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Graph Distance and Adaptive K-Nearest Neighbors Selection-Based Density Peak Clustering

IEEE ACCESS 2024年 12卷 71783-71796页

作者： Sun, Yuqin Wang, Jingcong Sun, Yuan Zhang, Pengcheng Wang, Tianyi Shanghai Univ Elect Power Sch Math & Phys Shanghai 201306 Peoples R China

Density Peak Clustering (DPC) is known for its rapid identification of cluster centers and successful clustering tasks. However, traditional DPC encounters several issues, which include simplifications in local density and distance metrics, a non-robust single-allocation strategy and limited fault tolerance. To address these challenges, this study introduces an innovative density peak clustering algorithm, named Graph Distance and Adaptive K-Nearest Neighbors Selection-Based Density Peak Clustering (GAK-DPC). Our goal with the approach is to enhance the algorithm's adaptability to non-linear and complex data structures. We achieve this by replacing the traditional Euclidean distance with graph distance. Additionally, we redefine the method for computing local density based on information from K-nearest neighbor data points. By introducing the concept of natural neighbors, the neighborhood radius r is obtained when all instances in the dataset have at least one natural neighbor. Then for the current data point, the number of data points falling within a circle centered on it with radius r is counted as the K-value of that data point. Thus, we achieve the adaptive selection of the K-value. This adaptive K-value strategy takes into account the dataset's characteristics and inter-point neighbor relationships, which enhances the algorithm's adaptability and robustness. Finally, we optimize the secondary allocation strategy for sample points to improve the algorithm's fault tolerance. By conducting comparisons with traditional clustering algorithms on UCI datasets and synthetic datasets, we demonstrate the effectiveness of GAK-DPC.

关键词： Clustering algorithms partitioning algorithms Resource management Kernel Robustness Manifolds Euclidean distance Nearest neighbor methods Adaptive K-neighbors allocation strategy density peak clustering graph distance natural neighbors

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Optimum Partition of Power Networks Using Singular Value Decomposition and Affinity Propagation

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IEEE TRANSACTIONS ON POWER SYSTEMS 2024年第5期39卷 6359-6371页

作者： Eddin, Maymouna Ez Massaoudi, Mohamed Abu-Rub, Haitham Shadmand, Mohammad Abdallah, Mohamed Texas A&M Univ Dept Elect Engn College Stn TX 77840 USA Texas A&M Univ Dept Elect Engn Doha 23874 Qatar Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA Hamad Bin Khalifa Univ Coll Sci & Engn Div Informat & Comp Technol Doha 34110 Qatar

Due to coupling and correlation between nodes and buses in the power system, Power Grid partitioning (PGP) is a promising approach to analyze large power systems and provide timely actions during disturbances. From this perspective, this paper proposes an efficient framework for fast and optimal PGP, based on singular value decomposition analysis of the graph's Laplacian. An Affinity Propagation clustering algorithm-based PGP is tailored for automatically forming highly interconnected clusters based on pairwise similarities without requiring a predefined number of partitions. The core objective is to quantify the clustering performance based on internal clustering validity indices, such as the Silhouette Index, Calinski-Harabasz Index, and Davies-Bouldin Index. The adopted methodology aims to enhance partitioning efficiency substantially while preserving a high level of partitioning quality. The proposed framework is verified on IEEE 14, 39, 118, and 2000-bus systems and compared to nine other well-known and widely used clustering techniques, including K-Means and Gaussian Mixture models. The simulation results demonstrate the scalability of the proposed approach and its high-quality partitioning output with a Silhouette index of 0.6162, 0.6597, 0.6664, and 0.6555 for the IEEE 14, 39, 118, and 2000-bus systems, respectively.

关键词： Indexes Clustering algorithms partitioning algorithms Power systems Power grids Scalability Laplace equations complex networks grid partitioning machine learning power system analysis

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HRCM: A Hierarchical Regularizing Mechanism for Sparse and Imbalanced Communication in Whole Human Brain Simulations

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2024年第6期35卷 901-918页

作者： Du, Xin Wang, Minglong Lu, Zhihui Duan, Qiang Liu, Yuhao Feng, Jianfeng Wang, Huarui Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Minist Educ Engn Res Ctr Cyber Secur Auditing & Monitoring Shanghai 200433 Peoples R China Shanghai Blockchain Engn Res Ctr Shanghai 200433 Peoples R China Penn State Univ Sch Informat Sci & Technol State Coll PA 16802 USA Fudan Univ Inst Sci & Technol Brain Inspired Intelligence Shanghai 200437 Peoples R China

Brain simulation is one of the most important measures to understand how information is represented and processed in the brain, which usually needs to be realized in supercomputers with a large number of interconnected graphical processing units (GPUs). For the whole human brain simulation, tens of thousands of GPUs are utilized to simulate tens of billions of neurons and tens of trillions of synapses for the living brain to reveal functional connectivity patterns. However, as an application of the irregular spares communication problem on a large-scale system, the sparse and imbalanced communication patterns of the human brain make it particularly challenging to design a communication system for supporting large-scale brain simulations. To face this challenge, this paper proposes a hierarchical regularized communication mechanism, HRCM. The HRCM maintains a hierarchical virtual communication topology (HVCT) with a merge-forward algorithm that exploits the sparsity of neuron interactions to regularize inter-process communications in brain simulations. HRCM also provides a neuron-level partition scheme for assigning neurons to simulation processes to balance the communication load while improving resource utilization. In HRCM, neuron partition is formulated as a k-way graph partition problem and solved efficiently by the proposed hybrid multi-constraint greedy (HMCG) algorithm. HRCM has been implemented in human brain simulations at the scale of up to 86 billion neurons running on 10000 GPUs. Results obtained from extensive simulation experiments verify the effectiveness of HRCM in significantly reducing communication delay, increasing resource usage, and shortening simulation time for large-scale human brain models.

关键词： Brain modeling Neurons Computational modeling Load modeling partitioning algorithms Supercomputers Topology Parallel computing irregular communications brain simulations k-way graph partition hierarchical virtual topology

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Adjustable Multi-Stream Block-Wise Farthest Point Sampling Acceleration in Point Cloud Analysis

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第7期71卷 3523-3527页

作者： Zhou, Changchun Fu, Yuzhe Ma, Yanzhe Han, Eryi He, Yifan Jiao, Hailong Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen 518055 Peoples R China Reconova Technol Co Ltd Xiamen 361008 Peoples R China

Point cloud is increasingly used in a variety of applications. Farthest Point Sampling (FPS) is typically employed for down-sampling to reduce the size of point cloud and enhance the representational capability by preserving contour points in point cloud analysis. However, due to low parallelism and high computational complexity, high energy consumption and long latency are caused, which becomes a bottleneck of hardware acceleration. In this brief, we propose an adjustable multi-stream block-wise FPS algorithm, adjusted by four configurable parameters, according to hardware and accuracy requirements. A unified hardware architecture with one parameter is designed to implement the adjustable multi-stream block-wise FPS algorithm. Furthermore, we present a rapid searching algorithm to select the optimal configuration of the five parameters. Designed in an industrial 28-nm CMOS technology, the proposed hardware architecture achieves a latency of 0.005 (1.401) ms and a frame energy consumption of 0.09 (27.265) $\mu $ J/frame for 1 k (24 k) input points at 200 MHz and 0.9 V supply voltage. Compared to the state of the art, the proposed hardware architecture reduces the latency by up to 99.9%, saves the energy consumption by up to 99.5%, and improves the network accuracy by up to 9.34%.

关键词： Hardware Point cloud compression Computer architecture Energy consumption Parallel processing Prediction algorithms partitioning algorithms Point cloud neural networks mapping parallelism FPS acceleration framework energy consumption network accuracy hardware architecture

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partitioning Stateful Data Stream Applications in Dynamic Edge Cloud Environments

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2022年第4期15卷 2368-2381页

作者： Ding, Shaoshuai Yang, Lei Cao, Jiannong Cai, Wei Tan, Mingkui Wang, Zhenyu South China Univ Technol Sch Software Engn Guangzhou 510641 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518000 Guangdong Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518000 Peoples R China

Computation partitioning is an important technique to improve the application performance by selectively offloading some computations from the mobile devices to the nearby edge cloud. In a dynamic environment in which the network bandwidth to the edge cloud may change frequently, the partitioning of the computation needs to be updated accordingly. The frequent updating of partitioning leads to high state migration cost between the mobile side and edge cloud. However, existing works don't take the state migration overhead into consideration. Consequently, the partitioning decisions may cause significant network congestion and increase overall completion time tremendously. In this article, with considering the state migration overhead, we propose a set of novel algorithms to update the partitioning based on the changing network bandwidth. To the best of our knowledge, this is the first work on computation partitioning for stateful data stream applications in dynamic environments. The algorithms aim to alleviate the network congestion and minimize the make-span through selectively migrating state in dynamic edge cloud environments. Extensive simulations show our solution not only could selectively migrate state but also outperforms other classical benchmark algorithms in terms of make-span. The proposed model and algorithms will enrich the scheduling theory for stateful tasks, which has not been explored before.

关键词： Cloud computing Mobile handsets partitioning algorithms Heuristic algorithms Object tracking Data models Servers Edge cloud computation partitioning stateful data stream applications

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A Probabilistic Niching Evolutionary Computation Framework Based on Binary Space partitioning

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第1期52卷 51-64页

作者： Huang, Ting Gong, Yue-Jiao Chen, Wei-Neng Wang, Hua Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Univ Victoria ISILC Melbourne Vic 8001 Australia

Multimodal optimization problems have multiple satisfactory solutions to identify. Most of the existing works conduct the search based on the information of the current population, which can be inefficient. This article proposes a probabilistic niching evolutionary computation framework that guides the future search based on more sufficient historical information, in order to locate diverse and high-quality solutions. A binary space partition tree is built to structurally organize the space visiting information. Based on the tree, a probabilistic niching strategy is defined to reinforce exploration and exploitation by making full use of the structural historical information. The proposed framework is universal for incorporating various baseline niching algorithms. In this article, we integrate the proposed framework with two niching algorithms: 1) a distance-based differential evolution algorithm and 2) a topology-based particle swarm optimization algorithm. The two new algorithms are evaluated on 20 multimodal optimization test functions. The experimental results show that the proposed framework helps the algorithms obtain competitive performance. They outperform a number of state-of-the-art niching algorithms on most of the test functions.

关键词： Sociology Statistics partitioning algorithms Topology Vegetation Probabilistic logic Optimization Binary space partition (BSP) evolutionary algorithm (EA) multimodal optimization probabilistic niching computation

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Exploring Evolutionary Spectral Clustering for Temporal-Smoothed Clustered Cell-Free Networking

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第2期14卷 494-498页

作者： Wang, Junyuan Wu, Tianyao Zhou, Ouyang Zhu, Yaping Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Tongji Univ Sch Comp Sci & Technol Shanghai 201804 Peoples R China

Clustered cell-free networking, which dynamically partitions the whole network into nonoverlapping subnetworks, has been recently proposed to mitigate the cell-edge problem in cellular networks. However, prior works only focused on optimizing clustered cell-free networking in static scenarios with fixed users. This could lead to a large number of handovers in the practical dynamic environment with moving users, seriously hindering the implementation of clustered cell-free networking in practice. This letter considers user mobility and aims to simultaneously maximize the sum rate and minimize the number of handovers. By transforming the multi-objective optimization problem into a time-varying graph partitioning problem and exploring evolutionary spectral clustering, a temporal-smoothed clustered cell-free networking algorithm is proposed, which is shown to be effective in smoothing network partitions over time and reducing handovers while maintaining similar sum rate.

关键词： Handover Optimization Clustering algorithms Cellular networks partitioning algorithms Transforms Uplink Signal to noise ratio Precoding Heuristic algorithms Clustered cell-free networking user mobility temporal smoothness handover evolutionary spectral clustering

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An Improved Global Perturbation Observation Algorithm for Partially Shaded Photovoltaic Arrays

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IEEE ACCESS 2024年 12卷 165017-165025页

作者： Wang, Yulin Sun, Liying Liaoning Univ Technol Sch Elect Engn Jinzhou 121001 Peoples R China

This paper introduces an improved global perturbation observation (IGP&O) algorithm for maximum power point tracking (MPPT) of photovoltaic (PV) arrays under partial shading conditions. The algorithm is divided into two stages. In the first stage, the algorithm uses the working point of the traditional MPPT mobile PV to make the ratio of the output voltage and current of the PV equal to the set load value. In the second stage, the output point is moved to the maximum power point (MPP) by the IGP&O algorithm. The proposed algorithm is modeled and simulated using MATLAB/Simulink. The simulation results in different cases show that compared with the traditional MPPT algorithm, the average tracking accuracy of the IGP&O algorithm is more than 98.5%, which has the advantage of jumping out of the local extreme point. The power fluctuation of the algorithm is between 0.001W-2.698 W, which has the advantage of small output power fluctuation. Compared with the intelligent optimization algorithm with an average tracking time of 0.14s-0.79s, the average tracking time of the IGP&O algorithm is only 0.005s-0.06s, which has a speed advantage.

关键词： partitioning algorithms Resistance Maximum power point trackers Lighting Solar power generation Photovoltaic systems Real-time systems Perturbation methods Mathematical models Voltage control Maximum power point tracking (MPPT) partial shading perturbation observation method

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MADFM: Multi-Layer Adaptive Driver Fatigue Monitoring Model Based on Steering Wheel Signals

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Zhu, Jinglong Liu, Qiang Zhao, Zhangzhen Lu, Yu Yao, Zikai Li, Qing Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen 518107 Peoples R China Guangdong Marshell Elect Technol Co Zhaoqing 526238 Peoples R China Sun Yat Sen Univ Guangzhou Automobile Res Inst Joint Lab Intelligent Transportat & Artificial Int Shenzhen 518107 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Univ Sydney Sch Aerosp Mech & Mechatron Engn Sydney NSW 2006 Australia

Driver fatigue poses a serious threat to road safety. Although most vehicles are equipped with driver fatigue monitoring models, the performance of existing models is not satisfactory due to the complexity and unclear reliability of fatigue features. To address these issues, this study proposed a multi-layer adaptive driver fatigue monitoring (MADFM) model, based on the steering wheel signals obtained from 25 participants in a driving simulator. The steering wheel signals were sequentially processed through an adaptive window partitioning algorithm based on sample entropy, feature extraction, and an adaptive outlier point removal algorithm based on local outlier factor. Finally, a multi-scale adaptive feature selection (AFS) algorithm based on global-local correlation was applied to obtain the optimal feature set D-o. AFS algorithm was then validated using a random forest model and compared with existing feature selection algorithms. The results showed that after adopting the AFS algorithm, the model's fatigue monitoring accuracy reached 93.71%, which was 10.21% higher than the algorithm without using feature selection. Compared to four common feature selection algorithms, AFS algorithm demonstrated superior performance in terms of monitoring accuracy, precision, recall and interpretability. The interpretability analysis results revealed that steering wheel angle and lateral acceleration features were the most sensitive features affecting the accuracy of the fatigue monitoring model. This model could improve the monitoring performance of driver fatigue, prompting safer driving behaviors, and reducing traffic accidents risk.

关键词： Fatigue Monitoring Feature extraction Wheels Vehicles Adaptation models partitioning algorithms Hidden Markov models Accuracy Real-time systems Driving fatigue feature selection adaptive model machine learning traffic safety

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Structured Graph-Based Ensemble Clustering

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第6期37卷 3728-3738页

作者： Zheng, Xuan Lu, Yihang Wang, Rong Nie, Feiping Li, Xuelong Northwestern Polytech Univ Sch Cybersecur Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China Northwestern Polytech Univ Sch Comp Sci Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China

Ensemble clustering can utilize the complementary information among multiple base clusterings, and obtain a clustering model with better performance and more robustness. Despite its great success, there are still two problems in the current ensemble clustering methods. First, most ensemble clustering methods often treat all base clusterings equally. Second, the final ensemble clustering result often relies on k-means or other discretization procedures to uncover the clustering indicators, thus obtaining unsatisfactory results. To address these issues, we proposed a novel ensemble clustering method based on structured graph learning, which can directly extract clustering indicators from the obtained similarity matrix. Moreover, our methods take sufficient consideration of correlation among the base clusterings and can effectively reduce the redundancy among them. Extensive experiments on artificial and real-world datasets demonstrate the efficiency and effectiveness of our methods.

关键词： Clustering methods Clustering algorithms partitioning algorithms Trajectory Training Robustness Redundancy Optics Machine learning algorithms Laplace equations Clustering ensemble learning structured graph learning

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