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Dynamic grouping control of electric vehicles based on improved k-means algorithm for wind power fluctuations suppression 认领 引用 被引量：8
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作者 Yang Yu Mai Liu +2 位作者 Dongyang Chen Yuhang Huo Wentao Lu 《Global Energy Interconnection》 EI CSCD 2023年第5期542-553,共12页
To address the significant lifecycle degradation and inadequate state of charge(SOC)balance of electric vehicles(EVs)when mitigating wind power fluctuations,a dynamic grouping control strategy is proposed for EVs base... To address the significant lifecycle degradation and inadequate state of charge(SOC)balance of electric vehicles(EVs)when mitigating wind power fluctuations,a dynamic grouping control strategy is proposed for EVs based on an improved k-means algorithm.First,a swing door trending(SDT)algorithm based on compression result feedback was designed to extract the feature data points of wind power.The gating coefficient of the SDT was adjusted based on the compression ratio and deviation,enabling the acquisition of grid-connected wind power signals through linear interpolation.Second,a novel algorithm called IDOA-KM is proposed,which utilizes the Improved Dingo Optimization Algorithm(IDOA)to optimize the clustering centers of the k-means algorithm,aiming to address its dependence and sensitivity on the initial centers.The EVs were categorized into priority charging,standby,and priority discharging groups using the IDOA-KM.Finally,an two-layer power distribution scheme for EVs was devised.The upper layer determines the charging/discharging sequences of the three EV groups and their corresponding power signals.The lower layer allocates power signals to each EV based on the maximum charging/discharging power or SOC equalization principles.The simulation results demonstrate the effectiveness of the proposed control strategy in accurately tracking grid power signals,smoothing wind power fluctuations,mitigating EV degradation,and enhancing the SOC balance. 展开更多
关键词 Electric vehicles Wind power fluctuation smoothing Improved k-means Power allocation Swing door trending
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A Hybrid Method Combining Improved K-means Algorithm with BADA Model for Generating Nominal Flight Profiles 认领 引用 被引量：1
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作者 Tang Xinmin Gu Junwei +2 位作者 Shen Zhiyuan Chen Ping Li Bo 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2016年第4期414-424,共11页
A high-precision nominal flight profile,involving controllers′intentions is critical for 4Dtrajectory estimation in modern automatic air traffic control systems.We proposed a novel method to effectively improve the a... A high-precision nominal flight profile,involving controllers′intentions is critical for 4Dtrajectory estimation in modern automatic air traffic control systems.We proposed a novel method to effectively improve the accuracy of the nominal flight profile,including the nominal altitude profile and the speed profile.First,considering the characteristics of trajectory data,we developed an improved K-means algorithm.The approach was to measure the similarity between different altitude profiles by integrating the space warp edit distance algorithm,thereby to acquire several fitted nominal flight altitude profiles.This approach breaks the constraints of traditional K-means algorithms.Second,to eliminate the influence of meteorological factors,we introduced historical gridded binary data to determine the en-route wind speed and temperature via inverse distance weighted interpolation.Finally,we facilitated the true airspeed determined by speed triangle relationships and the calibrated airspeed determined by aircraft data model to extract a more accurate nominal speed profile from each cluster,therefore we could describe the airspeed profiles above and below the airspeed transition altitude,respectively.Our experimental results showed that the proposed method could obtain a highly accurate nominal flight profile,which reflects the actual aircraft flight status. 展开更多
关键词 air transportation flight profile K-means algorithm space warp edit distance(SWED)algorithm trajectory prediction
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Polarimetric Meteorological Satellite Data Processing Software Classification Based on Principal Component Analysis and Improved K-Means Algorithm 认领 引用 被引量：2
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作者 Manyun Lin Xiangang Zhao +3 位作者 Cunqun Fan Lizi Xie Lan Wei Peng Guo 《Journal of Geoscience and Environment Protection》 2017年第7期39-48,共10页
With the increasing variety of application software of meteorological satellite ground system, how to provide reasonable hardware resources and improve the efficiency of software is paid more and more attention. In th... With the increasing variety of application software of meteorological satellite ground system, how to provide reasonable hardware resources and improve the efficiency of software is paid more and more attention. In this paper, a set of software classification method based on software operating characteristics is proposed. The method uses software run-time resource consumption to describe the software running characteristics. Firstly, principal component analysis (PCA) is used to reduce the dimension of software running feature data and to interpret software characteristic information. Then the modified K-means algorithm was used to classify the meteorological data processing software. Finally, it combined with the results of principal component analysis to explain the significance of various types of integrated software operating characteristics. And it is used as the basis for optimizing the allocation of software hardware resources and improving the efficiency of software operation. 展开更多
关键词 Principal Component Analysis Improved K-Mean Algorithm Meteorological Data Processing Feature Analysis Similarity Algorithm
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An Improved K-means Algorithm for Clustering Categorical Data 认领 引用 被引量：1
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作者 Ming Lei Pilian He Zhichao Li 《通讯和计算机（中英文版）》 2006年第8期20-24,共5页
Most of the earlier work on clustering is mainly focused on numerical data the inherent geometric properties of which can be exploited to naturally define distance functions between the data points. However, the compu... Most of the earlier work on clustering is mainly focused on numerical data the inherent geometric properties of which can be exploited to naturally define distance functions between the data points. However, the computational cost makes most of the previous algorithms unacceptable for clustering very large databases. The k-means algorithm is well known for its efficiency in this respect. At the same time, working only on numerical data prohibits them from being used for clustering categorical data. This paper shows how to apply the notion of ＂cluster centers＂ to a dataset of categorical objects, and a k-means-like algorithm for clustering categorical data is introduced. 展开更多
关键词 K-means Algorithm Categorical Data Data Mining K-modes Algorithm
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An Improved K-Means Algorithm Based on Initial Clustering Center Optimization 认领 引用
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作者 LI Taihao NAREN Tuya +2 位作者 ZHOU Jianshe REN Fuji LIU Shupeng 《ZTE Communications》 2017年第B12期43-46,共4页
The K-means algorithm is widely known for its simplicity and fastness in text clustering.However,the selection of the initial clus?tering center with the traditional K-means algorithm is some random,and therefore,the ... The K-means algorithm is widely known for its simplicity and fastness in text clustering.However,the selection of the initial clus?tering center with the traditional K-means algorithm is some random,and therefore,the fluctuations and instability of the clustering results are strongly affected by the initial clustering center.This paper proposed an algorithm to select the initial clustering center to eliminate the uncertainty of central point selection.The experiment results show that the improved K-means clustering algorithm is superior to the traditional algorithm. 展开更多
关键词 clustering K-means algorithm initial clustering center
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An improved Alpha-shape algorithm for extracting section contours of the super-high steel bridge tower using point clouds 认领 引用 被引量：2
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作者 ZHANG Yiming ZHAO Tianhao +2 位作者 LIAO Ruixuan LI Haoqing WANG Hao 《Journal of Southeast University(English Edition)》 EI CAS 2026年第1期26-35,共10页
The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,a... The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,and temperature fluctuations can compromise the accuracy of contour extraction.To address these limitations,an improved Alpha-shape-based point cloud contour extraction method is proposed.The proposed approach uses a hierarchical strategy to process three-dimensional laser scanning point clouds.The processed data are then subjected to curvatureadaptive voxel filtering to reduce acquisition noise.In addition,an enhanced iterative closest point(ICP)variant with correspondence validation accurately aligns the discrete point cloud segments.The proposed curvature-responsive Alpha-shape framework enables multiscale contour delineation through topology-adaptive threshold modulation,which resolves boundary ambiguities in geometrically complex cross-sections.The method was experimentally validated using field-acquired measurement datasets from the Zhangjinggao Yangtze River Bridge tower segments,confirming its capability to reconstruct noncanonical cross-sectional geometries.Three contour extraction methods,including Poisson reconstruction,the conventional Alpha-shape algorithm,and random sample consensus with ICP(RANSAC-ICP),were compared to evaluate the performance of the proposed Alpha-shape algorithm.The results demonstrate that the proposed method achieves superior contour extraction accuracy and data reduction efficiency,highlighting its effectiveness in contour extraction tasks. 展开更多
关键词 super-high steel bridge tower point cloud contour extraction improved Alpha-shape algorithm
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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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Equivalent Modeling with Passive Filter Parameter Clustering for Photovoltaic Power Stations Based on a Particle Swarm Optimization K-Means Algorithm 认领 引用
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作者 Binjiang Hu Yihua Zhu +3 位作者 Liang Tu Zun Ma Xian Meng Kewei Xu 《Energy Engineering》 EI 2026年第1期431-459,共29页
This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the compl... This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research. 展开更多
关键词 Photovoltaic power station multi-machine equivalentmodeling particle swarmoptimization K-means clustering algorithm
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Techno-economic co-optimization of CO2enhanced oil recovery strategies in a tight oil reservoir using coupled improved evolutionary algorithm and machine learning framework 认领 引用
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作者 Shu-Qin Wen Bing Wei +2 位作者 Jun-Yu You Nan-Jiang Leng William Ampomah 《Petroleum Science》 SCIE EI CAS CSCD 2026年第5期2639-2654,共16页
Massive carbon dioxide(CO2)emissions drive climate change.Injecting CO2into unconventional reservoirsachieves both enhanced oil recovery(EOR)and geological sequestration.However,simultaneously optimizing oil exc... Massive carbon dioxide(CO2)emissions drive climate change.Injecting CO2into unconventional reservoirsachieves both enhanced oil recovery(EOR)and geological sequestration.However,simultaneously optimizing oil exchange ratio,CO2storage,and net present value remainschallenging.This study develops an integrated machine learning(ML)-based framework for multi-objective optimization of CO2-EOR.A high-resolution reservoir simulation was constructed from field data,and Latin hypercube sampling generateddiverse scenarios for proxy training.Mantel's test quantified correlations between input parameters and performance metrics,showing that injection strategy strongly controls net present value,whereas geological properties dominate CO2storage.Three ML models—random forest(RF),support vector regression,and artificial neural networks—were evaluated,with RF selected for its superior performance on small datasets.RF was embedded into an improved non-dominatedsorting genetic algorithm II,enhanced with grey difference degree,crowding distance,and adaptive differential evolution to improve diversity and efficiency.Finally,the technique for order preference by similarity to ideal solution ranked Pareto-optimal solutions through integrating oil productivity,storage,and economics.The proposed framework operationalizes simultaneoushigh-efficiency tight oil recovery and field-scale CO2geological storage,delivering quantitative design rules that embed low-carbon practice into upstream operations and advance the energy sector's greenerand sustainable transition. 展开更多
关键词 CO2enhanced oil recovery Multi-objective optimization Improved non-dominated sorting genetic algorithm II Unconventional oil reservoir
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Pedestrian detection of infrared images based on an improved FCOS algorithm 认领 引用
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作者 Fuzhen ZHU Hao HAN +1 位作者 Hengfei JIA Bing ZHU 《Optoelectronics Letters》 EI 2026年第2期105-110,共6页
The current infrared image pedestrian detectors have problems with high rates of false positives and false negatives. To solve these problems, we proposed an improved anchor-free fully convolutional one-stage object d... The current infrared image pedestrian detectors have problems with high rates of false positives and false negatives. To solve these problems, we proposed an improved anchor-free fully convolutional one-stage object detection(FCOS) algorithm. Firstly, we introduced the channel attention module squeeze excitation(SE)-Block in the FCOS backbone network, which was used to learn how to model the relative importance between different feature channels, and to achieve the weight recalibration of the features extracted from the convolution neural network, and improve the weight values that are more important for pedestrian target detection. Secondly, soft non-maximum suppression(Soft-NMS) replaced the conventional NMS within the algorithm's post-processing phase, which was used to reduce the probability of missed detection for occluded pedestrians. The experimental results show that our improved FCOS algorithm improves the average precision(AP) by 6.71% on the original dataset and 7.97% on the augmented KAIST pedestrian dataset compared with the original FCOS algorithm. Our improvements effectively meet the real-time requirements and there is no significant decrease in speed compared with the original FCOS algorithm, and decreased the false positives and false negatives for infrared image pedestrian detection. 展开更多
关键词 weight recalibration pedestrian detection infrared image pedestrian detectors channel attention module learn how model relative importance different feature channels improved FCOS algorithm infrared images Squeeze Excitation SE block
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Fault Self-Healing Cooperative Strategy of New Energy Distribution Network Based on Improved Ant Colony-Genetic Hybrid Algorithm 认领 引用 被引量：1
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作者 Fengchao Chen Aoqi Mei +2 位作者 Zheng Liu Ruhao Wu Qiwei Li 《Energy Engineering》 EI 2026年第4期247-267,共21页
With the high proportion of new energy access,the traditional fault self-healing mechanism of the distribution network is challenged.Aiming at the demand for fast recovery of new distribution network faults,this paper... With the high proportion of new energy access,the traditional fault self-healing mechanism of the distribution network is challenged.Aiming at the demand for fast recovery of new distribution network faults,this paper proposes a fault self-healing cooperative strategy for the new energy distribution network based on an improved ant colony-genetic hybrid algorithm.Firstly,the graph theory adjacency matrix is used to characterize the topology of the distribution network,and the dynamic positioning of new energy nodes is realized.Secondly,based on the output model and load characteristic model of wind,photovoltaic,and energy storage,a two-layer cooperative self-healing model of the distribution network is constructed.The upper layer is based on the improved depth-breadth hybrid search(DFS-BFS)to divide the island,with the maximum weight load recovery and the minimum number of switching actions as the goal,combined with the load priority to dynamically restore the key load.The lower layer uses the improved ant colony-genetic hybrid algorithm to solve the fault recovery path with the minimum total power loss load and the minimum network loss as the goal,generate the optimal switching sequence,and verify the power flow constraints.Finally,the simulation results based on the IEEE 33-bus system show that the proposed method can guarantee the power supply of key loads in the distribution network with high-tech energy penetration,restore the power supply of more load nodes with the least switching operation,and effectively reduce the line loss,which verifies the effectiveness and superiority of the method. 展开更多
关键词 Fault recovery identification of topology improved ant colony-genetic hybrid algorithm distribution network self-healing
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Integrated diagnosis of abnormal energy consumption in converter steelmaking using GWO-SVM-K-means algorithms 认领 引用
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作者 Fei-Xiang Dai Xiang-Jun Bao +2 位作者 Lu Zhang Xiao-Jing Yang Guang Chen 《Journal of Iron and Steel Research International》 SCIE EI CSCD 2026年第1期458-468,共11页
To address the issue of abnormal energy consumption fluctuations in the converter steelmaking process,an integrated diagnostic method combining the gray wolf optimization(GWO)algorithm,support vector machine(SVM),and ... To address the issue of abnormal energy consumption fluctuations in the converter steelmaking process,an integrated diagnostic method combining the gray wolf optimization(GWO)algorithm,support vector machine(SVM),and K-means clustering was proposed.Eight input parameters—derived from molten iron conditions and external factors—were selected as feature variables.A GWO-SVM model was developed to accurately predict the energy consumption of individual heats.Based on the prediction results,the mean absolute percentage error and maximum relative error of the test set were employed as criteria to identify heats with abnormal energy usage.For these heats,the K-means clustering algorithm was used to determine benchmark values of influencing factors from similar steel grades,enabling root-cause diagnosis of excessive energy consumption.The proposed method was applied to real production data from a converter in a steel plant.The analysis reveals that heat sample No.44 exhibits abnormal energy consumption,due to gas recovery being 1430.28 kg of standard coal below the benchmark level.A secondary contributing factor is a steam recovery shortfall of 237.99 kg of standard coal.This integrated approach offers a scientifically grounded tool for energy management in converter operations and provides valuable guidance for optimizing process parameters and enhancing energy efficiency. 展开更多
关键词 Converter smelting process Abnormal energy diagnosis Gray wolf optimization algorithm Support vector machine K-means clustering algorithm
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An improved YOLOv8 apple leaf disease detection algorithm 认领 引用
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作者 Xinyu PEI Wei YUAN +1 位作者 Yuexiu ZHANG Lianjun SONG 《Optoelectronics Letters》 EI 2026年第5期314-320,共7页
This paper suggests an improved you only look once version 8n(YOLOv8n)algorithm for apple leaf disease detection,abbreviated as ALWB-YOLOv8n.The model is comprised of four essential components.Initially,arbitrary kern... This paper suggests an improved you only look once version 8n(YOLOv8n)algorithm for apple leaf disease detection,abbreviated as ALWB-YOLOv8n.The model is comprised of four essential components.Initially,arbitrary kernel convolution(AKConv)replaces the convolution module,which significantly decreases both the model’s parameter count and its overall size.Secondly,the large selective kernel network(LSKNet)attention mechanism is added in the Backbone,which can dynamically adjust the spatial sensory domain,and experiments have proved that this method is extremely advantageous for small target detection.Third,a weighted bi-directional feature pyramid network is introduced,which enables the model to achieve multi-scale feature fusion and is more concise and faster.Finally,wise intersection over union(WIoU)is used to replace complete intersection over union(CIoU)in YOLOv8,and the idea of focal loss is introduced,which effectively solves the detection problems in cases such as apple leaves occluding each other and blurred boundaries of diseased leaves.The improved algorithm exhibits superior performance compared to other common object detection algorithms.Compared with YOLOv8n,the improved algorithm achieves 2.3%improvement in precision,3.8%improvement in recall,and 2.5%and 2.7%improvement in mAP0.5 and mAP0.5:0.95,respectively.Compared with YOLOv8n,the improved model reduces the number of parameters and size of the model and realizes real-time monitoring with a frames per second(FPS)of 50.5. 展开更多
关键词 large selective kernel network arbitrary kernel convolution large selective kernel network lsknet attention kernel convolution akconv replaces weighted bi directional feature pyramid network adjust spatial sensory domainand improved YOLOv n algorithm apple leaf disease detection
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Improved k-means clustering algorithm 认领 引用 被引量：26
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作者 夏士雄 李文超 +2 位作者 周勇 张磊 牛强 《Journal of Southeast University(English Edition)》 EI CAS 2007年第3期435-438,共4页
In allusion to the disadvantage of having to obtain the number of clusters of data sets in advance and the sensitivity to selecting initial clustering centers in the k-means algorithm, an improved k-means clustering a... In allusion to the disadvantage of having to obtain the number of clusters of data sets in advance and the sensitivity to selecting initial clustering centers in the k-means algorithm, an improved k-means clustering algorithm is proposed. First, the concept of a silhouette coefficient is introduced, and the optimal clustering number Kopt of a data set with unknown class information is confirmed by calculating the silhouette coefficient of objects in clusters under different K values. Then the distribution of the data set is obtained through hierarchical clustering and the initial clustering-centers are confirmed. Finally, the clustering is completed by the traditional k-means clustering. By the theoretical analysis, it is proved that the improved k-means clustering algorithm has proper computational complexity. The experimental results of IRIS testing data set show that the algorithm can distinguish different clusters reasonably and recognize the outliers efficiently, and the entropy generated by the algorithm is lower. 展开更多
关键词 clustering k-means algorithm silhouette coefficient
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Plant Leaf Diseases Classification Using Improved K-Means Clustering and SVM Algorithm for Segmentation 认领 引用 被引量：1
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作者 Mona Jamjoom Ahmed Elhadad +1 位作者 Hussein Abulkasim Safia Abbas 《Computers, Materials & Continua》 SCIE EI 2023年第7期367-382,共16页
Several pests feed on leaves,stems,bases,and the entire plant,causing plant illnesses.As a result,it is vital to identify and eliminate the disease before causing any damage to plants.Manually detecting plant disease ... Several pests feed on leaves,stems,bases,and the entire plant,causing plant illnesses.As a result,it is vital to identify and eliminate the disease before causing any damage to plants.Manually detecting plant disease and treating it is pretty challenging in this period.Image processing is employed to detect plant disease since it requires much effort and an extended processing period.The main goal of this study is to discover the disease that affects the plants by creating an image processing system that can recognize and classify four different forms of plant diseases,including Phytophthora infestans,Fusarium graminearum,Puccinia graminis,tomato yellow leaf curl.Therefore,this work uses the Support vector machine(SVM)classifier to detect and classify the plant disease using various steps like image acquisition,Pre-processing,Segmentation,feature extraction,and classification.The gray level co-occurrence matrix(GLCM)and the local binary pattern features(LBP)are used to identify the disease-affected portion of the plant leaf.According to experimental data,the proposed technology can correctly detect and diagnose plant sickness with a 97.2 percent accuracy. 展开更多
关键词 SVM machine learning GLCM algorithm K-means clustering LBP
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Hybrid Flow Shop Rescheduling Approach Based on Hybrid-Driven Mechanism and Improved Multi-Objective WOA 认领 引用
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作者 Feng Lv Xin Xu +1 位作者 Cheng Yang Yixuan Tang 《Computers, Materials & Continua》 SCIE EI 2026年第7期1982-2009,共28页
To ensure an effective disturbance response and maintain continuous production in hybrid flow shops,this paper focuses on the design of a rescheduling method.A rescheduling model is constructed that minimizes the make... To ensure an effective disturbance response and maintain continuous production in hybrid flow shops,this paper focuses on the design of a rescheduling method.A rescheduling model is constructed that minimizes the makespan,total tardiness,and scheme deviation degree.A hybrid rescheduling driving mechanism based on the latest completion time is designed to effectively trigger rescheduling.The Whale Optimization Algorithm(WOA)is improved by integrating the good point set theory,nonlinear control parameter strategy,and Differential Evolution(DE)algorithm.Moreover,non-dominated sorting and a dynamic external archive mechanism based on crowding distance are introduced to make it suitable for multi-objective optimization problems.The superiority of the Improved Multi-objective Whale Optimization Algorithm(IMOWOA)and the effectiveness of the improved mechanisms are verified through comparative experiments and ablation experiments.Taking the final assembly production line of an agricultural machinery equipment enterprise as an example,a rescheduling scheme is generated based on the practical production requirements,which verifies the feasibility and effectiveness of the proposed method. 展开更多
关键词 Hybrid flow shop production disturbance production rescheduling rescheduling driving mechanism improved multi-objective whale optimization algorithm
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Deep Learning Mixed Hyper-Parameter Optimization Based on Improved Cuckoo Search Algorithm 认领 引用 被引量：1
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作者 TONG Yu CHEN Rong HU Biling 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2025年第2期195-204,共10页
Deep learning algorithm is an effective data mining method and has been used in many fields to solve practical problems.However,the deep learning algorithms often contain some hyper-parameters which may be continuous,... Deep learning algorithm is an effective data mining method and has been used in many fields to solve practical problems.However,the deep learning algorithms often contain some hyper-parameters which may be continuous,integer,or mixed,and are often given based on experience but largely affect the effectiveness of activity recognition.In order to adapt to different hyper-parameter optimization problems,our improved Cuckoo Search(CS)algorithm is proposed to optimize the mixed hyper-parameters in deep learning algorithm.The algorithm optimizes the hyper-parameters in the deep learning model robustly,and intelligently selects the combination of integer type and continuous hyper-parameters that make the model optimal.Then,the mixed hyper-parameter in Convolutional Neural Network(CNN),Long-Short-Term Memory(LSTM)and CNN-LSTM are optimized based on the methodology on the smart home activity recognition datasets.Results show that the methodology can improve the performance of the deep learning model and whether we are experienced or not,we can get a better deep learning model using our method. 展开更多
关键词 improved Cuckoo Search algorithm mixed hyper-parameter optimization deep learning
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Damage Detection of X-ray Image of Conveyor Belts with Steel Rope Cores Based on Improved FCOS Algorithm 认领 引用 被引量：1
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作者 WANG Baomin DING Hewei +1 位作者 TENG Fei LIU Hongqin 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第2期309-318,共10页
Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is propose... Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is proposed based on the improved fully convolutional one-stage object detection(FCOS)algorithm.The regression performance of bounding boxes was optimized by introducing the complete intersection over union loss function into the improved algorithm.The feature fusion network structure is modified by adding adaptive fusion paths to the feature fusion network structure,which makes full use of the features of accurate localization and semantics of multi-scale feature fusion networks.Finally,the network structure was trained and validated by using the X-ray image dataset of damages in conveyor belts with steel rope cores provided by a flaw detection equipment manufacturer.In addition,the data enhancement methods such as rotating,mirroring,and scaling,were employed to enrich the image dataset so that the model is adequately trained.Experimental results showed that the improved FCOS algorithm promoted the precision rate and the recall rate by 20.9%and 14.8%respectively,compared with the original algorithm.Meanwhile,compared with Fast R-CNN,Faster R-CNN,SSD,and YOLOv3,the improved FCOS algorithm has obvious advantages;detection precision rate and recall rate of the modified network reached 95.8%and 97.0%respectively.Furthermore,it demonstrated a higher detection accuracy without affecting the speed.The results of this work have some reference significance for the automatic identification and detection of steel core conveyor belt damage. 展开更多
关键词 conveyer belts with steel rope cores damage X-ray image image detection improved fully convo-lutional one-stage object detection(FCOS)algorithm
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Design of high phase-sensitivity BlueP/TMDC heterostructure-based SPR biosensor using improved artificial bee colony algorithm 认领 引用
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作者 Chong Yue Mantong Chen +1 位作者 Yaopu Lang Qinggang Liu 《Nanotechnology and Precision Engineering》 EI CAS CSCD 2025年第2期113-122,共10页
This paper uses an innovative improved artificial bee colony(IABC)algorithm to aid in the fabrication of a highly responsive phasemodulation surface plasmon resonance(SPR)biosensor.In this biosensor’s sensing structu... This paper uses an innovative improved artificial bee colony(IABC)algorithm to aid in the fabrication of a highly responsive phasemodulation surface plasmon resonance(SPR)biosensor.In this biosensor’s sensing structure,a double-layer Ag-Au metal film is combined with a blue phosphoreneransition metal dichalcogenide(BlueP/TMDC)hybrid structure and graphene.In the optimization function of the IABC method,the reflectivity at resonance angle is incorporated as a constraint to achieve high phase sensitivity.The performance of the Ag-Au-BlueP/TMDC-graphene heterostructure as optimized by the IABC method is compared with that of a similar structure optimized using the traditional ABC algorithm.The results indicate that optimization using the IABC method gives significantly more phase sensitivity,together with lower reflectivity,than can be achieved with the traditional ABC method.The highest phase sensitivity of 3.662×106 °/RIU is achieved with a bilayer of BlueP/WS2 and three layers of graphene.Moreover,analysis of the electric field distribution demonstrates that the optimal arrangement can be utilized for enhanced detection of small biomolecules.Thus,given the exceptional sensitivity achieved,the proposed method based on the IABC algorithm has great promise for use in the design of high-performance SPR biosensors with a variety of multilayer structures. 展开更多
关键词 SPR Phase modulation Sensitivity Improved artificial bee colony algorithm BlueP/TMDC Heterostructure
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基于K-means聚类和改进蚁群算法的跨境电商仓储选址优化研究 认领 引用 被引量：1
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作者 邱国斌 易玉涛 《物流研究》 2026年第1期84-92,共9页
为解决传统选址方法无法动态适配跨境场景的问题,本文针对跨境电商仓储选址的复杂性与灵活性,结合跨境电商特有的国际物流成本、关税政策、区域市场需求、汇率波动等核心要素,构建基于K-means聚类和改进蚁群算法的跨境电商仓储选址模型... 为解决传统选址方法无法动态适配跨境场景的问题,本文针对跨境电商仓储选址的复杂性与灵活性,结合跨境电商特有的国际物流成本、关税政策、区域市场需求、汇率波动等核心要素,构建基于K-means聚类和改进蚁群算法的跨境电商仓储选址模型。本研究通过在多约束条件下的MATLAB软件仿真模拟,将现有选址与优化后选址进行比较。研究表明,该模型能够有效优化跨境电商仓储选址方案,为企业在全球供应链布局中提供科学决策支持。 展开更多
关键词 跨境电商 仓储选址 改进蚁群算法 MATLAB仿真 K-means聚类
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