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Image clustering algorithm using superpixel segmentation and non-symmetric Gaussian-Cauchy mixture model

IET IMAGE PROCESSING 2020年第16期14卷 4132-4143页

作者： Ji, Sifan Zhu, Hongqing Wang, Pengyu Ling, Xiaofeng East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China

In this study, an unsupervised clustering algorithm is proposed to label superpixel density images. Firstly, the authors propose a novel superpixel segmentation algorithm driven by a modified fuzzy C-means objective function, Kullback-Leibler (KL) divergence, and an entropy term, which generate superpixels with good boundary adherence and intensity homogeneity. In this model, the logarithm of Gaussian distribution as a new distance metric is used to improve the accuracy of boundary pixel classification, the KL divergence is applied to regularise the fuzzy objective function. Based on this model, the generated superpixel intensity images with a highly distinctive background colour from the colour of the target are obtained. Grouping cues generated by superpixels can affect the performance of image clustering greatly. Next, according to the small amount of clustering data generated by the superpixel intensity images, they construct a non-symmetric mixture model based on a mixture of Gaussian distribution and Cauchy distribution for implementing image clustering. Thus, clustering of colour images is transformed into clustering of these newly generated data. The advantage of this model is its well adaption to different shapes of observed data. Experimental results on publicly available data sets are provided to demonstrate the effectiveness of the proposed algorithm.

关键词： image colour analysis Gaussian distribution image segmentation pattern clustering mixture models image classification computer vision fuzzy set theory entropy unsupervised learning image clustering algorithm-based superpixel segmentation nonsymmetric Gaussian-Cauchy mixture model computer vision research unsupervised clustering algorithm superpixel density images novel superpixel segmentation algorithm Kullback-Leibler divergence good boundary adherence intensity homogeneity Gaussian distribution KL divergence fuzzy objective function generated superpixel intensity images clustering data nonsymmetric mixture model natural colour images newly generated data clustering model

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A Gamma Beta Mixture model for Channel Multipath Components clustering 14

A Gamma Beta Mixture Model for Channel Multipath Components ...

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14th European Conference on Antennas and Propagation (EuCAP)

作者： Sun, Cheng Li, Yupeng Tang, Pan Zhang, Jianhua Tian, Lei Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

ISBN: (纸本)9788831299008

In this paper, a Gamma Beta Mixture model (GBMM) is proposed to cluster channel multipath components (MPCs) where the gamma distribution is utilized to fit the delay data and angle data is fitted with beta distribution. We utilize the expectation-maximization (EM) algorithm to get the optimal GBMM parameters. Specially, in the M step of the EM algorithm, the Newton-Raphson method is utilized to optimize the GBMM parameters since we could not get closed solutions. To verify the clustering effect, an outdoor-to indoor (O21) measurement activity at 3.5 GHz was conducted. Simulation results based on real channel measurement data indicate that, compared with Gaussian mixture model (GMM), GBMM has better clustering performance.

关键词： clustering model EM algorithm Newton-Raphson Method channel measurement

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A Novel clustering Method for Extracting Representative Photovoltaic Scenarios Considering Power, Energy, and Variability

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IEEE Transactions on Power Systems 2025年

作者： Fu, Xueqian Lu, Na Sun, Hongbin Zhang, Youmin China Agricultural University College of Information and Electrical Engineering Beijing China National Innovation Center for Digital Fishery Beijing China Tsinghua University Department of Electrical Engineering Beijing China Concordia University Department of Mechanical Industrial and Aerospace Engineering MontrealQC Canada

Due to the significant uncertainty in photovoltaic (PV) power generation, grid operation scenarios with a high proportion of PV integration are complex and varied. To accurately extract representative scenarios for PV power generation, this paper proposes a novel clustering model that simultaneously considers PV power, energy, and variability. Compared to traditional clustering models that rely on Euclidean distance, the proposed clustering model not only takes into account the Euclidean distance, but also incorporates the daily PV power generation and the characteristics of PV power curves, enabling a more accurate quantification and analysis of the impact of PV on the electricity networks. To solve the proposed clustering model, an alternating optimization algorithm is proposed, based on linear optimization, Lagrange multipliers, and eigenvalue decomposition. The highlights of this paper are the dual verification of the proposed method through theoretical proof and simulation examples. Theoretically, the computational complexity of the algorithm is illustrated, and the convergence of the algorithm is demonstrated. The proposed method is tested using real PV data from Australia and the IEEE 69-bus system, successfully generating 13 representative PV generation scenarios with a maximum similarity distance of the morphological trend as low as 0.3062, ensuring the most representative PV generation peak times. © 1969-2012 IEEE.

关键词： alternating optimization solution clustering model photovoltaic probabilistic power flow representative scenario

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Shannon Entropy for the GI⁰ model: A New Segmentation Approach

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2020年 13卷 2547-2553页

作者： Ferreira, Jodavid de A. Nascimento, Abraao D. C. Univ Fed Pernambuco Dept Stat BR-50740540 Recife PE Brazil

Synthetic aperture radar (SAR) has been successfully used as a remote sensing tool. However, SAR images are contaminated by speckle noise and require specialized postprocessing procedures;e.g, tailored segmenters. The G(I)(0) distribution is a flexible model for SAR intensities because of its ability at describing heterogeneous clutters. Furthermore, applying information theory measures (e.g., entropy) to extract features in SAR imagery processing has achieved a prominent position. In this article, we derive both a closed-form expression for the G(I)(0) Shannon entropy and some of its mathematical properties. Consequently new entropy-based segmentation procedures for multidimensional SAR intensities-assuming independence or some dependence pattern-are also proposed. Finally, applications to real SAR imagery point out the proposed entropy-based segmenters can be more efficient than other well-defined methods, like the clustering by gamma mixture models.

关键词： G(I)(0) distribution clustering model information theory (IT) stochastic entropy

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Single and interval valued neutrosophic group decision making problem based on dynamic programming cluster model

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COMPUTATIONAL & APPLIED MATHEMATICS 2024年第5期43卷 1-36页

作者： Kanchana, A. Nagarajan, D. Jacob, Kavikumar Saveetha Univ Saveetha Sch Engn Dept Math Saveetha Inst Med & Tech Sci Chennai India Rajalakshmi Inst Technol Dept Math Chennai India Univ Tun Hussein Onn Malaysia Fac Appl Sci & Technol Dept Math & Stat Campus Pagoh Pagoh 84600 Malaysia Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Biosci Chennai India

In this study, we address the difficult group decision-making situations in which the preference data are represented by linguistic variables using the dynamic programming approach. Making decisions with exact numbers is difficult for decision-makers due to the ambiguity and complexity of reality. Since they cannot be computed directly, the language variables were encoded using single and interval neutrosophic sets. In a real-world context, neutrosophic sets are used to manage indeterminacy. Then, new models of similarity and distance are developed to quantify the relations between interval and single neutrosophic sets. Next, an interval-based clustering approach based on dynamic programming is proposed and used to group the decision-makers. In addition, a novel method is provided to calculate interval weights for decision-makers and clusters, considering both the cluster centre and group size. Next, a centroid-based ranking method is used to compare and rank the possibilities, and the effectiveness of the proposed strategy is demonstrated through illustrative trials. The purpose of the comparisons and discussions is to demonstrate its superiority.

关键词： Centroid-based ranking method clustering model Dynamic programming Group decision-making Interval neutrosophic sets Single neutrosophic sets

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An improved collaborative filtering model based on time weighted correlation coefficient and inter-cluster separation

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2023年第10期14卷 3543-3560页

作者： Lan, Ruike Tian, Donghong Wu, Qianqian Li, Min Southwest Petr Univ Sch Sci Chengdu Sichuan Peoples R China

The recommendation system is a good choice to provide users with personalized services. Facing with a large amount of data, clustering technology can group similar users into one category, which can make more rapid and accurate recommendations for target users. Due to user's interests changing over time, how to make reasonable recommendations is an important issue. In this article, an improved k-means clustering algorithm based on cuckoo search is proposed and an improved time correlation coefficient is added to the algorithm to improve the accuracy of recommendation system. The proposed clustering algorithm integrates intra-cluster compactness and inter-cluster separation, which can improve the similarity of users in the same cluster. The proposed time correlation coefficient creatively considers both the inherent connection between user's preference and time, and the impact of the periodicity and continuity of time on user rating patterns.

关键词： Recommendation system clustering model Inter-cluster separation Time weighted correlation coefficient Collaborative filtering User similarity

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A Linguistic Large Group Decision Making Method Based on the Cloud model

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2018年第6期26卷 3314-3326页

作者： Wang, Pei Xu, Xuanhua Huang, Shuai Cai, Chenguang Cent S Univ Sch Business Changsha 410083 Hunan Peoples R China Cent S Univ Sch Math & Stat Changsha 410083 Hunan Peoples R China Cent South Univ Forestry & Technol Sch Transportat & Logist Changsha 410004 Hunan Peoples R China

Serving as an effective tool for describing both randomness and fuzziness of qualitative concepts, the cloud model has become a common topic of research. By realizing the uncertain transformation between qualitative concepts and quantitative data, the cloud model provides a new way to deal with group decision making problems in the linguistic environment. However, classical methods based on the cloud model mainly focus on addressing problems with a small number of decision makers. In this paper, we apply the cloud model to the decision making problems that involve a large group of decision makers. Specifically, we first define a new measure of fuzzy distance for clouds based on the alpha - cuts. On the basis of the proposed fuzzy distance measure, we then present a new similarity measure between clouds. Next, we construct an improved clustering approach based on the traditional hierarchical clustering algorithm. Furthermore, we develop a hybrid weight scheme to obtain the cluster weight vector, which takes both the subgroup size and the variance into consideration. Moreover, we present a consensus-based method based on the cloud model for large group decision making with linguistic information. Finally, in order to confirm the validity and effectiveness of the proposed method, we give an application in the context of the Belt and Road Initiative of China, and perform some detailed comparisons to show the advantages of the proposed method.

关键词： Cloud model clustering model fuzzy distance large group similarity

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A novel analysis model of China's new energy talents

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RENEWABLE ENERGY 2013年 58卷 21-27页

作者： Liu, Lin Li, Guodong Luo, Han North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China

Sustainable development of the new energy industry requires continuous input from professional talents. The new energy industry will continue to grow in importance, and the characteristics of the necessary cross-discipline talents that will promote its development are becoming more and more defined. This article puts forward an analysis model starting with the addition of talents carriers subdivided by specialties, and takes this subdivision as a starting point with the purpose of maximizing talents selection throughout the entire nation. First of all, we define each talent as a specific vector. Then a clustering algorithm is used to perform an analysis and the new energy talents database is created. Finally, we use an algorithm based on graph theory and discrete mathematics to calculate the essential value of the talents vector. This value provides a measurement of the importance of the talents. The current talents structure is analyzed in order to clarify understanding of the present situation so as to enable informed planning of targeted training needs and structural adjustments, thus meeting the sustainable development requirements for the new energy industry in China. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： New energy talents clustering model Primary key Binary relation Primary attribute

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On the Product Development model of Service Innovation in Manufacturing Industry

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ANTHROPOLOGIST 2014年第3期17卷 713-724页

作者： Hsu, Min-Wei Lo, Tsai-Yun Bien, Shoujen Tsai, Janson Yang, Wen-Hao Jinwen Univ Sci & Technol Dept Visual Commun Design Tatung Univ Grad Inst Design Sci New Taipei City 23154 Taiwan Tatung Univ Dept & Grad Sch Ind Design Taipei 104 Taiwan Jinwen Univ Sci & Technol Inst Culture Ind & Innovat Design New Taipei city 23154 Taiwan Jin Wen Univ Dept Visual Commun Design New Taipei City 23154 Taiwan Jinwen Univ Sci & Technol Inst Culture Ind & Innovat Design New Taipei City 23154 Taiwan

The study objects comprise of manufacturers in transition to manufacturing servitization which Taiwan Government strongly advocates recently. The study conducts in-depth interview with specialized counseling units in addition to supplementing specific internal data analysis through the discussion on the success cases in the application for government counseling. Using qualitative research method, the study expounds the new mode shaping, causes in forming cooperation-strategy model and the association with corporate business model in the development of product service innovation. The model of service innovation development not yet revealed in prior literatures are now unfolded through the specific flow chart of the operation of product development model for the representative manufacturers mentioned in the study, providing an important reference for Asian manufacturing industries in terms of shortening R&D process time. Students of engineering education can apply the concept of manufacturing servitization to assist them with industrial thinking and connect with in-school learning. Such concept can also effectively offer an important discourse for reference during the shifting of manufacturing R&D after the students enter the workplace.

关键词： Manufacturing Servitization Service Design Service Innovation model clustering model

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Validation of an early vascular aging construct model for comprehensive cardiovascular risk assessment using external risk indicators for improved clinical utility: data from the EVasCu study

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CARDIOVASCULAR DIABETOLOGY 2024年第1期23卷 1-17页

作者： Cavero-Redondo, Ivan Saz-Lara, Alicia Martinez-Garcia, Irene Otero-Luis, Iris Martinez-Rodrigo, Arturo Univ Castilla La Mancha Hlth & Social Res Ctr Cuenca Spain Univ Autonoma Chile Fac Ciencias Salud Talca Chile Univ Castilla La Mancha Res Grp Elect Biomed & Telecommun Engn Cuenca Spain

BackgroundCardiovascular diseases (CVDs) remain a major global health concern, necessitating advanced risk assessment beyond traditional factors. Early vascular aging (EVA), characterized by accelerated vascular changes, has gained importance in cardiovascular risk *** EVasCu study in Spain examined 390 healthy participants using noninvasive measurements. A construct of four variables (Pulse Pressure, Pulse Wave Velocity, Glycated Hemoglobin, Advanced Glycation End Products) was used for clustering. K-means clustering with principal component analysis revealed two clusters, healthy vascular aging (HVA) and early vascular aging (EVA). External validation variables included sociodemographic, adiposity, glycemic, inflammatory, lipid profile, vascular, and blood pressure *** cluster participants were older and exhibited higher adiposity, poorer glycemic control, dyslipidemia, altered vascular properties, and higher blood pressure. Significant differences were observed for age, smoking status, body mass index, waist circumference, fat percentage, glucose, insulin, C-reactive protein, diabetes prevalence, lipid profiles, arterial stiffness, and blood pressure levels. These findings demonstrate the association between traditional cardiovascular risk factors and *** study validates a clustering model for EVA and highlights its association with established risk factors. EVA assessment can be integrated into clinical practice, allowing early intervention and personalized cardiovascular risk management.

关键词： Cardiovascular Diseases Early vascular aging Risk assessment clustering model Cardiovascular risk factors.

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