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A Distributed Robust Voltage Control Framework for Active Distribution Systems

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第2期61卷 1896-1906页

作者： Xu, Junjun Mu, Yu Cheng, Wei Gong, Guangjing Huang, Jungao Zhang, Tengfei Hu, Qinran Chen, Kang Wu, Andrew Yang Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China State Grid Elect Power Res Inst NARI Grp Corp Nanjing 211106 Peoples R China Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Hong Kong Polytech Univ Sch Profess Educ & Execut Dev Hong Kong 999077 Peoples R China

Voltage fluctuations in active distribution systems (ADS) have increased with the high penetration of uncertain energy resources, making unified governance more challenging. This paper proposes a novel distributed robust voltage control (DRVC) method for ADS based on coordinated clustering of photovoltaic and energy storage systems, aiming to aggregate decentralized resources into interrelated clusters to achieve distributed voltage regulation. Firstly, an improved Louvain algorithm based clustering method for ADS is introduced, further incorporating the voltage sensitivity and regulation capability of generators into the modularized function. After that, a local robust voltage regulation model for each cluster is constructed, and the global multi-objective optimization function is solved through a distributed robust solution method for integrating intelligent optimization to further enhance the robustness. Case studies and results demonstrate the performance of the proposed voltage control framework in resisting uncertainties by contrast with existing models.

关键词： Voltage control Clustering algorithms Data models Power system stability Sensitivity Reactive power partitioning algorithms Robust control Regulation Microgrids Distributed generator distribution system robust voltage control uncertainty

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Time-Aware and Direction-Constrained Collective Spatial Keyword Query

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第3期26卷 3039-3055页

作者： Feng, Zhe Li, Guohui Li, Jianjun Jin, Changlong Du, Xiaokun Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Software Wuhan 430074 Peoples R China Shandong Univ Sch Mech Elect & Informat Engn Weihai 250100 Peoples R China South Cent Minzu Univ Coll Comp Sci Wuhan 430079 Peoples R China

Collective spatial keyword query (CoSKQ) is an important variant of spatial keyword queries and has become a research hotspot. In real life, user behavior usually has a certain directionality, so they may want to obtain the result object that conforms to a specific direction, which is what the direction-constrained query studies. In addition, query time information also plays an important role in location-based query processing. To this end, this paper takes the lead in studying the Time-aware and Direction-constrained Collective Spatial Keyword Query (TDCoSKQ). To facilitate direction-related operations, space objects are organized using the polar coordinate system. Firstly, an efficient space partition method is designed, and on this basis, a new hybrid index structure KRPQT is designed. Based on KRPQT, several pruning strategies are proposed to prune irrelevant regions and objects from the perspective of keyword, time, and direction, and the basic algorithm KRPQB is proposed. To further improve the efficiency of query processing, the possible areas of the result objects are analyzed and shrunk to greatly reduce the number of candidate objects, and three optimization algorithms KRPSW, KRPSW+LFO, and KRPSW+KRPQB are proposed. Then, we discuss how to extend the proposed methods to deal with TDCoSKQ queries with other distance functions and TDCoSKQ queries with weight objects. Finally, the efficiency of the proposed algorithm is verified by simulation experiments.

关键词： Query processing Roads Lead partitioning algorithms Indexes Optimization Intelligent transportation systems Collective spatial keyword queries direction-constrained time-aware polar coordinate space

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A Task Scheduling Method for Minimizing Completion Time in Edge Collaboration Environment

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IEEE INTERNET OF THINGS JOURNAL 2025年第5期12卷 5423-5434页

作者： Zhao, Hui Zhang, Chao Lu, Xiaoqin Wang, Jing Yang, Pengfei Wan, Bo Wang, Quan Xidian Univ Key Lab Smart Human Comp Interact & Wearable Techn Xian 710071 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

In the edge computing environment, the uneven geographical distribution of tasks may lead to unbalanced load on the edge server. In addition, some larger tasks are difficult to completely offload to edge servers, which cannot fully utilize edge server resources. To solve the above problems, we propose a task scheduling method to minimize the completion time by combining the horizontal edge collaboration and fine-grained task partial offloading technology. First, combining horizontal edge collaboration and fine-grained task partial offloading technology, considering the location relationship between users and edge servers in multiuser multiedge server scenario, a task partial offloading optimization problem is established to minimize task completion time. Second, due to the nonconvex and variables coupling, we decompose the original problem into resource allocation, user-server association, and offloading strategy subproblems. A task scheduling algorithm based on improved teaching-learning-based optimization (ITLBO) is proposed to obtain the best task scheduling decision which includes task offloading location and offloading ratio. Simulation results show that the proposed method can effectively reduce the task completion time in edge collaboration environment.

关键词： Servers Collaboration Optimization Resource management Cloud computing Scheduling Internet of Things Energy consumption Edge computing partitioning algorithms Edge collaboration partial offloading task scheduling teaching-learning-based optimization

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High-Performance and Low-Complexity Multitouch Detection for Variable Ground States

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IEEE SENSORS JOURNAL 2025年第2期25卷 3138-3150页

作者： Sim, Doeun Byun, Younghoon Kim, Inseob Lee, Youngjoo Pohang Univ Sci & Technol POSTECH Dept Elect Engn Pohang 37673 South Korea HiDeep Inc Seoul 13493 South Korea

Multitouch detection algorithms are crucial for precise interaction with touch screens. However, as existing multitouch detection algorithms only target the good ground mass (GGM) environment, performance drops sharply when the ground state is unstable, such as the low ground mass (LGM) environment. This article introduces an enhanced multitouch detection algorithm tailored for both ground environments, addressing deficiencies in recognizing large touch areas and precise coordinates. First, the center of thumb search (CTS) method combined with an adaptive valley point division (VPD) skip process for large circular touches, such as thumb touches, enables detection without excessive segmentation. Second, conditional VPD thresholding is designed to distinguish similar single and multitouch in LGM environment. This algorithm posed challenges that negatively impacted the detection performance in the GGM environment;however, these issues were addressed through the development of ground state classifier (GSC). At last, CTS algorithm facilitates the distinction of the center of a largesized thumb touch, enhancing the resolution in closely spaced touch scenarios by properly partitioning touch groups. Experimental results demonstrate significant improvements in accuracy and linearity. We have quantitatively confirmed substantial enhancements in performance from a user standpoint, achieving 96.07% in accuracy for the total dataset and x13.36 better in linearity. These innovations collectively advance the state of touch detection technology in challenging LGM environments, presenting a robust framework for future applications.

关键词： Thumb Accuracy Detection algorithms Sensors Stationary state Classification algorithms Touch sensitive screens Performance evaluation partitioning algorithms Capacitance Connected component analysis (CCA) multitouch detection overlap split touch-screen panel (TSP)

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A Fast CU Partition Algorithm for AVS3 Based on Adaptive Tree Search and Pruning Optimization

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IEEE TRANSACTIONS ON BROADCASTING 2025年第1期71卷 125-141页

作者： Yin, Jihang Qi, Honggang Zhong, Liang Zhao, Zhiyuan Wang, Qiang Wu, Jingran Zhang, Xianguo Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 101408 Peoples R China Tencent Shannon Lab CAPD TEG Shenzhen 518000 Guangdong Peoples R China

In the third generation of the Audio Video Coding Standard (AVS3), the size of Coding Tree Units (CTUs) has been expanded to four times larger than the previous generation, and more Coding Unit (CU) partition modes have been introduced, enhancing adaptability and efficiency in video encoding. CU partition in AVS3 not only brings improvements in encoding performance but also significantly increases the computational complexity, posing substantial challenges to real-time encoding. We propose a fast algorithm for CU partition, which features adaptive tree search and pruning optimization. Firstly, it adjusts the tree search order based on neighbor CU and lookahead information. Specifically, the analysis order of sub-blocks and parent blocks is adaptively adjusted: the potential optimal partition is prioritized, the non-optimal partitions are deferred, and an optimized order of first-full-then-sub or first-sub-then-full is selected. Secondly, the pruning optimization algorithm utilizes analyzed information to skip non-optimal partitions to reduce computational complexity. Due to the adjusted tree search order and the prioritization of potential optimal partitions, more analyzed information becomes available when evaluating non-optimal partitions, thereby improving the recall and precision rates of non-optimal partitions detection, saving more time, and introducing negligible loss in coding performance. The proposed algorithm has been implemented in the open-source encoder uavs3e. Experimental results indicate that under the three encoding configurations of AI, LD B, and RA, the algorithm achieves significant time saving of 51.41%, 40.57%, and 40.57%, with BDBR increases of 0.64%, 1.61%, and 1.04%, respectively. These results outperform the state-of-the-art fast CU partition algorithms.

关键词： Encoding partitioning algorithms Costs Standards Optimization Streaming media Channel coding Video coding Prediction algorithms Real-time systems AVS3 video coding fast cu partition tree search

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Higher Order Local Search Assisted Community Detection in Signed Networks

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2025年

作者： Xiao, Jing Dang, Zi-Zheng Yang, Zhi-Xiang Xu, Xiao-Ke Shenzhen Technol Univ Coll Big Data & Internet Shenzhen 518118 Peoples R China Dalian Minzu Univ Coll Informat & Commun Engn Dalian 116600 Peoples R China Beijing Normal Univ Sch Journalism & Commun Beijing 100875 Peoples R China

Community detection in signed networks reveals mesoscale structures and sign characteristics, facilitating accurate and realistic understanding of real-life signed networks. Given the complexity of signed community structures and the NP-hard challenge of achieving optimal partitions, modularity optimization using memetic algorithms has emerged as a promising approach. However, the identified signed partitions still face challenges in modularity quality and precision, with many misassigned nodes or being stuck at local optima, particularly in ambiguous and complex structures. To address this issue, this article introduces a new concept: central-node signed motif, a higher order local topological feature that effectively identifies signed communities, providing richer and more efficient topological information. Furthermore, we utilize these central-node signed motifs to improve community detection through a framework named higher order local search assisted memetic modularity (HLSMM). In HLSMM, we design a central-node signed motif-based local search strategy to optimize node assignments and improve modularity quality and structural balance of optimized signed communities. Experimental results from synthetic and real-world datasets demonstrate the effectiveness of the local search strategy in enhancing accuracy and modularity quality. HLSMM exhibits a general advantage in modularity quality over currently representative methods on real-world datasets, particularly in ambiguous and complex community structures.

关键词： Optimization Image edge detection Search problems Memetics Vectors partitioning algorithms Faces Accuracy Topology Measurement Community detection higher order local search memetic algorithm signed motifs signed network

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Examining the Impact of Fingerprint Vector Size on Similarity Determination and Clustering Performance of a Pattern-Based Similarity Metric

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IEEE ACCESS 2025年 13卷 51660-51668页

作者： Yaro, Abdulmalik Shehu Maly, Filip Prazak, Pavel Univ Hradec Kralove Fac Informat & Management Dept Informat & Quantitat Methods Hradec Kralove 50003 Czech Republic Ahmadu Bello Univ Dept Elect & Telecommun Engn Zaria 810106 Nigeria

The correlation-based context similarity coefficient (CSC) metric is a pattern-based fingerprint similarity metric that has gained interest in fingerprint database clustering operations. The performance of the traditional distance-based metric in fingerprint vector similarity determination has been known to be influenced by the size of the fingerprint vector. However, as for the correlation-based CSC metric, there is no comprehensive research on how fingerprint vector size affects its performance in similarity determination and subsequently clustering performance. As such, this paper examines the impact of fingerprint vector size on the similarity determination performance of the correlation-based CSC metric with the k-medoids algorithm employed for clustering. The analysis is performed across four synthetic and two experimentally generated fingerprint databases with varying fingerprint vector sizes. The impact analysis is carried out with the clustering algorithm set to generate K is an element of [3, 5] clusters. Additionally, the results are compared against three distance-based metrics: squared Euclidean, Manhattan, and cosine. With silhouette score as the clustering performance metric, the simulation result shows that the size of the fingerprint vector influences the similarity determination performance of the correlation-based CSC metric. Additionally, the number of clusters in which the clustering algorithm is set to generate also contributes to how the correlation-based CSC metric performs in similarity determination. The similarity determination time complexity of the correlation-based CSC metric increases with fingerprint vector size, making efficient clustering more challenging as fingerprint vector sizes increase. For optimal performance, the correlation-based CSC metric is recommended for us as a similarity metric on a database with a fingerprint vector size of N <= 4 and a clustering algorithm configured to generate no more than 3 clusters.

关键词： Fingerprint recognition Vectors Measurement Clustering algorithms Databases Measurement uncertainty Accuracy Mathematical models partitioning algorithms Location awareness Clustering context similarity coefficient fingerprint vector size pattern-based metric similarity determination

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Low-Overlap Point Cloud Registration by Semiglobal Block Matching

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第3期21卷 2491-2500页

作者： Guo, Shiyi Wu, Yihong Xie, Binjian Liu, Bingxi Jia, Tong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Southern Univ Sci & Technol Beijing Peoples R China Southern Univ Sci & Technol Shenzhen 518055 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China Northeastern Univ Minist Educ Key Lab Data Anal & Optimizat Smart Ind Shenyang 110819 Peoples R China

In recent years, the local feature-based point cloud registration methods have attracted more attention for their high robustness. The state-of-the-art local feature-based pipeline consists of local feature extraction, feature matching, and outlier rejection. However, the accuracy of this pipeline decreases significantly between low-overlap point clouds. In this article, we propose a novel hand-crafted framework to realize efficient low-overlap point cloud registration through point cloud partitioning, semiglobal point cloud block matching, and best transformation selection with refinement. Experimental results on benchmark datasets show that the proposed algorithm presents the superior performance over the previous methods on low-overlap scenes under different features and achieves very competitive accuracy on non-low-overlap data.

关键词： Point cloud compression Feature extraction Three-dimensional displays Accuracy Pipelines Robustness partitioning algorithms Informatics Image color analysis Convolution 3-D feature block matching low overlap point cloud registration

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Compact Workspace Decomposition Based on a Bottom-Up Approach

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IEEE ACCESS 2025年 13卷 3917-3928页

作者： Skorobogatov, Georgy Calvo, Toni Barrado, Cristina Salami, Esther Tech Univ Catalonia Comp Architecture Dept Castelldefels 08860 Spain

In this paper, we present an algorithm that addresses the challenge of dividing a workspace among multiple UAVs. The workspace can be any convex or non-convex polygon and may contain holes of various shapes that represent no-fly zones. The UAVs can be heterogeneous, with different levels of autonomy, speed, and range. The goal of the workspace division is to obtain areas whose sizes are best matched to the capabilities of the UAVs while maximizing compactness. The algorithm decomposes the polygon representing the workspace into a triangular grid, followed by an iterative process of accumulating adjacent triangles while maximizing the compactness of the resulting regions. The performance of the algorithm and the quality of the partitions generated by the algorithm are compared to existing methods. Results show that this approach outperforms others in several metrics, achieving a 5% to 10% improvement in compactness, while maintaining reasonable performance, with a time overhead of up to approximately two seconds when splitting a polygon into ten parts.

关键词： partitioning algorithms Autonomous aerial vehicles Drones Robots Clustering algorithms Classification algorithms Shape Approximation algorithms Sensors Area measurement UAV multi-robot systems workspace division polygon partition

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Sparse Multi-View K-Means Clustering

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IEEE ACCESS 2025年 13卷 46773-46793页

作者： Yang, Miin-Shen Parveen, Shazia Chung Yuan Christian Univ Dept Appl Math Taoyuan 32023 Taiwan

In machine learning, k-means clustering is an unsupervised leaning technique to partition the data into k clusters that are homogeneous within the cluster and heterogeneous between clusters. The k-means algorithm assigns equal importance to all features in a single view. Several multi-view clustering techniques have been developed over the last few decades, including multi-view k-means (MVKM). In MVKM, patterns can be detected more accurately, and clustering performance is improved by applying each view individually for pattern discovery. The demand for highly accurate and flexible methods in machine learning and artificial intelligence has increased the use of multi-view datasets. These datasets are complicated and pose challenges for researchers in real-life situations. A novel type of sparse k-means clustering algorithm, sparse MVKM (S-MVKM), is proposed in this study for multi-view data. The main aim of our proposed S-MVKM algorithm is to achieve sparsity in features and shrink unimportant features to exactly zero using Lasso (least absolute shrinkage and selection operator). For considering theoretic behaviors of the S-MVKM algorithm, we create a convergence theorem to ensure that any convergent subsequence in S-MVKM will ultimately tend to an optimal solution. The proposed S-MVKM algorithm is then compared with several existing algorithms, such as traditional MVKM, feature reduction MVKM (FR-MVKM), MVKM with adaptive sparse membership weight allocation (M-VASM), and redundant and sparse feature learning (RSF-MVKM). Based on the three clustering performance measures of accuracy rate, Rand index, and normalized mutual information, we make the comparisons between them by using several synthetic and real-world datasets. The comparative results demonstrate that the proposed S-MVKM clustering algorithm effectively surpasses these competing clustering algorithms in terms of these three performance measures. Finally, we make concluding remarks that provide potential g

关键词： Clustering algorithms Linear programming Machine learning algorithms Convergence Accuracy Data mining Social networking (online) partitioning algorithms Internet of Things Industries K-means multi-view k-means (MVKM) sparsity sparse MVKM (S-MVKM) feature reduction performance measures

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