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Identification of small impact craters in Chang’e-4 landing areas using a new multi-scale fusion crater detection algorithm 认领 引用
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作者 FangChao Liu HuiWen Liu +7 位作者 Li Zhang Jian Chen DiJun Guo Bo Li ChangQing Liu ZongCheng Ling Ying-Bo Lu JunSheng Yao 《Earth and Planetary Physics》 EI CSCD 2026年第1期92-104,共13页
Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious an... Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy. 展开更多
关键词 impact craters Chang’e-4 landing area multi-scale automatic detection YOLO11 Fusion algorithm
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Erratum to“False Data Injection Attacks on Data-Driven Algorithms in Smart Grids Utilizing Distributed Power Supplies”[Engineering 51(2025)62-74] 认领 引用
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作者 Zengji Liu Mengge Liu +1 位作者 Qi Wang Yi Tang 《Engineering》 SCIE EI CSCD 2026年第5期362-362,共1页
The publisher regrets the CRediT authorship contribution statement was inserted incorrectly and the correct statement should be updated as below:Zengji Liu:Writing-review&editing,Writing-original draft,Visualizati... The publisher regrets the CRediT authorship contribution statement was inserted incorrectly and the correct statement should be updated as below:Zengji Liu:Writing-review&editing,Writing-original draft,Visualization,Validation,Supervision,Software,Resources,Project administration,Methodology,Investigation,Funding acquisition,Formal analysis,Data curation,Conceptualization.Mengge Liu:Writing-review&editing,Writing-original draft,Investigation.Qi Wang:Writing-review&editing,Writing-original draft.Yi Tang:Writing-review&editing,Writing-original draft. 展开更多
关键词 data driven algorithms credit authorship contribution statement false data injection attacks smart grids distributed power supplies
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Optimizing IoT-Driven Smart Cities with the Dynamic Leader Sibha Algorithm:A Novel Approach to Feature Selection and Hyperparameter Tuning 认领 引用
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作者 Safaa Zaman Marwa M.Eid +2 位作者 Ebrahim A.Mattar Doaa Sami Khafaga El-Sayed M.El-Kenawy 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期899-958,共60页
The rapid growth of Internet of Things(IoT)technologies has transformed modern urban environments into complex smart cities,generating vast amounts of high-dimensional,heterogeneous data.Effectively analyzing this dat... The rapid growth of Internet of Things(IoT)technologies has transformed modern urban environments into complex smart cities,generating vast amounts of high-dimensional,heterogeneous data.Effectively analyzing this data is crucial for optimizing urban infrastructure,enhancing quality of life,and supporting sustainable development.However,smart city data presents significant challenges,including non-linear dependencies,noisy signals,and high dimensionality.To address these challenges,this study proposes the Dynamic Leader Sibha Algorithm(DLSA),a novel metaheuristic optimization technique inspired by the structured counting dynamics of the Sibha.The DLSA was applied to the Smart Cities Index dataset,leveraging copula functions to model complex,multivariate dependencies and enhance predictive accuracy.The baseline machine learning(ML)evaluation revealed that the ExtraTreesRegressor achieved the lowest mean squared error(MSE)of 0.007462409,highlighting its superior initial performance.Following feature selection using the binary Dynamic Leader Sibha Algorithm(bSiba),the average error was reduced to 0.373245769,significantly improving data quality and model efficiency.Subsequent ML evaluation after feature selection further reduced the MSE of the ExtraTreesRegressor to 0.00151927,reflecting the effectiveness of dimensionality reduction.Finally,hyperparameter optimization using the DLSA achieved a remarkable MSE of 1.32249×10−6 with the Siba+ExtraTreesRegressor combination,demonstrating the algorithm’s powerful optimization capabilities.These findings indicate that the DLSA framework can significantly enhance the predictive performance of IoT-driven smart city models,offering valuable insights for urban planners,policymakers,and technology developers seeking to build smarter,more resilient cities. 展开更多
关键词 Smart cities analytics Internet of Things(IoT) Dynamic Leader Sibha Algorithm(DLSA) feature selection and optimization high-dimensional data analysis
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Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to Data Clustering 认领 引用
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作者 Jeng-Shyang Pan Mengfei Zhang +2 位作者 Shu-Chuan Chu Xingsi Xue Václav Snášel 《Computers, Materials & Continua》 SCIE EI 2025年第4期475-496,共22页
Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data analysis.Traditional clustering algorithms,such as K-means,are widely used due to their sim... Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data analysis.Traditional clustering algorithms,such as K-means,are widely used due to their simplicity and efficiency.This paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering performance.The SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)algorithm.Firstly,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population diversity.Secondly,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence speed.Finally,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation effectively.The performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic algorithms.To further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven datasets.Experimental results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering approaches.This study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks. 展开更多
关键词 Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm
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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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Design and Test Verification of Energy Consumption Perception AI Algorithm for Terminal Access to Smart Grid 认领 引用
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作者 Sheng Bi Jiayan Wang +2 位作者 Dong Su Hui Lu Yu Zhang 《Energy Engineering》 EI 2025年第10期4135-4151,共17页
By comparing price plans offered by several retail energy firms,end users with smart meters and controllers may optimize their energy use cost portfolios,due to the growth of deregulated retail power markets.To help s... By comparing price plans offered by several retail energy firms,end users with smart meters and controllers may optimize their energy use cost portfolios,due to the growth of deregulated retail power markets.To help smart grid end-users decrease power payment and usage unhappiness,this article suggests a decision system based on reinforcement learning to aid with electricity price plan selection.An enhanced state-based Markov decision process(MDP)without transition probabilities simulates the decision issue.A Kernel approximate-integrated batch Q-learning approach is used to tackle the given issue.Several adjustments to the sampling and data representation are made to increase the computational and prediction performance.Using a continuous high-dimensional state space,the suggested approach can uncover the underlying characteristics of time-varying pricing schemes.Without knowing anything regarding the market environment in advance,the best decision-making policy may be learned via case studies that use data from actual historical price plans.Experiments show that the suggested decision approach may reduce cost and energy usage dissatisfaction by using user data to build an accurate prediction strategy.In this research,we look at how smart city energy planners rely on precise load forecasts.It presents a hybrid method that extracts associated characteristics to improve accuracy in residential power consumption forecasts using machine learning(ML).It is possible to measure the precision of forecasts with the use of loss functions with the RMSE.This research presents a methodology for estimating smart home energy usage in response to the growing interest in explainable artificial intelligence(XAI).Using Shapley Additive explanations(SHAP)approaches,this strategy makes it easy for consumers to comprehend their energy use trends.To predict future energy use,the study employs gradient boosting in conjunction with long short-term memory neural networks. 展开更多
关键词 Energy consumption perception terminal access smart grid AI Model SHAP Q-learning algorithm
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设置SMA碟簧群的高速铁路轨道-桥梁系统地震响应及易损性分析 认领 引用
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作者 刘正楠 张维科 +1 位作者 林发明 周小华 《中国铁道科学》 EI CAS CSCD 北大核心 2026年第1期116-126,共11页
为评估设置SMA碟簧群的高速铁路轨道-桥梁系统的抗震能力,以含等效路基段的8跨简支梁桥为研究对象,分别在活动支座侧和固定支座侧布置SMA碟簧群,采用OpenSees建立结构的有限元分析模型,并从太平洋地震工程研究中心数据库选取30条适合于... 为评估设置SMA碟簧群的高速铁路轨道-桥梁系统的抗震能力,以含等效路基段的8跨简支梁桥为研究对象,分别在活动支座侧和固定支座侧布置SMA碟簧群,采用OpenSees建立结构的有限元分析模型,并从太平洋地震工程研究中心数据库选取30条适合于Ⅱ类场地的地震动记录,进行SMA碟簧群设置前后轨道-桥梁系统的地震响应研究;基于增量动力分析方法,开展设置SMA碟簧群的高速铁路轨道-桥梁系统的地震易损性分析。结果表明:SMA碟簧群对轨道系统中各部件位移响应控制效果有限,但对剪力齿槽、固定支座的最大和残余位移控制效果显著,同时还可降低桥墩的残余位移;“V型”地形条件下,SMA碟簧群的设置仅对活动支座、固定支座、剪力齿槽和抗剪钢筋的位移响应敏感,桥墩变高度布置时SMA碟簧群对剪力齿槽的最大位移响应控制效果较差;设置SMA碟簧群可降低剪力齿槽的损伤概率,尤其可以降低固定支座在不同损伤状态下的超越概率。 展开更多
关键词 高速铁路 轨道-桥梁系统 SMA碟簧群 地震响应 易损性
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阀用谐振式无线供电SMA驱动器的驱动特性研究 认领 引用
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作者 魏列江 卢红煜 +2 位作者 强彦 魏小玲 张海东 《机床与液压》 北大核心 2026年第4期10-17,共8页
针对某些需要密封的场合,可以采用无线供电形状记忆合金(SMA)驱动方式进行阀门驱动,但密封层厚度、外界操控装置的位置姿态和谐振频率等均会对该SMA驱动器的驱动特性产生严重影响。基于磁耦合谐振电路互感理论和传热学理论,建立谐振式... 针对某些需要密封的场合,可以采用无线供电形状记忆合金(SMA)驱动方式进行阀门驱动,但密封层厚度、外界操控装置的位置姿态和谐振频率等均会对该SMA驱动器的驱动特性产生严重影响。基于磁耦合谐振电路互感理论和传热学理论,建立谐振式无线供电SMA驱动器的电-热驱动模型。通过COMSOL与MATLAB/Simulink联合仿真,分析谐振式无线供电系统谐振频率、原边线圈和副边线圈之间的轴向距离、径向偏移、偏转角度等对SMA驱动特性的影响规律,并搭建试验平台进行试验验证。结果表明:在100~250 kHz范围内,系统谐振频率越高,SMA丝的响应速度越快,并且在频率高于200 kHz时,长度70 mm的SMA丝才能达到最大预期位移;此外,原、副边线圈之间轴向距离对无线供电SMA驱动器的驱动特性的影响最大,径向偏移的影响次之,偏转角度的影响最小,且随原、副边线圈轴向距离增大,径向偏移和偏转角度对SMA丝驱动特性的影响也越大。研究结果为全密封应用环境下阀门的能量供给与驱动方式提供理论支持与设计参考。 展开更多
关键词 SMA驱动器 谐振式无线供电 驱动特性
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CAGE-YOLO:A Dense Small Object Detection Model for Aquaculture Net Cages Based on Remote Sensing Images 认领 引用 被引量：1
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作者 ZHANG Wenbo JIANG Yijue +2 位作者 SONG Wei HE Qi ZHANG Wenbo 《智慧农业（中英文）》 CSCD 2026年第2期98-117,共20页
[Objective]Detecting dense and small aquaculture net cages in complex backgrounds is difficult,the purpose of this study is to build a specialized dataset and design a targeted detection model that enhances recognitio... [Objective]Detecting dense and small aquaculture net cages in complex backgrounds is difficult,the purpose of this study is to build a specialized dataset and design a targeted detection model that enhances recognition accuracy and robustness for practical aquaculture management.[Methods]A dataset of aquaculture net cages was constructed using highresolution remote sensing imagery collected from seven representative farming regions(Australia,Canada,Chile,Croatia,Greece,China,and the Faroe Islands),and Cage-YOLO,a deep learning model based on YOLOv5,was proposed for detecting dense and small aquaculture net cages.First,an adaptive dense perception algorithm was introduced,which automatically selects and generates feature maps that reflect the high-density distribution of small aquaculture net cages.Second,an enhanced module based on spatial pyramid pooling fast was integrated to effectively reduce background noise interference and improve global feature extraction capabilities.Finally,a mixed attention block was incorporated to further enhance the model's perception of dense and small objects.[Results and Discussions]Experimental results showed that the proposed Cage-YOLO achieved improvements over the original YOLOv5 in terms of precision,recall,and mean average precision by 5.6,21.8,and 17.4 percentage points,respectively.The model size was maintained at 16.9 MB,demonstrating both strong performance and deployment advantages.[Conclusions]This study provides a new approach for dense and small object detection and offers technical support for the intelligent management of marine cage aquaculture. 展开更多
关键词 aquaculture net cage small object detection adaptive dense perception algorithm enhancement spatial pyramid pooling mix attention block
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基于SMA线缆自复位装置与结构研究进展 认领 引用
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作者 周云 阿力马斯·叶尔布拉提 +1 位作者 石菲 列文琛 《工程力学》 EI CSCD 北大核心 2026年第3期1-20,共20页
形状记忆合金(Shape Memory Alloy,SMA)线缆凭借超弹性特性所形成的旗型滞回,具有优越的自复位能力、良好的耗能性能及足够的承载能力,在地震工程中展现出广阔应用前景。该文系统梳理近年来团队研究工作,对SMA线缆在自复位装置及结构抗... 形状记忆合金(Shape Memory Alloy,SMA)线缆凭借超弹性特性所形成的旗型滞回,具有优越的自复位能力、良好的耗能性能及足够的承载能力,在地震工程中展现出广阔应用前景。该文系统梳理近年来团队研究工作,对SMA线缆在自复位装置及结构抗震中的应用进展进行了全面总结。从材料层面归纳总结SMA线缆在锚固系统、循环稳定性、温度效应、疲劳性能以及应力松弛与蠕变特性等方面的实验研究成果,揭示SMA线缆力学机理与服役演化规律,指出预拉力保持与训练策略的关键性。围绕SMA线缆自复位支撑构件的创新设计与性能优化,介绍了SMA线缆支撑、SMA线缆-黏弹阻尼自复位支撑、SMA线缆-摩擦复合自复位支撑以及双阶段自复位支撑的构造特征、工作机理及力学性能,建立相应的宏观力学模型与退化模型。在结构层面梳理了基于SMA线缆支撑钢框架结构的抗震性能研究,包括振动台试验验证、数值模拟分析、极罕遇地震下的韧性提升作用以及主-余震序列下的抗倒塌性能评估。该文构建了贯通“材料-构件-结构”的多层次研究框架,为SMA线缆在高性能抗震结构中的工程化应用与规范化设计提供了系统的理论支撑与实验依据。 展开更多
关键词 形状记忆合金 SMA线缆 自复位装置 振动台实验 抗震性能
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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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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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柔肝清肺方治疗特发性肺间质纤维化的疗效观察及对血清TGF-β1、COL-I、α-SMA水平的影响 认领 引用 被引量：1
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作者 汪美琴 臧敏 +2 位作者 赵晨充 杨美霞 王浩铭 《时珍国医国药》 CAS CSCD 北大核心 2026年第6期1092-1095,共4页
目的 观察特发性肺间质纤维化(IPF)患者应用柔肝清肺方治疗的临床疗效,并分析其潜在作用机制。方法 80例IPF患者,随机分为观察组与对照组各40例。对照组给予西医基础药物和强的松治疗,研究组给予西医基础药物联合柔肝清肺方治疗。两组... 目的 观察特发性肺间质纤维化(IPF)患者应用柔肝清肺方治疗的临床疗效,并分析其潜在作用机制。方法 80例IPF患者,随机分为观察组与对照组各40例。对照组给予西医基础药物和强的松治疗,研究组给予西医基础药物联合柔肝清肺方治疗。两组治疗周期均为8周。比较两组患者治疗前后的临床疗效,并比较两组患者治疗前后血清TGF-β1、α-SMA、COL-I水平。结果 治疗后,观察组的总有效率显著高于对照组(P0.05)。两组不良反应发生率均为12.5%(5/40),均未出现严重不良反应。结论 柔肝清肺方可有效改善IPF患者的临床症状,其作用机制可能与降低血清TGF-β1、α-SMA、COL-I水平有关。 展开更多
关键词 特发性肺间质纤维化 柔肝清肺方 TGF-β1 α-SMA COL-I
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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://gffzz188fe103f8f1460asnqoxc5bpwq5u6906.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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