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Salt and Pepper Noise Filter Based on GA-BP Algorithm Noise Detector 认领 引用 被引量：2
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作者 宋寅卯 李晓娟 《光电工程》 CAS CSCD 北大核心 2011年第2期59-64,共6页
基于噪声检测的中值滤波器已广泛用于消除图像中的椒盐噪声，然而在高噪声密度情况下，对噪声像素的定位不准确很容易造成图像边缘的模糊。本文提出了一种基于GA-BP的椒盐噪声滤波算法，克服了这一缺陷。算法首先用遗传算法优化的BP网... 基于噪声检测的中值滤波器已广泛用于消除图像中的椒盐噪声，然而在高噪声密度情况下，对噪声像素的定位不准确很容易造成图像边缘的模糊。本文提出了一种基于GA-BP的椒盐噪声滤波算法，克服了这一缺陷。算法首先用遗传算法优化的BP网络对图像中的噪声像素定位，然后引入保边函数和PRP算法求目标函数的极值进而实现图像的去噪处理。实验结果表明，该算法比传统滤波算法效果有明显改善，且具有良好的泛化性、鲁棒性和自适应性。 展开更多
关键词 GA-BP算法 椒盐噪声 噪声检测 保边函数 PRP算法
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Neural Network Based on GA-BP Algorithm and its Application in the Protein Secondary Structure Prediction 认领 引用 被引量：8
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作者 YANG Yang LI Kai-yang 《Chinese Journal of Biomedical Engineering(English Edition)》 CAS 2006年第1期1-9,共9页
The advantages and disadvantages of genetic algorithm and BP algorithm are introduced. A neural network based on GA-BP algorithm is proposed and applied in the prediction of protein secondary structure, which combines... The advantages and disadvantages of genetic algorithm and BP algorithm are introduced. A neural network based on GA-BP algorithm is proposed and applied in the prediction of protein secondary structure, which combines the advantages of BP and GA. The prediction and training on the neural network are made respectively based on 4 structure classifications of protein so as to get higher rate of predication---the highest prediction rate 75.65%,the average prediction rate 65.04%. 展开更多
关键词 BP algorithm genetic algorithm, Neural network, Structure classification, Protein secondary structure prediction
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An improved Alpha-shape algorithm for extracting section contours of the super-high steel bridge tower using point clouds 认领 引用 被引量：2
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作者 ZHANG Yiming ZHAO Tianhao +2 位作者 LIAO Ruixuan LI Haoqing WANG Hao 《Journal of Southeast University(English Edition)》 EI CAS 2026年第1期26-35,共10页
The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,a... The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,and temperature fluctuations can compromise the accuracy of contour extraction.To address these limitations,an improved Alpha-shape-based point cloud contour extraction method is proposed.The proposed approach uses a hierarchical strategy to process three-dimensional laser scanning point clouds.The processed data are then subjected to curvatureadaptive voxel filtering to reduce acquisition noise.In addition,an enhanced iterative closest point(ICP)variant with correspondence validation accurately aligns the discrete point cloud segments.The proposed curvature-responsive Alpha-shape framework enables multiscale contour delineation through topology-adaptive threshold modulation,which resolves boundary ambiguities in geometrically complex cross-sections.The method was experimentally validated using field-acquired measurement datasets from the Zhangjinggao Yangtze River Bridge tower segments,confirming its capability to reconstruct noncanonical cross-sectional geometries.Three contour extraction methods,including Poisson reconstruction,the conventional Alpha-shape algorithm,and random sample consensus with ICP(RANSAC-ICP),were compared to evaluate the performance of the proposed Alpha-shape algorithm.The results demonstrate that the proposed method achieves superior contour extraction accuracy and data reduction efficiency,highlighting its effectiveness in contour extraction tasks. 展开更多
关键词 super-high steel bridge tower point cloud contour extraction improved Alpha-shape algorithm
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发电上市公司社会责任会计信息披露质量评价——基于因子分析和GA-BP神经网络模型 认领 引用 被引量：1
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作者 何姣 李娜 湛忠灿 《会计之友》 北大核心 2026年第2期83-91,共9页
企业社会责任会计信息披露作为衡量公司履行社会责任的重要手段,对于提升企业透明度和社会形象至关重要。通过构建发电上市公司社会责任会计信息披露质量评价指标体系,运用因子分析和GA-BP神经网络模型对2019—2023年连续独立发布社会... 企业社会责任会计信息披露作为衡量公司履行社会责任的重要手段,对于提升企业透明度和社会形象至关重要。通过构建发电上市公司社会责任会计信息披露质量评价指标体系,运用因子分析和GA-BP神经网络模型对2019—2023年连续独立发布社会责任报告的10家典型发电上市公司进行披露质量评价。研究发现:10家公司社会责任会计信息披露质量整体呈现逐年改善趋势,但披露质量水平有待进一步提升;大唐发电、华能国际等5家中央企业所属上市公司披露质量相对较高;不同公司受社会责任投入、绿色环保因子等因素综合影响导致披露质量差异较大。文章分别从建立完善的披露体系、加强外部独立鉴证和提高企业自身披露水平三个方面提出对策建议,以期为提升发电上市公司社会责任会计信息披露质量提供借鉴。 展开更多
关键词 社会责任会计 信息披露 GA-BP神经网络 因子分析
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Novel Sea Otter Optimization Algorithm for WSN Coverage Intelligence Optimization 认领 引用 被引量：2
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作者 WU Jin GAO Yaqiong +2 位作者 SU Zhengdong CHONG Gege XIONG Hao 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第4期828-842,I0002,共15页
A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for... A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for food in seawater,grooming their fur,feeding with the aid of stones,and escaping from danger.In the exploration stage,a wetness factor is introduced to control the behavior of sea otters in foraging and grooming;a danger factor is introduced to control the behavior of sea otters in feeding and avoiding dangers in the exploitation stage,and the behaviors of sea otters in responding to different dangers are mathematically modeled.The proposed algorithm is compared with 9 well-known intelligent optimization algorithms,and evaluated in 13 benchmark functions as well as wireless sensor network coverage optimization problems to verify the effectiveness of the proposed algorithm.The experimental results show that the node coverage after SOOA optimization reaches 91.2%in 2D environment and 90.47%in 3D environment.Compared with other algorithms,SOOA is superior and possesses the ability to solve complex optimization problems. 展开更多
关键词 sea otter optimization algorithm(SOOA) swarm intelligence optimization wireless sensor network coverage optimization
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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：2
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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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基于GA-BP神经网络的露天矿山排土场边坡失稳预测 认领 引用 被引量：1
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作者 谢尊贤 马浩浩 +1 位作者 江松 武潇云 《中国安全科学学报》 EI CAS CSCD 北大核心 2026年第3期81-88,共8页
为提高矿山排土场边坡失稳预测的准确性与可靠性,构建一种基于改进遗传算法(GA)优化反向传播(BP)神经网络的露天矿山排土场边坡失稳预测模型。利用GA全局优化BP神经网络的权值和阈值,并引入Levenberg-Marquardt(LM)算法以提升网络收敛效... 为提高矿山排土场边坡失稳预测的准确性与可靠性,构建一种基于改进遗传算法(GA)优化反向传播(BP)神经网络的露天矿山排土场边坡失稳预测模型。利用GA全局优化BP神经网络的权值和阈值,并引入Levenberg-Marquardt(LM)算法以提升网络收敛效率;选取台阶坡面角、岩土内应力、台阶高度、地表位移、孔隙水压力等10个关键指标作为输入,以边坡安全系数为输出,并通过150组矿山案例数据进行模型训练与验证。结果表明:相较于传统BP模型,GA-BP模型的均方误差(MSE)、平均绝对误差(MAE)和平均绝对百分比误差(MAPE)分别降低46.9%、25.4%和5.38%,预测值更贴近安全系数阈值(Fs=1.2),预测灵敏度和稳定性显著提升。皮尔森相关性分析进一步显示,地表位移与内部位移(0.98)、孔隙水压力与降雨量(0.75)呈强相关性,验证了输入指标的合理性。 展开更多
关键词 遗传算法(GA) 反向传播(BP)神经网络 露天矿山 排土场 边坡失稳预测 安全系数
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GA-BP优化混合模型在强人工干预下复杂流域洪水演进预测中的应用 认领 引用
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作者 雷晓辉 石鑫龙 +5 位作者 吴旭 陈一帆 龙岩 贾昊 段晨斐 刘冬冬 《南水北调与水利科技（中英文）》 CAS CSCD 北大核心 2026年第4期817-828,共12页
针对强人工干预下流域洪水演进过程非线性耦合强、传统机理模型难以精准刻画的问题,采用一种融合人工干预特征与智能优化的洪水预测方法,以大清河北支北河店断面为对象,选取安格庄出库、北易水及落宝滩流量作为多源输入,构建干预强度比... 针对强人工干预下流域洪水演进过程非线性耦合强、传统机理模型难以精准刻画的问题,采用一种融合人工干预特征与智能优化的洪水预测方法,以大清河北支北河店断面为对象,选取安格庄出库、北易水及落宝滩流量作为多源输入,构建干预强度比量化指标,并结合遗传算法(genetic algorithm,GA)优化BP神经网络(genetic algorithm-backpropagation,GA-BP),建立t+3 h实时预测模型。结果表明:优化后的GA-BP模型结构为6-5双隐藏层,其预测精度显著优于传统BP模型,测试集均方误差(mean squared error,EMS)降低40.6%至0.0011,决定系数R2提升至0.94;敏感性分析证实,水库调度信号(安格庄出库)是预测精度的主导因素;模型在各类工况下均表现稳定,其中在防洪决策的关键泄洪工况下精度最高(R2=0.99)。本研究为强人工干预流域的洪水实时精准预报提供了可解释、高可靠的技术解决方案。 展开更多
关键词 强人工干预 洪水预测 遗传算法 BP神经网络 GA-BP混合模型 大清河北支流域
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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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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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考虑PSSI效应的地震动传递可解释性GA-BP预测模型研究 认领 引用
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作者 许浩 李佳禹 +3 位作者 何文福 赵慧玲 杨杰 詹浩南 《振动工程学报》 EI CSCD 北大核心 2026年第8期2435-2444,共10页
非基岩场地中,土体条件及桩-土-结构相互作用(PSSI,pile-soil-structure interaction)会显著影响地震动在地基-结构体系中的传递与放大,但其作用机制及传递规律尚不明确。为此,本文通过数值模拟构建了考虑PSSI效应的地震动传递关系数据... 非基岩场地中,土体条件及桩-土-结构相互作用(PSSI,pile-soil-structure interaction)会显著影响地震动在地基-结构体系中的传递与放大,但其作用机制及传递规律尚不明确。为此,本文通过数值模拟构建了考虑PSSI效应的地震动传递关系数据库,并采用遗传算法优化的反向传播神经网络(GA-BP,genetic algorithm optimization of backpropagation neural networks)建立了地表加速度反应谱放大系数预测模型,随后基于SHAP(shapley additive explanations)算法对模型进行了可解释性分析。结果表明,各谱周期下预测值与训练值的决定系数平均值在0.92以上,且计算反应谱与预测反应谱的DTW(dynamic time warping)距离平均值小于0.52,不同场地条件和地震动参数下预测模型均具有较高精度。地震动峰值、基岩深度及地表下30 m范围内的平均剪切波速对放大系数的影响较大,而结构周期和结构与桩基础刚度比的影响较小。加速度反应谱放大系数对主要参数的响应具有明显的周期差异:短周期下,较大的基岩深度会减小放大系数,而较大的剪切波速和地震动峰值会增大放大系数;长周期下上述影响趋势相反。 展开更多
关键词 场地放大 PSSI效应 地震动 GA-BP神经网络 可解释机器学习
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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GA-BP神经网络与多目标优化算法的模具钢铣削工艺参数研究 认领 引用
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作者 吴玉文 于洋 +1 位作者 王建军 王帅 《模具技术》 2026年第1期115-120,共6页
采用DEFORM-3D建立高强人PC53模具钢铣削有限元模型,基于多组仿真数据,分别建立常规BP神经网络与反向传播神经网络模型和遗传算法优化反向传播神经网络模型(GA-BP)神经网络模型,对比两者在切削力和刀具磨损预测中的性能差异;进而设计神... 采用DEFORM-3D建立高强人PC53模具钢铣削有限元模型,基于多组仿真数据,分别建立常规BP神经网络与反向传播神经网络模型和遗传算法优化反向传播神经网络模型(GA-BP)神经网络模型,对比两者在切削力和刀具磨损预测中的性能差异;进而设计神经网络与多目标优化算法的耦合机制,以最大铣削效率、最小切削力和最小刀具磨损为目标,分别采用NSGA-II和NSGA-III算法优化,通过Pareto前沿分析算法性能。结果表明:GA-BP神经网络预测精度更高,所建模型优化框架可有效映射切削参数与目标的关系,且NSGA-II在三目标优化中表现更优。研究为DC53模具钢等难加工材料铣削参数优化提供了可行方法与数据支撑。 展开更多
关键词 DC53模具钢 铣削 GA-BP神经网络 多目标优化 工艺参数 DEFORM-3D
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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://gffzz188fe103f8f1460asnnvvqfw0xxqw6vqf.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
A Deep-Learning-Based Constitutive Method for Geomaterials Using a Neural Cutting Plane Algorithm 认领 引用
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作者 Qingxiang Meng Zijie He +1 位作者 Yajun Cao Weijiang Chu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期158-180,共23页
Constitutive modeling for geomaterials remains challenging because of limited data availability,strong nonlinearity,pressure sensitivity,and the non-smooth characteristics of commonly used yield surfaces.This study pr... Constitutive modeling for geomaterials remains challenging because of limited data availability,strong nonlinearity,pressure sensitivity,and the non-smooth characteristics of commonly used yield surfaces.This study presents a deep-learning-based constitutive method for geomaterials that incorporates a neural stress-integration procedure based on the cutting plane algorithm(CPA).Two compact fully connected networks are trained to learn the yield function and its stress gradient from an augmented stress-state dataset.The trained networks are then incorporated into a cutting plane return-mapping procedure,in which only first-order information is required for the plastic stress return.This avoids explicit analytical yield expressions and second-derivative evaluations and is therefore more naturally compatible with non-smooth Mohr-Coulomb-type yield-surface representations in a first-order returnmapping sense.Numerical results show that the proposed method reproduces the reference Mohr-Coulomb response along the examined monotonic triaxial compression paths.Compared with the finite-difference closest-point projection method(CPPM)implementation considered in this study,the CPA-based neural stress-update procedure requires fewer network calls per update,indicating a more economical implementation for the present learned constitutive framework. 展开更多
关键词 Geomaterials constitutive modeling deep learning cutting plane algorithm stress integration
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