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

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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Deep Spiking Neural Networks Driven by Adaptive Interval Membrane Potential for Temporal Credit Assignment Problem

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第1期9卷 717-728页

作者： Jiang, Jiaqiang Ding, Haohui Wang, Haixia Yan, Rui Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Peoples R China

One of the core challenges in biological and machine learning is how neural networks can discover the predictive features hidden in distracting streams of unrelated sensory activity via delayed feedback, commonly known as the "temporal credit-assignment (TCA) problem". By matching the output spikes of neurons to the number of features, the aggregated-label (AL) learning is a promising approach to target this problem. However, existing mainstream AL learning algorithms either only focus on a few information of features or consider the imprecise temporal loss. In this paper, we propose a novel adaptive interval membrane potential driven aggregated-label learning algorithm, named IMPD-AL. This algorithm not only captures the adaptive interval temporal information but also integrates the loss of all spike firing times in the precise interval most relevant to features. Experimental results on speech recognition (TIDIGITS), dynamic digit recognition (N-MNIST), dynamic image recognition (CIFAR10-DVS), and dynamic gesture recognition (DVS128 Gesture) show that the IMPD-AL algorithm outperforms the state-of-the-art (SOTA) AL algorithm in SNNs on both classification task and TCA task.

关键词： Neurons clustering algorithms Membrane potentials Task analysis Training Computational modeling Heuristic algorithms Temporal credit-assignment problem aggregate-label learning spiking neural network membrane potential driven synaptic plasticity

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Underwater Multiple AUV Cooperative Target Tracking Based on Minimal Reward Participation-Embedded MARL

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第5期24卷 4169-4182页

作者： Zhu, Shengchao Han, Guangjie Lin, Chuan Zhang, Fan Hohai Univ Coll Comp & Software Engn Nanjing 210013 Peoples R China Hohai Univ Key Lab Maritime Intelligent Network Informat Tech Minist Educ Nanjing 210013 Peoples R China Northeastern Univ Software Coll Shenyang 110819 Peoples R China Hohai Univ Coll Informat Sci & Engn Changzhou 213200 Peoples R China

Recently, the rapid advancement of Multi-Agent Reinforcement Learning (MARL) has introduced a new paradigm for intelligent underwater target tracking within Autonomous Underwater Vehicle (AUV) cluster networks, enabling these networks to intelligently collaborate in target tracking. However, the limited scalability of MARL poses significant challenges to the performance of AUV cluster networks in tracking tasks. Specifically, MARL models trained on a fixed agents lose their effectiveness when the agent count changes, underscoring the critical need to enhance MARL's scalability to accommodate an arbitrary number of agents. This paper addresses the pressing issue of MARL's scalability in the context of AUV cluster network-based target tracking. Specifically, we propose an Elastic Software-Defined Multi-Agent Reinforcement Learning (ESD-MARL) architecture to enhance the scalability of AUV cluster networks. Moreover, we propose an Incremental Multi-Agent Reinforcement Learning algorithm based on Minimal Reward Participation (IMARL-MRP) that allows for the expansion of the agents without retraining. By integrating the ESD-MARL with the IMARL-MRP, we propose an elastic underwater target tracking scheme, achieving high-performance target tracking with enhanced scalability. Evaluation results demonstrate that the proposed approach effectively enhances the scalability of MARL, enabling the arbitrary expansion of the AUV cluster network, thus supporting scalable and efficient underwater target tracking.

关键词： Scalability Target tracking Computer architecture clustering algorithms Reinforcement learning Training Mobile computing Network architecture Mathematical models Heuristic algorithms Multi-agent reinforcement learning autonomous underwater vehicle underwater target tracking software-defined networking

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Channel Knowledge Map-Aided Channel Prediction With Measurements-Based Evaluation

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IEEE TRANSACTIONS ON COMMUNICATIONS 2025年第5期73卷 3622-3636页

作者： Wang, Xianling Shi, Yi Wang, Tianci Huang, Yingyujiao Hu, Zeyu Chen, Lin Jiang, Zhiyuan Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ 07030 USA

Gaining accurate channel state information (CSI) through a low-cost scheme has always been difficult in wireless communication systems. One of the current research directions is to obtain the CSI from the channel knowledge map (CKM) based on the users' location. However, the direct utilization of CSI in CKM is hindered due to the sensitivity of instantaneous CSI to time-varying scattering environments and positioning errors. To address this issue, this paper proposes a channel prediction scheme that combines the CKM with historical user CSI to enhance the beamforming performance in multiple-input multiple-output (MIMO) systems. Specifically, the joint-orthogonal matching pursuit algorithm is used to accurately reconstruct the user channel with high precision using a limited number of pilots, and the multi-path components tracking algorithm is employed to extract the common and independent support sets of paths from the estimated channel and the CKM. Lastly, an adaptive and low-complexity predictor is utilized to obtain the future user CSI. The proposed scheme has been evaluated using multiple measured channel datasets, the results indicate a significant improvement in predicting channel cosine similarity compared to directly using the CSI from CKM and existing schemes.

关键词： Channel estimation Prediction algorithms clustering algorithms Wireless communication Vectors Matching pursuit algorithms Array signal processing Accuracy Time-frequency analysis Predictive models Channel knowledge map channel prediction channel sounding campaign joint-orthogonal matching pursuit MIMO

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Dual AAV Cluster-Assisted Maritime Physical-Layer Secure Communications via Collaborative Beamforming

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IEEE INTERNET OF THINGS JOURNAL 2025年第9期12卷 12589-12607页

作者： Huang, Jiawei Wang, Aimin Sun, Geng Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Nanyang Technol Univ Coll Comp & Data Sci Singapore 639798 Singapore Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China

Autonomous aerial vehicles (AAVs) can be utilized as relay platforms to assist maritime wireless communications. However, complex channels and multipath effects at sea can adversely affect the quality of AAV transmitted signals. Collaborative beamforming (CB) can enhance the signal strength and range to assist the AAV relay for remote maritime communications. However, due to the open nature of AAV channels, security issue requires special consideration. This article proposes a dual AAV cluster-assisted system via CB to achieve physical-layer security in maritime wireless communications. Specifically, one AAV cluster forms a maritime AAV-enabled virtual antenna array (MUVAA) relay to forward data signals to the remote legitimate vessel, and the other AAV cluster forms an MUVAA jammer to send jamming signals to the remote eavesdropper. In this system, we formulate a secure and energy-efficient maritime communication multiobjective optimization problem (SEMCMOP) to maximize the signal-to-interference-plus-noise ratio (SINR) of the legitimate vessel, minimize the SINR of the eavesdropping vessel and minimize the total flight energy consumption of AAVs. Since the SEMCMOP is an NP-hard and large-scale optimization problem, we propose an improved swarm intelligence optimization algorithm with chaotic solution initialization and hybrid solution update strategies to solve the problem. Simulation results indicate that the proposed algorithm outperforms other comparison algorithms, and it can achieve more efficient signal transmission by using the CB-based method.

关键词： Autonomous aerial vehicles Jamming Optimization Wireless communication Relays Maritime communications Security Eavesdropping clustering algorithms Antenna arrays Autonomous aerial vehicles (AAVs)-assisted collaborative beamforming maritime communications multiobjective optimization physical-layer secure

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An Interval-Probability-Based Distribution System State Estimation Quantification Framework Considering Nonlinear Correlations of Uncertain Distributed Generators With Limited Information

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IEEE TRANSACTIONS ON POWER SYSTEMS 2025年第3期40卷 2349-2362页

作者： Liu, Bi Wang, Huaifeng Huang, Qi Xu, Lijia Sichuan Agr Univ Coll Mech & Elect Engn Yaan 625014 Peoples R China Southwest Univ Sci & Technol SWUST Mianyang 621010 Peoples R China Univ Elect Sci & Technol China UESTC Chengdu 611731 Peoples R China

The distribution system state estimation (DSSE) is critical for the operation and control of electric distribution systems, but faces significant challenges due to the integration of distributed generators (DGs). The existing uncertain DSSE frameworks struggle with managing correlations, particularly nonlinear correlations among DGs, and it is exacerbated by limited available observations of DGs in practical distribution systems. In light of these problems, initially, this paper utilizes the partition around medoids clustering algorithm and evidence theory to propose a joint Dempster-Shafer (DS) structure for modeling the multiple DGs with limited information, while accounting for their nonlinear correlations. The entire uncertainty hyperspace of DGs is partitioned into a specific number of sub-hyperspaces with corresponding basic probability assignments, according to the limited observations. Subsequently, the uncertainties of DGs are propagated to DSSE outputs by integrating affine arithmetic with evidence theory and multidimensional parallelepiped model, while facilitating further correlation characterization among DGs. Eventually, a probability box (P-box) about each DSSE output, comprising finite intervals with interval probabilities, can be achieved for demonstrating its uncertainty. The proposed interval-probability-based DSSE framework's effectiveness, accuracy, computational efficiency, and scalability are validated through comprehensive tests across various distribution systems.

关键词： Uncertainty Correlation Mathematical models clustering algorithms Computational modeling Probabilistic logic Generators Accuracy State estimation Computational efficiency Uncertain distribution system state estimation Distributed generators Limited information Nonlinear correlations Interval probability

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MCC: Multi-Cluster Contrastive Semi-Supervised Segmentation Framework for Echocardiogram Videos

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IEEE ACCESS 2025年 13卷 30543-30554页

作者： Chen, Yu-Jen Lin, Shr-Shiun Shi, Yiyu Ho, Tsung-Yi Xu, Xiaowei Natl Tsing Hua Univ Dept Comp Sci Hsinchu 300044 Taiwan Chung Shan Med Univ Hosp Dept Emergency Med Taichung 40201 Taiwan Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Southern Med Univ Guangdong Prov Peoples Hosp Guangdong Acad Med Sci Guangzhou 510080 Peoples R China

Variability in sonographer expertise often leads to low-quality ultrasound imaging, presenting significant challenges for accurate echocardiogram video segmentation. Current methods require extensive annotations, which are impractical given the large number of frames and artifacts in videos. To address this, we propose a Multi-Cluster Contrastive (MCC) learning framework, a semi-supervised approach that minimizes annotation requirements while maintaining high segmentation performance. Leveraging contrastive loss to enhance foreground feature extraction, our method incorporates multi-cluster contrastive loss to utilize multiple annotated ground-truths per batch and an anchor frame selection algorithm to improve segmentation performance. Experimental results on two public echocardiography datasets (MCE and EchoNet-Dynamic) demonstrate the effectiveness of our method, achieving state-of-the-art performance. The MCC framework enhances segmentation practicality by reducing annotation requirements, particularly for developing new datasets, and facilitates efficient segmentation of low-quality echocardiogram videos. Our implementation is available at https://***/windstormer/MCC.

关键词： Videos Annotations Image segmentation Noise Myocardium Adaptation models Training Echocardiography clustering algorithms Ultrasonic imaging Semisupervised learning object segmentation echocardiography self-supervised learning

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Joint Correlations Sparse Bayesian Learning STAP With Prior Knowledge of Clutter Ridge

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 6820-6832页

作者： Cui, Junhao Chen, Zhangxin Liang, Jing Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

Space-time adaptive processing (STAP) based on sparse Bayesian learning (SBL) can significantly improve clutter suppression performance utilizing clutter sparsity. However, the existing SBL-STAP algorithms lack full use of correlations, which leads to unsatisfactory performance and slow convergence speed. In this article, we propose a joint correlations SBL-STAP (JCSBL-STAP) algorithm to improve clutter suppression performance. It comes from a rational idea that the clutter ridge in the space-time domain is not only the origin of clutter sparsity, but also the origin of correlations. Normally, the amplitude of scatterers along the clutter ridge are correlated between multiple samples and have clustered correlation properties in each sample. The JCSBL-STAP algorithm utilizes a joint correlations sparse prior to exploiting both correlations and provides a multisample correlation decoupling framework to update hyperparameters. The algorithm is executed on a proposed hybrid prior dictionary. Compared with the conventional uniform dictionary, the hybrid prior dictionary can easily express the clustered correlation properties and effectively alleviates the off-grid problem. Experimental results confirm the performance of the proposed method on both simulated data and measured Mountain-Top data.

关键词： Clutter Correlation Vectors Training clustering algorithms Dictionaries Doppler effect Bayes methods Sparse matrices Remote sensing Dictionary design joint correlations prior knowledge of clutter ridge space-time adaptive processing (STAP) sparse Bayesian learning (SBL)

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Reinforcing TSK Model Interpolation With Rule Weight Adjustment via Location View Analysis

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2025年第2期33卷 580-592页

作者： Jiang, Changhong Shang, Changjing Shen, Qiang Aberystwyth Univ Dept Comp Sci Aberystwyth SY23 3FL Wales

Fuzzy rule interpolation (FRI) has been successfully applied to address real-world problems where domain knowledge is incomplete, particularly in situations where observations do not directly match existing rules. However, most of the existing research focuses on fuzzy systems based on Mamdani models. Recently, a pioneering approach has been proposed for rule interpolation within Takagi-Sugeno-Kang (TSK) fuzzy models, employing two distinct techniques that cluster neighboring rules to facilitate interpolated outcomes. Although promising, these techniques have been developed through empirical methods, lacking formal representation and theoretical justification. This article formalizes such emerging empirical studies with the aim of strengthening the theoretical foundation of FRI. It introduces the location view of rules and rule bases regarding TSK models, analyzing the influence of individual rules within FRI techniques for such models. Through geometrizing sparse rule bases, comparative investigations of the empirical and theoretical results are carried out. Moreover, geometric projection of the rules is combined with a rule weight adjustment mechanism to reinforce the capability of the formalized FRI techniques. By embedding directional parameters into the rules, the resulting method increases the weighting of critical rules, strengthening the FRI algorithms' performance when many rules may be selected for interpolation. Experimental results confirm the effectiveness of the theoretical model and the consistency with the existing empirical findings. This highlights the potential and significance of formal studies in guiding algorithmic improvements on FRI with TSK models.

关键词： Interpolation Analytical models Fuzzy systems clustering algorithms Euclidean distance Data mining Complexity theory Cognition Takagi-Sugeno model Medical services Formalization fuzzy rule interpolation location view rule projection TSK model

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First Arrival Picking of Aircraft-Excited Seismic Waves Based on Energy Distribution

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Li, Shuang Jiang, Tao Pang, Zhongqin Jilin Univ Coll Instrumentat & Elect Engn Minist Educ China Changchun 130026 Peoples R China Jilin Univ Minist Educ China Key Lab Geophys Explorat Equipment Changchun 130026 Peoples R China

When the acoustic-to-seismic coupling effect is used to detect low-altitude aircrafts, picking up the exact first arrival of the seismic waves excited by aircrafts directly affects the accuracy of the detection. There are random noise and frequent impulse noise in seismic data collection due to wind blowing, grass hitting, and so on. The existing seismic wave picking algorithms are sensitive to the impulse noise, which leads to high false picking rate. In order to solve this problem, first arrival picking of aircraft-excited seismic waves based on energy distribution (ED) is proposed in this article. Compared with the commonly used pickup algorithms, ED can avoid the false pickup caused by single impulse noise and effectively reduce the false picking caused by impulse noise clusters consisted of multiple consecutive single impulse noise. ED ensures both high accuracy and high computational efficiency. More importantly, the actual collected data may contain multiple aircraft events, and ED can pick up all the aircraft events at the same time and does not need to intercept each aircraft signal one by one. By picking up the actual data, the effectiveness and practicability of ED are further verified. The advantages and limitations of the ED algorithm are also discussed to provide future research directions.

关键词： Couplings Accuracy Noise clustering algorithms Interference Data collection Aerospace electronics Computational efficiency Seismic waves Aircraft Acoustic-to-seismic coupling aircraft detection energy distribution (ED) first-arrival picking impulse noise cluster

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