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A K-means clustering based blind multiband spectrum sensing algorithm for cognitive radio 被引量:4
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作者 LEI Ke-jun TAN Yang-hong +1 位作者 YANG Xi WANG Han-rui 《Journal of Central South University》 SCIE EI CAS CSCD 2018年第10期2451-2461,共11页
In this paper,a blind multiband spectrum sensing(BMSS)method requiring no knowledge of noise power,primary signal and wireless channel is proposed based on the K-means clustering(KMC).In this approach,the KMC algorith... In this paper,a blind multiband spectrum sensing(BMSS)method requiring no knowledge of noise power,primary signal and wireless channel is proposed based on the K-means clustering(KMC).In this approach,the KMC algorithm is used to identify the occupied subband set(OSS)and the idle subband set(ISS),and then the location and number information of the occupied channels are obtained according to the elements in the OSS.Compared with the classical BMSS methods based on the information theoretic criteria(ITC),the new method shows more excellent performance especially in the low signal-to-noise ratio(SNR)and the small sampling number scenarios,and more robust detection performance in noise uncertainty or unequal noise variance applications.Meanwhile,the new method performs more stablely than the ITC-based methods when the occupied subband number increases or the primary signals suffer multi-path fading.Simulation result verifies the effectiveness of the proposed method. 展开更多
关键词 cognitive radio(CR) blind multiband spectrum sensing(BMSS) k-means clustering(KMC) occupied subband set(OSS) idle subband set(ISS) information theoretic criteria(ITC) noise uncertainty
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基于k-means算法的聚类个数确定方法改进 被引量:2
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作者 王丙参 王国长 魏艳华 《统计与决策》 北大核心 2025年第7期59-64,共6页
文章基于k-means算法探讨了最优聚类个数k*的确定方法:第一类是统计量方法;第二类是聚类算法不稳定性方法,即基于两次聚类结果间的距离,利用交叉验证、随机抽样取交集、自助法来构建聚类算法估计不稳定性指标,并根据投票、最小化均值方... 文章基于k-means算法探讨了最优聚类个数k*的确定方法:第一类是统计量方法;第二类是聚类算法不稳定性方法,即基于两次聚类结果间的距离,利用交叉验证、随机抽样取交集、自助法来构建聚类算法估计不稳定性指标,并根据投票、最小化均值方法确定k^(*)。数值模拟结果显示:在给定k^(*)的情况下,聚类结果与标签的距离或相似度可作为评价聚类结果的指标,为聚类算法评价提供了新的借鉴;基于k-means算法确定k^(*)的前提是数据集根据欧氏距离可明显分为几簇,相对而言,聚类算法不稳定性方法优于统计量方法;对于不稳定性指标,交叉验证估计方法与随机抽样取交集估计方法对抽样个数稳健,抽样个数依次建议略少于样本容量的1/3、80%;自助抽样估计方法由于利用了全部样本,因此效率更高;4种不稳定性指标没有显著差异,投票与最小化均值方法也没有显著差异。 展开更多
关键词 k-meanS算法 聚类个数 统计量 不稳定性
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基于深度自适应K-means++算法的电抗器声纹聚类方法
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作者 闵永智 郝大宇 +2 位作者 王果 何怡刚 贺建山 《电力系统保护与控制》 北大核心 2025年第8期1-13,共13页
在高压并联电抗器声纹信号监测系统中,长时海量无标签声纹的高维非平稳性导致特征提取困难、无监督聚类适应性差。由此提出了一种基于深度自适应K-means++算法(deep adaptive K-means++clustering algorithm,DAKCA)的750 kV电抗器声纹... 在高压并联电抗器声纹信号监测系统中,长时海量无标签声纹的高维非平稳性导致特征提取困难、无监督聚类适应性差。由此提出了一种基于深度自适应K-means++算法(deep adaptive K-means++clustering algorithm,DAKCA)的750 kV电抗器声纹聚类方法。首先通过采用两阶段无监督策略微调的改进堆叠稀疏自编码器(stacked sparse autoencoder,SSAE),对快速傅里叶变换后的归一化频域数据提取电抗器原始声纹32维深度特征。进一步提出了依据最近邻聚类有效性指标(clustering validation index based on nearest neighbors,CVNN)的自适应K-means++聚类算法,构建了能自适应确定最优聚类个数的电抗器声纹聚类模型。最后通过西北地区某750 kV电抗器实测声纹数据集进行了验证。结果表明,DAKCA算法对无标签声纹数据在不同样本均衡程度下能够稳定提取32维深度特征,并实现最优聚类,为直接高效利用电抗器无标签声纹数据提供了参考。 展开更多
关键词 750 kV电抗器 声纹聚类 自适应聚类算法 稀疏自编码器 深度自适应k-means++算法
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基于K-Means和IE模型的采空区地表安全性评价指标研究
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作者 赵博 俞奎 《金属矿山》 北大核心 2025年第6期221-229,共9页
随着智能算法在灾害评价领域的深入应用,构建合理的评价指标体系对于实现复杂采空区地表安全性的高效评价至关重要。然而,传统指标选取方法存在主观性强、干扰因素多、效率低及数字化程度不足等诸多瓶颈。为此,构建了一种基于K-Means聚... 随着智能算法在灾害评价领域的深入应用,构建合理的评价指标体系对于实现复杂采空区地表安全性的高效评价至关重要。然而,传统指标选取方法存在主观性强、干扰因素多、效率低及数字化程度不足等诸多瓶颈。为此,构建了一种基于K-Means聚类算法和IE理论的高效精确评价指标模型。该模型首先从采空区地表灾害作用机理出发,广泛筛选潜在评价指标;进而利用K-Means算法对这些指标进行聚类筛选,以降低指标信息表达的冗余性和复杂度;通过IE理论计算提炼出对安全性影响显著的关键指标,构建出一套采空区复杂场地安全性评价的指标体系。为验证指标体系的合理性,结合PCA和熵权法进行检验评估;将模型应用于某采空区地区,并与常用方法的评价结果进行对比。结果表明:该模型成功将38个初选指标精简至8个关键指标,所构建的评价指标体系仅用21.1%的指标特征便能表征87.9%的原始指标信息,显著降低了计算工作量,提升了评价效率。该研究成果不仅为采空区地表稳定性评价提供了一种新颖方法,而且为相关领域的研究提供了理论支撑,具有较高的理论价值和实践意义。 展开更多
关键词 采空区 评价指标 聚类算法 信息熵
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基于RSA模型和改进K-means算法的电商行业客户细分
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作者 杨静 《计算机应用与软件》 北大核心 2025年第8期125-131,172,共8页
针对新兴的网络购物客户数量大、客户流动性强和消费数据多的特点,提出RSA模型结合改进的K-means聚类算法实现客户细分。采用熵值法计算RSA模型各指标的权重,综合各个属性计算客户价值。结合K近邻算法和密度峰值算法,提出一种基于K近邻... 针对新兴的网络购物客户数量大、客户流动性强和消费数据多的特点,提出RSA模型结合改进的K-means聚类算法实现客户细分。采用熵值法计算RSA模型各指标的权重,综合各个属性计算客户价值。结合K近邻算法和密度峰值算法,提出一种基于K近邻和密度峰值聚类的K-means初始聚类中心选取方法,优化传统K-means算法实现客户细分。通过选取的标准数据集和某零售公司在线交易的真实数据进行实验验证,证明了RSA模型和改进K-means算法具有更加优异的性能。 展开更多
关键词 RSA模型 客户细分 k-meanS算法 密度峰值聚类 K近邻
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Optimization of jamming formation of USV offboard active decoy clusters based on an improved PSO algorithm 被引量:1
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作者 Zhaodong Wu Yasong Luo Shengliang Hu 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2024年第2期529-540,共12页
Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for t... Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for timing and deployment.To improve the response speed and jamming effect,a cluster of OADs based on an unmanned surface vehicle(USV)is proposed.The formation of the cluster determines the effectiveness of jamming.First,based on the mechanism of OAD jamming,critical conditions are identified,and a method for assessing the jamming effect is proposed.Then,for the optimization of the cluster formation,a mathematical model is built,and a multi-tribe adaptive particle swarm optimization algorithm based on mutation strategy and Metropolis criterion(3M-APSO)is designed.Finally,the formation optimization problem is solved and analyzed using the 3M-APSO algorithm under specific scenarios.The results show that the improved algorithm has a faster convergence rate and superior performance as compared to the standard Adaptive-PSO algorithm.Compared with a single OAD,the optimal formation of USV-OAD cluster effectively fills the blind area and maximizes the use of jamming resources. 展开更多
关键词 Electronic countermeasure Offboard active decoy USV cluster Jamming formation optimization Improved PSO algorithm
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Intuitionistic fuzzy C-means clustering algorithms 被引量:22
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作者 Zeshui Xu Junjie Wu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2010年第4期580-590,共11页
Intuitionistic fuzzy sets(IFSs) are useful means to describe and deal with vague and uncertain data.An intuitionistic fuzzy C-means algorithm to cluster IFSs is developed.In each stage of the intuitionistic fuzzy C-me... Intuitionistic fuzzy sets(IFSs) are useful means to describe and deal with vague and uncertain data.An intuitionistic fuzzy C-means algorithm to cluster IFSs is developed.In each stage of the intuitionistic fuzzy C-means method the seeds are modified,and for each IFS a membership degree to each of the clusters is estimated.In the end of the algorithm,all the given IFSs are clustered according to the estimated membership degrees.Furthermore,the algorithm is extended for clustering interval-valued intuitionistic fuzzy sets(IVIFSs).Finally,the developed algorithms are illustrated through conducting experiments on both the real-world and simulated data sets. 展开更多
关键词 intuitionistic fuzzy set(IFS) intuitionistic fuzzy Cmeans algorithm clustering interval-valued intuitionistic fuzzy set(IVIFS).
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Method of neural network modulation recognition based on clustering and Polak-Ribiere algorithm 被引量:4
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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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Ant colony ATTA clustering algorithm of rock mass structural plane in groups 被引量:11
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作者 李夕兵 王泽伟 +1 位作者 彭康 刘志祥 《Journal of Central South University》 SCIE EI CAS 2014年第2期709-714,共6页
Based on structural surface normal vector spherical distance and the pole stereographic projection Euclidean distance,two distance functions were established.The cluster analysis of structure surface was conducted by ... Based on structural surface normal vector spherical distance and the pole stereographic projection Euclidean distance,two distance functions were established.The cluster analysis of structure surface was conducted by the use of ATTA clustering methods based on ant colony piles,and Silhouette index was introduced to evaluate the clustering effect.The clustering analysis of the measured data of Sanshandao Gold Mine shows that ant colony ATTA-based clustering method does better than K-mean clustering analysis.Meanwhile,clustering results of ATTA method based on pole Euclidean distance and ATTA method based on normal vector spherical distance have a great consistence.The clustering results are most close to the pole isopycnic graph.It can efficiently realize grouping of structural plane and determination of the dominant structural surface direction.It is made up for the defects of subjectivity and inaccuracy in icon measurement approach and has great engineering value. 展开更多
关键词 rock mass discontinuity cluster analysis ant colony ATTA algorithm distance function Silhouette index
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Blind source separation by weighted K-means clustering 被引量:5
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作者 Yi Qingming 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2008年第5期882-887,共6页
Blind separation of sparse sources (BSSS) is discussed. The BSSS method based on the conventional K-means clustering is very fast and is also easy to implement. However, the accuracy of this method is generally not ... Blind separation of sparse sources (BSSS) is discussed. The BSSS method based on the conventional K-means clustering is very fast and is also easy to implement. However, the accuracy of this method is generally not satisfactory. The contribution of the vector x(t) with different modules is theoretically proved to be unequal, and a weighted K-means clustering method is proposed on this grounds. The proposed algorithm is not only as fast as the conventional K-means clustering method, but can also achieve considerably accurate results, which is demonstrated by numerical experiments. 展开更多
关键词 blind source separation underdetermined mixing sparse representation weighted k-means clustering.
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A Clustering-based Location Allocation Method for Delivery Sites under Epidemic Situations
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作者 Zhou Yaqiong Chen Junqi +2 位作者 Li Weishi Qiu Sihang Ju Rusheng 《系统仿真学报》 CAS CSCD 北大核心 2024年第12期2782-2796,共15页
To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location al... To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location allocation method for the delivery sites to deliver daily necessities during epidemic quarantines.After establishing the optimization objectives and constraints,we developed a relevant mathematical model based on the collected data and utilized traditional intelligent optimization algorithms to obtain Pareto optimal solutions.Building on the characteristics of these Pareto front solutions,we introduced an improved clustering algorithm and conducted simulation experiments using data from Changchun City.The results demonstrate that the proposed algorithm outperforms traditional intelligent optimization algorithms in terms of effectiveness,efficiency,and stability,achieving reductions of approximately 12%and 8%in time and labor costs,respectively,compared to the baseline algorithm. 展开更多
关键词 location problem clustering algorithm intelligent optimization algorithm Pareto front
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Background dominant colors extraction method based on color image quick fuzzy c-means clustering algorithm 被引量:2
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作者 Zun-yang Liu Feng Ding +1 位作者 Ying Xu Xu Han 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2021年第5期1782-1790,共9页
A quick and accurate extraction of dominant colors of background images is the basis of adaptive camouflage design.This paper proposes a Color Image Quick Fuzzy C-Means(CIQFCM)clustering algorithm based on clustering ... A quick and accurate extraction of dominant colors of background images is the basis of adaptive camouflage design.This paper proposes a Color Image Quick Fuzzy C-Means(CIQFCM)clustering algorithm based on clustering spatial mapping.First,the clustering sample space was mapped from the image pixels to the quantized color space,and several methods were adopted to compress the amount of clustering samples.Then,an improved pedigree clustering algorithm was applied to obtain the initial class centers.Finally,CIQFCM clustering algorithm was used for quick extraction of dominant colors of background image.After theoretical analysis of the effect and efficiency of the CIQFCM algorithm,several experiments were carried out to discuss the selection of proper quantization intervals and to verify the effect and efficiency of the CIQFCM algorithm.The results indicated that the value of quantization intervals should be set to 4,and the proposed algorithm could improve the clustering efficiency while maintaining the clustering effect.In addition,as the image size increased from 128×128 to 1024×1024,the efficiency improvement of CIQFCM algorithm was increased from 6.44 times to 36.42 times,which demonstrated the significant advantage of CIQFCM algorithm in dominant colors extraction of large-size images. 展开更多
关键词 Dominant colors extraction Quick clustering algorithm clustering spatial mapping Background image Camouflage design
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MR-CLOPE: A Map Reduce based transactional clustering algorithm for DNS query log analysis 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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Applying memetic algorithm-based clustering to recommender system with high sparsity problem 被引量:2
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作者 MARUNG Ukrit THEERA-UMPON Nipon AUEPHANWIRIYAKUL Sansanee 《Journal of Central South University》 SCIE EI CAS 2014年第9期3541-3550,共10页
A new recommendation method was presented based on memetic algorithm-based clustering. The proposed method was tested on four highly sparse real-world datasets. Its recommendation performance is evaluated and compared... A new recommendation method was presented based on memetic algorithm-based clustering. The proposed method was tested on four highly sparse real-world datasets. Its recommendation performance is evaluated and compared with that of the frequency-based, user-based, item-based, k-means clustering-based, and genetic algorithm-based methods in terms of precision, recall, and F1 score. The results show that the proposed method yields better performance under the new user cold-start problem when each of new active users selects only one or two items into the basket. The average F1 scores on all four datasets are improved by 225.0%, 61.6%, 54.6%, 49.3%, 28.8%, and 6.3% over the frequency-based, user-based, item-based, k-means clustering-based, and two genetic algorithm-based methods, respectively. 展开更多
关键词 memetic algorithm recommender system sparsity problem cold-start problem clustering method
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Spatial quality evaluation for drinking water based on GIS and ant colony clustering algorithm 被引量:4
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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 new clustering algorithm for large datasets 被引量:1
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作者 李清峰 彭文峰 《Journal of Central South University》 SCIE EI CAS 2011年第3期823-829,共7页
The Circle algorithm was proposed for large datasets.The idea of the algorithm is to find a set of vertices that are close to each other and far from other vertices.This algorithm makes use of the connection between c... The Circle algorithm was proposed for large datasets.The idea of the algorithm is to find a set of vertices that are close to each other and far from other vertices.This algorithm makes use of the connection between clustering aggregation and the problem of correlation clustering.The best deterministic approximation algorithm was provided for the variation of the correlation of clustering problem,and showed how sampling can be used to scale the algorithms for large datasets.An extensive empirical evaluation was given for the usefulness of the problem and the solutions.The results show that this method achieves more than 50% reduction in the running time without sacrificing the quality of the clustering. 展开更多
关键词 data mining Circle algorithm clustering categorical data clustering aggregation
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基于优化k-means的沂水麒麟雪茄庄园气象适宜性分析
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作者 郑明君 柳平增 +2 位作者 张艳 陈秀斋 仇京范 《中国农机化学报》 北大核心 2025年第8期346-352,共7页
为探究国内雪茄烟叶适宜种植区域,以沂水麒麟雪茄庄园为范例开展气象适宜性分析。通过采用内部指标DB Index优化聚类数k值,结合k-means++算法选择初始聚类中心点,对沂水麒麟雪茄庄园、比那尔德里奥、圣地亚哥、巴伊亚等11个国内外雪茄... 为探究国内雪茄烟叶适宜种植区域,以沂水麒麟雪茄庄园为范例开展气象适宜性分析。通过采用内部指标DB Index优化聚类数k值,结合k-means++算法选择初始聚类中心点,对沂水麒麟雪茄庄园、比那尔德里奥、圣地亚哥、巴伊亚等11个国内外雪茄产区大田期的关键气象因子进行聚类分析。结果表明:11个产区被划分为4个簇,其中沂水与比那尔德里奥、圣地亚哥、巴伊亚、什邡等高品质雪茄产区同属2号簇,显示其具有相似的气象条件。进一步通过隶属函数与气象适宜性指数评价表明,沂水麒麟雪茄庄园的适宜性指数CFI达0.931,属于最适宜种植等级。田间验证显示,该庄园8个雪茄品种的农艺性状指标均符合优质雪茄烟叶标准。本研究证实沂水麒麟雪茄庄园具备优质雪茄烟叶生产的气象条件,为国内雪茄产区选择提供科学依据。 展开更多
关键词 聚类算法 雪茄烟叶 生态适宜性 机器学习
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基于k-means聚类与标记分水岭算法的二氧化氯浓度测试方法
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作者 何家萌 黄豪中 +1 位作者 陈其勇 许桂霞 《广西大学学报(自然科学版)》 北大核心 2025年第1期186-199,共14页
人为使用二氧化氯检测试纸与标准比色卡进行比对时无法得出具体的浓度结果,且受主观因素影响较大,测量结果准确性差的问题,对二氧化氯检测试纸进行图像采集,根据其颜色与形状特征,提出基于三通道彩色图片的k-means聚类算法与标记分水岭... 人为使用二氧化氯检测试纸与标准比色卡进行比对时无法得出具体的浓度结果,且受主观因素影响较大,测量结果准确性差的问题,对二氧化氯检测试纸进行图像采集,根据其颜色与形状特征,提出基于三通道彩色图片的k-means聚类算法与标记分水岭算法结合的分割算法,快速准确地完成对二氧化氯检测试纸的分割及定位,并对二氧化氯检测试纸的颜色值与对应溶液的浓度进行相关性分析与曲线拟合,在定位二氧化氯检测试纸后,提取其颜色值并根据拟合曲线计算出对应的二氧化氯溶液浓度。结果表明,该算法分割速度快,分割效果好,对二氧化氯溶液浓度的测量准确,质量浓度对误差不超过15 mg/L,引用误差不超过4%,能有效避免人为比对时产生的主观因素干扰以及估算误差。 展开更多
关键词 二氧化氯检测试纸 消杀效果评价 k-meanS聚类算法 标记分水岭算法
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New density clustering-based approach for failure mode and effect analysis considering opinion evolution and bounded confidence
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作者 WANG Jian ZHU Jingyi +1 位作者 SHI Hua LIU Huchen 《Journal of Systems Engineering and Electronics》 CSCD 2024年第6期1491-1506,共16页
Failure mode and effect analysis(FMEA)is a preven-tative risk evaluation method used to evaluate and eliminate fail-ure modes within a system.However,the traditional FMEA method exhibits many deficiencies that pose ch... Failure mode and effect analysis(FMEA)is a preven-tative risk evaluation method used to evaluate and eliminate fail-ure modes within a system.However,the traditional FMEA method exhibits many deficiencies that pose challenges in prac-tical applications.To improve the conventional FMEA,many modified FMEA models have been suggested.However,the majority of them inadequately address consensus issues and focus on achieving a complete ranking of failure modes.In this research,we propose a new FMEA approach that integrates a two-stage consensus reaching model and a density peak clus-tering algorithm for the assessment and clustering of failure modes.Firstly,we employ the interval 2-tuple linguistic vari-ables(I2TLVs)to express the uncertain risk evaluations provided by FMEA experts.Then,a two-stage consensus reaching model is adopted to enable FMEA experts to reach a consensus.Next,failure modes are categorized into several risk clusters using a density peak clustering algorithm.Finally,the proposed FMEA is illustrated by a case study of load-bearing guidance devices of subway systems.The results show that the proposed FMEA model can more easily to describe the uncertain risk information of failure modes by using the I2TLVs;the introduction of an endogenous feedback mechanism and an exogenous feedback mechanism can accelerate the process of consensus reaching;and the density peak clustering of failure modes successfully improves the practical applicability of FMEA. 展开更多
关键词 failure mode and effect analysis(FMEA) interval 2-tuple linguistic variable(I2TLV) consensus reaching density peak clustering algorithm
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基于K-means算法的艾德莱斯绸色彩提取方法的优化设计
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作者 刘恒君 饶蕾 曹远荣 《毛纺科技》 北大核心 2025年第8期82-90,共9页
为了提高艾德莱斯绸的数据化以及数字化研究,针对艾德莱斯绸本身的工艺特征优化设计一种基于K-means聚类算法的色彩提取方法。首先采用非接触扫描仪扫描样本获得图像;通过中值滤波对比图像在不同窗口尺寸下的平滑降噪效果,确定最适合艾... 为了提高艾德莱斯绸的数据化以及数字化研究,针对艾德莱斯绸本身的工艺特征优化设计一种基于K-means聚类算法的色彩提取方法。首先采用非接触扫描仪扫描样本获得图像;通过中值滤波对比图像在不同窗口尺寸下的平滑降噪效果,确定最适合艾德莱斯绸图像预处理的窗口数值;再将图像的色彩信息从RGB空间转为更符合视觉分析的HSV空间;结合艾德莱斯绸本身纹样特征,对比2种常见的最佳类簇数目k值选取办法,并进行k值选取办法的优化和对比;最后将聚类算法与数据分析相结合,采用多个k值分别确定图像单个色彩。结果表明:该优化方式可以较为准确地提取出复杂的艾德莱斯绸色彩及其占比情况,为提取复杂图像色彩提供了新的研究思路,拓宽传统纹样图像的色彩研究方式。 展开更多
关键词 k-meanS聚类算法 艾德莱斯绸 色彩提取 数据分析
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