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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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A Quantum-Inspired Algorithm for Clustering and Intrusion Detection 认领 引用
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作者 Gang Xu Lefeng Wang +5 位作者 Yuwei Huang Yong Lu Xin Liu Weijie Tan Zongpeng Li Xiu-Bo Chen 《Computers, Materials & Continua》 SCIE EI 2026年第4期1180-1215,共36页
The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,convention... The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,conventional clustering-based methods face notable drawbacks,including poor scalability in handling high-dimensional datasets and a strong dependence of outcomes on initial conditions.To overcome the performance limitations of existing methods,this study proposes a novel quantum-inspired clustering algorithm that relies on a similarity coefficient-based quantum genetic algorithm(SC-QGA)and an improved quantum artificial bee colony algorithm hybrid K-means(IQABC-K).First,the SC-QGA algorithmis constructed based on quantum computing and integrates similarity coefficient theory to strengthen genetic diversity and feature extraction capabilities.For the subsequent clustering phase,the process based on the IQABC-K algorithm is enhanced with the core improvement of adaptive rotation gate and movement exploitation strategies to balance the exploration capabilities of global search and the exploitation capabilities of local search.Simultaneously,the acceleration of convergence toward the global optimum and a reduction in computational complexity are facilitated by means of the global optimum bootstrap strategy and a linear population reduction strategy.Through experimental evaluation with multiple algorithms and diverse performance metrics,the proposed algorithm confirms reliable accuracy on three datasets:KDD CUP99,NSL_KDD,and UNSW_NB15,achieving accuracy of 98.57%,98.81%,and 98.32%,respectively.These results affirm its potential as an effective solution for practical clustering applications. 展开更多
关键词 Intrusion detection clustering quantum artificial bee colony algorithm K-means quantum genetic algorithm
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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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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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Similarity matrix-based K-means algorithm for text clustering 认领 引用 被引量：2
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作者 曹奇敏 郭巧 吴向华 《Journal of Beijing Institute of Technology》 EI CAS 2015年第4期566-572,共7页
K-means algorithm is one of the most widely used algorithms in the clustering analysis. To deal with the problem caused by the random selection of initial center points in the traditional al- gorithm, this paper propo... K-means algorithm is one of the most widely used algorithms in the clustering analysis. To deal with the problem caused by the random selection of initial center points in the traditional al- gorithm, this paper proposes an improved K-means algorithm based on the similarity matrix. The im- proved algorithm can effectively avoid the random selection of initial center points, therefore it can provide effective initial points for clustering process, and reduce the fluctuation of clustering results which are resulted from initial points selections, thus a better clustering quality can be obtained. The experimental results also show that the F-measure of the improved K-means algorithm has been greatly improved and the clustering results are more stable. 展开更多
关键词 text clustering K-means algorithm similarity matrix F-measure
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An efficient enhanced k-means clustering algorithm 认领 引用 被引量：35
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作者 FAHIM A.M SALEM A.M +1 位作者 TORKEY F.A RAMADAN M.A 《Journal of Zhejiang University-SCIENCE A》 EI CAS 2006年第10期1626-1633,共8页
In k-means clustering, we are given a set of n data points in d-dimensional space R^d and an integer k and the problem is to determine a set of k points in R^d, called centers, so as to minimize the mean squared dista... In k-means clustering, we are given a set of n data points in d-dimensional space R^d and an integer k and the problem is to determine a set of k points in R^d, called centers, so as to minimize the mean squared distance from each data point to its nearest center. In this paper, we present a simple and efficient clustering algorithm based on the k-means algorithm, which we call enhanced k-means algorithm. This algorithm is easy to implement, requiring a simple data structure to keep some information in each iteration to be used in the next iteration. Our experimental results demonstrated that our scheme can improve the computational speed of the k-means algorithm by the magnitude in the total number of distance calculations and the overall time of computation. 展开更多
关键词 Clustering algorithms Cluster analysis k-means algorithm Data analysis
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Development of slope mass rating system using K-means and fuzzy c-means clustering algorithms 认领 引用 被引量：2
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作者 Jalali Zakaria 《International Journal of Mining Science and Technology》 EI CAS CSCD 2016年第6期959-966,共8页
Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experien... Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experience-based criteria. In order to eliminate linguistic criteria resulted from experience-based judgments and account for uncertainties in determining class boundaries developed by SMR system,the system classification results were corrected using two clustering algorithms, namely K-means and fuzzy c-means(FCM), for the ratings obtained via continuous and discrete functions. By applying clustering algorithms in SMR classification system, no in-advance experience-based judgment was made on the number of extracted classes in this system, and it was only after all steps of the clustering algorithms were accomplished that new classification scheme was proposed for SMR system under different failure modes based on the ratings obtained via continuous and discrete functions. The results of this study showed that, engineers can achieve more reliable and objective evaluations over slope stability by using SMR system based on the ratings calculated via continuous and discrete functions. 展开更多
关键词 SMR based on continuous functions Slope stability analysis K-means and FCM clustering algorithms Validation of clustering algorithms Sangan iron ore mines
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Clustering Algorithms to Analyze Molecular Dynamics Simulation Trajectories for Complex Chemical and Biological Systems 认领 引用
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作者 Jun-hui Peng Wei Wang +2 位作者 Ye-qing Yu Han-lin Gu Xuhui Huang 《Chinese Journal of Chemical Physics》 SCIE EI CAS CSCD 2018年第4期404-420,613,共17页
Molecular dynamics （MD） simulation has become a powerful tool to investigate the structure- function relationship of proteins and other biological macromolecules at atomic resolution and biologically relevant timesc... Molecular dynamics （MD） simulation has become a powerful tool to investigate the structure- function relationship of proteins and other biological macromolecules at atomic resolution and biologically relevant timescales. MD simulations often produce massive datasets con- taining millions of snapshots describing proteins in motion. Therefore, clustering algorithms have been in high demand to be developed and applied to classify these MD snapshots and gain biological insights. There mainly exist two categories of clustering algorithms that aim to group protein conformations into clusters based on the similarity of their shape （geometric clustering） and kinetics （kinetic clustering）. In this paper, we review a series of frequently used clustering algorithms applied in MD simulations, including divisive algorithms, ag- glomerative algorithms （single-linkage, complete-linkage, average-linkage, centroid-linkage and ward-linkage）, center-based algorithms （K-Means, K-Medoids, K-Centers, and APM）, density-based algorithms （neighbor-based, DBSCAN, density-peaks, and Robust-DB）, and spectral-based algorithms （PCCA and PCCA＋）. In particular, differences between geomet- ric and kinetic clustering metrics will be discussed along with the performances of diflhrent clustering algorithms. We note that there does not exist a one-size-fits-all algorithm in the classification of MD datasets. For a specific application, the right choice of clustering algo- rithm should be based on the purpose of clustering, and the intrinsic properties of the MD conformational ensembles. Therefore, a main focus of our review is to describe the merits and limitations of each clustering algorithm. We expect that this review would be helpful to guide researchers to choose appropriate clustering algorithms for their own MD datasets. 展开更多
关键词 Molecular dynamics simulation Clustering algorithms Markov state models,Protein dynamics
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Hybrid Genetic Algorithm with K-Means for Clustering Problems 认领 引用 被引量：2
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作者 Ahamed Al Malki Mohamed M. Rizk +1 位作者 M. A. El-Shorbagy A. A. Mousa 《Open Journal of Optimization》 2016年第2期71-83,共13页
The K-means method is one of the most widely used clustering methods and has been implemented in many fields of science and technology. One of the major problems of the k-means algorithm is that it may produce empty c... The K-means method is one of the most widely used clustering methods and has been implemented in many fields of science and technology. One of the major problems of the k-means algorithm is that it may produce empty clusters depending on initial center vectors. Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary principles of natural selection and genetics. This paper presents a hybrid version of the k-means algorithm with GAs that efficiently eliminates this empty cluster problem. Results of simulation experiments using several data sets prove our claim. 展开更多
关键词 Cluster Analysis Genetic Algorithm K-Means
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A State of Art Analysis of Telecommunication Data by k-Means and k-Medoids Clustering Algorithms 认领 引用
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作者 T. Velmurugan 《Journal of Computer and Communications》 2018年第1期190-202,共13页
Cluster analysis is one of the major data analysis methods widely used for many practical applications in emerging areas of data mining. A good clustering method will produce high quality clusters with high intra-clus... Cluster analysis is one of the major data analysis methods widely used for many practical applications in emerging areas of data mining. A good clustering method will produce high quality clusters with high intra-cluster similarity and low inter-cluster similarity. Clustering techniques are applied in different domains to predict future trends of available data and its uses for the real world. This research work is carried out to find the performance of two of the most delegated, partition based clustering algorithms namely k-Means and k-Medoids. A state of art analysis of these two algorithms is implemented and performance is analyzed based on their clustering result quality by means of its execution time and other components. Telecommunication data is the source data for this analysis. The connection oriented broadband data is given as input to find the clustering quality of the algorithms. Distance between the server locations and their connection is considered for clustering. Execution time for each algorithm is analyzed and the results are compared with one another. Results found in comparison study are satisfactory for the chosen application. 展开更多
关键词 k-Means Algorithm k-Medoids Algorithm Data Clustering Time Complexity Telecommunication Data
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Multifactor diagnostic model of converter energy consumption based on K-means algorithm and its application 认领 引用
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作者 Fei-xiang Dai Guang Chen +3 位作者 Xiang-jun Bao Gong-guo Liu Lu Zhang Xiao-jing Yang 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2025年第8期2359-2369,共11页
To address the challenge of identifying the primary causes of energy consumption fluctuations and accurately assessing the influence of various factors in the converter unit of an iron and steel plant,the focus is pla... To address the challenge of identifying the primary causes of energy consumption fluctuations and accurately assessing the influence of various factors in the converter unit of an iron and steel plant,the focus is placed on the critical components of material and heat balance.Through a thorough analysis of the interactions between various components and energy consumptions,six pivotal factors have been identified—raw material composition,steel type,steel temperature,slag temperature,recycling practices,and operational parameters.Utilizing a framework based on an equivalent energy consumption model,an integrated intelligent diagnostic model has been developed that encapsulates these factors,providing a comprehensive assessment tool for converter energy consumption.Employing the K-means clustering algorithm,historical operational data from the converter have been meticulously analyzed to determine baseline values for essential variables such as energy consumption and recovery rates.Building upon this data-driven foundation,an innovative online system for the intelligent diagnosis of converter energy consumption has been crafted and implemented,enhancing the precision and efficiency of energy management.Upon implementation with energy consumption data at a steel plant in 2023,the diagnostic analysis performed by the system exposed significant variations in energy usage across different converter units.The analysis revealed that the most significant factor influencing the variation in energy consumption for both furnaces was the steel grade,with contributions of−0.550 and 0.379. 展开更多
关键词 Equivalent energy consumption model Intelligent diagnostic model K-means clustering algorithm Online system Energy management
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Entropical K-means for clustering and an alternating algorithm 认领 引用
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作者 Jieli Ding Liming Wu 《Science China Mathematics》 SCIE CSCD 2026年第6期1615-1638,共24页
In this paper,we generalize the entropical optimal transport to the free entropical optimal transport,and use the latter as our tool for studying the nonpolynomial(NP)-hard problem of K-means.In fact,the semifree entr... In this paper,we generalize the entropical optimal transport to the free entropical optimal transport,and use the latter as our tool for studying the nonpolynomial(NP)-hard problem of K-means.In fact,the semifree entropical optimal transport conducts us to introduce a new entropical cost function of K-means for the clustering of a dataset or more generally for finding some good approximation of K-barycenters of a general probability distribution,instead of the least square cost in the classic K-means.The new entropical cost function leads naturally to a new probabilistic algorithm(called entropical K-means or EK-means simply,a probabilistic regularization of Lloyd's K-means),which is different from the known ones,and very fast in computation.Our free entropical optimal transport approach and algorithm for K-means work not only for distributions of data in the continuum space Rd,but also for distributions of discrete data. 展开更多
关键词 K-means clustering K-barycenters optimal transport entropical optimal transport algorithms
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基于K-means聚类的电力客户工单信息动态分配方法 认领 引用
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作者 李笑霏 桑田 +2 位作者 徐震 冯春生 何一康 《电子设计工程》 2026年第11期161-165,共5页
针对电力客户工单种类繁多,难以快速分类相似工单,导致无法根据工单信息和资源状态实时变化调整分配方案,降低了工单处理效率和资源利用率的问题,提出基于K-means聚类的电力客户工单信息动态分配方法。通过构建电力客户工单信息动态分... 针对电力客户工单种类繁多,难以快速分类相似工单,导致无法根据工单信息和资源状态实时变化调整分配方案,降低了工单处理效率和资源利用率的问题,提出基于K-means聚类的电力客户工单信息动态分配方法。通过构建电力客户工单信息动态分配工作效率最大化策略,将K-means聚类算法与遗传算法相结合,设计出最优工单信息分配策略。利用该策略在动态分配工作效率最大化策略中搜索最优的工单信息分配途径,这种层层递进、协同优化的方式实现了电力客户工单信息的最优分配。实验结果表明,设计方法的检修效率提升率高于0.2,技能匹配度指数高于0.6,技能利用均衡度高于0.6,说明设计方法显著提升了电力客户工单的处理效率和资源利用率。 展开更多
关键词 K-means聚类算法 遗传算法 检修技能值 适应度函数 电力客户工单信息 信息动态分配
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Research on Dynamic Clustering Routing Considering Node Load for Wireless Sensor Networks 认领 引用
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作者 Yi Sun Can Cui +1 位作者 Shanshan Ke Jun Lu 《Communications and Network》 2013年第3期508-511,共4页
Aiming at the problem that node load is rarely considered in existing clustering routing algorithm for Wireless Sensor Networks (WSNs), a dynamic clustering routing algorithm for WSN is presented in this paper called ... Aiming at the problem that node load is rarely considered in existing clustering routing algorithm for Wireless Sensor Networks (WSNs), a dynamic clustering routing algorithm for WSN is presented in this paper called DCRCL (Dynamic Clustering Routing Considering Load). This algorithm is comprised of three phases including cluster head (CH) selection, cluster setup and inter-cluster routing. First, the CHs are selected based on residual energy and node load. Then the non-CH nodes choose a cluster by comparing the cost function of its neighbor CHs. At last, each CH communicates with base station by using multi-hop communication. The simulation results show that comparing with the existing one, the techniques life cycle and date volume of the network are increased by 30.7 percent and 29.8 percent respectively by using the proposed algorithm DCRCL. 展开更多
关键词 Wireless Sensor Network Dynamic Routing Clustering Algorithm Node Load
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An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering 认领 引用 被引量：11
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作者 Taher NIKNAM Babak AMIRI +1 位作者 Javad OLAMAEI Ali AREFI 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS 2009年第4期512-519,共8页
The K-means algorithm is one of the most popular techniques in clustering. Nevertheless, the performance of the Kmeans algorithm depends highly on initial cluster centers and converges to local minima. This paper prop... The K-means algorithm is one of the most popular techniques in clustering. Nevertheless, the performance of the Kmeans algorithm depends highly on initial cluster centers and converges to local minima. This paper proposes a hybrid evolutionary programming based clustering algorithm, called PSO-SA, by combining particle swarm optimization (PSO) and simulated annealing (SA). The basic idea is to search around the global solution by SA and to increase the information exchange among particles using a mutation operator to escape local optima. Three datasets, Iris, Wisconsin Breast Cancer, and Ripley's Glass, have been considered to show the effectiveness of the proposed clustering algorithm in providing optimal clusters. The simulation results show that the PSO-SA clustering algorithm not only has a better response but also converges more quickly than the K-means, PSO, and SA algorithms. 展开更多
关键词 Simulated annealing （SA） Data clustering Hybrid evolutionary optimization algorithm K-means clustering Parti-cle swarm optimization （PSO）
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P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets 认领 引用
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作者 Ayman Altameem Ramesh Chandra Poonia +2 位作者 Ankit Kumar Linesh Raja Abdul Khader Jilani Saudagar 《Intelligent Automation & Soft Computing》 SCIE 2023年第1期553-566,共14页
Data clustering is crucial when it comes to data processing and analytics.The new clustering method overcomes the challenge of evaluating and extracting data from big data.Numerical or categorical data can be grouped.... Data clustering is crucial when it comes to data processing and analytics.The new clustering method overcomes the challenge of evaluating and extracting data from big data.Numerical or categorical data can be grouped.Existing clustering methods favor numerical data clustering and ignore categorical data clustering.Until recently,the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering methods.However,these algorithms could not use the concept of categorical data for clustering.Following that,suggestions for expanding traditional categorical data processing methods were made.In addition to expansions,several new clustering methods and extensions have been proposed in recent years.ROCK is an adaptable and straightforward algorithm for calculating the similarity between data sets to cluster them.This paper aims to modify the algo-rithm by creating a parameterized version that takes specific algorithm parameters as input and outputs satisfactory cluster structures.The parameterized ROCK algorithm is the name given to the modified algorithm(P-ROCK).The proposed modification makes the original algorithm moreflexible by using user-defined parameters.A detailed hypothesis was developed later validated with experimental results on real-world datasets using our proposed P-ROCK algorithm.A comparison with the original ROCK algorithm is also provided.Experiment results show that the proposed algorithm is on par with the original ROCK algorithm with an accuracy of 97.9%.The proposed P-ROCK algorithm has improved the runtime and is moreflexible and scalable. 展开更多
关键词 ROCK K-means algorithm clustering approaches unsupervised learning K-histogram
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DKP-SLAM:A Visual SLAM for Dynamic Indoor Scenes Based on Object Detection and Region Probability 认领 引用 被引量：2
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作者 Menglin Yin Yong Qin Jiansheng Peng 《Computers, Materials & Continua》 SCIE EI 2025年第1期1329-1347,共19页
In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and robustness.This paper prese... In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and robustness.This paper presents a dynamic SLAM algorithm that leverages object detection and regional dynamic probability.Firstly,a parallel thread employs the YOLOX object detectionmodel to gather 2D semantic information and compensate for missed detections.Next,an improved K-means++clustering algorithm clusters bounding box regions,adaptively determining the threshold for extracting dynamic object contours as dynamic points change.This process divides the image into low dynamic,suspicious dynamic,and high dynamic regions.In the tracking thread,the dynamic point removal module assigns dynamic probability weights to the feature points in these regions.Combined with geometric methods,it detects and removes the dynamic points.The final evaluation on the public TUM RGB-D dataset shows that the proposed dynamic SLAM algorithm surpasses most existing SLAM algorithms,providing better pose estimation accuracy and robustness in dynamic environments. 展开更多
关键词 Visual SLAM dynamic scene YOLOX K-means++clustering dynamic probability
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Genetic Algorithm Combined with the K-Means Algorithm:A Hybrid Technique for Unsupervised Feature Selection 认领 引用
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作者 Hachemi Bennaceur Meznah Almutairy Norah Alhussain 《Intelligent Automation & Soft Computing》 SCIE 2023年第9期2687-2706,共20页
The dimensionality of data is increasing very rapidly,which creates challenges for most of the current mining and learning algorithms,such as large memory requirements and high computational costs.The literature inclu... The dimensionality of data is increasing very rapidly,which creates challenges for most of the current mining and learning algorithms,such as large memory requirements and high computational costs.The literature includes much research on feature selection for supervised learning.However,feature selection for unsupervised learning has only recently been studied.Finding the subset of features in unsupervised learning that enhances the performance is challenging since the clusters are indeterminate.This work proposes a hybrid technique for unsupervised feature selection called GAk-MEANS,which combines the genetic algorithm(GA)approach with the classical k-Means algorithm.In the proposed algorithm,a new fitness func-tion is designed in addition to new smart crossover and mutation operators.The effectiveness of this algorithm is demonstrated on various datasets.Fur-thermore,the performance of GAk-MEANS has been compared with other genetic algorithms,such as the genetic algorithm using the Sammon Error Function and the genetic algorithm using the Sum of Squared Error Function.Additionally,the performance of GAk-MEANS is compared with the state-of-the-art statistical unsupervised feature selection techniques.Experimental results show that GAk-MEANS consistently selects subsets of features that result in better classification accuracy compared to others.In particular,GAk-MEANS is able to significantly reduce the size of the subset of selected features by an average of 86.35%(72%–96.14%),which leads to an increase of the accuracy by an average of 3.78%(1.05%–6.32%)compared to using all features.When compared with the genetic algorithm using the Sammon Error Function,GAk-MEANS is able to reduce the size of the subset of selected features by 41.29%on average,improve the accuracy by 5.37%,and reduce the time by 70.71%.When compared with the genetic algorithm using the Sum of Squared Error Function,GAk-MEANS on average is able to reduce the size of the subset of selected features by 15.91%,and improve the accuracy by 9.81%,but the time is increased by a factor of 3.When compared with the machine-learning based methods,we observed that GAk-MEANS is able to increase the accuracy by 13.67%on average with an 88.76%average increase in time. 展开更多
关键词 Genetic algorithm unsupervised feature selection k-Means clustering
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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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