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3D Shape Segmentation With Potential Consistency Mining and Enhancement

IEEE TRANSACTIONS ON MULTIMEDIA 2025年 27卷 133-144页

作者： Shu, Zhenyu Li, Shiyang Xin, Shiqing Liu, Ligang NingboTech Univ Sch Comp & Data Engn Ningbo 315104 Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310018 Peoples R China Shandong Univ Sch Comp Sci & technol Jinan 250355 Peoples R China Univ Sci & Technol China Sch Math Sci Graph & Geometr Comp Lab Hefei 230026 Anhui Peoples R China

3D shape segmentation is a crucial task in the field of multimedia analysis and processing, and recent years have seen a surge in research on this topic. However, many existing methods only consider geometric features of 3D shapes and fail to explore the potential connections between faces, limiting their segmentation performance. In this paper, we propose a novel segmentation approach that mines and enhances the potential consistency of 3D shapes to overcome this limitation. The key idea is to mine the consistency between different partitions of 3D shapes and to use the unique consistency enhancement strategy to continuously optimize the consistency features for the network. Our method also includes a comprehensive set of network structures to mine and enhance consistent features, enabling more effective feature extraction and better utilization of contextual information around each face when processing complex shapes. We evaluate our approach on public benchmarks through extensive experiments and demonstrate its effectiveness in achieving higher accuracy than existing methods.

关键词： Shape Three-dimensional displays Feature extraction Faces Vectors Point cloud compression partitioning algorithms Labeling Data mining Accuracy 3D shape segmentation consistency deep learning shape analysis

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Computing Tasks Saving Schemes Through Early Exit in Edge Intelligence-Assisted Systems

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IEEE TRANSACTIONS ON COMPUTERS 2025年第5期74卷 1565-1576页

作者： Niu, Xin Lv, Xianwei Chen, Wang Yu, Chen Jin, Hai Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Sch Comp Sci & Technol Serv Comp Technol & Syst LabCluster & Grid Comp L Wuhan 430074 Peoples R China

Edge intelligence (EI) is a promising paradigm where end devices collaborate with edge servers to provide artificial intelligence services to users. In most realistic scenarios, end devices often move unconsciously, resulting in frequent computing migrations. Moreover, a surge in computing tasks offloaded to edge servers significantly prolongs queuing latency. These two issues obstruct the timely completion of computing tasks in EI-assisted systems. In this paper, we formulate an optimization problem aiming to maximize computing task completion under latency constraints. To address this issue, we first categorize computing tasks into new computing tasks (NCTs) and partially completed computing tasks (PCTs). Subsequently, based on model partitioning, we design a new computing task saving scheme (NSS) to optimize early exit points for NCTs and computing tasks in the queuing queue. Furthermore, we propose a partially completed computing task saving scheme (PSS) to set early exit points for PCTs during computing migrations. Numerous experiments show that computing saving schemes can achieve at least 90% computing task completion rate and up to 61.81% latency reduction compared to other methods.

关键词： Computational modeling Servers Artificial intelligence Heuristic algorithms Performance evaluation partitioning algorithms Mathematical models Deep learning Computers Adaptation models Edge intelligence model early exit computing migration queuing latency

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Generalized Graph-Dependent Isolation of Collusive Attacks for Interconnected Systems

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第4期70卷 2274-2288页

作者： Zhao, Dan Ho, Daniel W. C. Wen, Guanghui Southeast Univ Dept Syst Sci Nanjing 211189 Peoples R China City Univ Hong Kong Dept Math Kowloon Hong Kong Peoples R China

This article addresses the isolation problem of collusive false data injection attacks and covert attacks for interconnected systems, particularly in establishing generalized graph-dependent conditions responsible for attack isolation. Two algorithms involving inclusive neighbor information and neighbor information are introduced to accomplish attack isolation. By exploring the mechanism of attack collusion, it is found that both the number of subsystems' one-hop neighbors and the size of cycles in the graph play significant roles in attack isolation. Besides, it is interesting to note that their impacts on attack isolation can be mutually compensated. Then, the worst attack collusion cases for the proposed algorithms under different cycle sizes are developed, based on which, several equivalent sufficient yet sharp graph conditions for the isolation of false data injection attacks and covert attacks are, respectively, provided with the notion of $(r,s)$-isolability. It is revealed that the inclusive neighbor information-based algorithm is more effective for isolating false data injection attacks. In contrast, the neighbor information-based algorithm is more suitable for isolating covert attacks. Finally, simulations demonstrate the effectiveness of the theoretical results.

关键词： Observers Vectors Interconnected systems Topology Couplings Power system dynamics partitioning algorithms Estimation Data models Urban areas Attack collusion attack isolation covert attacks false data injection attacks graph isolability interconnected systems

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Memristive Clustering: A Novel Sustainable Parameter Selection Based on Memristive Circuit Model

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING

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IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2025年第1期10卷 18-27页

作者： Qiao, Kaikai Ma, Ben Wang, Lidan Duan, Shukai Southwest Univ Coll Artificial Intelligence Chongqing 400715 Peoples R China Chongqing Key Lab Brain Inspired Comp & Intelligen Chongqing 400715 Peoples R China

In recent years, memristors have attracted much attention in the fields of nonvolatile memory, logic operation and neuromorphic computing. As a new type of two-terminal passive electronic component similar to sandwich structure, its main resistance mechanism is the formation and fracture of metal or oxygen vacancy conductive filaments. Traditional clustering algorithms own strong sensitivity to different parameter selection, including partition clustering algorithm and density clustering algorithm. In view of the non-volatile characteristics of memristor and the In-memory computing characteristics of memristive circuit, this paper designs a new memristive clustering paradigm, and further verifies the feasibility and effectiveness of the proposed analog circuit to improve the performance of clustering parameters by exploring the data mining and image segmentation problems of these two types of clustering algorithms.

关键词： Clustering algorithms partitioning algorithms Ions Resistance Programming Voltage Memristors Bilevel programming clustering method data mining in-memory computing optimization

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A Matheuristics for the Configuration of Automated Vertical Lift Modules Warehouses

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 7284-7295页

作者： Tresca, Giulia Cavone, Graziana Scarabaggio, Paolo Carli, Raffaele Dotoli, Mariagrazia Polytech Bari Dept Elect & Informat Engn I-70126 Bari Italy Roma Tre Univ Dept Civil Comp Sci & Aeronaut Technol Engn I-00146 Rome Italy

The design of the layout of Vertical Lift Module (VLM) warehouses is a non-trivial process that involves selecting dimensions, internal configuration, and allocation of each tray to avoid space loss while satisfying logistic constraints. Our contribution in this context is a two-phase matheuristics -an algorithm that combines exact mathematical methods and heuristics- to simplify the design of VLMs layout. The proposed matheuristics relies on three Mixed-Integer Linear Programming models, addressing the internal configuration of trays and the allocation of trays into columns based on industrial logistic constraints. This approach requires as input parameters the items features, predetermined tray types with different dimensions, matheuristic settings, and a priority rule for tray allocation. The algorithm outputs to the logistics operator types and quantities of trays needed, internal partitioning, item positions in each tray, and tray positions in each column. Extensive testing demonstrates the effectiveness of our approach under realistic scenarios. Additionally, we introduce a comprehensive set of priority rules for allocating trays into columns, providing a comparison to assist logistics operators in selecting the most suitable for specific scenarios. Note to Practitioners-VLMs are closed structures composed of columns that house a variable number of sliding trays and a lift-mounted module that handles the trays. Their operation revolves around the so-called "goods to the man" principle, where goods are automatically brought to the operator using a dedicated access bay. This design ensures that stored items are easily accessible to operators, reducing time for order picking and improving workplace safety and ergonomics. Designing a VLM for logistics companies is a complex and time-consuming task since it requires optimizing a target objective while satisfying a large set of constraints. The current manual approach lacks automated methods and relies on experience

关键词： Logistics Resource management Layout Throughput Software Biological system modeling Automation Software algorithms partitioning algorithms Mathematical models Automated warehouse logistics 4.0 vertical lift modules matheuristics

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An Efficient and Flexible Complexity Control Method for Versatile Video Coding

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IEEE TRANSACTIONS ON BROADCASTING 2025年第1期71卷 96-110页

作者： Zhao, Yan Zhu, Chen Xu, Jun Lu, Guo Song, Li Ma, Siwei Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence AI Inst Shanghai 200240 Peoples R China Peking Univ Natl Engn Res Ctr Visual Technol Sch Comp Sci Beijing 100871 Peoples R China

Recently, numerous complexity control approaches have been proposed to achieve the target encoding complexity. However, only few of them were developed for VVC encoders. This paper fills this gap by proposing an efficient and flexible complexity control approach for VVC. The support for both Acceleration Ratio Control (ARC) and Encoding Time Control (ETC) makes our method highly versatile for various applications. At first, we introduce a sequence-level complexity estimation model to merge the ARC and ETC tasks. Then, four key modules are involved for complexity control: complexity allocation, complexity estimation, encoding configuration decision, and feedback. Specifically, we hierarchically allocate the complexity budget to three coding levels: GOP, frame, and Basic Unit (BU). Each BU's allocation weight is decided by its SSIM distortion, whereby the perceptual quality can be ensured. The multi-complexity configurations are established by altering the partition depth and number of reference frames. Via tuning each BU's configuration according to its target acceleration ratio and adaptively updating the control strategies based on the feedback, our scheme can precisely realize any achievable acceleration targets within one-pass encoding. Moreover, each BU's un-accelerated reference encoding time, which is used to calculate its target acceleration ratio, is estimated by SVR models. Experiments prove that for both the ARC and ETC tasks, our scheme can precisely achieve a wide range of complexity targets (30% similar to 100%) with negligible RD loss in PSNR and SSIM, outperforming other state-of-the-art methods.

关键词： Complexity theory Encoding Estimation Resource management Visualization Standards partitioning algorithms Distortion Copper Video recording Versatile video coding (VVC) complexity control complexity estimation block partition motion estimation

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Vertical Federated Density Peaks Clustering Under Nonlinear Mapping

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第2期37卷 1004-1017页

作者： Li, Chao Ding, Shifei Xu, Xiao Guo, Lili Ding, Ling Wu, Xindong China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Peoples R China China Univ Min & Technol Sch Comp Sci & Technol Xuzhou Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ China Hefei 230009 Peoples R China

As the representative density-based clustering algorithm, density peaks clustering (DPC) has wide recognition, and many improved algorithms and applications have been extended from it. However, the DPC involving privacy protection has not been deeply studied. In addition, there is still room for improvement in the selection of centers and allocation methods of DPC. To address these issues, vertical federated density peaks clustering under nonlinear mapping (VFDPC) is proposed to address privacy protection issues in vertically partitioned data. Firstly, a hybrid encryption privacy protection mechanism is proposed to protect the merging process of distance matrices generated by client data. Secondly, according to the merged distance matrix, a more effective cluster merging under nonlinear mapping is proposed to ameliorate the process of DPC. Results on man-made, real, and multi-view data fully prove the improvement of VFDPC on clustering accuracy.

关键词： Clustering algorithms Protection Privacy Servers partitioning algorithms Fuzzy logic Federated learning Nearest neighbor methods Homomorphic encryption Polynomials Density peaks clustering (DPC) hybrid encryption similarity matrix vertical federated learning

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Fixed-Time Event-Triggered Bipartite Consensus of Multiagent Systems Under Time-Varying Disconnected Topologies

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年

作者： Liu, Xiaoyang He, Haibin Jiang, Zhuyan Fan, Lu Yu, Wenwu Jiangsu Normal Univ Sch Comp Sci & Technol Xuzhou 221116 Peoples R China Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Southeast Univ Sch Math Nanjing 210096 Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China

This article focuses on the fixed-time bipartite consensus problems of multiagent systems under time-varying disconnected topologies. Different from the jointly connected topology, an enhanced fixed-time local pinning algorithm is proposed to overcome challenges posed by disconnected signed networks without introducing additional topological assumptions. Especially, the motion tendencies of isolated agents in cooperative-competitive networks are thoroughly discussed. Event-triggered control with the impulsive effect is utilized to achieve the bipartite consensus of MASs with minimal energy consumption, where the total energy function does not need to be monotonic, and the Zeno behavior can be avoided. Finally, the efficacy of the designed protocol is demonstrated through two numerical examples.

关键词： Topology Event detection Network topology Sensors partitioning algorithms Multi-agent systems Control systems Protocols Cybernetics Time-varying systems Bipartite consensus disconnected topology fixed-time event-triggered control multiagent systems

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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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Cluster Optimization for Exponential, Right-Triangular, and Uniformly Distributed Data

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IEEE ACCESS 2025年 13卷 36273-36284页

作者： Bulivou, Gabiriele Reddy, Karuna G. Khan, M. G. M. Univ South Pacific Sch Informat Technol Engn Math & Phys Suva Fiji

Cluster analysis aims to categorize data objects into cohesive groups based on their intrinsic characteristics, often modeled by probability distributions. This paper presents a novel Mathematical Programming-Dynamic Programming (MP-DP) clustering method developed by the authors, applied to datasets characterized by exponential, right-triangular, and uniform distributions. The MP-DP technique optimizes cluster partitions by leveraging the probability distributions inherent in the data. We conducted a comparative evaluation to assess the performance of MP-DP against four established clustering methodologies: K-Means, Fuzzy C-Means, expectation-maximization, and Genie++ hierarchical clustering. Results from extensive simulations and real-world datasets consistently demonstrate the superior efficacy of MP-DP in achieving optimal clustering outcomes. Specifically, MP-DP excels in handling diverse data distributions and effectively mitigating the effects of noise and uncertainty, thereby enhancing clustering accuracy and reliability. This study highlights the significant advancement offered by MP-DP in clustering research. It underscores the method's potential for applications across various domains, such as healthcare, environmental monitoring, and manufacturing, where robust and efficient data clustering is essential for insightful data analysis and decision-making.

关键词： Clustering algorithms Probability distribution Dynamic programming Programming Noise Clustering methods partitioning algorithms Heuristic algorithms Distributed databases Uncertainty Cluster analysis dynamic programming technique mathematical programming problem optimum cluster partitions probability distribution function

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