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A Firefly Algorithm-Optimized CNN-BiLSTM Model for Automated Detection of Bone Cancer and Marrow Cell Abnormalities 认领 引用
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作者 Ishaani Priyadarshini 《Computers, Materials & Continua》 SCIE EI 2026年第3期1510-1535,共26页
Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a ... Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network(CNN)with a Bidirectional Long Short-Term Memory(BiLSTM)architecture,optimized using the Firefly Optimization algorithm(FO).The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data,capturing both local patterns and sequential dependencies in diagnostic features,while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance.The approach is evaluated on two benchmark biomedical datasets:one comprising diagnostic data for bone cancer detection and another for identifying marrow cell abnormalities.Experimental results demonstrate that the proposed method outperforms standard deep learning models,including CNN,LSTM,BiLSTM,and CNN-LSTM hybrids,significantly.The CNNBiLSTM-FO model achieves an accuracy of 98.55%for bone cancer detection and 96.04%for marrow abnormality classification.The paper also presents a detailed complexity analysis of the proposed algorithm and compares its performance across multiple evaluation metrics such as precision,recall,F1-score,and AUC.The results confirm the effectiveness of the firefly-based optimization strategy in improving classification accuracy and model robustness.This work introduces a scalable and accurate diagnostic solution that holds strong potential for integration into intelligent clinical decision-support systems. 展开更多
关键词 Firefly optimization algorithm(FO) marrow cell abnormalities bidirectional long short term memory(Bi-LSTM) temporal dependency modeling
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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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基于CNN-BiLSTM-SSA的锅炉再热器壁温预测模型 认领 引用 被引量：1
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作者 徐世明 何至谦 +6 位作者 彭献永 商忠宝 范景玮 王俊略 曲舒杨 刘洋 周怀春 《动力工程学报》 CAS CSCD 北大核心 2026年第1期121-130,共10页
针对锅炉高温再热器壁温动态特点,提出了一种基于稀疏自注意力(SSA)、卷积神经网络(CNN)及双向长短期记忆神经网络(BiLSTM)相融合的再热器壁温软测量模型。首先,采用核主成分分析(KPCA)算法对原始候选变量进行筛选降维,选择前26个主成... 针对锅炉高温再热器壁温动态特点,提出了一种基于稀疏自注意力(SSA)、卷积神经网络(CNN)及双向长短期记忆神经网络(BiLSTM)相融合的再热器壁温软测量模型。首先,采用核主成分分析(KPCA)算法对原始候选变量进行筛选降维,选择前26个主成分变量作为模型的最终输入。其次,考虑利用CNN捕捉局部相关性,BiLSTM学习数据的长期序列依赖性的优势,使用卷积神经网络-双向长短期记忆神经网络(CNN-BiLSTM)捕捉时序数据中的短期和长期依赖关系,引入稀疏自注意力SSA机制,通过为不同特征部分分配自适应权重,从而增强CNN-BiLSTM模型的特征提取与建模能力,最后利用在役1000 MW超超临界锅炉的历史数据进行仿真实验。结果表明:CNN-BiLSTM-SSA模型在高温再热器壁温预测中的均方根误差(RMSE)、平均绝对误差(MAE)及平均绝对百分比误差(MAPE)分别为4.92℃、3.81℃和0.6241%,相应的指标均优于CNN、LSTM、BiLSTM、CNN-LSTM和CNN-BiLSTM模型。 展开更多
关键词 再热器壁温软测量 深度学习 卷积神经网络 长短期记忆网络 注意力机制 核主成分分析 CNN-BiLSTM
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基于CNN-BiLSTM的新型电力系统用电负荷特征分层提取 认领 引用
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作者 岳云力 武冰清 +2 位作者 关皓闻 杨悦 高晋 《电子设计工程》 2026年第7期145-149,共5页
新型电力系统下,用电负荷数据多源异构性加剧、时空动态耦合复杂,难以同步捕捉负荷局部空间关联与长时序依赖规律,提取的特征区分度和完整度低。为此,提出基于CNN-BiLSTM的分层特征提取方法。该方法通过卷积神经网络提取负荷数据的空间... 新型电力系统下,用电负荷数据多源异构性加剧、时空动态耦合复杂,难以同步捕捉负荷局部空间关联与长时序依赖规律,提取的特征区分度和完整度低。为此,提出基于CNN-BiLSTM的分层特征提取方法。该方法通过卷积神经网络提取负荷数据的空间局部特征,结合双向长短期记忆网络捕捉时序双向依赖关系,并采用主成分分析法对多层次特征进行融合,实现负荷特征的逐层提取与集成。实验结果表明,所提方法在常规场景下的特征区分度、提取完整度和特征准确度平均达到0.93、0.92和0.96,较对比方法平均提升约19.2%、29.6%和12.9%;在极端场景下,其特征区分度、提取完整度与特征准确度仍分别保持0.94、0.90和0.92,优于对比方法。证明所提方法能够提升特征提取的区分度、完整度与准确度,可为新型电力系统的精准感知与优化调度提供可靠的数据支撑。 展开更多
关键词 CNN-BiLSTM算法 新型电力系统 用电负荷 特征提取 分层提取
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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CNN-BiLSTM模型的致密砂岩储层孔隙度预测方法 认领 引用 被引量：1
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作者 李黎 董政 +2 位作者 侯凯 吴成 彭己君 《物探与化探》 CAS 2026年第2期319-329,共11页
孔隙度是描述储层物理性质的重要参数,孔隙度预测的准确性对油气勘探开发、油气藏地质建模和精细评价具有重要意义。针对致密砂岩储层低孔、低渗及非均质性强的特点,本文提出一种融合卷积神经网络(convolutional neural network,CNN)和... 孔隙度是描述储层物理性质的重要参数,孔隙度预测的准确性对油气勘探开发、油气藏地质建模和精细评价具有重要意义。针对致密砂岩储层低孔、低渗及非均质性强的特点,本文提出一种融合卷积神经网络(convolutional neural network,CNN)和双向长短期记忆网络(bidirectional long short-term memory,BiLSTM)的储层孔隙度预测模型,用于从测井数据中提取特征并建立其与孔隙度参数之间的复杂非线性关联。首先,依据岩石物理模型筛选出与孔隙度显著相关的敏感弹性参数作为输入参数,随后借助卷积神经网络提取反演数据中的空间依赖特征,进一步通过双向长短时记忆神经网络捕捉其前后的时序依赖关系,最终实现孔隙度的有效预测。开平凹陷A油田3口井的测试结果表明,相比常规深度学习方法,CNN-BiLSTM模型的孔隙度预测结果具有更好的精度及横向分辨率,验证了该模型的有效性。在此基础上,利用该模型开展三维储层孔隙度预测,通过输入储层反演获得的弹性参数数据体计算出孔隙度数据体,为海上开平凹陷A油田少井区储层孔隙度预测提供了新的研究思路。 展开更多
关键词 开平凹陷 CNN-BiLSTM模型 深度学习 孔隙度预测 致密砂岩
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融合InSAR与改进CNN-BiLSTM的镇雄县滑坡形变分析与预测方法研究 认领 引用
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作者 胡波 侯岳林 +5 位作者 李文 李粤彬 张润源 杨力 吴文清 李佳 《安全与环境学报》 CAS CSCD 北大核心 2026年第8期3224-3235,共12页
滑坡是严重威胁人民生命财产安全的自然灾害,及时准确地监测与预测形变对于灾害防治至关重要。针对云南省镇雄县的滑坡区域,采用小基线干涉测量技术(Small Baseline Subset Interferometric Synthetic Aperture Radar,SBAS-InSAR)监测... 滑坡是严重威胁人民生命财产安全的自然灾害,及时准确地监测与预测形变对于灾害防治至关重要。针对云南省镇雄县的滑坡区域,采用小基线干涉测量技术(Small Baseline Subset Interferometric Synthetic Aperture Radar,SBAS-InSAR)监测滑坡形变,并提出一种改进的卷积神经网络-双向长短期记忆网络(Convolutional Neural Network-Bidirectional Long Short-Term Memory Network,CNN-BiLSTM)模型,即鲸鱼迁徙优化算法-卷积神经网络-双向长短期记忆网络-注意力机制(Whale Migrating Algorithm-Convolutional Neural Network-Bi-directional Long Short-Term Memory-Attention,WMA-CNN-BiLSTM-Attention)进行滑坡形变预测。研究通过SBAS-InSAR技术获取滑坡区域的形变信息,并通过时序数据处理提取形变特征。滑坡形变通常具有复杂性和非线性,传统的优化方法难以找到最优解,因此基于鲸鱼迁徙算法(Whale Migration Algorithm,WMA)优化CNN-BiLSTM模型,并结合自注意力机制(Attention),高效地在高维参数空间中寻找最佳超参数组合,从而提升模型在滑坡形变预测中的表现。试验结果显示,WMA-CNN-BiLSTM-Attention在滑坡形变预测中的平均均方根误差(RMSE)仅为1.69 mm,与基准模型相比,误差降低了44%~56%。此外,最大和最小绝对偏差分别小于4 mm和0.1 mm,平均拟合优度(R2)超过0.94,表明该模型能够较好地预测滑坡形变的动态变化特征,可为相关滑坡位移预测提供参考。 展开更多
关键词 公共安全 InSAR技术 鲸鱼迁徙算法 WMA-CNN-BiLSTM-Attention 滑坡形变预测
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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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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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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://gffzz188fe103f8f1460asxfwvknkqn95c6vxw.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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面向非均衡数据集下基于贝叶斯优化CNN-BiLSTM的变压器故障诊断研究 认领 引用
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作者 刘对 唐金权 +2 位作者 金涛 蔡定国 黄建华 《变压器》 2026年第8期61-70,共10页
针对非均衡数据集下变压器故障诊断准确率低、模型泛化能力不足等问题,本文开展基于贝叶斯优化CNN-BiLSTM的故障诊断方法研究。首先,针对数据集的质量问题,设计融合箱线图离群值检测、L1范数特征提取与LSTM数据增强的预处理缓解,可以有... 针对非均衡数据集下变压器故障诊断准确率低、模型泛化能力不足等问题,本文开展基于贝叶斯优化CNN-BiLSTM的故障诊断方法研究。首先,针对数据集的质量问题,设计融合箱线图离群值检测、L1范数特征提取与LSTM数据增强的预处理缓解,可以有效缓解数据集样本偏少及类别分布非均衡问题。其次,构建CNN-BiLSTM混合模型,利用CNN提取故障信号的空间特征,通过BiLSTM捕捉时序动态特征,结合贝叶斯优化算法对网络超参数进行全局寻优,避免人工调参的主观性与盲目性,提升模型结构合理性与训练稳定性。试验算例结果表明,该方法在非均衡数据集上的故障诊断准确率显著优于传统机器学习与单一深度学习模型,且经实际工程数据验证,其泛化能力与实用价值突出,为解决变压器故障诊断中非均衡数据难题提供了可靠的技术方案,也为同类电气设备的智能诊断研究提供了参考。 展开更多
关键词 变压器 故障诊断 油中溶解气体分析 CNN-BiLSTM 贝叶斯优化
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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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RRT*-GSQ:A hybrid sampling path planning algorithm for complex orchard scenarios 认领 引用
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作者 ZHU Qingzhen ZHAO Jiamuyang +1 位作者 DAI Xu YU Yang 《农业工程学报》 EI CAS CSCD 北大核心 2026年第3期13-25,共13页
Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT*),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narr... Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT*),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narrow passages,slow convergence,and high computational costs.To address these challenges,this paper proposes a novel hybrid global path planning algorithm integrating Gaussian sampling and quadtree optimization(RRT*-GSQ).This methodology aims to enhance path planning by synergistically combining a Gaussian mixture sampling strategy to improve node generation in critical regions,an adaptive step-size and direction optimization mechanism for enhanced obstacle avoidance,a Quadtree-AABB collision detection framework to lower computational complexity,and a dynamic iteration control strategy for more efficient convergence.In obstacle-free and obstructed scenarios,compared with the conventional RRT*,the proposed algorithm reduced the number of node evaluations by 67.57%and 62.72%,and decreased the search time by 79.72%and 78.52%,respectively.In path tracking tests,the proposed algorithm achieved substantial reductions in RMSE of the final path compared to the conventional RRT*.Specifically,the lateral RMSE was reduced by 41.5%in obstacle-free environments and 59.3%in obstructed environments,while the longitudinal RMSE was reduced by 57.2%and 58.5%,respectively.Furthermore,the maximum absolute errors in both lateral and longitudinal directions were constrained within 0.75 m.Field validation experiments in an operational orchard confirmed the algorithm's practical effectiveness,showing reductions in the mean tracking error of 47.6%(obstacle-free)and 58.3%(with obstructed),alongside a 5.1%and 7.2%shortening of the path length compared to the baseline method.The proposed algorithm effectively enhances path planning efficiency and navigation accuracy for robots,presenting a superior solution for high-precision autonomous navigation of agricultural robots in orchard environments and holding significant value for engineering applications. 展开更多
关键词 robot path planning orchard improved RRT*algorithm Gaussian sampling autonomous navigation
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