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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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Identification of column edges of DNA fragments by using k-means clustering and mean algorithm on lane histograms of DNA agarose gel electrophoresis images 7

Identification of column edges of DNA fragments by using K-m...

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7th International Conference on Digital Image Processing (ICDIP)

作者： Turan, Muhammed kamil Sehirli, Eftal Elen, Abdullah karas, Ismail Rakip Karabuk Univ Karabuk Turkey

ISBN: (纸本)9781628418293

Gel electrophoresis (GE) is one of the most used method to separate DNA, RNA, protein molecules according to size, weight and quantity parameters in many areas such as genetics, molecular biology, biochemistry, microbiology. The main way to separate each molecule is to find borders of each molecule fragment. This paper presents a software application that show columns edges of DNA fragments in 3 steps. In the first step the application obtains lane histograms of agarose gel electrophoresis images by doing projection based on x-axis. In the second step, it utilizes k-means clustering algorithm to classify point values of lane histogram such as left side values, right side values and undesired values. In the third step, column edges of DNA fragments is shown by using mean algorithm and mathematical processes to separate DNA fragments from the background in a fully automated way. In addition to this, the application presents locations of DNA fragments and how many DNA fragments exist on images captured by a scientific camera.

关键词： Lane histogram k-means clustering algorithm mean algorithm DNA fragments

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Information Architecture (IA): Using Multidimensional Scaling (MDS) and k-means clustering algorithm for Analysis of Card Sorting Data

JOURNAL OF USABILITY STUDIES

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JOURNAL OF USABILITY STUDIES 2018年第3期13卷 138-157页

作者： Paea, Sione Baird, Ross Univ South Pacific Sch Comp Informat & Math Sci Suva Fiji Optimal Workshop Www Optimalworkshop Com Wellington New Zealand

We present a method for visualizing and analyzing card sorting data aiming to develop an in-depth and effective information architecture and navigation structure. One of the well-known clustering techniques for analyzing large data sets is with the k-means algorithm. However, that algorithm has yet to be widely applied to analyzing card sorting data sets to measure the similarity between cards and result displays using multidimensional scaling. The multidimensional scaling, which employs particle dynamics to the error function minimization, is a good candidate to be a computational engine for interactive card sorting data. In this paper, we apply the combination of a similarity matrix, a k-means algorithm, and multidimensional scaling to cluster and calculate an information architecture from card sorting data sets. We chose card sorting to improve an information architecture. The proposed algorithm handled the overlaps between cards in the card sorting data quite well and displayed the results in a basic layout showing all clusters and card coordinates. For outliers, the algorithm allows grouping of single cards to their closest core clusters. The algorithm handled outliers well choosing cards with the strongest similarities from the similarity matrix. We tested the clustering algorithm on real-world data sets and compared to other techniques. The results generated clear knowledge on relevant usability issues in visualizing information architecture. The identified usability issues point to a need for a more in-depth search of design solutions that are tailored for the targeted group of people who are struggling with complicated visualizing techniques. This study is for people who need support to easily visualize information architecture from data sets.

关键词： information architecture card sorting multidimensional scaling k-means clustering algorithm analysis similarity matrix

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Construction of aesthetic education evaluation model based on k-means clustering analysis in the context of artificial intelligence

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年第1期25卷 728-743页

作者： Li, Mo Guo, Shasha Chen, Jie Hebei Univ Engn Student Affairs Off Handan Peoples R China Hebei Univ Engn Sch Earth Sci & Engn 19Taiji RdEcon Devel Zone Handan 056038 Hebei Province Peoples R China

Enhancing aesthetic education can boost individuals' skills in recognizing, understanding, appreciating, and creating beauty. Nevertheless, traditional methods for evaluating aesthetic education are often subjective and have limited capacity to handle complex features effectively. This article was based on artificial intelligence to develop an aesthetic education evaluation model, and used an improved k-means clustering algorithm and a pre-trained ResNet-101 model for feature extraction. This article used WikiArt as an experimental data set, which contains 77,229 artworks from 15 art styles. This study used silhouette coefficient, Davies-Bouldin index, Calinski-Harabasz index, and Dunn index to evaluate the model clustering effect. Experimental results indicated that with a clustering \(k\) value of 10, the enhanced algorithm significantly advanced clustering quality, computational efficiency, and memory usage. This model has enhanced the objectivity and efficiency of aesthetic education evaluation and introduced new approaches for classifying and assessing artistic works.

关键词： k-means clustering algorithm aesthetic education evaluation artificial intelligence feature extraction ResNet-101 model

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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 new data-adaptive network design methodology based on the k-means clustering and modified ISODATA algorithm for regional gravity field modeling via spherical radial basis functions

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JOURNAL OF GEODESY 2022年第11期96卷 1-29页

作者： Ulug, Rasit karslioglu, Mahmut Onur Middle East Tech Univ Dept Geodet & Geog Informat Technol TR-068000 Ankara Turkey Middle East Tech Univ Dept Civil Engn Geomat Engn Div Ankara Turkey

In this study, a new data-adaptive network design methodology called k-SRBF is presented for the spherical radial basis functions (SRBFs) in regional gravity field modeling. In this methodology, the cluster centers (centroids) obtained by the k-means clustering algorithm are post-processed to construct a network of SRBFs by replacing the centroids with the SRBFs. The post-processing procedure is inspired by the heuristic method, Iterative Self-Organizing Data Analysis Technique (ISODATA), which splits clusters within the user-defined criteria to avoid over- and under-parameterization. These criteria are the minimum spherical distance between the centroids and the minimum number of samples for each cluster. The bandwidth (depth) of each SRBF is determined using the generalized cross-validation (GCV) technique in which only the observations within the radius of impact area (RIA) are used. The numerical tests are carried out with real and simulated data sets to investigate the effect of the user-defined criteria on the network design. Different bandwidth limits are also examined, and the appropriate lower and upper bandwidth limits are chosen based on the empirical signal covariance function and user-defined criteria. Also, additional tests are performed to verify the performance of the proposed methodology in combining different types of observations, such as terrestrial and airborne data available in Colorado. The results reveal that k-SRBF is an effective methodology to establish a data-adaptive network for SRBFs. Moreover, the proposed methodology improves the condition number of normal equation matrix so that the least-squares procedure can be applied without regularization considering the user-defined criteria and bandwidth limits.

关键词： Regional gravity field modeling Spherical radial basis functions Data-adaptive network design Poisson kernel k-means clustering algorithm ISODATA

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Research on Grounding Grid Corrosion Diagnosis Based on Genetic k-means algorithm

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ENERGY SCIENCE & ENGINEERING 2025年

作者： Huang, Longsheng Xiao, Xianghui Huang, Mingxian Zhang, Zhenshan Song, Yunhao Guan, Luchang Foshan Univ Sch Mechatron Engn & Automat Foshan Peoples R China Foshan Univ Guangdong Prov Key Lab Ind Intelligent Inspect Tec Foshan Peoples R China

Grounding grid corrosion is one of the main reasons that affect the stable operation of electrical equipment in substations and endanger personal safety. After many years of operation, the grounding conductors will be eroded by soil. It may even cause major accidents and economic losses. Therefore, it is of great significance to diagnose the corrosion faults of the grounding grid and find out the corroded conductors. In this paper, the genetic k-means algorithm (GkA) is proposed to solve the mathematical model and judge the corrosion of grounding conductors. This algorithm combines GA's global searching ability and k-means's local searching ability, which improves the diagnosis result. In the simulation experiment, compared with the single GA's diagnosis, the diagnosis results of GkA were improved, and the number of misdiagnosed branches decreased by 66.7%. The simulation results show that the proposed algorithm takes less time to run, can eliminate the misdiagnosed branches commendably, and improve the accuracy of diagnosis. The proposed method provides a new idea to evaluate the corrosion degree of the grounding grid. The clustering algorithm is used to classify branches with similar corrosion degrees to achieve the purpose of corrosion diagnosis.

关键词： corrosion diagnosis electrical network theory genetic algorithm grounding grid k-means clustering algorithm

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Mapping global human thermal comfort: Spatiotemporal variability of UTCI regions under climate change

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SUSTAINABLE CITIES AND SOCIETY 2025年 119卷

作者： Zheng, Jing Du, Yaxing Zhu, Xian Dong, Wenjie Sun Yat Sen Univ Sch Atmospher Sci Key Lab Trop Atmosphere Ocean Syst Southern Marine Sci & Engn Guangdong Lab ZhuhaiMi Zhuhai 519082 Peoples R China

Human Thermal Comfort (HTC) is directly related to human health, quality of life, and sustainable urban planning. However, the global evaluation of outdoor thermal comfort via Universal Thermal Climate Index (UTCI) is still inadequate. This study systematically analyzed global HTC, via UTCI between 1951 and 2100 based on the Coupled Model Intercomparison Project Phase 6, covering historical trends and four Shared Socioeconomic Pathways (SSPs) future scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). The following key findings were presented: (1) The global average UTCI exhibits a latitudinal gradient distribution and a clear, consistent increase over time. (2) Ten characteristic thermal regions were identified by combining Empirical Orthogonal Function (EOF) and kmeans clustering. (3) EOF analysis revealed significant global warming and complex regional contrasts. (4) Low-latitude thermal regions expand poleward in all scenarios, with SSP5-8.5 causing major thermal property shifts and region redistributions. These results underscore a significant impact of changes inhuman thermal comfort under climate change, offering recommendations for interventions and policy guidance to address the challenges of climate change.

关键词： Human thermal comfort (HTC) Universal Thermal Climate Index (UTCI) k-means clustering algorithm Empirical orthogonal function (EOF) analysis

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GPR Diffraction Separation by Incorporating Multilevel Wavelet Transform and Multiple Singular Spectrum Analysis

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APPLIED SCIENCES-BASEL 2025年第6期15卷

作者： Wang, Haolin Wang, Honghua Hou, Zhiyang Zhou, Fei Guilin Univ Technol Coll Earth Sci Guilin 541004 Peoples R China

By leveraging amplitude differences between reflected and diffracted signals in Ground Penetrating Radar (GPR) data, multiple singular spectrum analysis (MSSA) is considered an attractive approach to separate diffraction, which has identified great potential in their detectability of small-scale geological structures. However, conventional MSSA encounters difficulties in pinpointing the singular value threshold that corresponds to reflection, diffraction, and noise within the singular spectrum, leading to a resolution loss of the extracted diffraction profile. To address this issue, this paper develops a new technique that incorporates multilevel wavelet transform (MWT) and MSSA to separate GPR diffraction. By first implementing the MWT on GPR data decompose, the strategy can obtain various approximate detailed coefficients of multiple transformation levels for the subsequent inverse MWT to construct the corresponding coefficient profile. The issue of coefficient profiles that depict reflections often contains residual diffractions is also addressed by performing multiple singular spectrum SVDs based on the Hankel matrix within the dominant frequency domain. Building upon this, the k-means clustering algorithm is introduced to perform MSSA for classifying singular values into k categories. The diffraction wavefield is rebuilt by combining these outcomes with the coefficient profiles that depict diffractions at various transformation levels. Numerical tests showcase that the biorthogonal wavelet basis function bior4.4 provides remarkably efficient GPR diffraction separation performance, and the number of clusters in the k-means clustering algorithm typically ranges from 9 to 15, accounting for the complexity of the wave components. Compared to plane wave deconstruction (PWD), the proposed MWT-MSSA approach reduces energy loss at the diffraction vertex, decreases residual diffraction energy within the reflection profile, and enhances computational efficiency by approx

关键词： ground penetrating radar (GPR) multilevel wavelet transform (MWT) multiple singular spectrum analysis (MSSA) k-means clustering algorithm diffraction separation

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