期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到247,865篇文章
< 1 2 … 250 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
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
1
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
暂未订购 下载PDF
Low-complexity APSK demodulation algorithm based on K-means clustering in LEO satellite communication systems 认领 引用
2
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
暂未订购 下载PDF
Phased-Enhancement Marine Predators Algorithm for Global Optimization and Medical Insurance Fraud Detection 认领 引用
3
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
Intelligent identification for discrete memristive neuron map:An adaptive chaos game optimization algorithm studied from the perspectives of different sample sizes and objective functions 认领 引用
4
作者 Yuexi Peng Xinyi Luo +2 位作者 Zhijun Li Mengjiao Wang Minglin Ma 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第6期276-291,共16页
Discrete memristive neuron systems have attracted considerable attention due to their nonlinear dynamical properties,low computational overhead,and ease of hardware implementation.For the practical engineering applica... Discrete memristive neuron systems have attracted considerable attention due to their nonlinear dynamical properties,low computational overhead,and ease of hardware implementation.For the practical engineering applications of discrete memristive neuron systems,effective control remains a key issue.Parameter identification using intelligent optimization algorithms is an important approach for controlling complex nonlinear systems.However,classical algorithms are prone to falling into local optima and often exhibit high computational complexity,resulting in slow convergence.Therefore,a new algorithm named adaptive chaos game optimization(ACGO)is proposed to address these issues.By introducing a differential evolution mutation strategy and a Cauchy adaptive parameter mechanism,the ACGO algorithm can effectively balance global exploration and local exploitation capabilities.To verify the effectiveness of the proposed algorithm,it is applied to parameter identification in five discrete memristive neuron maps(DMNMs)and compared with seven intelligent optimization algorithms.Simulation results demonstrate that the ACGO algorithm achieves higher accuracy and faster convergence.In addition,an in-depth investigation is conducted into the effects of sample size and objective function on identification performance.The results indicate that setting the sample size to 4 and selecting the mean squared error(MSE)as the objective function can achieve better identification performance and a high level of robustness. 展开更多
关键词 discrete memristive neuron map parameter identification chaos game optimization algorithm sample size
暂未订购 下载PDF
Identification of small impact craters in Chang’e-4 landing areas using a new multi-scale fusion crater detection algorithm 认领 引用
5
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
暂未订购 下载PDF
An APO Algorithm Based on Taguchi Methods and Its Application in Multi-Level Image Segmentation 认领 引用
6
作者 Jeng-Shyang Pan Yan-Na Wei +3 位作者 Ling-Da Chi Shu-Chuan Chu Ru-Yu Wang Junzo Watada 《Computers, Materials & Continua》 SCIE EI 2026年第5期814-837,共24页
Multilevel image segmentation is a critical task in image analysis,which imposes high requirements on the global search capability and convergence efficiency of segmentation algorithms.In this paper,an improved Artifi... Multilevel image segmentation is a critical task in image analysis,which imposes high requirements on the global search capability and convergence efficiency of segmentation algorithms.In this paper,an improved Artificial Protozoa Optimization algorithm,termed the two-stage Taguchi-assisted Gaussian–Levy Artificial Protozoa Optimization(TGAPO)algorithm,is proposed and applied tomultilevel image segmentation.The proposed algorithm adopts a two-stage evolutionary mechanism.In the first stage,Gaussian perturbation is introduced to enhance local search capability;in the second stage,Levy flight is incorporated to expand the global search range;and finally,the Taguchi strategy is employed to further refine the optimal solution.Consequently,the global optimization performance and robustness of the algorithm are significantly improved.To evaluate the effectiveness of the proposed TGAPO algorithm,comparative experiments are conducted with representative optimization algorithms,including the Grey Wolf Optimizer(GWO)and Particle Swarm Optimization(PSO),in the context ofmultilevel image segmentation.The segmentation quality is assessed using the minimum cross-entropy function as the performance metric.Experimental results demonstrate that the TGAPO algorithm outperforms the comparison algorithms in terms of segmentation accuracy and convergence speed,and exhibits superior stability in high-threshold segmentation tasks.Furthermore,the proposedmethod achieves excellentmulti-threshold segmentation performance for color images and shows strong potential for practical applications. 展开更多
关键词 Meta-heuristic algorithm multilevel image segmentation taguchi strategy minimum cross-entropy threshold artificial protozoa optimization(APO)
暂未订购 下载PDF
SSA*-PDWA:A Hierarchical Path Planning Framework with Enhanced A*Algorithm and Dynamic Window Approach for Mobile Robots 认领 引用
7
作者 Lishu Qin Yu Gao Xinyuan Lu 《Computers, Materials & Continua》 SCIE EI 2026年第4期2069-2094,共26页
With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper pro... With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper proposes an intelligent path planning framework based on grid map modeling.First,an improved Safe and Smooth A*(SSA*)algorithm is employed for global path planning.By incorporating obstacle expansion and cornerpoint optimization,the proposed SSA*enhances the safety and smoothness of the planned path.Then,a Partitioned Dynamic Window Approach(PDWA)is integrated for local planning,which is triggered when dynamic or sudden static obstacles appear,enabling real-time obstacle avoidance and path adjustment.A unified objective function is constructed,considering path length,safety,and smoothness comprehensively.Multiple simulation experiments are conducted on typical port grid maps.The results demonstrate that the improved SSA*significantly reduces the number of expanded nodes and computation time in static environmentswhile generating smoother and safer paths.Meanwhile,the PDWA exhibits strong real-time performance and robustness in dynamic scenarios,achieving shorter paths and lower planning times compared to other graph search algorithms.The proposedmethodmaintains stable performance across maps of different scales and various port scenarios,verifying its practicality and potential for wider application. 展开更多
关键词 Dynamic window approach improved A*algorithm dynamic path planning trajectory optimization
暂未订购 下载PDF
A Review of Genetic Algorithms:Principles, Procedures, and Applications in Optimization 认领 引用
8
作者 M.A.El-Shorbagy 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期138-184,共47页
This paper provides a thorough examination of Genetic Algorithms(GAs),a category of evolutionary computation methods derived from the concepts of natural selection and genetics.The main concept and operational princip... This paper provides a thorough examination of Genetic Algorithms(GAs),a category of evolutionary computation methods derived from the concepts of natural selection and genetics.The main concept and operational principle of GAs are elucidated,highlighting the evolution of populations of candidate solutions across multiple generations to get optimal or near-optimal solutions for complicated problems.The paper delineates the sequential phases of a conventional GA,encompassing problem formulation,solution encoding,initialization of population,fitness evaluation,selection,crossover,mutation,and termination criteria,so offering a coherent framework for comprehending the algorithm’s functionality.Moreover,numerous prominent genetic operators,including crossover and mutation,are examined,highlighting their distinct forms and processes for fostering diversity and exploration within the search space.Also,the paper emphasizes the benefits of GAs,including their capacity to address nonlinear,multimodal,and high-dimensional optimization challenges without necessitating gradient information,along with their adaptability in resolving both continuous and discrete issues.The limitations and constraints of GAs,such as computing expense,parameter optimization,and the risk of premature convergence,are thoroughly analyzed.The paper examines various applications of GAs across fields,including engineering design,control systems,combinatorial optimization,machine learning,operations research,and multi-objective optimization,demonstrating the versatility and practical significance of this evolutionary method.This work establishes a robust basis for scholars and practitioners seeking to implement GAs in intricate optimization challenges.The review indicates that GAs have greatly progressed from Holland’s original formulation to specialized variations,such as real-valued,permutation,and tree-based encodings,each tailored to certain issue categories.The critical study indicates that although classical GAs are proficient in global exploration,their hybridization with local search techniques(memetic algorithms),swarm intelligence(GA-PSO),and surrogate models significantly improves convergence time and solution accuracy.The study highlights ongoing research deficiencies,such as the disparity between theoretical convergence proofs and the actual performance of algorithms,as well as the necessity for systematic recommendations in the design of hybrid algorithms. 展开更多
关键词 Genetic algorithm evolutionary computation global optimization nonlinear optimization computational intelligence optimization techniques
暂未订购 下载PDF
Methods,Algorithms,Codes,and Software for Theoretical and Computational Chemistry 认领 引用
9
作者 Bin Jiang 《Chinese Journal of Chemical Physics》 SCIE EI CAS CSCD 2026年第3期301-302,共2页
Theoretical and computational chemistry has profoundly impacted a wide range of disciplines,from chemistry and physics to biology and materials science.In recent years,remarkable advances in electronic structure theor... Theoretical and computational chemistry has profoundly impacted a wide range of disciplines,from chemistry and physics to biology and materials science.In recent years,remarkable advances in electronic structure theory,molecular dynamics,and machine learning methods——coupled with increasingly powerful algorithms and software—have equipped chemists with an unprecedented arsenal of tools to tackle complex chemical problems. 展开更多
关键词 theoretical computational chemistry electronic structure theorymolecular dynamicsand algorithms codes software methods materials sciencein theoretical chemistry
暂未订购 下载PDF
Hierarchical algorithm for large-scale irregular packing problems 认领 引用
10
作者 Xiao LIU 《ENGINEERING Information Technology & Electronic Engineering》 SCIE EI CSCD 2026年第3期69-80,共12页
To address the challenge of large-scale packing problems,this paper proposes a novel hierarchical algorithm based on the geometrical classification of parts.The algorithm begins by classifying parts into three levels ... To address the challenge of large-scale packing problems,this paper proposes a novel hierarchical algorithm based on the geometrical classification of parts.The algorithm begins by classifying parts into three levels based on their area and fullness and then applies distinct packing strategies to each category.An innovative“shape matching”method is introduced,which,together with the“box stacking”(for rectangular parts)and“gravity packing,”forms a comprehensive hierarchical packing system.Level-1 comprises large rectangular parts,which are arranged using the box stacking algorithm.By aligning the corner points of the parts’bounding boxes,this method avoids the hooking issue commonly encountered in gravity packing.Level-2 includes both large,irregular parts and medium-sized parts.They are first processed using the shape matching algorithm,where rotation and translation are applied to achieve contour complementarity.The quality of the match is evaluated using the shape matching coefficient(SMC).If the SMC fails to reach the preset quality threshold,the system switches to box stacking(for large,irregular parts)or gravity packing(for medium-sized parts).Level-3 comprises the remaining smaller parts and those that failed to pack in the previous two levels.For these parts,shape matching is attempted first,and the system resorts to gravity packing in case of failure.The experimental and comparative results demonstrate that the proposed hierarchical algorithm achieves higher material utilization than the traditional gravity packing algorithm.This improvement is facilitated by the box stacking and shape matching strategies,which promote a more orderly and compact arrangement of parts. 展开更多
关键词 Large-scale packing Hierarchical algorithm Box stacking Shape matching Gravity packing Principle of minimum potential energy
暂未订购 下载PDF
Pigeon-Inspired Optimization Algorithm:Definition,Variants,and Its Applications in Unmanned Aerial Vehicles 认领 引用
11
作者 Yu-Xuan Zhou Kai-Qing Zhou +2 位作者 Wei-Lin Chen Zhou-Hua Liao Di-Wen Kang 《Computers, Materials & Continua》 SCIE EI 2026年第4期186-225,共40页
ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the ... ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the algorithmhas attracted considerable academic and industrial interest owing to its effective balance between exploration and exploitation,coupled with advantages in real-time performance and robustness.Nevertheless,as applications have diversified,limitations in convergence precision and a tendency toward premature convergence have become increasingly evident,highlighting a need for improvement.This reviewsystematically outlines the developmental trajectory of the PIO algorithm,with a particular focus on its core applications in UAV navigation,multi-objective formulations,and a spectrum of variantmodels that have emerged in recent years.It offers a structured analysis of the foundational principles underlying the PIO.It conducts a comparative assessment of various performance-enhanced versions,including hybrid models that integrate mechanisms from other optimization paradigms.Additionally,the strengths andweaknesses of distinct PIOvariants are critically examined frommultiple perspectives,including intrinsic algorithmic characteristics,suitability for specific application scenarios,objective function design,and the rigor of the statistical evaluation methodologies employed in empirical studies.Finally,this paper identifies principal challenges within current PIO research and proposes several prospective research directions.Future work should focus on mitigating premature convergence by refining the two-phase search structure and adjusting the exponential decrease of individual numbers during the landmark operator.Enhancing parameter adaptation strategies,potentially using reinforcement learning for dynamic tuning,and advancing theoretical analyses on convergence and complexity are also critical.Further applications should be explored in constrained path planning,Neural Architecture Search(NAS),and other real-worldmulti-objective problems.For Multi-objective PIO(MPIO),key improvements include controlling the growth of the external archive and designing more effective selection mechanisms to maintain convergence efficiency.These efforts are expected to strengthen both the theoretical foundation and practical versatility of PIO and its variants. 展开更多
关键词 Pigeon-inspired optimization metaheuristic algorithm algorithmvariants swarmintelligence variants UAVs convergence analysis
暂未订购 下载PDF
Design of Consensus Algorithm for UAV Swarm Identity Authentication Based on Lightweight Blockchain 认领 引用
12
作者 Yuji Sang Lijun Liu +2 位作者 Long Lv Husheng Wu Hemin Yin 《Computers, Materials & Continua》 SCIE EI 2026年第5期639-663,共25页
Aiming at the challenges of low throughput,excessive consensus latency and high communication complexity in the Practical Byzantine Fault Tolerance(PBFT)algorithm in blockchain networks,its application in identity ver... Aiming at the challenges of low throughput,excessive consensus latency and high communication complexity in the Practical Byzantine Fault Tolerance(PBFT)algorithm in blockchain networks,its application in identity verification for distributed networking of a drone cluster is limited.Therefore,a lightweight blockchainbased identity authentication model for UAV swarms is designed,and a Credit-score and Grouping-mechanism Practical Byzantine Fault Tolerance(CG-PBFT)algorithm is proposed.CG-PBFT introduces a reputation score evaluation mechanism,classifies the reputation levels of nodes in the network,and optimizes the consensus process based on grouping consensus and BLS aggregate signature technology.Experimental results demonstrate that under identical experimental conditions,compared with the PBFT algorithm,CG-PBFT achieves a 250%increase in average throughput,a 70%reduction in average latency,and simultaneous enhancement in security,thus making it more suitable for UAV swarm networks. 展开更多
关键词 UAV swarm network blockchain PBFT consensus algorithm credit score
暂未订购 下载PDF
Predictive modeling for mechanical properties of cold-rolled strip steel based on random forest regression and whale optimization algorithm 认领 引用
13
作者 Hong-Lei Cai Yi-Ming Fang +3 位作者 Le Liu Li-Hui Ren Zhen-Dong Liu Xiao-Dong Zhao 《Journal of Iron and Steel Research International》 SCIE EI CSCD 2026年第3期73-87,共15页
In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method n... In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method named RFR-WOA is developed based on random forest regression(RFR)and whale optimization algorithm(WOA).Firstly,using Pearson and Spearman correlation analysis and Gini coefficient importance ranking on an actual production dataset containing 37,878 samples,22 key variables are selected as model inputs from 112 variables that affect mechanical properties.Subsequently,an RFR-based predictive model for the mechanical properties of cold-rolled strip steel is constructed.Then,with the combination of the coefficient of determination(R2)and root mean square error as the optimization objective,the hyperparameters of RFR model are iteratively optimized using WOA,and better predictive effectiveness is obtained.Finally,the mechanical properties prediction model based on RFR-WOA is compared with models established using deep neural networks,convolutional neural networks,and other methods.The test results on 9469 samples of actual production data show that the model developed present has better predictive accuracy and generalization capability. 展开更多
关键词 Cold-rolled strip steel Mechanical property Predictive modeling Random forest regression Whale optimization algorithm
暂未订购 下载PDF
Algorithmically Enhanced Data-Driven Prediction of Shear Strength for Concrete-Filled Steel Tubes 认领 引用
14
作者 Shengkang Zhang Yong Jin +5 位作者 Soon Poh Yap Haoyun Fan Shiyuan Li Ahmed El-Shafie Zainah Ibrahim Amr El-Dieb 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期374-398,共25页
Concrete-filled steel tubes(CFST)are widely utilized in civil engineering due to their superior load-bearing capacity,ductility,and seismic resistance.However,existing design codes,such as AISC and Eurocode 4,tend to ... Concrete-filled steel tubes(CFST)are widely utilized in civil engineering due to their superior load-bearing capacity,ductility,and seismic resistance.However,existing design codes,such as AISC and Eurocode 4,tend to be excessively conservative as they fail to account for the composite action between the steel tube and the concrete core.To address this limitation,this study proposes a hybrid model that integrates XGBoost with the Pied Kingfisher Optimizer(PKO),a nature-inspired algorithm,to enhance the accuracy of shear strength prediction for CFST columns.Additionally,quantile regression is employed to construct prediction intervals for the ultimate shear force,while the Asymmetric Squared Error Loss(ASEL)function is incorporated to mitigate overestimation errors.The computational results demonstrate that the PKO-XGBoost model delivers superior predictive accuracy,achieving a Mean Absolute Percentage Error(MAPE)of 4.431%and R2 of 0.9925 on the test set.Furthermore,the ASEL-PKO-XGBoost model substantially reduces overestimation errors to 28.26%,with negligible impact on predictive performance.Additionally,based on the Genetic Algorithm(GA)and existing equation models,a strength equation model is developed,achieving markedly higher accuracy than existing models(R2=0.934).Lastly,web-based Graphical User Interfaces(GUIs)were developed to enable real-time prediction. 展开更多
关键词 Asymmetric squared error loss genetic algorithm machine learning pied kingfisher optimizer quantile regression
暂未订购 下载PDF
A Workflow Scheduling Method Based on the Combination of Tunicate Swarm Algorithm and Highest Response Ratio Next Scheduling 认领 引用
15
作者 Yujie Tian Ming Zhu +2 位作者 Jing Li Cong Liu Ziyang Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第5期1950-1963,共14页
Workflow scheduling is critical for efficient cloud resource management.This paper proposes Tunicate Swarm-Highest Response Ratio Next,a novel scheduler that synergistically combines the Tunicate Swarm Algorithm with ... Workflow scheduling is critical for efficient cloud resource management.This paper proposes Tunicate Swarm-Highest Response Ratio Next,a novel scheduler that synergistically combines the Tunicate Swarm Algorithm with the Highest Response Ratio Next policy.The Tunicate Swarm Algorithm generates a cost-minimizing task-to-VM mapping scheme,while the Highest Response Ratio Next dynamically dispatches tasks in the ready queue with the highest-priority.Experimental results demonstrate that the Tunicate Swarm-Highest Response RatioNext reduces costs by up to 94.8%compared to meta-heuristic baselines.It also achieves competitive cost efficiency vs.a learning-based method while offering superior operational simplicity and efficiency,establishing it as a highly practical solution for dynamic cloud environments. 展开更多
关键词 Workflow scheduling cloud computing tunicate swarm algorithm highest response ratio next scheduling
暂未订购 下载PDF
Energy Aware Task Scheduling of IoT Application Using a Hybrid Metaheuristic Algorithm in Cloud Computing 认领 引用
16
作者 Ahmed Awad Mohamed Eslam Abdelhakim Seyam +4 位作者 Ahmed R.Elsaeed Laith Abualigah Aseel Smerat Ahmed M.AbdelMouty Hosam E.Refaat 《Computers, Materials & Continua》 SCIE EI 2026年第3期1786-1803,共18页
In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task schedul... In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption. 展开更多
关键词 Energy-efficient tasks internet of things(IoT) cloud fog computing artificial ecosystem-based optimization salp swarm algorithm cloud computing
暂未订购 下载PDF
Enhanced diagnosis of diabetic retinopathy:integrating advanced algorithms for automated detection and classification 认领 引用
17
作者 E Murali T Prasad +2 位作者 JHari Krishna Abdul Hussain Sharief Peta Nandini 《International Journal of Ophthalmology(English edition)》 SCIE CAS 2026年第4期637-645,共9页
AIM:To develop an automated diagnostic system for early detection of diabetic retinopathy(DR)using fundus images by identifying exudates,hemorrhages,and microaneurysms with advanced image processing and machine learni... AIM:To develop an automated diagnostic system for early detection of diabetic retinopathy(DR)using fundus images by identifying exudates,hemorrhages,and microaneurysms with advanced image processing and machine learning techniques.METHODS:Fundus images from the IDRiD dataset and additional Kaggle datasets were used.A wavelet-based band-pass filter was applied for edge enhancement of retinal features.Gaussian mixture model(GMM)clustering was used to segment and extract texture features.These extracted features were classified using machine learning algorithms,including a random forest classifier and a multilayer perceptron neural network.Performance metrics such as sensitivity,specificity,and accuracy were computed to evaluate the proposed model’s diagnostic effectiveness.RESULTS:The random forest-based classification system achieved a sensitivity of 95.08%,specificity of 86.67%,and overall accuracy of 95.20%in detecting DR lesions.The combination of wavelet-based edge enhancement,GMM clustering,and neural network-based feature classification demonstrated high reliability in lesion identification.CONCLUSION:The proposed method effectively detects early signs of DR from fundus images,offering a highaccuracy,automated,and scalable solution for assisting ophthalmologists.Its application can support large-scale screening programs,particularly in regions with limited access to specialized eye care. 展开更多
关键词 diabetic retinopathy random forest algorithm Gaussian mixture modelclustering multilayer perceptron fundus image analysis
暂未订购 下载PDF
Medical data mining for diabetic retinopathy:Imaging modalities and algorithms 认领 引用
18
作者 Bobbinpreet Kaur Sheenam +1 位作者 Aleem Ali Fakhrun Jamal 《Medical Data Mining》 CAS 2026年第3期62-73,共12页
Diabetic retinopathy(DR)is one of the primary issue of vision loss.It is caused by damages of blood vessels of the retina.Advances in imaging technology and the computational intelligence are turning out to be an effe... Diabetic retinopathy(DR)is one of the primary issue of vision loss.It is caused by damages of blood vessels of the retina.Advances in imaging technology and the computational intelligence are turning out to be an effective tool to accurately and early diagnosis of Diabetic retinopathy.This review article highlights a critical analysis of the existing literature on machine learning and deep learning model applied to fundus photography,optical coherence tomography(OCT),and RetCam imaging.Public datasets such as EyePACS,IDRiD,and Messidor have been widely used but remain challenged by variability,class imbalance,and annotation quality.Data mining techniques—such as clustering to discern disease progression trends,feature selection to minimize dimensionality are essential for deriving relevant clinical insights.The results demonstrate that deep learning-based CAD systems surpass typical machine learning methods,with classification accuracies greater than 90%in multi-stage DR severity assessment.Fundus photography integrated with CNN-based models exhibits significant promise for extensive screening,but OCT-based methods offer improved structural examination of retinal layers.Therefore,an overall computer-aided diagnosis(CAD)supported by medical data mining can enable cost-effective,scalable,and precise DR screening,thereby reducing the global burden of diabetes-related blindness. 展开更多
关键词 diabetic retinopathy medical data mining computer-aided diagnosis retinal imaging datasets deep learning algorithms health informatics
暂未订购 下载PDF
Empirical tropospheric zenith wet delay models with strong generalization capability based on a robust machine learning fusion algorithm 认领 引用
19
作者 Jiahao Zhang Qin Liang Yunqing Huang 《Geodesy and Geodynamics》 EI CSCD 2026年第2期211-224,共14页
Tropospheric zenith wet delay(ZWD)plays a vital role in the analysis of space geodetic observations.In recent years,machine learning methods have been increasingly applied to improve the accuracy of ZWD calculations.H... Tropospheric zenith wet delay(ZWD)plays a vital role in the analysis of space geodetic observations.In recent years,machine learning methods have been increasingly applied to improve the accuracy of ZWD calculations.However,a single machine learning model has limited generalization capabilities.To address these limitations,this study introduces a novel machine learning fusion(MLF)algorithm with stronger generalization capabilities to enhance ZWD modeling and prediction accuracy.The MLF algorithm utilizes a two-layer structure integrating extra trees(ET),backpropagation neural network(BPNN),and linear regression models.By comparing the root mean square error(RMSE)of these models,we found that both ET-based and MLF-based models outperform RF-based and BPNN-based models in terms of internal and external accuracy,across both surface meteorological data-based and blind models.The improvement in exte rnal accuracy is particularly significant in the blind models.Our re sults show that the MLF(with an RMSE of 3.93 cm)and ET(3.99 cm)models outperform the traditional GPT3model(4.07 cm),while the RF(4.21 cm)and BPNN(4.14 cm)have worse external accuracies than the GPT3 model.It is worth noting that the BPNN suffered from overfitting during external accuracy tests,which was avoided by the MLF.In summary,regardless of the availability of surface meteorological data,the MLF-based empirical models demonstrate superior internal and external accuracy compared to the other tested models in this study. 展开更多
关键词 Tropospheric zenith wet delay Machine learning Extra trees Machine learning fusion algorithm Empirical models
暂未订购 下载PDF
A Robust Damage Identification Method Based on Modified Holistic Swarm Optimization Algorithm and Hybrid Objective Function 认领 引用
20
作者 Xiansong Xie Xiaoqian Qian 《Structural Durability & Health Monitoring》 EI 2026年第2期235-259,共25页
Correlation function of acceleration responses-based damage identificationmethods has been developed and employed,while they still face the difficulty in identifying local orminor structural damages.To deal with this ... Correlation function of acceleration responses-based damage identificationmethods has been developed and employed,while they still face the difficulty in identifying local orminor structural damages.To deal with this issue,a robust structural damage identification method is developed,integrating a modified holistic swarm optimization(MHSO)algorithm with a hybrid objective function.The MHSO is developed by combining Hammersley sequencebased population initialization,chaotic search around the worst solution,and Hooke-Jeeves pattern search around the best solution,thereby improving both global exploration and local exploitation capabilities.A hybrid objective function is constructed by merging acceleration correlation function-based and strain correlation function-based objective functions,effectively leveraging the complementary sensitivities of global and local responses.To further suppress spurious solutions and promote sparsity in parameter estimation,an additional L0.5 regularization term is introduced.The effectiveness of the proposed method is validated through numerical simulations on a simply supported beam and a steel girder benchmark structure.Comparative studies with sequential quadratic programming,genetic algorithm,andHSO demonstrate that theMHSOachieves superior accuracy and convergence efficiency,even with limited sensors and 20%noise-contaminated measurements.Results highlight that the hybrid objective function significantly enhances the detection of both major and minor damages,while the inclusion of sparse regularization improves robustness against noise and model uncertainties.The findings indicate that the proposed framework provides a reliable and computationally efficient solution for simultaneous localization and quantification of structural damages,offering promising applicability to real-world structural health monitoring scenarios. 展开更多
关键词 Damage identification holistic swarm optimization algorithm combined correlation function hybrid objective function sparse regularization grid structure
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 250 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈