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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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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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Rock discontinuity extraction from 3D point clouds using pointwise clustering algorithm 认领 引用 被引量：1
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作者 Xiaoyu Yi Wenxuan Wu +2 位作者 Wenkai Feng Yongjian Zhou Jiachen Zhao 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第7期4429-4444,共16页
Recognizing discontinuities within rock masses is a critical aspect of rock engineering.The development of remote sensing technologies has significantly enhanced the quality and quantity of the point clouds collected ... Recognizing discontinuities within rock masses is a critical aspect of rock engineering.The development of remote sensing technologies has significantly enhanced the quality and quantity of the point clouds collected from rock outcrops.In response,we propose a workflow that balances accuracy and efficiency to extract discontinuities from massive point clouds.The proposed method employs voxel filtering to downsample point clouds,constructs a point cloud topology using K-d trees,utilizes principal component analysis to calculate the point cloud normals,and employs the pointwise clustering(PWC)algorithm to extract discontinuities from rock outcrop point clouds.This method provides information on the location and orientation(dip direction and dip angle)of the discontinuities,and the modified whale optimization algorithm(MWOA)is utilized to identify major discontinuity sets and their average orientations.Performance evaluations based on three real cases demonstrate that the proposed method significantly reduces computational time costs without sacrificing accuracy.In particular,the method yields more reasonable extraction results for discontinuities with certain undulations.The presented approach offers a novel tool for efficiently extracting discontinuities from large-scale point clouds. 展开更多
关键词 Rock mass discontinuity 3D point clouds Pointwise clustering(PWC)algorithm Modified whale optimization algorithm(MWOA)
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An Efficient Clustering Algorithm for Enhancing the Lifetime and Energy Efficiency of Wireless Sensor Networks 认领 引用
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作者 Peng Zhou Wei Chen Bingyu Cao 《Computers, Materials & Continua》 SCIE EI 2025年第9期5337-5360,共24页
Wireless Sensor Networks(WSNs),as a crucial component of the Internet of Things(IoT),are widely used in environmental monitoring,industrial control,and security surveillance.However,WSNs still face challenges such as ... Wireless Sensor Networks(WSNs),as a crucial component of the Internet of Things(IoT),are widely used in environmental monitoring,industrial control,and security surveillance.However,WSNs still face challenges such as inaccurate node clustering,low energy efficiency,and shortened network lifespan in practical deployments,which significantly limit their large-scale application.To address these issues,this paper proposes an Adaptive Chaotic Ant Colony Optimization algorithm(AC-ACO),aiming to optimize the energy utilization and system lifespan of WSNs.AC-ACO combines the path-planning capability of Ant Colony Optimization(ACO)with the dynamic characteristics of chaotic mapping and introduces an adaptive mechanism to enhance the algorithm’s flexibility and adaptability.By dynamically adjusting the pheromone evaporation factor and heuristic weights,efficient node clustering is achieved.Additionally,a chaotic mapping initialization strategy is employed to enhance population diversity and avoid premature convergence.To validate the algorithm’s performance,this paper compares AC-ACO with clustering methods such as Low-Energy Adaptive Clustering Hierarchy(LEACH),ACO,Particle Swarm Optimization(PSO),and Genetic Algorithm(GA).Simulation results demonstrate that AC-ACO outperforms the compared algorithms in key metrics such as energy consumption optimization,network lifetime extension,and communication delay reduction,providing an efficient solution for improving energy efficiency and ensuring long-term stable operation of wireless sensor networks. 展开更多
关键词 Internet of Things wireless sensor networks ant colony optimization clustering algorithm energy efficiency
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Optimized algorithm for balancing clusters in wireless sensor networks 认领 引用
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作者 Mucheol KIM Sunhong KIM +1 位作者 Hyungjin BYUN Sangyong HAN 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS 2009年第10期1404-1412,共9页
Wireless sensor networks consist of hundreds or thousands of sensor nodes that involve numerous restrictions in-cluding computation capability and battery capacity.Topology control is an important issue for achieving ... Wireless sensor networks consist of hundreds or thousands of sensor nodes that involve numerous restrictions in-cluding computation capability and battery capacity.Topology control is an important issue for achieving a balanced placement of sensor nodes.The clustering scheme is a widely known and efficient means of topology control for transmitting information to the base station in two hops.The automatic routing scheme of the self-organizing technique is another critical element of wireless sensor networks.In this paper we propose an optimal algorithm with cluster balance taken into consideration,and compare it with three well known and widely used approaches,i.e.,LEACH,MEER,and VAP-E,in performance evaluation.Experimental results show that the proposed approach increases the overall network lifetime,indicating that the amount of energy required for com-munication to the base station will be reduced for locating an optimal cluster. 展开更多
关键词 Wireless sensor networks Self-organizing sensor algorithm Clustering algorithm Optimization
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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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Flight Trajectory Option Set Generation Based on Clustering Algorithms 认领 引用
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作者 WANG Shijin SUN Min +1 位作者 LI Yinglin YANG Baotian 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2025年第6期767-788,共22页
Addressing the issue that flight plans between Chinese city pairs typically rely on a single route,lacking alternative paths and posing challenges in responding to emergencies,this study employs the“quantile-inflecti... Addressing the issue that flight plans between Chinese city pairs typically rely on a single route,lacking alternative paths and posing challenges in responding to emergencies,this study employs the“quantile-inflection point method”to analyze specific deviation trajectories,determine deviation thresholds,and identify commonly used deviation paths.By combining multiple similarity metrics,including Euclidean distance,Hausdorff distance,and sector edit distance,with the density-based spatial clustering of applications with noise(DBSCAN)algorithm,the study clusters deviation trajectories to construct a multi-option trajectory set for city pairs.A case study of 23578 flight trajectories between the Guangzhou airport cluster and the Shanghai airport cluster demonstrates the effectiveness of the proposed framework.Experimental results show that sector edit distance achieves superior clustering performance compared to Euclidean and Hausdorff distances,with higher silhouette coefficients and lower Davies⁃Bouldin indices,ensuring better intra-cluster compactness and inter-cluster separation.Based on clustering results,19 representative trajectory options are identified,covering both nominal and deviation paths,which significantly enhance route diversity and reflect actual flight practices.This provides a practical basis for optimizing flight paths and scheduling,enhancing the flexibility of route selection for flights between city pairs. 展开更多
关键词 flight trajectory clustering trajectory option set sector edit distance density-based spatial clustering of applications with noise(DBSCAN)algorithm deviation trajectories
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Driving manipulation analysis and control reconfiguration of heavy-haul trains 认领 引用
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作者 LI Zi-yi ZHOU Yan-li +2 位作者 YANG Hui YU Yong-sheng LI Guang-wei 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第1期506-522,共17页
The safe driving and operation of trains is a necessary condition for ensuring the safe operation of trains.In particular,heavy-haul trains are characterized by the difficulty in driving and operation.Considering the ... The safe driving and operation of trains is a necessary condition for ensuring the safe operation of trains.In particular,heavy-haul trains are characterized by the difficulty in driving and operation.Considering the uncertainties in train driving and operation,this paper analyzes the relationship between the safety of heavy-haul electric locomotive hauled trains and driving and operation.It studies the auxiliary intelligent driving safety operation control methods.Through K-means to identify the characteristics of drivers'driving manipulation,the hidden Markov model adaptively adjusts the train driving and operation sequence,and conducts auxiliary driving reconstruction for heavy-haul locomotive driving and operation.Based on the train running curve and the locomotive traction/braking characteristics,it smoothly controls the exertion of the traction/braking force of heavy-haul locomotives,thereby optimizing the driving safety control of heavy-haul trains in the vehicle-environment-track system.Finally,the train operation simulation and optimized driving verification are carried out by simulating some track sections.The results show that the proposed method can correct and pre-optimize driving operations,improving the smoothness of heavy-haul trains by approximately 10%.It verifies the effectiveness of the proposed train assisted driving control reconstruction method,facilitating the smooth and safe operation of heavy-haul trains. 展开更多
关键词 heavy-haul trains driving manipulation K-means clustering algorithm hidden Markov model control reconfiguration
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A Resilient BIRCH-Based Smart Framework for Real-Time IoT Data Clustering 认领 引用
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作者 Prabhat Das Dibya Jyoti Bora +2 位作者 Sajal Saha Cheng-Chi Lee Hirak Mazumdar 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期864-898,共35页
Real-time data processing is essential in the evolving landscape of IoT applications,ensuring efficiency,reliability,and adaptability.However,conventional clustering algorithms often face difficulties in managing high... Real-time data processing is essential in the evolving landscape of IoT applications,ensuring efficiency,reliability,and adaptability.However,conventional clustering algorithms often face difficulties in managing highfrequency,continuous IoT data streams due to limited adaptability and high computational overhead.To address these challenges,this study proposes a resilient adaptation of the BIRCH(Balanced Iterative Reducing and Clustering using Hierarchies)algorithm,tailored specifically for streaming IoT data.The enhanced approach dynamically recalculates clusters and determines the optimal number of clusters using the KneeLocator method.Unlike the original batchoriented BIRCH,the modified version processes data incrementally,enabling continuous adaptation to changing data distributions.The proposed method was validated on benchmark IoT datasets and compared against K-Means,DBSCAN,standard BIRCH,and other state-of-the-art streaming-based clustering algorithms.Results consistently show that the modified BIRCH outperforms existing approaches in execution speed,memory efficiency,scalability,and clustering accuracy.In addition,the algorithm has been deployed within a web-based application featuring interactive visualization and anomaly detection,highlighting its practical relevance for smart city and industrial IoT scenarios.To promote reproducibility and future research,the complete framework and source code have been made publicly available. 展开更多
关键词 IoT applications clustering algorithms smart city applications real-time data processing industrial IoT real-time clustering
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Method of Modulation Recognition Based on Combination Algorithm of K-Means Clustering and Grading Training SVM 认领 引用 被引量：14
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作者 Faquan Yang Ling Yang +3 位作者 Dong Wang Peihan Qi Haiyan Wang 无 《China Communications》 SCIE CSCD 2018年第12期55-63,共9页
For the existing support vector machine,when recognizing more questions,the shortcomings of high computational complexity and low recognition rate under the low SNR are emerged.The characteristic parameter of the sign... For the existing support vector machine,when recognizing more questions,the shortcomings of high computational complexity and low recognition rate under the low SNR are emerged.The characteristic parameter of the signal is extracted and optimized by using a clustering algorithm,support vector machine is trained by grading algorithm so as to enhance the rate of convergence,improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram in this paper.Simulation results show that the average recognition rate based on this algorithm is enhanced over 30%compared with methods that adopting clustering algorithm or support vector machine respectively under the low SNR.The average recognition rate can reach 90%when the SNR is 5 dB,and the method is easy to be achieved so that it has broad application prospect in the modulating recognition. 展开更多
关键词 clustering algorithm feature extraction grading algorithm support vector machine modulation recognition
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Method of neural network modulation recognition based on clustering and Polak-Ribiere algorithm 认领 引用 被引量：6
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作者 Faquan Yang Zan Li +2 位作者 Hongyan Li Haiyan Huang Zhongxian Pan 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2014年第5期742-747,共6页
To improve the recognition rate of signal modulation recognition methods based on the clustering algorithm under the low SNR, a modulation recognition method is proposed. The characteristic parameter of the signal is ... To improve the recognition rate of signal modulation recognition methods based on the clustering algorithm under the low SNR, a modulation recognition method is proposed. The characteristic parameter of the signal is extracted by using a clustering algorithm, the neural network is trained by using the algorithm of variable gradient correction （Polak-Ribiere） so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram. Simulation results show that the recognition rate based on this algorithm is enhanced over 30% compared with the methods that adopt clustering algorithm or neural network based on the back propagation algorithm alone under the low SNR. The recognition rate can reach 90% when the SNR is 4 dB, and the method is easy to be achieved so that it has a broad application prospect in the modulating recognition. 展开更多
关键词 clustering algorithm feature extraction algorithm of Polak-Ribiere neural network （NN） modulation recognition.
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Spatial quality evaluation for drinking water based on GIS and ant colony clustering algorithm 认领 引用 被引量：7
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作者 侯景伟 米文宝 李陇堂 《Journal of Central South University》 SCIE EI CAS 2014年第3期1051-1057,共7页
To develop a better approach for spatial evaluation of drinking water quality, an intelligent evaluation method integrating a geographical information system(GIS) and an ant colony clustering algorithm(ACCA) was used.... To develop a better approach for spatial evaluation of drinking water quality, an intelligent evaluation method integrating a geographical information system(GIS) and an ant colony clustering algorithm(ACCA) was used. Drinking water samples from 29 wells in Zhenping County, China, were collected and analyzed. 35 parameters on water quality were selected, such as chloride concentration, sulphate concentration, total hardness, nitrate concentration, fluoride concentration, turbidity, pH, chromium concentration, COD, bacterium amount, total coliforms and color. The best spatial interpolation methods for the 35 parameters were found and selected from all types of interpolation methods in GIS environment according to the minimum cross-validation errors. The ACCA was improved through three strategies, namely mixed distance function, average similitude degree and probability conversion functions. Then, the ACCA was carried out to obtain different water quality grades in the GIS environment. In the end, the result from the ACCA was compared with those from the competitive Hopfield neural network(CHNN) to validate the feasibility and effectiveness of the ACCA according to three evaluation indexes, which are stochastic sampling method, pixel amount and convergence speed. It is shown that the spatial water quality grades obtained from the ACCA were more effective, accurate and intelligent than those obtained from the CHNN. 展开更多
关键词 geographical information system （GIS） ant colony clustering algorithm （ACCA） quality evaluation drinking water spatial analysis
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A Physical Layer Algorithm for Estimation of Number of Tags in UHF RFID Anti-Collision Design 认领 引用 被引量：5
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作者 Zhong Huang Jian Su +4 位作者 Guangjun Wen Wenxian Zheng Chu Chu Yijun Zhang Yibo Zhang 《Computers, Materials & Continua》 SCIE EI 2019年第7期399-408,共10页
A priori knowledge of the number of tags is crucial for anti-collision protocols in slotted UHF RFID systems.The number of tags is used to decide optimal frame length in dynamic frame slotted ALOHA(DFSA)and to adjust ... A priori knowledge of the number of tags is crucial for anti-collision protocols in slotted UHF RFID systems.The number of tags is used to decide optimal frame length in dynamic frame slotted ALOHA(DFSA)and to adjust access probability in random access protocols.Conventional researches estimate the number of tags in MAC layer based on statistics of empty slots,collided slots and successful slots.Usually,a collision detection algorithm is employed to determine types of time slots.Only three types are distinguished because of lack of ability to detect the number of tags in single time slot.In this paper,a physical layer algorithm is proposed to detect the number of tags in a collided slot.Mean shift algorithm is utilized,and some properties of backscatter signals are investigated.Simulation results verify the effectiveness of the proposed solution in terms of low estimation error with a high SNR range,outperforming the existing MAC layer approaches. 展开更多
关键词 UHF RFID anti-collision cluster algorithm
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Advanced Fuzzy C-Means Algorithm Based on Local Density and Distance 认领 引用 被引量：4
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作者 Shaochun PANG Yijie +1 位作者 SHAO Sen JIANG Keyuan 《Journal of Shanghai Jiaotong university(Science)》 EI 2018年第5期636-642,共7页
This paper presents an advanced fuzzy C-means(FCM) clustering algorithm to overcome the weakness of the traditional FCM algorithm, including the instability of random selecting of initial center and the limitation of ... This paper presents an advanced fuzzy C-means(FCM) clustering algorithm to overcome the weakness of the traditional FCM algorithm, including the instability of random selecting of initial center and the limitation of the data separation or the size of clusters. The advanced FCM algorithm combines the distance with density and improves the objective function so that the performance of the algorithm can be improved. The experimental results show that the proposed FCM algorithm requires fewer iterations yet provides higher accuracy than the traditional FCM algorithm. The advanced algorithm is applied to the influence of stars' box-office data, and the classification accuracy of the first class stars achieves 92.625%. 展开更多
关键词 objective function clustering center fuzzy C-means （FCM） clustering algorithm degree of member-ship
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A Nonuniform Clustering Routing Algorithm Based on an Improved K-Means Algorithm 认领 引用 被引量：4
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作者 Xinliang Tang Man Zhang +3 位作者 Pingping Yu Wei Liu Ning Cao Yunfeng Xu 《Computers, Materials & Continua》 SCIE EI 2020年第9期1725-1739,共15页
In a large-scale wireless sensor network(WSN),densely distributed sensor nodes process a large amount of data.The aggregation of data in a network can consume a great amount of energy.To balance and reduce the energy ... In a large-scale wireless sensor network(WSN),densely distributed sensor nodes process a large amount of data.The aggregation of data in a network can consume a great amount of energy.To balance and reduce the energy consumption of nodes in a WSN and extend the network life,this paper proposes a nonuniform clustering routing algorithm based on the improved K-means algorithm.The algorithm uses a clustering method to form and optimize clusters,and it selects appropriate cluster heads to balance network energy consumption and extend the life cycle of the WSN.To ensure that the cluster head(CH)selection in the network is fair and that the location of the selected CH is not concentrated within a certain range,we chose the appropriate CH competition radius.Simulation results show that,compared with LEACH,LEACH-C,and the DEEC clustering algorithm,this algorithm can effectively balance the energy consumption of the CH and extend the network life. 展开更多
关键词 WSN node energy consumption nonuniform clustering routing algorithm
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MR-CLOPE: A Map Reduce based transactional clustering algorithm for DNS query log analysis 认领 引用 被引量：2
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作者 李晔锋 乐嘉锦 +2 位作者 王梅 张滨 刘良旭 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第9期3485-3494,共10页
DNS(domain name system) query log analysis has been a popular research topic in recent years. CLOPE, the represented transactional clustering algorithm, could be readily used for DNS query log mining. However, the alg... DNS(domain name system) query log analysis has been a popular research topic in recent years. CLOPE, the represented transactional clustering algorithm, could be readily used for DNS query log mining. However, the algorithm is inefficient when processing large scale data. The MR-CLOPE algorithm is proposed, which is an extension and improvement on CLOPE based on Map Reduce. Different from the previous parallel clustering method, a two-stage Map Reduce implementation framework is proposed. Each of the stage is implemented by one kind Map Reduce task. In the first stage, the DNS query logs are divided into multiple splits and the CLOPE algorithm is executed on each split. The second stage usually tends to iterate many times to merge the small clusters into bigger satisfactory ones. In these two stages, a novel partition process is designed to randomly spread out original sub clusters, which will be moved and merged in the map phrase of the second phase according to the defined merge criteria. In such way, the advantage of the original CLOPE algorithm is kept and its disadvantages are dealt with in the proposed framework to achieve more excellent clustering performance. The experiment results show that MR-CLOPE is not only faster but also has better clustering quality on DNS query logs compared with CLOPE. 展开更多
关键词 DNS data mining MR-CLOPE algorithm transactional clustering algorithm Map Reduce framework
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Research of Improved Fuzzy c-means Algorithm Based on a New Metric Norm 认领 引用 被引量：2
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作者 毛力 宋益春 +2 位作者 李引 杨弘 肖炜 《Journal of Shanghai Jiaotong university(Science)》 EI 2015年第1期51-55,共5页
For the question that fuzzy c-means(FCM)clustering algorithm has the disadvantages of being too sensitive to the initial cluster centers and easily trapped in local optima,this paper introduces a new metric norm in FC... For the question that fuzzy c-means(FCM)clustering algorithm has the disadvantages of being too sensitive to the initial cluster centers and easily trapped in local optima,this paper introduces a new metric norm in FCM and particle swarm optimization(PSO)clustering algorithm,and proposes a parallel optimization algorithm using an improved fuzzy c-means method combined with particle swarm optimization(AF-APSO).The experiment shows that the AF-APSO can avoid local optima,and get the best fitness and clustering performance significantly. 展开更多
关键词 fuzzy c-means(FCM) particle swarm optimization(PSO) clustering algorithm new metric norm
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Collision-Based Chosen-Message Simple Power Clustering Attack Algorithm 认领 引用 被引量：6
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作者 陈艾东 许森 +1 位作者 陈运 秦志光 《China Communications》 SCIE CSCD 2013年第5期114-119,共6页
Chosen-message pair Simple Power Analysis (SPA) attacks were proposed by Boer, Yen and Homma, and are attack methods based on searches for collisions of modular multiplication. However, searching for collisions is dif... Chosen-message pair Simple Power Analysis (SPA) attacks were proposed by Boer, Yen and Homma, and are attack methods based on searches for collisions of modular multiplication. However, searching for collisions is difficult in real environments. To circumvent this problem, we propose the Simple Power Clustering Attack (SPCA), which can automatically identify the modular multiplication collision. The insignificant effects of collision attacks were validated in an Application Specific Integrated Circuit (ASIC) environment. After treatment with SPCA, the automatic secret key recognition rate increased to 99%. 展开更多
关键词 crypt analysis side channel attack collision attack chosen-message attack clustering algorithm
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Identification of High-Risk Scenarios for Cascading Failures in New Energy Power Grids Based on Deep Embedding Clustering Algorithms 认领 引用 被引量：1
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作者 Xueting Cheng Ziqi Zhang +1 位作者 Yueshuang Bao Huiping Zheng 《Energy Engineering》 EI 2023年第11期2517-2529,共13页
At present,the proportion of new energy in the power grid is increasing,and the random fluctuations in power output increase the risk of cascading failures in the power grid.In this paper,we propose a method for ident... At present,the proportion of new energy in the power grid is increasing,and the random fluctuations in power output increase the risk of cascading failures in the power grid.In this paper,we propose a method for identifying high-risk scenarios of interlocking faults in new energy power grids based on a deep embedding clustering(DEC)algorithm and apply it in a risk assessment of cascading failures in different operating scenarios for new energy power grids.First,considering the real-time operation status and system structure of new energy power grids,the scenario cascading failure risk indicator is established.Based on this indicator,the risk of cascading failure is calculated for the scenario set,the scenarios are clustered based on the DEC algorithm,and the scenarios with the highest indicators are selected as the significant risk scenario set.The results of simulations with an example power grid show that our method can effectively identify scenarios with a high risk of cascading failures from a large number of scenarios. 展开更多
关键词 New energy power system deep embedding clustering algorithms cascading failures
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Clustering analysis algorithm for security supervising data based on semantic description in coal mines 认领 引用 被引量：1
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作者 孟凡荣 周勇 夏士雄 《Journal of Southeast University(English Edition)》 EI CAS 2008年第3期354-357,共4页
In order to mine production and security information from security supervising data and to ensure security and safety involved in production and decision-making,a clustering analysis algorithm for security supervising... In order to mine production and security information from security supervising data and to ensure security and safety involved in production and decision-making,a clustering analysis algorithm for security supervising data based on a semantic description in coal mines is studied.First,the semantic and numerical-based hybrid description method of security supervising data in coal mines is described.Secondly,the similarity measurement method of semantic and numerical data are separately given and a weight-based hybrid similarity measurement method for the security supervising data based on a semantic description in coal mines is presented.Thirdly,taking the hybrid similarity measurement method as the distance criteria and using a grid methodology for reference,an improved CURE clustering algorithm based on the grid is presented.Finally,the simulation results of a security supervising data set in coal mines validate the efficiency of the algorithm. 展开更多
关键词 semantic description clustering analysis algorithm similarity measurement
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