lingjun Murali, Gauthaman Samadi, Kambiz Lim, Sung Kyu Georgia Inst Technol Dept Elect & Comp Engn Atlanta GA 30332 USA Qualcomm Technol Inc Res & Dev Dept San Diego CA 92121 USA Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

Tier partitioning is one of the most critical stages in monolithic 3-D (M3D) integrated circuits (ICs) implementation flows. It transforms 2-D netlists into 3-D by performing tier assignment for each design instance, which directly impacts the power, performance, and area (PPA) metrics of final 3-D full-chip designs. However, the current state-of-the-art tier partitioning approach named bin-based min-cut algorithm has fundamental flaws that lead to severe drawbacks, such as timing degradation, 3-D routing overhead, and redundant monolithic intertier vias (MIVs) insertion. To overcome these issues, in this article, we propose TP-GNN, an unsupervised graph learning-based tier partitioning framework that utilizes graph neural networks (GNNs) and advanced machine learning (ML) techniques to perform tier partitioning. The proposed framework comprehends design- and technology-related parameters properly so that it is generalizable to various netlists and technologies. In addition, it can be integrated with any style of M3D design flows that require tier assignments of standard cells. In the experiments, we validate the proposed framework on seven industrial designs with two different fashions of M3D implementation flows: 1) partitioning-first (Snap3D) and 2) partitioning-last (Shrunk2D and Compact2D) flows. We demonstrate that our framework, TP-GNN, significantly improves the 3-D quality of results (QoR) across most testing designs in a large margin compared with the bin-based min-cut tier partitioning algorithm. Specifically, in OpenPiton, an RISC-V-based multicore system, we observe 27.4%, 7.7%, and 20.3% improvements in performance, wirelength, and energy-per-cycle, respectively. Finally, we perform a case study by applying the proposed framework to a heterogeneous M3D design flow, Pin3D, on a commercial CPU design and observe that TP-GNN reaches better partitioning solutions than the existing partitioning approaches for heterogeneous 3-D ICs.

关键词： Three-dimensional displays Integrated circuits partitioning algorithms Routing Timing Degradation Three-dimensional integrated circuits 3D integrated circuits (ICs) circuit partitioning physical design

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Inertia-Based Indices to Determine the Number of Clusters in K-Means: An Experimental Evaluation

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IEEE ACCESS 2024年 12卷 11761-11773页

作者： Rykov, Andrei de Amorim, Renato Cordeiro Makarenkov, Vladimir Mirkin, Boris Natl Res Univ Higher Sch Econ Dept Data Anal & Machine Intelligence Moscow 101000 Russia Univ Essex Comp Sci & Elect Engn Dept Wivenhoe CO4 3SQ England Imagia Cybernet Montreal PQ H3C 3P8 Canada Mila Quebec AI Inst Montreal PQ H2S 3H1 Canada Univ London Dept Comp Sci & Informat Syst London WC1E 7HX England

This paper gives an experimentally supported review and comparison of several indices based on the conventional K-means inertia criterion for determining the number of clusters, K, in datasets, using the popular Silhouette width index as a benchmark. Our experiments involve a novel version of the Elbow index, defined using values of K two or three steps apart. We also discuss alternative ways of computing the inertia and summarizing its values. Even though there are no overall winners in our experiments, some of our results are very conclusive and can be used as a guide for indices determining the number of clusters in K-means.

关键词： Indexes Clustering algorithms Euclidean distance Amplitude modulation partitioning algorithms Computer science K-means number of clusters inertia elbow method Calinski-Harabasz index Hartigan rule

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One-Step Multiview Fuzzy Clustering With Collaborative Learning Between Common and Specific Hidden Space Information

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第10期35卷 14031-14044页

作者： Zhang, Wei Deng, Zhaohong Zhang, Te Choi, Kup-Sze Wang, Shitong Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Peoples R China Jiangsu Key Lab Digital Design & Software Technol Wuxi 214122 Peoples R China Key Lab Computat Neurosci & Brain Inspired Intelli Shanghai 200433 Peoples R China ZJLab Shanghai 200433 Peoples R China Univ Nottingham Sch Comp Sci Lab Uncertainty Data & Decis Making LUCID Nottingham NG8 1BB England Hong Kong Polytech Univ Ctr Smart Hlth Hong Kong Peoples R China

Multiview data are widespread in real-world applications, and multiview clustering is a commonly used technique to effectively mine the data. Most of the existing algorithms perform multiview clustering by mining the commonly hidden space between views. Although this strategy is effective, there are two challenges that still need to be addressed to further improve the performance. First, how to design an efficient hidden space learning method so that the learned hidden spaces contain both shared and specific information of multiview data. Second, how to design an efficient mechanism to make the learned hidden space more suitable for the clustering task. In this study, a novel one-step multiview fuzzy clustering (OMFC-CS) method is proposed to address the two challenges by collaborative learning between the common and specific space information. To tackle the first challenge, we propose a mechanism to extract the common and specific information simultaneously based on matrix factorization. For the second challenge, we design a one-step learning framework to integrate the learning of common and specific spaces and the learning of fuzzy partitions. The integration is achieved in the framework by performing the two learning processes alternately and thereby yielding mutual benefit. Furthermore, the Shannon entropy strategy is introduced to obtain the optimal views weight assignment during clustering. The experimental results based on benchmark multiview datasets demonstrate that the proposed OMFC-CS outperforms many existing methods.

关键词： Clustering methods Learning systems Clustering algorithms Data mining Collaboration Feature extraction partitioning algorithms Common space fuzzy clustering multiview data one-step clustering specific space

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Track Consensus-Based Labeled Multi-Target Tracking in Mobile Distributed Sensor Network

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第6期23卷 7351-7362页

作者： Verma, Janardan Kumar Chhabra, Jitender Kumar Ranga, Virender Natl Inst Technol Dept Comp Engn Kurukshetra 136119 Haryana India Delhi Technol Univ Dept Informat Technol Delhi 110042 India

This paper proposes an efficient algorithm for tracking multiple targets using a network of static and mobile sensors (robots). Multi-target tracking has a broad array of applications, including crowd monitoring, vehicle tracking, warehouse automation, and pedestrian safety, among others. The problem of distributed labeled multi-target tracking comprises constraints on sensing range, communication, label consistency, and motion. Hence, our algorithm strives to minimize label mismatching, communication, movement of robots, and tracking error, which are serious concerns in the existing solutions. The problem is decomposed into two sub-problems: distributed estimation and adaptive movement control. We present a novel track consensus algorithm for estimating the number and tracks of targets, complemented by an efficient label consensus method. This algorithm can effectively identify similar tracks and fuse them in cluttered scenarios. Various movement control strategies are proposed to minimize the moving distance of the robots while keeping the maximum number of targets in the sensing range. The maximum target sensing problem is NP-hard;therefore, we propose and compare heuristic, approximation, and randomized algorithms. We have also verified our proposed solution through extensive simulations and compared the distributed estimation and movement control algorithms with other prominent solutions. We also analyze estimation accuracy using the Optimal Sub-Pattern Assignment (OSPA) metric, asymptotic performance, and communication cost and confirm the real-time computation of our proposed algorithms.

关键词： Target tracking Robot sensing systems Filtering algorithms Sensors Information filters Prediction algorithms partitioning algorithms Multi-target tracking distributed fusion track consensus label consensus movement control target tracking

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Multimodel Self-Learning Predictive Control Method With Industrial Application

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2024年第11期71卷 14842-14852页

作者： Huang, Keke Ying, Xinyu Wu, Dehao Yang, Chunhua Gui, Weihua Cent South Univ Sch Automat Changsha 410083 Peoples R China

In industrial sites, system operation conditions fluctuate due to changes in raw material and equipment status, making it critical to identify the operation conditions and obtain appropriate controllers accurately. Additionally, even for a specific operation condition, fixed control strategies may result in mismatches due to varying operational stages. To address the accurate control of industrial processes across multiple operation conditions, this article proposes a multimodel self-learning predictive control (MSLPC) method to simultaneously improve the accuracy of offline condition partition and online control performance. Specifically, in the offline stage, for complex and multidimensional industrial data, condition indicators are selected based on expert systems and data analytics, and a "presetting precise-fusion" two-stage operation condition learning (TSOCL) algorithm is proposed to accurately identify the operation conditions of the system. In the online stage, a self-learning predictive control algorithm is proposed, which improves adaptability and control performance by adjusting controllers. This maintains a high match between the control strategy and system state. Simulation experiments demonstrate that the MSLPC method achieves higher control accuracy and faster control rate in the presence of varying operation conditions. Finally, the proposed method is deployed in a real industrial roaster to validate its effectiveness and excellent control performance.

关键词： partitioning algorithms Predictive control Control systems Adaptation models Time series analysis Predictive models Prediction algorithms Multimodel operation condition learning predictive control self-learning zinc roasting process

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Analytical Die-to-Die 3-D Placement With Bistratal Wirelength Model and GPU Acceleration

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2024年第6期43卷 1624-1637页

作者： Liao, Peiyu Zhao, Yuxuan Guo, Dawei Lin, Yibo Yu, Bei Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Peking Univ Ctr Energy Efficient Comp & Applicat Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China Peking Univ Sch Integrated Circuits Beijing 100871 Peoples R China Peking Univ Ctr Integrated Circuits Beijing 100871 Peoples R China Peking Univ Inst Elect Design Automat Wuxi 214031 Peoples R China

In this article, we present a new analytical 3-D placement framework with a bistratal wirelength model for faceto-face-bonded 3-D ICs with heterogeneous technology nodes based on the electrostatic-based density model. The proposed framework, enabling GPU acceleration, is capable of efficiently determining node partitioning and locations simultaneously, leveraging the dedicated 3-D wirelength model and density model. The experimental results on ICCAD 2022 contest benchmarks demonstrate that our proposed 3-D placement framework can achieve up to 6.1% wirelength improvement and 4.1% on average compared to the first-place winner with much fewer vertical interconnections and up to 9.8x runtime speedup. Notably, the proposed framework also outperforms the state-of-the-art 3-D analytical placer by up to 3.3% wirelength improvement and 2.1% on average with up to 8.8x acceleration on large cases using GPUs.

关键词： Three-dimensional displays Integrated circuits Solid modeling Pins Optimization Integrated circuit modeling partitioning algorithms 3-D integrated circuits physical design placement

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