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Pythagorean fuzzy information processing based on centroid distance measure and its applications

EXPERT SYSTEMS WITH APPLICATIONS 2024年 236卷

作者： Sun, Gang Wang, Mingxin Guizhou Univ Commerce Comp & Informat Engn Coll Guiyang 550014 Peoples R China Hunan Inst Technol Sch Math & Phys Hengyang 421002 Peoples R China

By using Pythagorean fuzzy set (PFS), people can quantitatively describe and process information with fuzziness, and then make reasonable and effective judgments or decisions. In this process, distance measure often serves as a useful and important tool. The purpose of this paper is to explore some information processing methods and techniques in Pythagorean fuzzy environment. Firstly, the research progress of PFS is reviewed, and the main research directions and some hot issues in relevant research fields are discussed. Secondly, by analyzing the information of hesitation degree of Pythagorean fuzzy number (PFN), definitions of hesitation factor and centroid distance measure based on the representation of centroid coordinates of the hesitation regions are proposed, and properties of hesitation factor and centroid distance measure are discussed. Finally, the novel clustering algorithm, ranking method and multi criteria decision making (MCDM) method are developed in Pythagorean fuzzy environment, which are based on the new centroid distance measure. The effectiveness of the proposed algorithm and methods are verified through numerical examples and comparative analysis. The research results indicate that the centroid distance measure proposed in this paper can effectively characterize the distance between PFNs, thus making the clustering algorithm of PFNs, the ranking method of PFNs, and the Pythagorean fuzzy MCDM method have obvious superiorities and advantages.

关键词： Pythagorean fuzzy number (PFN) Centroid distance measure clustering algorithm Ranking method Multi criteria decision making (MCDM) method

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Estimation of water quality variables based on machine learning model and cluster analysis-based empirical model using multi-source remote sensing data in inland reservoirs, South China

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ENVIRONMENTAL POLLUTION 2024年 342卷 123104页

作者： Tian, Di Zhao, Xinfeng Gao, Lei Liang, Zuobing Yang, Zaizhi Zhang, Pengcheng Wu, Qirui Ren, Kun Li, Rui Yang, Chenchen Li, Shaoheng Wang, Meng He, Zhidong Zhang, Zebin Chen, Jianyao Sun Yat Sen Univ Sch Geog & Planning Guangzhou 510275 Peoples R China Zhuhai Ecol Environm Monitoring Stn Guangdong Prov Zhuhai 519070 Peoples R China Chinese Acad Sci Key Lab Vegetat Restorat & Management Degraded Eco South China Bot Garden Guangzhou 510650 Peoples R China Chinese Acad Geol Sci Inst Karst Geol Key Lab Karst Dynam Minist Nat Resources & Guangxi 50 Qixing Rd Guilin 541004 Guangxi Peoples R China

Reservoirs play important roles in the drinking water supply for urban residents, agricultural water provision, and the maintenance of ecosystem health. Satellite optical remote sensing of water quality variables in medium and micro-sized inland waters under oligotrophic and mesotrophic status is challenging in terms of the spatiotemporal resolution, weather conditions and frequent nutrient status changes in reservoirs, etc., especially when quantifying non-optically active components (non-OACs). This study was based on the surface reflectance products of unmanned aerial vehicle (UAV) multispectral images, Sentinel-2B Multispectral instrument (MSI) images and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) by utilizing fuzzy C-means (FCM) clustering algorithm was combined with band combination (BC) model to construct the FCM-BC empirical model, and used mixed density network (MDN), extreme gradient boosting (XGBoost), deep neural network (DNN) and support vector regression (SVR) machine learning (ML) models to invert 12 kinds of optically active components (OACs) and non-OACs. Compared with the unclustered BC (UC) model, the mean coefficient of determination (MR) of the FCM-BC models was improved by at least 46.9%. MDN model showed best accuracy (R2 in the range of 0.60-0.98) and stability (R2 decreased by up to 13.2%). The accuracy of UAV was relatively higher in both empirical methods and machine learning methods. Additionally, the spatio-temporal distribution maps of four water quality variables were mapped based on the MDN model and UAV images, all platforms showed good consistency. An inversion strategy of water quality variables in various monitoring frequencies and weather conditions were proposed finally. The purpose of introducing the UAV platform was to cooperate with the satellite to improve the monitoring response ability of OACs and non-OACs in small and micro-sized oligotrophic and mesotrophic water bodies.

关键词： Inland water quality monitoring Multi-source remote sensing clustering algorithm Band combination model Machine learning

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Refined seismic fragility curves of substation equipment considering ground motion classifications

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SOIL DYNAMICS AND EARTHQUAKE ENGINEERING 2024年 187卷

作者： Zhu, Wang Bian, Xiaoxu Xie, Qiang Tongji Univ Dept Struct Engn Siping Rd 1239 Shanghai 200092 Peoples R China

Seismic fragility of substation equipment is vital in risk analyses, as well as post-earthquake prediction of failure probabilities of multiple equipment for deciding the inspection order. However, the existing fragility curves of equipment are commonly generated without adequately considering intensity measures (IMs) of ground motions. During the post-earthquake use, they are deemed applicable to any earthquake occurring in the region, which is insufficiently precise. Thus, this paper employs a new method to derive more refined fragility curves for substation equipment based on ground motion classifications. It uses principal component analysis to convert IMs into a small number of principal components (PCs). Taking the PCs as indices, the selected ground motions are clearly categorized into distinct classes using the K-means clustering algorithm. Then, a simulation model of equipment is developed and the time-history dynamic analyses are performed to calculate seismic responses under different classes of ground motions. Finally, the refined fragility curves are derived via multiple stripe analysis for each ground motion class. A 500 kV power transformer is used as a case to implement the method. The results show that a small number of PCs can effectively reflect the multiple ground motion characteristics. Different ground motion classes have distinct distributions of IMs and PCs. There are notable discrepancies in the fragility curves of the power transformer when subjected to different ground motion classes. The significant disparities between the refined and original unclassified fragility curves highlight the importance of considering ground motion classifications.

关键词： Seismic fragility curves Substation equipment Ground motion classification Principal component analysis clustering algorithm Power transformer

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Dynamic Regularized Adaptive Cluster Optimization (DRACO) for Quantitative Cardiac Cine MRI in Complex Arrhythmias

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JOURNAL OF MAGNETIC RESONANCE IMAGING 2025年第1期61卷 248-262页

作者： Ming, Zhengyang Pogosyan, Arutyun Christodoulou, Anthony G. Finn, J. Paul Ruan, Dan Nguyen, Kim-Lien UCLA VA Greater Angeles Healthcare Syst 11301 Wilshire Blvd MC 111E Los Angeles CA 90073 USA UCLA David Geffen Sch Med 200 Med PlazaB269 Los Angeles CA 90095 USA Univ Calif Los Angeles Phys & Biol Med Grad Program Los Angeles CA USA UCLA David Geffen Sch Med Dept Radiol Sci Los Angeles CA USA UCLA David Geffen Sch Med Div Cardiol Los Angeles CA USA VA Greater Angeles Healthcare Syst Los Angeles CA USA Univ Calif Los Angeles Dept Bioengn Los Angeles CA USA UCLA David Geffen Sch Med Dept Radiat Oncol Los Angeles CA USA

Background: Irregular cardiac motion can render conventional segmented cine MRI nondiagnostic. clustering has been proposed for cardiac motion binning and may be optimized for complex arrhythmias. Purpose: To develop an adaptive cluster optimization method for irregular cardiac motion, and to generate the corresponding time-resolved cine images. Study Type: Prospective. Subjects: Thirteen with atrial fibrillation, four with premature ventricular contractions, and one patient in sinus rhythm. Field Strength/Sequence: Free-running balanced steady state free precession (bSSFP) with sorted golden-step, reference real-time sequence. Assessment: Each subject underwent both the sorted golden-step bSSFP and the reference Cartesian real-time imaging. Golden-step bSSFP images were reconstructed using the dynamic regularized adaptive cluster optimization (DRACO) method and k-means clustering. Image quality (4-point Likert scale), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), edge sharpness, and ventricular function were assessed. Statistical TestsPaired t-tests, Friedman test, regression analysis, Fleiss' Kappa, Bland-Altman analysis. Significance level P < 0.05. Results: The DRACO method had the highest percent of images with scores >= 3 (96% for diastolic frame, 93% for systolic frame, and 93% for multiphase cine) and the percentages were significantly higher compared with both the k-means and real-time methods. Image quality scores, SNR, and CNR were significantly different between DRACO vs. k-means and between DRACO vs. real-time. Cardiac function analysis showed no significant differences between DRACO vs. the reference real-time. Conclusion: DRACO with time-resolved reconstruction generated high quality images and has early promise for quantitative cine cardiac MRI in patients with complex arrhythmias including atrial fibrillation.

关键词： clustering algorithm atrial fibrillation arrhythmia golden-step acquisition total variation quantitative MRI

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Field Measurement and Channel Modeling Around Wailingding Island for Maritime Wireless Communication

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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2024年第6期23卷 1934-1938页

作者： Zhang, Hanzhong Zhou, Ting Xu, Tianheng Cheng, Min Hu, Honglin Shanghai Univ Sch Microelect Shanghai 200444 Peoples R China Chinese Acad Sci Shanghai Adv Res Inst Shanghai 201210 Peoples R China CETC Potevio Sci & Technol Co Ltd Guangzhou 510310 Peoples R China

Due to the influence of sparse scattering, wave movement and variable climate, maritime wireless channel modeling is particularly difficult. In this letter, a maritime channel measurement scheme is designed and implemented. Aclustering algorithmbased channel modeling framework is further proposed to process the measurement data and build a maritime propagation model. Specifically, the clustering algorithm is introduced in the framework to adaptively discover data characteristics in complex environments, which avoids error accumulation. Numerical results illustrate that the proposed model outperforms existing empirical path loss models in describing the signal propagation at sea, which provides good guidance for designing and deploying of maritime communication systems.

关键词： Channel measurement clustering algorithm maritime channel modeling

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An energy-balanced unequal clustering approach for circular wireless sensor networks

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AD HOC NETWORKS 2022年 132卷

作者： Zhao, Chengkun Wu, Qian Lin, Deyu Zhang, Zhiqiang Zhang, Yujie Kong, Linghe Guan, Yong Liang Nanchang Univ Sch Software Nanchang 330047 Jiangxi Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China Univ Leeds Sch Elect & Elect Engn Leeds England Nanyang Technol Univ Sch Elect & Elect Engn Nanyang Singapore

The network lifetime of the Wireless Sensor Networks (WSNs) is a critical factor for the relevant applications due to the limited energy supply. However, the imbalanced energy consumption of the whole network always leads to the "Hot Spot Problem" and the decline of network lifetime inevitably. To this end, the schemes of energybalanced unequal clustering and energy-efficient cluster head rotation are considered in this paper. To be specific, we conduct detailed theoretical derivation and mathematical calculation based on the concept of gradient to obtain the optimal number of CHs, with the aim of balancing the energy consumption for the whole network topology. In addition, a fuzzy logic-based mechanism for CHs rotation is also proposed to balance the energy distribution among different cluster heads. Subsequently, a novel Energy-Balanced unequal clustering Approach (EBCA) for circular wireless sensor networks is proposed and detailed. Finally, we conduct extensive simulations to verify its performance. The experimental results indicate that EBCA is able to balance the energy consumption of cluster heads in different gradient, reduce the energy consumption and prolong the network lifetime effectively compared with the classic and latest clustering algorithms.

关键词： WSNs Energy balance Network lifetime clustering algorithm

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Automatic High-Resolution Operational Modal Identification of Thin-Walled Structures Supported by High-Frequency Optical Dynamic Measurements

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MATERIALS 2024年第20期17卷 4999页

作者： Deng, Tongfa Wang, Yuexin Huang, Jinwen Cao, Maosen Sumarac, Dragoslav Jiangxi Univ Sci & Technol Sch Civil & Surveying & Mapping Engn Ganzhou 341000 Peoples R China Jiangxi Univ Sci & Technol Jiangxi Prov Key Lab Environm Geotech Engn & Hazar Ganzhou 341000 Peoples R China Hohai Univ Coll Mech & Engn Sci Nanjing 211100 Peoples R China State Univ Novi Pazar Dept Tech Sci Civil Engn Novi Pazar 36300 Serbia

High-frequency optical dynamic measurement can realize multiple measurement points covering the whole surface of the thin-walled structure, which is very useful for obtaining high-resolution spatial information for damage localization. However, the noise and low calculation efficiency seriously hinder its application to real-time, online structural health monitoring. To this end, this paper proposes a novel high-resolution frequency domain decomposition (HRFDD) modal identification method, combining an optical system with an accelerometer for measuring high-accuracy vibration response and introducing a clustering algorithm for automated identification to improve efficiency. The experiments on the cantilever aluminum plate were carried out to evaluate the effectiveness of the proposed approach. Natural frequency and damping ratios were obtained by the least-squares complex frequency domain (LSCF) method to process the acceleration responses;the high-resolution mode shapes were acquired by the singular value decomposition (SVD) processing of global displacement data collected by high-speed cameras. Finally, the complete set of the first nine order modal parameters for the plate within the frequency range of 0 to 500 Hz has been determined, which is closely consistent with the results obtained from both experimental modal analysis and finite element analysis;the modal parameters could be automatically picked up by the DBSCAN algorithm. It provides an effective method for applying optical dynamic technology to real-time, online structural health monitoring, especially for obtaining high-resolution mode shapes.

关键词： thin-walled structure optical dynamic measurement automated operational modal identification clustering algorithm

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A point symmetry clustering algorithm based on PSO

A point symmetry clustering algorithm based on PSO

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International Conference of Information Science and Management Engineering (ISME)

作者： Qu, Jianhua Shandong Normal Univ Sch Management Sci & Engn Jinan Peoples R China

ISBN: (纸本)9781845648299;9781845648282

This paper proposed an efficient PSO clustering algorithm with point symmetry distance based on cooperative evolution strategy. It not only determined the number of clusters, but also detected the proper partitions in data sets when the data sets possess the property of symmetry. In the algorithm, a new point symmetry distance is used to compute the similarity instead of the Euclid distance. Cooperative evolution strategy with multi-populations is introduced to prevent the PSO algorithm from trapping into the local optimal solution. The performance of the proposed algorithm is tested in two artificial data sets. The simulation results show that the performance of the algorithm is better than other algorithms mentioned in this paper.

关键词： clustering algorithm cooperative evolution point symmetry distance PSO

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A Self-learning clustering algorithm Based on clustering Coefficient

A Self-learning Clustering Algorithm Based on Clustering Coe...

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15th International Conference on Web Information Systems Engineering (WISE)

作者： Zhong, MingJie Ding, ZhiJun Sun, HaiChun Wang, PengWei Tongji Univ Minist Educ Key Lab Embedded Syst & Serv Comp Shanghai 200092 Peoples R China Univ Pisa Dept Comp Sci I-56127 Pisa Italy

ISBN: (纸本)9783319117492;9783319117485

This paper presents a novel clustering algorithm based on clustering coefficient. It includes two steps: First, k-nearest-neighbor method and correlation convergence are employed for a preliminary clustering. Then, the results are further split and merged according to intra-class and inter-class concentration degree based on clustering coefficient. The proposed method takes correlation between each other in a cluster into account, thereby improving the weakness existed in previous methods that consider only the correlation with center or core data element. Experiments show that our algorithm performs better in clustering compact data elements as well as forming some irregular shape clusters. It is more suitable for applications with little prior knowledge, e. g. hotspots discovery.

关键词： clustering algorithm clustering coefficient self-learning clustering

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Study on online English learning resource push based on Bayesian inference

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2024年第1-2期74卷 90-98页

作者： Zhang, Wenfeng Jiujiang Univ Sch Foreign Languages Jiujiang Jiangxi Peoples R China

The current online English learning resource push methods have problems of poor customer satisfaction, low reliability of pushed resources and low recall rate of resource pushes. Therefore, this paper proposes an online English learning resource push method based on Bayesian inference. Firstly, obtain online English learning resource data and classify online learning resources. Then, by mining and analysing learner learning data, clustering algorithms are used to locate and infer the learner's learning style. Finally, based on Bayesian inference, a Naive Bayesian classifier is developed, and a network English online learning resource push model is developed to achieve effective network English online learning resource distribution. Through relevant experiments, it has been confirmed that the customer satisfaction of this method varies from 96.0% to 99.8%, the push reliability varies from 90.5% to 99.8% and the resource push recall rate is 99.9%, which has the characteristic of good push effect.

关键词： Bayesian inference online English learning resource push classify clustering algorithm Naive Bayesian classifier

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