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10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Liu, Yuan-yuan Zhou, Shi-yu Sun, Jia-hui Jilin Agr Univ JLAU Coll Informat Technol Changchun Jilin Peoples R China Agr Sci & Technol Network Collaborat Innovat Ctr Changchun Jilin Peoples R China Internet Things Engn Res Technol Ctr Jilin Prov Changchun Jilin Peoples R China

ISBN: (纸本)9781538619377

Diseases like black spot, anthracnose and Cylindrocarpon destructans always appear on ginseng leaf. The traditional way to distinguish the disease in farmland is checking by planter in person. This way takes more time and energy. In this paper, according to cicatrices are different with normal leaf in color, we use k-means clustering algorithm and combine the difference between a and b components in Lab color space to discern the cicatrices in leaf image. Firstly, we can distinguish cicatrices and normal leaves. Secondly, we take out useless part like petiole. Lastly, we calculate the ratio of area of cicatrices and normal leaf. The ratio can be used as reference of the ginseng disease and its degree. The result of experiment shows that k-means clustering algorithm can segment the leaf image, and the segmentation is based on the difference of color, then we can calculate the ratio of pixel in each part, and judge the degree of disease.

关键词： digital image processing k-means clustering algorithm detection of ginseng's disease RGB color space Lab color space

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Suitable error evaluation criteria selection in the wind energy assessment via the k-means clustering algorithm

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INTERNATIONAL JOURNAL OF GREEN ENERGY 2016年第11期13卷 1145-1162页

作者： Wu, Jie Wang, Jianzhou Qin, Shanshan Lu, Haiyan Northwest Univ Nationalities Sch Math & Comp Sci Lanzhou Peoples R China Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Lanzhou Univ Res Sch Arid Environm & Climate Change MOE Key Lab Western Chinas Environm Syst Lanzhou Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW Australia

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the kolmogorov-Smirnov test error (kSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the k-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the k-means clustering algorithm, by which the CSE is clustered into a different group with kSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.

关键词： Anomalous value k-means clustering algorithm parameter optimization wind energy wind power classification

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Recognition and localization of occluded apples using k-means clustering algorithm and convex hull theory: a comparison

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MULTIMEDIA TOOLS AND APPLICATIONS 2016年第6期75卷 3177-3198页

作者： Wang, Dandan Song, Huaibo Tie, Zhihui Zhang, Weiyuan He, Dongjian Northwest A&F Univ Coll Mech & Elect Engn Yangling 712100 Shaanxi Peoples R China

For apple harvesting robot, it is difficult to acquire the coordinates of occluded apples accurately in natural scenes, which is important in implementing picking tasks. In this paper, a method on automatic recognition and localization of occluded apples was proposed. Firstly, an apple recognition algorithm based on k-means clustering theory was described. Secondly, convex hull information which was obtained by following the contours of extracted apple regions was used to extract the real apple edges. Finally, three points from these real edges were selected to estimate the centers and radius of apples. This algorithm was tested and compared with traditional Hough transform method (HT method) and contour curvature method (CC method) and 125 apple images were used to test the effectiveness of these methods. Four parameters including Segmentation Error (SE), False Positive Rate (FPR), False Negative Rate (FNR) and Overlap Index (OI) were used to evaluate the performance of these methods. Experimental results showed that SE of the presented method was decreased by 14.399 and 30.782 % when compared to CC method and HT method respectively, FPR by 7.234 and 11.728 % and OI was increased by 18.644 and 30.938 %. FNR of the proposed method was 0.912 % lower than CC method, while it was 5.869 % higher than HT method. The experimental results indicated that the proposed method could get much better localization rate than Hough transform method and contour curvature method, thus it could be concluded that the algorithm is an efficient means for the recognition and localization of occluded apples.

关键词： Occluded apple Localization Recognition k-means clustering algorithm Convex hull

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Identification of new candidate drugs for lung cancer using chemical-chemical interactions, chemical-protein interactions and a k-means clustering algorithm

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JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS 2016年第4期34卷 906-917页

作者： Lu, Jing Chen, Lei Yin, Jun Huang, Tao Bi, Yi kong, Xiangyin Zheng, Mingyue Cai, Yu-Dong Yantai Univ Collaborat Innovat Ctr Adv Drug Delivery Syst & B Minist Educ Sch PharmKey Lab Mol Pharmacol & Drug Evaluat Yantai 264005 Peoples R China Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China Shanghai Jiao Tong Univ Sch Med SJTUSM Inst Hlth Sci Key Lab Stem Cell Biol Shanghai 200025 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci Shanghai 200025 Peoples R China Shanghai Inst Mat Med Drug Discovery & Design Ctr Shanghai 201203 Peoples R China Shanghai Univ Coll Life Sci Shanghai 200444 Peoples R China

Lung cancer, characterized by uncontrolled cell growth in the lung tissue, is the leading cause of global cancer deaths. Until now, effective treatment of this disease is limited. Many synthetic compounds have emerged with the advancement of combinatorial chemistry. Identification of effective lung cancer candidate drug compounds among them is a great challenge. Thus, it is necessary to build effective computational methods that can assist us in selecting for potential lung cancer drug compounds. In this study, a computational method was proposed to tackle this problem. The chemical-chemical interactions and chemical-protein interactions were utilized to select candidate drug compounds that have close associations with approved lung cancer drugs and lung cancer-related genes. A permutation test and k-means clustering algorithm were employed to exclude candidate drugs with low possibilities to treat lung cancer. The final analysis suggests that the remaining drug compounds have potential anti-lung cancer activities and most of them have structural dissimilarity with approved drugs for lung cancer.

关键词： chemical-protein interaction k-means clustering algorithm lung cancer chemical-chemical interaction

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Researches on the Calculation Method of Saturation Flow Rate at Signalized Intersection Based on k-means clustering algorithm

Researches on the Calculation Method of Saturation Flow Rate...

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International Conference on Computer Technology, Electronics and Communication

作者： Weiming Luo Jianhua Yuan Qing Ma Lin Xu Traffic Management Research Institute of Ministry of Public Security Ministry Wuxi Jiangsu China

ISBN: (纸本)9781538657850

This paper studies k-means clustering algorithm for the calculation of saturation flow rate at signalized intersection. The calculation results are based on the measured traffic data, which are surveyed at the intersection of Xinhua Road and Youyi Road in Tangshan. By analyzing the distribution characteristics of average headway in a day, this paper presents the negative influences of unsaturated or oversaturated traffic flow on headway. A portion of the average headway which affected by unsaturated or oversaturated condition are removed by k-means clustering algorithm. The developed method is used to calculate the saturation flow rate within different conditions. The calculation results illustrate that the proposed method can offset the effects of unsaturated or oversaturated condition.

关键词： The saturation flow rate Headway Signalized intersection k-means clustering algorithm

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Open cluster membership probability based on k-means clustering algorithm

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EXPERIMENTAL ASTRONOMY 2016年第1期42卷 49-59页

作者： Abd El Aziz, Mohamed Selim, I. M. Essam, A. Zagazig Univ Dept Math Fac Sci Zagazig 44519 Egypt Nahda Univ Fac Comp Sci Bani Suwayf Egypt Natl Res Inst Astron & Geophys Dept Astron Cairo Egypt HTI Cairo Egypt

In the field of galaxies images, the relative coordinate positions of each star with respect to all the other stars are adapted. Therefore the membership of star cluster will be adapted by two basic criterions, one for geometric membership and other for physical (photometric) membership. So in this paper, we presented a new method for the determination of open cluster membership based on k-means clustering algorithm. This algorithm allows us to efficiently discriminate the cluster membership from the field stars. To validate the method we applied it on NGC 188 and NGC 2266, membership stars in these clusters have been obtained. The color-magnitude diagram of the membership stars is significantly clearer and shows a well-defined main sequence and a red giant branch in NGC 188, which allows us to better constrain the cluster members and estimate their physical parameters. The membership probabilities have been calculated and compared to those obtained by the other methods. The results show that the k-means clustering algorithm can effectively select probable member stars in space without any assumption about the spatial distribution of stars in cluster or field. The similarity of our results is in a good agreement with results derived by previous works.

关键词： Galaxy Open clusters and associations Individual k-means clustering algorithm NGC 2266 NGC 188 Color-magnitude diagram (CMD)

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A Prediction Model of Hard landing Based on RBF Neural Network with k-means clustering algorithm

A Prediction Model of Hard landing Based on RBF Neural Netwo...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Qiao, Xiaoduo Chang, Wenbing Zhou, Shenghan Lu, Xuefeng Beihang Univ Sch Reliabil & Syst Engn Beijing Peoples R China

ISBN: (纸本)9781509036653

This paper proposes a prediction model for forecasting the hard landing problem. The landing phase has been demonstrated the most dangerous phase in flight cycle for fatal accidents. The landing safety problem has become one of the hot research problems in engineering management field. The study concentrates more on the prediction and advanced warning of hard landing. Firstly, flight data is preprocessed with data slicing method based on flight height and dimension reduction. Subsequently, the radial basis function (RBF) neural network model is established to predict the hard landing. Then, the structure parameters of the model are determined by the k-means clustering algorithm. In the end, compared with Support Vector Machine and BP neural network, the RBF neural network based on k-means clustering algorithm model is adopted and the prediction accuracy of hard landing is better than traditional ways.

关键词： flight data hard landing prediction model RBF neural network k-means clustering algorithm

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Customer Segmentation Using k-means clustering and the Hybrid Particle Swarm Optimization algorithm

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COMPUTER JOURNAL 2023年第4期66卷 941-962页

作者： Li, Yue Qi, Jianfang Chu, Xiaoquan Mu, Weisong China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Minist Agr Key Lab Viticulture & Enol Beijing 100083 Peoples R China

In a competitive market, it is of great significance to divide customer groups to develop customer-centered personalized products. In this paper, we propose a customer segmentation method based on the k-means algorithm and the improved particle swarm optimization (PSO) algorithm. As the PSO algorithm easily falls into local extremum, the improved hybrid particle swarm optimization (IHPSO) algorithm is proposed to improve optimization accuracy. The full factorial design is used to determine the optimal parameter combination;the roulette operator is used to select excellent particles;then, the selected particles are crossed according to their adaptive crossover probabilities;when the population falls into a local optimum, the particles are mutated according to their adaptive mutation probabilities. Aimed at the k-means' sensitivity to selecting the initial cluster centers, IHPSO is used to optimize the cluster centers (IHPSO-kM). We compare IHPSO with the PSO, LDWPSO, GA, GA-PSO and ALPSO algorithms on nine benchmark functions. We also conduct comparative experiments to compare IHPSO-kM with several conventional and state-of-the-art approaches on five UCI datasets. All results show that the two proposed methods outperform existing models. Finally, IHPSO-kM is applied in customer segmentation. The experimental results also prove the rationality and applicability of IHPSO-kM for customer segmentation.

关键词： k-means clustering algorithm particle swarm optimization algorithm hybrid mechanism cluster analysis customer segmentation

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A k-means clustering-Based Multiple Importance Sampling algorithm for Integral Global Optimization

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Journal of the Operations Research Society of China 2023年第1期11卷 157-175页

作者： Chen Wang Dong-Hua Wu Department of Mathematics Shanghai UniversityShanghai200444China

In this paper, we propose a k-means clustering-based integral level-value estimation algorithm to solve a kind of box-constrained global optimization problem. For this purpose, we introduce the generalized variance function associated with the level-value of the objective function to be minimized. The variance function has a good property when Newton’s method is used to solve a variance equation resulting by setting the variance function to zero. We prove that the largest root of the variance equation is equal to the global minimum value of the corresponding optimization problem. Based on the k-means clustering algorithm, the multiple importance sampling technique is proposed in the implementable algorithm. The main idea of the cross-entropy method is used to update the parameters of sampling density function. The asymptotic convergence of the algorithm is proved, and the validity of the algorithm is verified by numerical experiments.

关键词： Global optimization Generalized variance function Multiple importance sampling k-means clustering algorithm

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A Hybrid k-means Method based on Modified Rat Swarm Optimization algorithm for Data clustering 43

A Hybrid K-means Method based on Modified Rat Swarm Optimiza...

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43rd Chinese Control Conference (CCC)

作者： Lou, Taishan Guan, Guanguang Yu, Zhepeng Wang, Yu Tong, Shihao Zhengzhou Univ Light Ind Sch Elect & Informat Engn Zhengzhou 450002 Henan Peoples R China

ISBN: (纸本)9798350366907;9789887581581

The original k-means clustering algorithm is prone to local optima and sensitive to the initial clustering center, which have a great impact on accuracy and stability of clustering results in practical applications. To overcome this limitation, an innovative k-means clustering method based on modified rat swarm optimization (RSO) algorithm is proposed. A nonlinear convergence factor is introduced into the RSO to adjust convergence speed of different data sets and improve the global search ability. Then, a reverse initial population strategy is adopted to increase population diversity, thus improving the robustness of the algorithm to the initial conditions. The modified RSO algorithm is used to find the initial optimal cluster centroid, and then k-means algorithm is used to refine the optimized initial cluster centroid to improve the clustering accuracy. The experimental results show that compared with the original k-means clustering algorithm, the improved algorithm has achieved significant improvement in each index of iris, wine and glass datasets, which proves the effectiveness and superiority of the algorithm. This paper presents a new hybrid clustering algorithm which combines the improved swarm intelligent optimization algorithm with k-means clustering algorithm. This method effectively solves the problem that k-means clustering algorithm is sensitive to the initial cluster center.

关键词： k-means clustering algorithm Rat Swarm Optimization global search ability

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