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Research on Parallel K-Medoids algorithm based on MapReduce 认领 引用
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作者 Xianli QIN 《International Journal of Technology Management》 2015年第1期26-28,共3页
In order to solve the bottleneck problem of the traditional K-Medoids clustering algorithm facing to deal with massive data information at the time of memory capacity and processing speed of CPU, the paper proposed a ... In order to solve the bottleneck problem of the traditional K-Medoids clustering algorithm facing to deal with massive data information at the time of memory capacity and processing speed of CPU, the paper proposed a parallel algorithm MapReduce programming model based on the research of K-Medoids algorithm. This algorithm increase the computation granularity and reduces the communication cost ratio based on the MapReduce model. The experimental results show that the improved parallel algorithm compared with other algorithms, speedup and operation efficiency is greatly enhanced. 展开更多
关键词 K-Medoids MapReduce Parallel computing Hadoop
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基于改进k-medoids聚类和碳约束的变压器状态异常大数据诊断方法研究 认领 引用
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作者 宋金伟 李俊妮 +1 位作者 宣东海 吴海涵 《电测与仪表》 CSCD 北大核心 2026年第5期20-29,共10页
为了进一步提升碳约束型变压器运行状态的诊断准确率和诊断效率,基于改进k-medoids聚类理论提出了变压器状态异常诊断方法,将变压器运行状态划分为正常、预警、异常、故障四种状态作为k-medoids聚类目标。模型获得变压器运行状态大数据... 为了进一步提升碳约束型变压器运行状态的诊断准确率和诊断效率,基于改进k-medoids聚类理论提出了变压器状态异常诊断方法,将变压器运行状态划分为正常、预警、异常、故障四种状态作为k-medoids聚类目标。模型获得变压器运行状态大数据,其中声纹特征指标采用梅尔倒谱特征作为衡量变压器异常振动声响的标准,对变压器运行大数据基于其物理特性进行归一化和标准化,便于聚类模型输入,针对传统k-medoids聚类过程引入三项措施进行改进,分别为基于局部密度评估的初始聚类中心生成、自适应替代方向调整以及聚类更新的混沌搜索,通过改进措施优化初始聚类中心生成并提升寻优效率,避免寻优过早陷入局部最优聚类点,将某地区碳约束型电网变压器历史运行大数据划分为训练组和测试组,基于训练组来进行k-medoids聚类网络的优化,并将训练完成的k-medoids聚类用于测试组的变压器运行状态聚类和诊断,验证了所提出模型的正确性和有效性。 展开更多
关键词 k-medoids聚类 变压器状态诊断 声纹特征 局部密度评估 自适应替代 混沌搜索
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基于暂稳态多特征融合与K-Medoids聚类的故障选线方法 认领 引用
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作者 包佳辉 李红月 《黑龙江工业学院学报（综合版）》 2026年第2期118-123,共6页
针对小电流接地系统单相故障选线易受过渡电阻及噪声干扰等问题,提出一种基于暂稳态多特征融合与K-Medoids聚类的选线方法。该方法选取暂态幅值比、基波相位差、波形相关系数、小波能量熵及5次谐波含量这5种具有互补性的特征构建特征集... 针对小电流接地系统单相故障选线易受过渡电阻及噪声干扰等问题,提出一种基于暂稳态多特征融合与K-Medoids聚类的选线方法。该方法选取暂态幅值比、基波相位差、波形相关系数、小波能量熵及5次谐波含量这5种具有互补性的特征构建特征集,并进行标准化处理。在此基础上,采用K-Medoids算法对线路进行聚类分析,并设计“簇大小比较-最大距离”双重判据以精准辨识故障线路。仿真验证表明,在不同故障初相角、过渡电阻、故障距离及噪声干扰下均能准确选线,有效克服了单一特征方法的局限性,具有较强的鲁棒性与适用性。 展开更多
关键词 K-Medoids聚类 小电流接地系统 多特征融合 故障选线
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融合K-medoids聚类算法的多维数据处理技术 认领 引用
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作者 郭宏文 李晖 +2 位作者 郑灶贤 刘浩 刘鑫 《信息技术》 2026年第3期194-198,共5页
针对传统算法在处理各类电网数据中存在异常数据识别准确度低以及校核能力差等问题,文中设计了一种融合K-medoids聚类和多维数据处理技术的改进算法。通过K-medoids聚类算法对数据进行分析,并划分为不同的簇,在每个簇内利用多维数据处... 针对传统算法在处理各类电网数据中存在异常数据识别准确度低以及校核能力差等问题,文中设计了一种融合K-medoids聚类和多维数据处理技术的改进算法。通过K-medoids聚类算法对数据进行分析,并划分为不同的簇,在每个簇内利用多维数据处理技术对数据特征进行校核,然后再对异常数据进行识别。利用多维数据之间的关系自主检查数据的一致性和准确性,对数据异常值进行分析与标记,提高了对异常数据的识别能力。在MATLAB中以电力财务数据为样本对改进算法进行验证,结果显示所提算法的异常数据识别准确率稳定在95%以上,识别效率为96.5%。 展开更多
关键词 K-medoids聚类算法 多维数据处理 电力数据 模糊数据处理算法
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基于K-medoids聚类的分布式光伏集群方法及短期功率预测 认领 引用
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作者 蒋亚雪 钱晶 +3 位作者 何昊城 张皓彦 曹雷 毛西蒙 《太阳能学报》 EI CAS CSCD 北大核心 2026年第6期645-655,共11页
为实现对光伏集群的科学聚类划分,该文融合考虑气象因素和地理位置对光伏电站聚类的影响,将出力特性作为子集群的划分特征,提出一种基于改进曼哈顿距离的K中心点聚类算法(K-中心点)。光伏集群聚类的两个关键是气象特征的有效识别和各电... 为实现对光伏集群的科学聚类划分,该文融合考虑气象因素和地理位置对光伏电站聚类的影响,将出力特性作为子集群的划分特征,提出一种基于改进曼哈顿距离的K中心点聚类算法(K-中心点)。光伏集群聚类的两个关键是气象特征的有效识别和各电站的相似性度量,为此采用变异系数法与秩和比法相结合对气象输入特征进行提取,可提高特征的识别有效性。另一方面将改进曼哈顿距离引入K-中心点聚类算法中,充分挖掘数据的动态特性,并采用3种内部有效指标优化聚类划分数,避免常规划分带来的分散性和偏差,提高聚类的整体效果。此外,在聚类基础上进行预测验证,为解决预测模型的参数设置问题,在预测模型中融入优化算法,采用改进灰狼优化算法(IGWO)对iTransformer和极度梯度提升(XGBoost)模型中的参数进行优化,功率预测结果表明所提聚类算法的有效性以及较高的精度。 展开更多
关键词 光伏发电 聚类分析 功率预测 改进灰狼优化算法 iTransformer 极度梯度提升
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基于改进MPE和K-medoids的变压器绕组松动故障诊断 认领 引用 被引量：2
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作者 马宏忠 薛健侗 +2 位作者 倪一铭 万可力 迮恒鹏 《高压电器》 CAS CSCD 北大核心 2025年第9期73-80,共8页
为了更加有效地对变压器绕组松动故障进行诊断,针对变压器有载运行时的振动信号,提出了一种基于改进多尺度排列熵(MPE)和K-medoids的变压器绕组松动故障诊断方法。首先采用粒子群优化(PSO)的MPE算法对绕组不同状态下的变压器振动信号进... 为了更加有效地对变压器绕组松动故障进行诊断,针对变压器有载运行时的振动信号,提出了一种基于改进多尺度排列熵(MPE)和K-medoids的变压器绕组松动故障诊断方法。首先采用粒子群优化(PSO)的MPE算法对绕组不同状态下的变压器振动信号进行特征提取,以减少MPE算法中参数设置对故障类型识别精度的影响,然后通过K-medoids聚类算法诊断变压器绕组松动故障,以完成故障的分类识别。对某10 kV变压器的绕组松动故障模拟实验结果表明,绕组不同状态下变压器振动信号的MPE值经PSO参数优化后存在明显差异,诊断效果优于传统经验设置参数的MPE算法,且稳定性得到提高。 展开更多
关键词 变压器 绕组松动诊断 粒子群优化的MPE算法 特征提取 K-medoids算法
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基于K-medoids-GBDT-PSO-LSTM组合模型的短期光伏功率预测 认领 引用 被引量：12
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作者 戴朝辉 陈昊 +3 位作者 刘莘轶 夏长青 郭嘉毅 于立军 《太阳能学报》 EI CAS CSCD 北大核心 2025年第1期654-661,共8页
为保障电网供需平衡和安全稳定运行,提高大型光伏电站功率预测的精度,提出一种基于K中心点聚类算法(K-medoids)、梯度提升树(GBDT)和粒子群优化算法(PSO)组合优化的长短期记忆神经网络(LSTM)的光伏功率短期预测模型。首先,采用K-medoid... 为保障电网供需平衡和安全稳定运行,提高大型光伏电站功率预测的精度,提出一种基于K中心点聚类算法(K-medoids)、梯度提升树(GBDT)和粒子群优化算法(PSO)组合优化的长短期记忆神经网络(LSTM)的光伏功率短期预测模型。首先,采用K-medoids聚类算法对大规模光伏发电数据样本中的天气数据进行不同类别聚类,分为晴天、阴天和雨/雪天3种天气类型;然后,在已有数据基础上构造特征工程,使用GBDT算法分别进行特征重要性分析,筛选出对光伏功率预测具有显著影响的特征,并构建合适大小结构的优化数据集;最后,将重构后的数据集代入PSO算法优化的LSTM模型进行训练,以建立短期预测模型。实验结果表明,该模型拥有更高预测精度,相比单一LSTM模型,在雨/雪天下的RMSE指标降低了12.19%。 展开更多
关键词 光伏发电 功率预测 机器学习 长短期记忆网络 优化算法 粒子群算法
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Study on the destabilizing damage precursors of cemented tailings backfill based on critical slowing down theory combined with multiple denoising algorithms under consideration of initial defect conditions 认领 引用 被引量：1
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作者 ZHAO Kang ZHONG Jun-cheng +3 位作者 YAN Ya-jing LIU Yang WEN Dao-tan XIAO Wei-ling 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第1期375-399,共25页
The cemented tailings backfill(CTB)with initial defects is more prone to destabilization damage under the influence of various unfavorable factors during the mining process.In order to investigate its influence on the... The cemented tailings backfill(CTB)with initial defects is more prone to destabilization damage under the influence of various unfavorable factors during the mining process.In order to investigate its influence on the stability of underground mining engineering,this paper simulates the generation of different degrees of initial defects inside the CTB by adding different contents of air-entraining agent(AEA),investigates the acoustic emission RA/AF eigenvalues of CTB with different contents of AEA under uniaxial compression,and adopts various denoising algorithms(e.g.,moving average smoothing,median filtering,and outlier detection)to improve the accuracy of the data.The variance and autocorrelation coefficients of RA/AF parameters were analyzed in conjunction with the critical slowing down(CSD)theory.The results show that the acoustic emission RA/AF values can be used to characterize the progressive damage evolution of CTB.The denoising algorithm processed the AE signals to reduce the effects of extraneous noise and anomalous spikes.Changes in the variance curves provide clear precursor information,while abrupt changes in the autocorrelation coefficient can be used as an auxiliary localization warning signal.The phenomenon of dramatic increase in the variance and autocorrelation coefficient curves during the compression-tightening stage,which is influenced by the initial defects,can lead to false warnings.As the initial defects of the CTB increase,its instability precursor time and instability time are prolonged,the peak stress decreases,and the time difference between the CTB and the instability damage is smaller.The results provide a new method for real-time monitoring and early warning of CTB instability damage. 展开更多
关键词 initial defects cemented tailings backfill critical slowing down acoustic emission RA/AF values denoising algorithms
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Optimization of the frequency offset increment of FDA-MIMO based on cuckoo search algorithm 认领 引用 被引量：1
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作者 WANG Bo ZHAO Yu +2 位作者 LI Yonglin YANG Rennong XUE Junjie 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第1期157-170,共14页
Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic e... Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic environments.The effectiveness of interference suppression by FDA-MIMO is limited by the inherent range-angle coupling issue in the FDA beampattern.Existing literature primarily focuses on control methods for FDA-MIMO radar beam direction under the assumption of static beampatterns,with insufficient exploration of techniques for managing nonstationary beam directions.To address this gap,this paper initially introduces the FDA-MIMO signal model and the calculation formula for the FDA-MIMO array output using the minimum variance distortionless response(MVDR)beamformer.Building on this,the problem of determining the optimal frequency offset for the FDA is rephrased as a convex optimization problem,which is then resolved using the cuckoo search(CS)algorithm.Simulations confirm the effectiveness of the proposed approach,showing that the frequency offsets obtained through the CS algorithm can create a dot-shaped beam direction at the target location while effectively suppressing interference signals within the mainlobe. 展开更多
关键词 frequency diverse array multiple-input multiple-output(FDA-MIMO) convex optimization cuckoo search algorithm beampattern
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Low-complexity APSK demodulation algorithm based on K-means clustering in LEO satellite communication systems 认领 引用
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作者 Guangfu Wu Xiangrui Meng +1 位作者 Changlin Chen Biqun Xiang 《Digital Communications and Networks》 SCIE EI CSCD 2026年第2期343-353,共11页
Amplitude Phase Shift Keying(APSK)is more suitable for the nonlinear channels of Low Earth Orbit(LEO)satellite communication systems compared to Quadrature Amplitude Modulation(QAM).To tackle challenges posed by Direc... Amplitude Phase Shift Keying(APSK)is more suitable for the nonlinear channels of Low Earth Orbit(LEO)satellite communication systems compared to Quadrature Amplitude Modulation(QAM).To tackle challenges posed by Direct Current(DC)interference and high demodulation complexity,we propose an APSK demodulation algorithm based on K-means clustering.Initially,static DC components are calculated and removed from the received APSK signals.Subsequently,the estimated APSK constellation points serve as initial centers for K-means clustering.These centers are refined through the K-means process and act as theoretical APSK constellation points for the Max-Log-MAP demodulation algorithm,effectively eliminating residual DC.We then introduce a low-complexity APSK demodulation algorithm that utilizes the symmetry of constellation points along with the Euclidean distance between DC-eliminated signals and these constellation points to minimize the set of constellation points.Simulation results indicate that for 32-APSK,our proposed demodulation submodule reduces computational complexity to approximately one-third that of the Max-Log-MAP algorithm while improving Bit Error Rate(BER)performance by about 0.23 dB.Furthermore,end-to-end simulation experiments conducted within LEO satellite communication systems demonstrate that our approach not only maintains this complexity advantage but also enhances BER performance by approximately 1.1 dB. 展开更多
关键词 DC elimination APSK demodulation LEO satellite communication K-means algorithm Max-Log-MAP algorithm
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基于K-Medoids聚类的上市造纸企业业绩分析 认领 引用
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作者 舒服华 《中华纸业》 CAS 2025年第8期24-28,共5页
我国上市造纸企业的经营业绩良莠不齐,对其进行聚类分析可以揭示它们之间的差异和特点,为其制定有效的经营管理策略提供参考,以帮助企业提高经营效益。K-Medoids算法克服了K-Means容易受到异常值或畸形分布的影响导致分类不精确的缺点,... 我国上市造纸企业的经营业绩良莠不齐,对其进行聚类分析可以揭示它们之间的差异和特点,为其制定有效的经营管理策略提供参考,以帮助企业提高经营效益。K-Medoids算法克服了K-Means容易受到异常值或畸形分布的影响导致分类不精确的缺点,抗干扰能力强,分类质量好。运用K-Medoids算法对我国部分上市造纸企业的经营业绩进行了聚类分析,15家公司的经营业绩被划分为6个类别,并对这些类别的整体经营业绩的优劣进行了排序。 展开更多
关键词 上市造纸企业 经营业绩 聚类分析 K-Medoids算法
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A Metaheuristic Football Optimization Algorithm Integrated with Large Language Models for Automated Seismic Time-Series Modeling 认领 引用
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作者 Amal H.Alharbi Marwa M.Eid +2 位作者 Nima Khodadadi Ebrahim A.Mattar Sayed Elkenawy 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期947-987,共41页
Seismic time series forecasting remains challenging due to the nonlinearity,non-stationarity,and noise of earthquake data,and because deep learning models are sensitive to preprocessing and hyperparameter settings.Alt... Seismic time series forecasting remains challenging due to the nonlinearity,non-stationarity,and noise of earthquake data,and because deep learning models are sensitive to preprocessing and hyperparameter settings.Although recent studies have improved neural architectures and optimization techniques,preprocessing is often treated as a fixed or manually designed stage,with limited integration into model optimization.To address this,this paper proposes an integrated,data-driven modelling framework that combines guided preprocessing with systematic hyperparameter optimization for seismic prediction,specifically forecasting earthquake magnitude from seismic catalog time-series data,with experiments conducted on Canadian seismic records.The method uses a Large Language Model to guide data preparation and feature engineering,rather than fully automate them,and applies deep learning-based forecasting with the N-HITS architecture,optimized via metaheuristic-assisted feature selection and hyperparameter tuning.The Football Optimization Algorithm(FbOA),employed as a metaheuristic optimization strategy in this study,is evaluated and compared with several well-known optimizers under identical conditions.The results show significant performance gains,with FbOA achieving superior accuracy,robustness,and convergence compared to baseline and competing methods.Notably,error metrics are reduced(MSE 3.10×10-7,RMSE 5.57×103),with high performance indicators(r=0.982,R2=0.979,NSE=0.981,WI=0.985).These results highlight the value of integrating guided preprocessing with optimization and demonstrate a scalable framework for high-precision time-series prediction in geophysical and related domains. 展开更多
关键词 Seismic time-series forecasting large language models metaheuristic algorithms football optimization algorithm earthquake modeling
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A Quantum-Inspired Algorithm for Clustering and Intrusion Detection 认领 引用
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作者 Gang Xu Lefeng Wang +5 位作者 Yuwei Huang Yong Lu Xin Liu Weijie Tan Zongpeng Li Xiu-Bo Chen 《Computers, Materials & Continua》 SCIE EI 2026年第4期1180-1215,共36页
The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,convention... The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,conventional clustering-based methods face notable drawbacks,including poor scalability in handling high-dimensional datasets and a strong dependence of outcomes on initial conditions.To overcome the performance limitations of existing methods,this study proposes a novel quantum-inspired clustering algorithm that relies on a similarity coefficient-based quantum genetic algorithm(SC-QGA)and an improved quantum artificial bee colony algorithm hybrid K-means(IQABC-K).First,the SC-QGA algorithmis constructed based on quantum computing and integrates similarity coefficient theory to strengthen genetic diversity and feature extraction capabilities.For the subsequent clustering phase,the process based on the IQABC-K algorithm is enhanced with the core improvement of adaptive rotation gate and movement exploitation strategies to balance the exploration capabilities of global search and the exploitation capabilities of local search.Simultaneously,the acceleration of convergence toward the global optimum and a reduction in computational complexity are facilitated by means of the global optimum bootstrap strategy and a linear population reduction strategy.Through experimental evaluation with multiple algorithms and diverse performance metrics,the proposed algorithm confirms reliable accuracy on three datasets:KDD CUP99,NSL_KDD,and UNSW_NB15,achieving accuracy of 98.57%,98.81%,and 98.32%,respectively.These results affirm its potential as an effective solution for practical clustering applications. 展开更多
关键词 Intrusion detection clustering quantum artificial bee colony algorithm K-means quantum genetic algorithm
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Structured Random Cycle-Guided Algorithm (SRCA): An Adaptive Metaheuristic Combining Directionally-Guided and Stochastic Search Strategies 认领 引用
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作者 Giuseppe Marannano Antonino Cirello Tommaso Ingrassia 《Computers, Materials & Continua》 SCIE EI 2026年第6期1873-1898,共26页
In response to the growing need for adaptive optimization algorithms capable of handling complex,multimodal,and high-dimensional search spaces,this paper introduces the Structured Random Cycle-guided Algorithm(SRCA).S... In response to the growing need for adaptive optimization algorithms capable of handling complex,multimodal,and high-dimensional search spaces,this paper introduces the Structured Random Cycle-guided Algorithm(SRCA).SRCA is not presented as a fundamentally new optimization paradigm,but rather as an architectural synthesis and a unified adaptive framework for dynamic operator selection.Based on a cycle-structured architecture,directional and stochastic search behaviors are dynamically selected at the individual level.The algorithm orchestrates well-established structured movements with a diverse pool of stochastic exploration strategies,enabling a coherent and adaptive balance between exploration and exploitation throughout the optimization process.Unlike traditional metaheuristics that rely on fixed behavioral roles or static movement schemes,SRCA allows each individual to adapt its search strategy based on real-time population feedback,monitored through convergence and dispersion indicators.The performance of SRCA is quantitatively assessed under strictly identical experimental conditions on a comprehensive set of 23 benchmark functions,including multimodal and high-dimensional problems,as well as on six classical constrained engineering design problems.Numerical results demonstrate competitive convergence reliability and robustness across diverse optimization tasks,confirming the effectiveness of the proposed adaptive cycle-based framework. 展开更多
关键词 Metaheuristic algorithms optimization constrained optimization benchmark functions structured random cycle-guided algorithm
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Optimization of a self-tuning force control system for the milling process using a dynamic enhanced genetic algorithm 认领 引用
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作者 Yao Li Zhengcai Zhao +3 位作者 Ning Qian Lei Zhang Wenfeng Ding Yucan Fu 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第2期33-43,共11页
When milling structural components with varying axial depths and widths,cutting forces tend to fluctuate,negatively impacting tool life and machining accuracy.To mitigate the force fluctuations and enhance tool longev... When milling structural components with varying axial depths and widths,cutting forces tend to fluctuate,negatively impacting tool life and machining accuracy.To mitigate the force fluctuations and enhance tool longevity,developing a simple,reliable,and easy-to-implement force control system for milling is essential,which is an important step toward advancing intelligent manufacturing.This paper explores the use of genetic algorithms(GA) for powerful optimization capabilities in developing self-tuning milling force controllers.A comprehensive framework for optimizing a fuzzy logic controller using an enhanced GA is specifically designed for the milling process.The optimization integrates the GA with a simulation model,fine-tuning membership functions and optimizing fuzzy rule selection.The enhanced GA incorporates the Integral of Time-weighted Absolute Error(ITAE) as the fitness criterion to improve the robustness and responsiveness of the controller.The optimized fuzzy logic controller is implemented within a computer numerical control system,adjusting feed rates in real-time to control milling forces.The performance of the proposed controller is validated through step and slope milling tests,demonstrating an average control accuracy of 95.52%.Comparative evaluations with other controllers show that the proposed system offers a significant improvement,achieving up to 4.58% better control accuracy in step milling tests. 展开更多
关键词 Optimization Self-tuning Force control system Milling process Genetic algorithm
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基于K-Medoids聚类的上市纺织企业经营业绩分析 认领 引用
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作者 舒服华 《国际纺织导报》 2025年第4期53-58,共6页
我国上市纺织企业的经营业绩表现参差不齐。对这些企业进行聚类分析可以揭示它们之间的差异和特性,为企业制定有效的经营管理策略提供参考,助力其提高经营效益。K-Medoids算法能够规避K-Means算法因异常值或数据畸形分布导致的分类不精... 我国上市纺织企业的经营业绩表现参差不齐。对这些企业进行聚类分析可以揭示它们之间的差异和特性,为企业制定有效的经营管理策略提供参考,助力其提高经营效益。K-Medoids算法能够规避K-Means算法因异常值或数据畸形分布导致的分类不精准问题,其抗干扰能力强,分类质量好。运用K-Medoids算法对我国部分上市纺织企业的经营业绩进行了聚类分析。15家公司的经营业绩被划分为6个类别。对各类别的整体经营业绩优劣进行了排序。 展开更多
关键词 纺织企业 业绩评价 聚类分析 K-Medoids算法
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An Efficient Evolutionary Algorithm for Few-for-Many Optimization 认领 引用
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作者 Ke Shang Hisao Ishibuchi +1 位作者 Zexuan Zhu Qingfu Zhang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1362-1377,共16页
Few-for-many(F4M)optimization,recently introduced as a novel paradigm in multi-objective optimization,aims to find a small set of solutions that effectively handle a large number of conflicting objectives.Unlike tradi... Few-for-many(F4M)optimization,recently introduced as a novel paradigm in multi-objective optimization,aims to find a small set of solutions that effectively handle a large number of conflicting objectives.Unlike traditional many-objective optimization methods,which typically attempt comprehensive coverage of the Pareto front,F4M optimization emphasizes finding a small representative solution set to efficiently address highdimensional objective spaces.Motivated by the computational complexity and practical relevance of F4M optimization,this paper proposes a new evolutionary algorithm explicitly tailored for efficiently solving F4M optimization problems.Inspired by Smetric selection evolutionary multi-objective optimization algorithm(SMS-EMOA),our proposed approach employs a(μ+1)-evolution strategy guided by the objective of F4M optimization.Furthermore,to facilitate rigorous performance assessment,we propose a novel benchmark test suite specifically designed for F4M optimization by leveraging the similarity betw een the R2indicator and F4M formulations.Our test suite is highly flexible,allowing any existing multi-objective optimization problem to be transformed into a corresponding F4M instance via scalarization using the weighted Tchebycheff function.Comprehensive experimental evaluations on benchmarks demonstrate the superior performance of our algorithm compared to existing state-of-the-art algorithms,especially on instances involving a large number of objectives.The source code of the proposed algorithm will be released publicly.Source code is available at http://gffzz188fe103f8f1460asqu9xoobqwvxq6996.ffgz.tsg.suse.edu.cn/MOL-SZU/SoM-EMOA. 展开更多
关键词 Evolutionary algorithm few-for-many optimization many-objective optimization (MOO) multi-objective optimization
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An algorithm-assisted high-resolution D-TOF imaging system with reconfigurable macropixel-based SPAD image sensor 认领 引用
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作者 Zhe Wang Jia-xing Song +8 位作者 Na Tian Xing-jia Ni Xu Yang Run-jiang Dou Peng Feng Jian Liu Nan-jian Wu Li-yuan Liu Shuang-ming Yu 《Journal of Semiconductors》 EI CAS CSCD 2026年第7期61-71,共11页
Single-photon avalanche diode(SPAD)image sensors are widely used in direct time-of-flight(D-TOF)imaging,but their ranging performance is often constrained by limited laser power.This article presents a SPAD-based D-TO... Single-photon avalanche diode(SPAD)image sensors are widely used in direct time-of-flight(D-TOF)imaging,but their ranging performance is often constrained by limited laser power.This article presents a SPAD-based D-TOF imaging system that combines a reconfigurable macro-pixel sensor architecture with a lightweight depth completion algorithm to achieve long-range depth imaging with enhanced spatial resolution under low optical power.The proposed sensor adopts a back-side illuminated(BSI)3D-stacked architecture with programmable macro-pixels that enhance detection sensitivity and enable flexible sensitivity–resolution trade-offs.An injection-locked ring-oscillator-based time-to-digital converter(RO-TDC)array achieves a time resolution of 152.5 ps,enabling accurate TOF measurement at an optical power of 10 mW.To compensate for macropixel-induced resolution loss,a probabilistic normalized convolutional neural network(pNCNN)is employed for depth completion using sparse depth inputs only.Experimental results demonstrate that up to 30×effective resolution enhancement of the system can be achieved via the depth completion algorithm without changing the physical resolution of the sensor.Additionally,the proposed system achieves a maximum ranging distance of 90 m and a range-to-power figure-of-merit(FOM)of9 m/mW,which validates the effectiveness of the system. 展开更多
关键词 SPAD reconfigurable macro-pixel time-to-digital converter(TDC) depth completion algorithm
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Flood predictions from metrics to classes by multiple machine learning algorithms coupling with clustering-deduced membership degree 认领 引用
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作者 ZHAI Xiaoyan ZHANG Yongyong +5 位作者 XIA Jun ZHANG Yongqiang TANG Qiuhong SHAO Quanxi CHEN Junxu ZHANG Fan 《Journal of Geographical Sciences》 SCIE CSCD 2026年第1期149-176,共28页
Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting... Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach. 展开更多
关键词 flood regime metrics class prediction machine learning algorithms hydrological model
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Phased-Enhancement Marine Predators Algorithm for Global Optimization and Medical Insurance Fraud Detection 认领 引用
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作者 Wen Long Yujia Wang +2 位作者 Qinghua Long Yang Yang Ming Xu 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第2期1088-1111,共24页
The Marine Predators Algorithm(MPA),while promising for complex optimization,suffers from limited solution precision,imbalanced exploration–exploitation,and premature convergence.To address these shortcomings,this pa... The Marine Predators Algorithm(MPA),while promising for complex optimization,suffers from limited solution precision,imbalanced exploration–exploitation,and premature convergence.To address these shortcomings,this paper proposes a phased-enhancement variant named PEMPA,which integrates three novel strategies into distinct phases of MPA:1)embedding historical best positions in the high-velocity ratio phase to refine solution quality;2)introducing an adaptive inertia weight based on an inverted Sigmoid function in the unit-velocity ratio phase to systematically balance exploration and exploitation;and 3)designing a two-stage opposition-based learning operator in the low-velocity ratio phase to prevent premature convergence.The performance of PEMPA is comprehensively evaluated across 23 classical benchmark functions,the IEEE Congress on Evolutionary Computation(CEC)2017 test suite,21 feature selection tasks,and a real-world medical insurance fraud detection problem.Experimental results confirm that the proposed strategies significantly enhance the efficiency and robustness of MPA.Furthermore,PEMPA demonstrates highly competitive performance compared with several state-of-the-art metaheuristic algorithms,validating its effectiveness and scalability for diverse optimization challenges. 展开更多
关键词 Marine predators algorithm Opposite-based learning Inertia weight Numerical optimization Feature selection
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