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10,910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：
Structural Reliability Analysis Based on Differential Evolution Algorithm and Hypersphere Integration 认领 引用
1
作者 CHEN Zhenzhong HAN Zhuo +4 位作者 WANG Peiyu PAN Qianghua LI Xiaoke GAN Xuehui CHEN Ge 《Journal of Donghua University(English Edition)》 CAS 2026年第1期118-130,共13页
In reliability analyses,the absence of a priori information on the most probable point of failure(MPP)may result in overlooking critical points,thereby leading to biased assessment outcomes.Moreover,second-order relia... In reliability analyses,the absence of a priori information on the most probable point of failure(MPP)may result in overlooking critical points,thereby leading to biased assessment outcomes.Moreover,second-order reliability methods exhibit limited accuracy in highly nonlinear scenarios.To overcome these challenges,a novel reliability analysis strategy based on a multimodal differential evolution algorithm and a hypersphere integration method is proposed.Initially,the penalty function method is employed to reformulate the MPP search problem as a conditionally constrained optimization task.Subsequently,a differential evolution algorithm incorporating a population delineation strategy is utilized to identify all MPPs.Finally,a paraboloid equation is constructed based on the curvature of the limit-state function at the MPPs,and the failure probability of the structure is calculated by using the hypersphere integration method.The localization effectiveness of the MPPs is compared through multiple numerical cases and two engineering examples,with accuracy comparisons of failure probabilities against the first-order reliability method(FORM)and the secondorder reliability method(SORM).The results indicate that the method effectively identifies existing MPPs and achieves higher solution precision. 展开更多
关键词 reliability analysis design point positioning differential evolution algorithm hypersphere integration
暂未订购 下载PDF
SL-COA:Hybrid Efficient and Enhanced Coati Optimization Algorithm for Structural Reliability Analysis 认领 引用 被引量：1
2
作者 Yunhan Ling Huajun Peng +4 位作者 Yiqing Shi Chao Xu Jingzhen Yan Jingjing Wang Hui Ma 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第4期767-808,共42页
Thetraditional first-order reliability method(FORM)often encounters challengeswith non-convergence of results or excessive calculation when analyzing complex engineering problems.To improve the global convergence spee... Thetraditional first-order reliability method(FORM)often encounters challengeswith non-convergence of results or excessive calculation when analyzing complex engineering problems.To improve the global convergence speed of structural reliability analysis,an improved coati optimization algorithm(COA)is proposed in this paper.In this study,the social learning strategy is used to improve the coati optimization algorithm(SL-COA),which improves the convergence speed and robustness of the newheuristic optimization algorithm.Then,the SL-COAis comparedwith the latest heuristic optimization algorithms such as the original COA,whale optimization algorithm(WOA),and osprey optimization algorithm(OOA)in the CEC2005 and CEC2017 test function sets and two engineering optimization design examples.The optimization results show that the proposed SL-COA algorithm has a high competitiveness.Secondly,this study introduces the SL-COA algorithm into the MPP(Most Probable Point)search process based on FORM and constructs a new reliability analysis method.Finally,the proposed reliability analysis method is verified by four mathematical examples and two engineering examples.The results show that the proposed SL-COA-assisted FORM exhibits fast convergence and avoids premature convergence to local optima as demonstrated by its successful application to problems such as composite cylinder design and support bracket analysis. 展开更多
关键词 Hybrid reliability analysis single-loop interactive hybrid analysis most probability point metaheuristic algorithms coati optimization algorithm
暂未订购 下载PDF
Gradient Optimization Assisted Bubble Sampling Method for Structural Reliability Analysis 认领 引用
3
作者 Zeng Meng LiwenjieShen +2 位作者 Changquan Li Jiaxiang Yi Gang Dong 《Acta Mechanica Solida Sinica》 SCIE EI CSCD 2026年第6期891-904,共14页
The sampling method is an important numerical technique for solving reliability problems in engineering systems.However,the evaluation of the failure probability using classical sampling methods is time-consuming for ... The sampling method is an important numerical technique for solving reliability problems in engineering systems.However,the evaluation of the failure probability using classical sampling methods is time-consuming for complex engineering structure.To address this issue,this paper proposes a gradient optimization assisted bubble sampling method(GOBSM)to reduce the computational costs,which enhances the coverage range of bubbles,thereby improving the computational efficiency without sacrificing the accuracy.Furthermore,the bubble gradient iterative algorithm is developed to efficiently construct bubbles.Eight complex numerical examples are tested for assessing the failure probability,and the results demonstrate the performance of GOBSM. 展开更多
关键词 Reliability analysis Bubble sampling method Bubble gradient iterative algorithm Failure probability
暂未订购 下载PDF
A high-resolution time-frequency analysis tool for fault diagnosis of rotating machinery 认领 引用
4
作者 Gang Yu Zhenghao Cui 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第1期145-154,共10页
Fault features in mechanical systems often manifest as transient impulses,which can be effectively analyzed using time-frequency analysis(TFA)methods.Recently,a new TFA technique known as the time-reassigned multi-syn... Fault features in mechanical systems often manifest as transient impulses,which can be effectively analyzed using time-frequency analysis(TFA)methods.Recently,a new TFA technique known as the time-reassigned multi-synchrosqueezing transform(TMssT)was proposed to capture these transient impulses for fault diagnosis.However,the TMSST,which is based on the short-time Fourier transform(STFT),suffers from unclear high-frequency re-presentations owing to the fixed sliding window used in the STFT.To address this limitation,the current study combined TMSST with the S-transform and a local maximum method to enhance the time-frequency representation for improved signal analysis.Furthermore,an extractive reconstruction algorithm that binds the maximum value of the spectral envelope is proposed for spectral decomposition.To validate the proposed technique,a simulated noise-added signal and four experimental bearing defect datasets were used.The results demonstrate that the proposed technique can effectively and accurately extract fault features from bearing signals regardless of whether the bearings operate under constant or varying speed conditions.This study offers a novel and efficient approach for fault diagnosis in mechanical systems with complex dynamic behaviors. 展开更多
关键词 Time-frequency analysis S-transform Extractive reconstruction algorithm Mechanical systems
暂未订购 下载PDF
Back analysis of rock mass parameters in mechanized twin tunnels based on coupled auto machine learning and multi-objective optimization algorithm 认领 引用 被引量：1
5
作者 Chengwen Wang Xiaoli Liu +4 位作者 Jiubao Li Enzhi Wang Nan Hu Wenli Yao Zhihui He 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第11期7038-7055,共18页
Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approache... Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations. 展开更多
关键词 Back analysis of rock parameters Auto machine learning Multi-objective optimization algorithm Mechanized twin tunnels Parametric modeling
暂未订购 下载PDF
Teaching Reform and Practice of Algorithm Design and Analysis Course 认领 引用
6
作者 Kejia Zhang Haiwei Pan +3 位作者 Zhiqiang Ma Shaoqiang Zhu Yingxin Qin Lan Zhang 《国际计算机前沿大会会议论文集》 2025年第1期140-149,共10页
This paper reforms the shortcomings and difficulties in the teaching process of the“Algorithm Design and Analysis”course.The knowledge graph optimizes the teaching content,abandons the traditional teaching method,ca... This paper reforms the shortcomings and difficulties in the teaching process of the“Algorithm Design and Analysis”course.The knowledge graph optimizes the teaching content,abandons the traditional teaching method,captures the direction of talent demand,adjusts the class time allocation,reorganizes the assessment method,focuses on practical hands-on ability,and designs a multistage teaching quality evaluation system to promote the overall improvement of teaching quality.The practice of course reform has proven that the“Algorithm Design and Analysis”course has achieved good teaching results after a series of teaching reform measures. 展开更多
关键词 Algorithm Design Analysis Teaching Reform Talent Cultivation
Using Large Language Models to Promote Vocational Skills Improvement:Reform and Practice of the“Algorithm Design and Analysis”Course in Higher Education 认领 引用
7
作者 Kejia Zhang Haiwei Pan +3 位作者 Zhiqiang Ma Shaoqiang Zhu Xiaoliang Qin Lan Zhang 《国际计算机前沿大会会议论文集》 2025年第1期150-159,共10页
“Algorithm Design and Analysis”is not only one of the important courses in the undergraduate teaching of computer science and technology but also a key part of computer professional skills.In recent years,with the r... “Algorithm Design and Analysis”is not only one of the important courses in the undergraduate teaching of computer science and technology but also a key part of computer professional skills.In recent years,with the rise and widespread application of big language models,many teaching reform plans have been produced to promote the quality and efficiency of teaching.This paper studies how to refer to software development professional skills standards,investigates the knowledge points of“Algorithm Design and Analysis”courses in other educational institutions,uses cutting-edge core technology big language models to drive the improvement of teaching evaluation methods,improves teaching efficiency,and carries out reforms and practices in teaching content for undergraduate students in computer science. 展开更多
关键词 Algorithm Design and Analysis Large Language Model Professional Skill Standards Teaching Reform
Pigeon-Inspired Optimization Algorithm:Definition,Variants,and Its Applications in Unmanned Aerial Vehicles 认领 引用
8
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
Multi-objective optimization and biomechanical injury analysis of rear-seat occupant restraint systems in small overlap collision 认领 引用
9
作者 Jia Yu Jingyu Xu +5 位作者 Tao Xiong Meng Xu Hao Deng Zhiyong Yin Xiuju Yang Shengxiong Liu 《Theoretical & Applied Mechanics Letters》 EI CAS CSCD 2026年第1期118-126,共9页
In small overlap collision,rear-seat occupants face elevated injury risks.This study conducts multi-objective opti-mization of the rear-seat occupant restraint system to reduce these injury risks and enhance overall v... In small overlap collision,rear-seat occupants face elevated injury risks.This study conducts multi-objective opti-mization of the rear-seat occupant restraint system to reduce these injury risks and enhance overall vehicle safety performance.Based on real-world traffic accident data,a full-vehicle crash simulation model was established and validated with the actual injury data.Weighted injury criteria(WIC)and neck injury metrics(Nij)were selected as optimization objectives.The rear-seat restraint system was optimized using NSGA-Ⅱ and TOPSIS algorithms to determine the optimal parameter configurations.The optimized parameters were subsequently reintegrated into the simulation model for validation.The results demonstrate a significant reduction in occupant injuries,with WIC and Nij reduced by 30.3%and 20.7%,respectively. 展开更多
关键词 Small overlap collision Rear-seat occupant injury Biomechanics analysis Multi-objective algorithm
暂未订购 下载PDF
A Comprehensive Framework for Nature-Inspired Photovoltaic Model Calibration and Explainable Surrogate-Based Sensitivity Analysis 认领 引用
10
作者 Yan-Hao Huang Chung-Ming Kao 《Computers, Materials & Continua》 SCIE EI 2026年第6期2354-2379,共26页
Photovoltaic(PV)equivalent-circuit models are widely used for performance evaluation and diagnostics,but their usefulness relies on both accurate calibration and interpretable understanding of how parameters shape cur... Photovoltaic(PV)equivalent-circuit models are widely used for performance evaluation and diagnostics,but their usefulness relies on both accurate calibration and interpretable understanding of how parameters shape current-voltage(Ⅰ-Ⅴ)behavior.For nonlinear and strongly coupled PV models,conventional global sensitivity analysis can be computationally demanding and offer limited insight into effect direction and operating-point dependence.This study presents an method-oriented framework that integrates nature-inspired optimization with surrogate-based explainable global sensitivity analysis under a specified operating condition.The Starfish Optimization Algorithm(SFOA)is first used for parameter identification by searching for the optimal parameter set that minimizes the discrepancy between measured and model-predicted Ⅰ-Ⅴ data for the Single-Diode Model(SDM)and Double-Diode Model(DDM).A Random Forest(RF)surrogate is trained to approximate the mapping from voltage and parameters to output current.Its accuracy is evaluated on an independent test set,achieving RMSE/R2 of 0.001331/0.999366 for SDM and 0.003090/0.999394 for DDM.Sensitivity is quantified primarily using Shapley Additive Explanations(SHAP),with mean decrease in impurity(MDI)and one-factor-at-a-time(OFAT)analysis used for cross-validation.Under identical settings,SFOA achieves the best accuracy among competing optimizers,with best root-mean-square error(RMSE)values of 0.0008818 for SDM and 0.0008811 for DDM.The integrated SHAP,MDI,and OFAT analyses yield consistent importance structures,and the overall ranking for DDM follows the same trend as that for SDM.Within the examined±5%neighborhood around the calibrated optimum,the photocurrent is the dominant factor governing Ⅰ-Ⅴ behavior,whereas diode-branch parameters show secondary and condition-dependent effects,and resistive parameters mainly contribute fine-scale adjustments within the examined neighborhood.Overall,the proposed framework provides accurate calibration and interpretable global sensitivity insights that can support a practical workflow for model-based PV analysis under the considered condition. 展开更多
关键词 Photovoltaic parameter identification sensitivity analysis starfish optimization algorithm random forest nature-inspired algorithms
暂未订购 下载PDF
Optimizing IoT-Driven Smart Cities with the Dynamic Leader Sibha Algorithm:A Novel Approach to Feature Selection and Hyperparameter Tuning 认领 引用
11
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
暂未订购 下载PDF
Enhanced diagnosis of diabetic retinopathy:integrating advanced algorithms for automated detection and classification 认领 引用
12
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
Economic Optimization of Wind Solar Energy Storage Microgrid in the Northwest Gobi Region of China Based on Improved MDA Algorithm 认领 引用
13
作者 Qingguo Nie Yongfang Nie 《Energy Engineering》 EI 2026年第6期443-468,共26页
This study proposes an optimized design method for wind-solar-storage microgrid systems in the Gobi Desert region of northwest China.The core innovation is the development of a Modified Dragonfly Algorithm(MDA)to addr... This study proposes an optimized design method for wind-solar-storage microgrid systems in the Gobi Desert region of northwest China.The core innovation is the development of a Modified Dragonfly Algorithm(MDA)to address the challenges of optimal system sizing and operation under complex desert conditions characterized by high renewable volatility and demanding environmental constraints.To strengthen the algorithm’s global search capability and convergence speed,three key enhancements are introduced:optimal point set initialization for even population distribution,cosine similarity guidance for balanced exploration-exploitation,and a nonlinear convergence factor for adaptive adjustment.The multi-objective optimization model is evaluated using a comprehensive set of technical,economic,and environmental metrics.Simulation results for a case study demonstrate the effectiveness of the proposed approach.The optimized microgrid configuration achieves a total net present cost of 40.062 million CNY,a competitive levelized cost of energy of 0.452 CNY/kWh,and a high renewable energy penetration rate of 88.73%.Environmentally,the system significantly reduces carbon dioxide emissions by approximately 1403.35 t annually compared to conventional power supply.A detailed sensitivity analysis reveals that energy storage capacity,local wind speed variability,and load fluctuations are the most critical factors influencing system economy and operational stability.Furthermore,a financial feasibility assessment yields a positive net present value of 9.237 million CNY and an investment payback period of approximately 8.7 years.These results collectively confirm the proposed MDAoptimized microgrid design offers strong economic viability,technical reliability,and substantial environmental benefits for sustainable development in arid and remote desert regions. 展开更多
关键词 Wind solar energy storage microgrid dragonfly optimization algorithm life cycle cost penetration rate of renewable energy financial analysis
暂未订购 下载PDF
A fully automated quantitative analysis method based on deep learning algorithms for immunohistochemical staining expression intensities 认领 引用
14
作者 Yongjian Deng Bojun Cai Xiaomei Wang 《Intelligent Oncology》 2025年第3期256-264,共9页
This paper focuses primarily on exploring the application of deep learning techniques and image processing algorithms in immunohistochemistry analysis,specifically targeting automated quantitative methods for nu-clear... This paper focuses primarily on exploring the application of deep learning techniques and image processing algorithms in immunohistochemistry analysis,specifically targeting automated quantitative methods for nu-clear,membrane,and cytoplasmic expressions of animal cells in whole-slide images.Cell nuclei,membranes,and cytoplasm were precisely identified and quantified by employing optical density separation techniques to differentiate between hematoxylin and 3,3'-diaminobenzidine staining components in combination with the CellViT nuclear segmentation algorithm and the region growing algorithm.Experimental validation demon-strates that the proposed algorithm performs excellently in terms of accuracy and recall.Compared to traditional manual interpretation,this algorithm achieve greater accuracy in specific quantitative metrics. 展开更多
关键词 Deep learning Immunohistochemistry analysis Image processing algorithm Optical density separation Quantification of whole-slide images
暂未订购 下载PDF
Research on Automatic Mining Method of Behavior Rule Based on Apriori Algorithm 认领 引用
15
作者 ZHU Aiqun LU Jin 《US-China Education Review(A)》 2026年第5期295-302,共8页
Behaviour rule mining extracts valuable patterns from large amounts of behavioural data,which is crucial for analysing user behaviour,monitoring systems,and detecting security threats.Traditional manual or statistical... Behaviour rule mining extracts valuable patterns from large amounts of behavioural data,which is crucial for analysing user behaviour,monitoring systems,and detecting security threats.Traditional manual or statistical methods often fail to reveal complex,hidden associations.This study therefore proposes an automated behaviour rule mining method based on an improved Apriori algorithm.This method adapts data preprocessing and feature encoding to behavioural characteristics,introduces an adaptive support threshold and incremental updating to enhance efficiency,and automates the generation and filtering of association rules from frequent behavioural sequences.When evaluated using accuracy,recall,and interpretability metrics on public user behaviour data and simulated system logs,the method was found to effectively mine meaningful rules while maintaining high efficiency with large-scale data.This work offers a scalable and interpretable approach to automated behaviour rule mining that supports intelligent analysis and decision-making. 展开更多
关键词 Apriori algorithm behavior mining frequent patterns association rules automated analysis data mining
暂未订购 下载PDF
Hybrid adaptive machine learning approach for detection and mitigation of GNSS spoofing through enhanced osprey optimization algorithm 认领 引用
16
作者 KOTI Sushmitha SANDHYA Rachamalla 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第3期1059-1080,共22页
Global Navigation Satellite Systems(GNSSs)are the specific term utilized with satellite constellation to acquire regional or global services.GNSS sensors use pseudo-distance measurement to estimate the position,veloci... Global Navigation Satellite Systems(GNSSs)are the specific term utilized with satellite constellation to acquire regional or global services.GNSS sensors use pseudo-distance measurement to estimate the position,velocity,and time(PVT).Several GNSS devices are exposed to detect spoofing attacks due to the use of unsafe locations.In addition,misleading signals are intentionally used to generate timing and position,and GNSS signal spoofing provides a constant risk to consumers.In past works,the implementation of the Global Positioning System(GPS)in autonomous vehicle navigation might be endangered by spoofing.To mitigate these issues,this task develops a hybrid machine-learning method for mitigating and detecting GNSS spoofing attacks.The developed model is processed with three phases:data collection,feature extraction,and detection.Initially,the required data is taken from the standard resource.Then,the data is given to the feature extraction phase.The features of the data are retrieved using the principal component analysis(PCA)and t-distributed stochastic neighbor embedding(t-SNE)model.The features obtained from the collected data are transferred to the detection phase.In the final phase,the GNSS spoofing detection and mitigation is executed using a machine learning method called as hybridized adaptive Bayesian learning and multi-layer perceptron(HABMLP).Enhanced osprey optimization algorithm(EOOA)is utilized for optimizing the variables to enhance the efficacy of models and achieves greater performance than other standard models. 展开更多
关键词 Global Navigation Satellite System(GNSS) detection and mitigation of GNSS t-distributed stochastic neighbor embedding(t-SNE) enhanced osprey optimization algorithm principal component analysis(PCA) hybridized adaptive Bayesian learning and multi-layer perceptron(HABMLP)
暂未订购 下载PDF
Analysis and Design of Surgical Instrument Localization Algorithm 认领 引用 被引量：4
17
作者 Siyu Lu Jun Yang +4 位作者 Bo Yang Zhengtong Yin Mingzhe Liu Lirong Yin Wenfeng Zheng 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第10期669-685,共17页
With the help of surgical navigation system,doctors can operate on patients more intuitively and accurately.The positioning accuracy and real-time performance of surgical instruments are very important to the whole sy... With the help of surgical navigation system,doctors can operate on patients more intuitively and accurately.The positioning accuracy and real-time performance of surgical instruments are very important to the whole system.In this paper,we analyze and design the detection algorithm of surgical instrument location mark,and estimate the posture of surgical instrument.In addition,we optimized the pose by remapping.Finally,the algorithm of location mark detection proposed in this paper and the posture analysis data of surgical instruments are verified and analyzed through experiments.The final result shows a high accuracy. 展开更多
关键词 Surgical navigation system surgical instruments positioning positioning mark detection algorithm matching algorithm posture analysis
暂未订购 下载PDF
Finite Element Model Updating of Bridge Structures Based on Sensitivity Analysis and Optimization Algorithm 认领 引用 被引量：6
18
作者 HUANG Minshui ZHU Hongping 《Wuhan University Journal of Natural Sciences》 CAS 2008年第1期87-92,共6页
The dynamic characteristics of bridge structures, such as the natural frequencies, mode shapes and model damping ratio, are the basis of structural dynamic computation, seismic analysis, vibration control and structur... The dynamic characteristics of bridge structures, such as the natural frequencies, mode shapes and model damping ratio, are the basis of structural dynamic computation, seismic analysis, vibration control and structural health condition monitoring. In this paper, a three-dimensional finite-element model is established for a highway bridge over a railway on No.312 National Highway and the ambient test is carried out in site, the dynamic characteristics of the bridge are studied using the finite-element analysis and ambient vibration measurements. Comparison between the theoretical and experimental results shows that the frequency differences of the modes range between 0.44% and 8.77%. If the measurement is more reliable, the finite element model updating is necessary. Thus, a set of design variables is selected based on sensitivity analysis, then the finite element model of the bridge is updated based on optimization algorithm. The results of model updating show that the proposed updating method in this paper is more simple and effective, the updated finite element model can reflect the dynamic characteristics of the bridge better, the analytical results can provide the theoretical basis for damage identification and health condition monitoring of the bridge. 展开更多
关键词 sensitivity analysis optimization algorithm model updating bridge structure
暂未订购 下载PDF
Integrated classification method of tight sandstone reservoir based on principal component analysise simulated annealing genetic algorithmefuzzy cluster means 认领 引用 被引量：4
19
作者 Bo-Han Wu Ran-Hong Xie +3 位作者 Li-Zhi Xiao Jiang-Feng Guo Guo-Wen Jin Jian-Wei Fu 《Petroleum Science》 SCIE EI CAS CSCD 2023年第5期2747-2758,共12页
In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tig... In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method. 展开更多
关键词 Tight sandstone Integrated reservoir classification Principal component analysis Simulated annealing genetic algorithm Fuzzy cluster means
暂未订购 下载PDF
A Hybrid Particle Swarm Optimization and Genetic Algorithm for Model Updating of A Pier-Type Structure Using Experimental Modal Analysis 认领 引用 被引量：3
20
作者 Alireza MOJTAHEDI Shahriar BAYBORDI +1 位作者 Amin FATHI Aliakbar YAGHUBZADEHb 《China Ocean Engineering》 SCIE EI CSCD 2020年第5期697-707,共11页
Conventional design of pier structures is based on the assumption of fully rigid joints. In practice, the real connections are semi-rigid that cause changes in dynamic characteristics. In this study, quality of the jo... Conventional design of pier structures is based on the assumption of fully rigid joints. In practice, the real connections are semi-rigid that cause changes in dynamic characteristics. In this study, quality of the joints is investigated by considering changes in natural frequencies. For this purpose, numerical and experimental modal analyses are carried out on related physical model of a pier type structure. When numerical results are evaluated,natural frequencies generally do not match the expected experimental results. Uncertainties in different aspects of engineering problems are always a challenge for researchers. The numerical models which are constructed on the basis of highly idealized scheme may not be able to represent all of the physical aspects of the physical one. For this study, determination of percentage of semi-rigid joints is considered as an optimization problem based on the numerical and experimental frequencies. Probabilistic sensitivity analysis is also used to determine the search space.A new technique of optimization problem is solved by a combination of smart particle swarm optimization(PSO)and genetic algorithms, and a complicated and efficient system for model updating process is introduced. It is observed that the hybrid PSO-Genetic algorithm is applicable and appropriate in model updating process. It performs better than PSO algorithm, considering the good agreement between theoretical frequencies and experimental ones,before and after model updating. 展开更多
关键词 pier structure probabilistic sensitivity analysis hybrid PSO-Genetic algorithm dynamic characteristics
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