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Service quality evaluation of agricultural cold chain logistics supply chain based on k-means clustering algorithm

International Journal of Manufacturing Technology and Management 2025年第1-2期39卷 59-73页

作者： Zhang, YuXi School of Economics and Trade Henan Polytechnic Institute Nanyang473000 China

In order to improve the reliability of the cold chain logistics supply chain and shorten the response time of the supply chain, a quality evaluation method of agricultural cold chain logistics supply chain based on k-means clustering algorithm was proposed. Firstly, build the quality evaluation system of agricultural cold chain logistics supply chain. Secondly, the root method is selected to check the consistency of the judgment matrix, and the weight vector of the logistics supply chain quality evaluation is calculated. Finally, the k-means clustering algorithm is used to evaluate the supply chain service quality. The experimental results show that the supply chain quality reliability of this method is 0.98, and the service response time is only six minutes;the service satisfaction rate can reach 99.6%. © 2025 Inderscience Enterprises Ltd.

关键词： agriculture products analytic hierarchy process cold chain logistics k-means clustering algorithm service quality assessment weight vector

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Sports competition data analysis and strategy optimization using k-means clustering algorithm

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年第1期25卷 888-902页

作者： An, Ni Guangdong Pharmaceut Univ Dept Phys Educ 280 Outer Ring East Rd Guangzhou 510006 Guangdong Peoples R China

Sports competition data analysis and strategy optimization are important ways to enhance athlete competitiveness and team collaboration. The current competition analysis and strategy formulation have strong subjectivity, making it difficult to deeply understand the performance characteristics and patterns of athletes and teams. Traditional analysis methods cannot accurately identify the performance differences of different athletes, and there are limitations in their feature recognition and classification. In order to enhance the scientificity of strategy formulation and improve the performance of athletes in competitions, this article combined the k-means clustering algorithm and focused on basketball sports to conduct an in-depth analysis of sports competition data analysis and strategy optimization. Firstly, the competition data was collected and preprocessed. Then, feature selection was carried out from three dimensions: competition results, player performance, and team characteristics. Finally, the k-means clustering algorithm was used to perform hierarchical clustering on the original data through a hierarchical method. To verify its effectiveness, this article conducted practical analysis on the data of nearly 5 basketball competitions in 10 university basketball leagues in a certain province and optimized strategies based on cluster analysis. The results showed that in terms of player performance, compared to before optimization, the average number of rebounds, assists, and steals of team players optimized based on algorithm strategy increased by about 38.9%, 25.0%, and 63.2%, respectively. The conclusion indicates that the application of k-means clustering algorithm in sports competition data analysis and strategy optimization can help improve the competitive level of athletes and enhance their performance.

关键词： sports competition data analysis strategy optimization k-means clustering algorithm basketball performance

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k-means clustering algorithm for multimedia applications with flexible HW/SW co-design

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JOURNAL OF SYSTEMS ARCHITECTURE 2013年第3期59卷 155-164页

作者： An, Fengwei Mattausch, Hans Juergen Hiroshima Univ Res Inst Nanodevice & Bio Syst Higashihiroshima 724 Japan

In this paper, we report a hardware/software (MW/SW) co-designed k-means clustering algorithm with high flexibility and high performance for machine learning, pattern recognition and multimedia applications. The contributions of this work can be attributed to two aspects. The first is the hardware architecture for nearest neighbor searching, which is used to overcome the main computational cost of a k-means clustering algorithm. The second aspect is the high flexibility for different applications which comes from not only the software but also the hardware. High flexibility with respect to the number of training data samples, the dimensionality of each sample vector, the number of clusters, and the target application, is one of the major shortcomings of dedicated hardware implementations for the k-means algorithm. In particular, the HW/SW k-means algorithm is extendable to embedded systems and mobile devices. We benchmark our multi-purpose k-means system against the application of handwritten digit recognition, face recognition and image segmentation to demonstrate its excellent performance, high flexibility, fast clustering speed, short recognition time, good recognition rate and versatile functionality. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Hardware/software co-design k-means clustering algorithm Nearest neighbor searching Handwritten digit recognition Face recognition Image segmentation

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k-means clustering algorithm for data distribution in cloud computing environment

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INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING 2021年第3期12卷 322-331页

作者： Pan, Hailan Lei, Yongmei Yin, Shi Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Shanghai Polytech Univ Res Ctr Resource Recycling Sci & Engn Shanghai 201209 Peoples R China Shanghai Polytech Univ Sch Econ & Management Shanghai 201029 Peoples R China

This study analyses the data structure in cluster analysis. It is a clustering method that randomly selects a known number of points and then continues to expand. Through the comparative experiments on the clustering accuracy of different similarity matrices, the experimental analysis on the effectiveness of the model, the distribution of e-commerce data under cloud computing and the calculation time of different clustering algorithms, we can better understand the k-means clustering algorithm and the status of e-commerce in cloud computing environment. The experimental results show that if the appropriate similarity function is selected, the result of spectral clustering is usually not lower than that of simple k-means clustering. When the number of users reaches 4000, the list reading time of the k-means clustering algorithm is 3.15 s, while the other three algorithms consume more time.

关键词： cloud computing data distribution oriented k-means clustering algorithm e-commerce platform data structure data distribution

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k-means clustering algorithm and LSTM based Short-term Load Forecasting for Distribution Transformer 5

K-Means Clustering Algorithm and LSTM based Short-term Load ...

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5th Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Li, Shan Lu, Xin Ouyang, Jianna Zhou, Yangjun Zhang, Wei Guangxi Power Grid Co Ltd Elect Power Res Inst Guangxi Power Grid Equipment Monitoring & Diag En Nanning Peoples R China

ISBN: (纸本)9781665490542

It is critical to forecast the electric load for a region. Traditional electric load forecasting frequently predicts the load of multiple transformers in the region after directly summing them, but directly predicting the total load after accumulating the load of each distribution transformer will weaken the individual time-series characteristics and reduce prediction accuracy, so it is necessary to forecast the total load of the region while appropriately retaining the individual time-series characteristics. To address the aforementioned issues, this paper clusters transformer load curves, narrows the load characteristics of the same category of transformers using the weighting concept. It stores the load characteristics of multiple transformers in a neural network. This paper develops a virtual transformer load forecasting model in which the sum of all transformer loads in the region is a number of times the virtual transformer load forecasting result.

关键词： load forecasting k-means clustering algorithm long short-term memory

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k-means clustering algorithm for Large-scale Chinese Commodity Information Web Based on Hadoop 14

K-means Clustering Algorithm for Large-scale Chinese Commodi...

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International Symposium Distributed Computing and Applications Business Engineering and Science

作者： Geng Yushui Zhang Lishuo Qilu Univ Technol Sch Informat Jinan 250353 Peoples R China

ISBN: (纸本)9781467365932

With the growing popularity of the network, product information filled in the many pages of the Internet, which you want to get the information you need on these pages tend to consider clustering information, and the current explosive growth of data so that the information mass storage condition occurs, clustering to facing the problems such as large calculation complexity and time consuming, then the traditional k-means clustering algorithm does not meet the needs of large data environments today, so this article combined with the advantages of the Hadoop platform and MapReduce programming model is proposed the k-means clustering algorithm for large-scale chinese commodity information Web based on Hadoop. Map function calculates the distance from the cluster center for each sample and mark to their category, Reduce function intermediate results are summarized and calculated new clustering center for the next round of iteration. Experimental results show that this method can better improve the clustering processing speed.

关键词： k-means clustering algorithm Hadoop platform MapReduce Cloud computing Big Data

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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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Analysis of space-time adaptive processing performance using k-means clustering algorithm for normalisation method in non-homogeneity detector process

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IET SIGNAL PROCESSING 2011年第2期5卷 113-120页

作者： kang, S. Ryu, J. Lee, J. Jeong, J. Korea Univ Dept Comp & Radio Commun Engn Seoul 136713 South Korea Korea Univ Dept Brain & Cognit Engn Seoul 136713 South Korea

This study describes the performance analysis of the non-homogeneity detector (NHD) with various normalisation methods for the space-time adaptive processing (STAP) of airborne radar signals under the non-homogeneous clutter environments. The authors can calculate a threshold value from the statistical analysis of generalised inner product (GIP) using the normalisation method using mean, median and the k-means clustering algorithm of training data snapshots in the NHD process. The selected homogeneous data using the threshold value are used to recalculate covariance matrix of the total interference. To evaluate the performance of the covariance matrix, the authors calculated the eigenspectra and signal to interference noise ratio (SINR) loss. The accuracy of the recalculated covariance matrix is verified by the modified sample matrix inversion (MSMI) test statistic for the target detection. Projection statistics (PS) based on GIP is also used to compare the performance of detecting single and multiple targets. The authors' simulation results demonstrate that the k-means clustering algorithm as a normalisation method for both GIP and GIP-based PS can improve the STAP performance in the severe non-homogeneous clutter environment even under the multiple targets scenarios, compared to the other normalisation methods.

关键词： airborne radar signals Radar theory statistical analysis covariance matrix airborne radar target detection space-time adaptive processing generalised inner product normalisation method k-means clustering algorithm non-homogeneity detector Radar equipment, systems and applications modified sample matrix inversion test statistic projection statistics Signal processing and detection Other topics in statistics

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Nonlinear seismic site response classification using k-means clustering algorithm: Case study of the September 6, 2018 Mw6.6 Hokkaido Iburi-Tobu earthquake, Japan

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SOIL DYNAMICS AND EARTHQUAkE ENGINEERING 2020年 128卷 105907-000页

作者： Ji, kun Wen, Ruizhi Ren, Yefei Dhakal, Yadab P. China Earthquake Adm Key Lab Earthquake Engn & Engn Vibrat Inst Engn Mech 29 Xuefu Rd Harbin 150080 Heilongjiang Peoples R China Natl Res Inst Earth Sci & Disaster Resilience 3-1 Tennodai Tsukuba Ibaraki 3050006 Japan

Spectral ratio methods have been widely used in evaluation of nonlinear seismic site response. Nevertheless, it remains inefficient and subjective to identify stations with nonlinear site response according to empirical threshold values of spectral ratio nonlinear degree indicators. This study, which was the first to apply the machine learning clustering algorithm to address this problem, used the September 6, 2018 M(w)6.6 Hokkaido Iburi-Tobu earthquake (Japan) as an example. First, we calculated the surface/borehole and horizontal/vertical spectral ratios using strong ground motion data recorded by kik-net vertical array and k-NET stations, respectively. The degree of nonlinear site response (DNL) and percentage of nonlinear site response (PNL) were computed using the difference between the strong motion of the mainshock and weak aftershocks as the reference for linear site response. Then, the k-means clustering algorithm was incorporated in the identification of nonlinear site response using the DNL, PNL, strength of ground motion (PGA) and site condition (V-S20 or V-S30) as explanatory variables. After careful multicollinearity diagnosis and confirmation of the optimum clustering number, we successfully classified the stations into two clusters with nonlinear and linear site responses. Overall, the clustering results were found in good agreement with the classification results based on empirical thresholds of several nonlinear indicators. For the stations identified with nonlinear site response, the reduction of amplification and frequency shift could be observed from the spectral ratio curves regarding the ground motions in the mainshock and the reference weak aftershocks, demonstrating typical nonlinearity response characteristics. Furthermore, a comprehensive indicator of nonlinear site response occurrence probability (NLscore) was obtained from a linear weighted combination of the normalized variables (PGA, V-S30/V-S20, DNL and PNL). The NLscore ranking of th

关键词： Nonlinear site response Spectral ratio method k-means clustering algorithm 2018 Hokkaido Iburi-Tobu earthquake

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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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