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fuzzy c-means clustering method with the fuzzy distance definition applied on symmetric triangular fuzzy numbers

JOURNAL OF INTELLIGENT & fuzzy SYSTEMS 2020年第3期38卷 2891-2905页

作者： Hossein-Abad, Hadi Mahdipour Shabanian, Mohsen Kazerouni, Iman Abaspur Esfarayen Univ Technol Dept Elect Engn Esfarayen North Khorasan Iran Salman Farsi Univ Kazerun Dept Elect Engn Kazerun Fars Iran Univ Limerick CRIS Limerick Ireland

The conventional fuzzy c-means (FcM) clustering method can be applied on data, where data features are crisp;however, when the features are fuzzy, the conventional FcM cannot be utilized. Recently, some researchers applied FcM on fuzzy numbers when the used metric has a crisp value. Since difference between two fuzzy numbers can be represented by a fuzzy value better than crisp one, in this paper, it is going to extend the FcM method for clustering symmetric triangular fuzzy numbers, where the used metric has a fuzzy value. It will be shown that the proposed fuzzy distance expresses the distance between two fuzzy numbers much better than crisp metrics. Then the proposed method has been applied on simulated and various real data, where it is compared with several new methods. The experimental results show better performance of the proposed method in compare to other ones.

关键词： clustering fuzzy c-means clustering method triangular fuzzy number fuzzy smaller

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Evaluation of Water Inrush Risk in the Fault Zone of the coal Seam Floor in Madaotou coal Mine, Shanxi Province, china

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WATER 2025年第2期17卷 259-259页

作者： Yu, Shuai Ding, Hanghang Yang, Moyuan Zhang, Menglin Capital Normal Univ Coll Resource Environm & Tourism Beijing 100048 Peoples R China Beijing Water Sci & Technol Inst Beijing 100048 Peoples R China Chinese Acad Sci Expt Sch Affiliated Beijing 100020 Peoples R China

As coal seams are mined at greater depths, the threat of high water pressure from the confined aquifer in the floor that mining operations face has become increasingly prominent. Taking the Madaotou mine field in the Datong coalfield as the research object, in the context of mining under pressure, for the main coal seams in the mining area, first of all, an improved evaluation method for the vulnerability of floor water inrush is adopted for hazard prediction. Secondly, numerical simulation is used to conduct a simulation analysis on the fault zones in high-risk areas. By using the fuzzy c-means clustering method (FccM) to improve the classification method for the normalized indicators in the original variable-weight vulnerability evaluation, the risk zoning for water inrush from the coal seam floor is determined. Then, through the numerical simulation method, a simulation analysis is carried out on high-risk areas to simulate the disturbance changes of different mining methods on the fault zones so as to put forward reasonable mining methods. The results show that the classification of the variable-weight intervals of water inrush from the coal seam floor is more suitable to be classified by using fuzzy clustering, thus improving the prediction accuracy. Based on the time effect of the delayed water inrush of faults, different mining methods determine the duration of the disturbance on the fault zones. Therefore, by reducing the disturbance time on the fault zones, the risk of karst water inrush from the floor of the fault zones can be reduced. Through prediction evaluation and simulation analysis, the evaluation of the risk of water inrush in coal mines has been greatly improved, which is of great significance for ensuring the safe and efficient mining of mines.

关键词： fuzzy c-means clustering method vulnerability assessment floor water inrush graben

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Indirect estimation of deformation modulus of an in situ rock mass: an ANFIS model based on grid partitioning, fuzzy c-means clustering and subtractive clustering

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GEOScIENcES JOURNAL 2016年第5期20卷 681-690页

作者： Fattahi, Hadi Arak Univ Technol Dept Min Engn Arak *** Iran

Deformability of rock masses influencing their behavior is an important geomechanical property for the rock structures design. Due to the problems in determining the deformability of jointed rock masses at the laboratory-scale, various in situ test methods such as plate loading tests, dilatometer etc. have been developed. Although these methods are currently the best techniques, they are expensive and time consuming, and present operational problems. Furthermore, the influence of the test volume on modulus of deformation depending on the technique used is also important. For these reasons, in this paper, the adaptive network-based fuzzy inference system (ANFIS) was used to build a prediction model for the indirect estimation of deformation modulus of a rock mass. Three ANFIS models were implemented by grid partitioning (GP), subtractive clustering method (ScM) and fuzzy c-means clustering method (FcM). The estimation abilities offered using three ANFIS models were presented by using field data of achieved from road and railway construction sites in Korea. In these models, rock mass rating (RMR), depth, uniaxial compressive strength of intact rock (UcS) and elastic modulus of intact rock (E-i) were utilized as the input parameters, while the deformation modulus of a rock mass was the output parameter. Various statistical performance indexes were utilized to compare the performance of those estimation models. The results achieved indicate that the ANFIS-ScM model has strong potential to indirect estimation of deformation modulus of a rock mass with high degree of accuracy and robustness.

关键词： deformation modulus ANFIS grid partitioning subtractive clustering method fuzzy c-means clustering method

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A cell-based dosimetry model for radium-223 dichloride therapy using bone micro-cT images and GATE simulations

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PHYSIcS IN MEDIcINE AND BIOLOGY 2020年第4期65卷 045010-045010页

作者： Pinto, Gabriella M. Bonifacio, Daniel A. B. de Sa, Lidia, V Lima, Luis Felipe c. Vieira, Igor F. Lopes, Ricardo T. Univ Fed Rio de Janeiro Nucl Instrumentat Lab PEN COPPE Rio De Janeiro RJ Brazil Inst Radioprotect & Dosimetry IRD CNEN Dept Med Phys Rio De Janeiro RJ Brazil Univ Hosp Dept Imaging & Pathol Nucl Med & Mol Imaging Herestr 49 B-3000 Leuven Belgium Katholieke Univ Leuven Herestr 49 B-3000 Leuven Belgium

Dosimetry at the cellular level has outperformed macrodosimetry in terms of agreement with toxicity effects in clinical studies. This fact has encouraged dosimetry studies aiming to quantify the absorbed doses needed to reach radiotoxicity at the cellular level and to inform recommendations on the administration of radium-223. The aim of this work is to qualitatively and quantitatively evaluate the absorbed doses of radium-223 and the interactions of the doses at the cellular level. The analysis was performed by Monte carlo simulations in GATE using micro-cT image of a mouse. Two physics lists available in the GATE code were tested. The influence of single and multiple scattering models on the absorbed dose distribution and number of particle hits was also studied. In addition, the fuzzy c-means clustering method was used for data segmentation. The segmentation method was suitable for these analyses, particularly given that it was unsupervised. There was no significant difference in the estimated absorbed dose between the two proposed physics lists. The absorbed dose values were not significantly influenced by scattering, although single scattering resulted in twice as many interactions as multiple scattering. The absorbed dose histogram at the voxel level shows heterogeneous absorbed dose values within each shell, but the observations from the graph of the medians were comparable to those in the literature. The interaction histogram indicates 104 events, although some voxels had no interactions with alpha particles. However, the voxels did not show absorbed doses capable of deterministic effects in the deepest part of the bone marrow. The absorbed dose distribution in images of mouse trabecular bone was compatible with simple geometric models, with absorbed doses capable of deterministic effects near the bone surface. The interaction distributions need to be correlated with in vivo studies for better interpretation.

关键词： cell-based dosimetry radium-223 therapy skeletal dosimetry fuzzy c-means clustering method

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Magnetic parameters, trace elements, and multivariate statistical studies of river sediments from southeastern India: a case study from the Vellar River

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ENVIRONMENTAL EARTH ScIENcES 2011年第2期63卷 297-310页

作者： chaparro, Marcos A. E. chaparro, Mauro A. E. Rajkumar, P. Ramasamy, V. Sinito, Ana M. Consejo Nacl Invest Cient & Tecn Inst Fis Arroyo Seco UNCPBA RA-1033 Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn Inst Multidisciplinario Ecosistemas & Desarrollo RA-1033 Buenos Aires DF Argentina Paavai Engn Coll Dept Phys Pachal 637018 Namakkal India Annamalai Univ Dept Phys Annamalainagar 608002 Tamil Nadu India

This contribution constitutes a new study using magnetic parameters and trace element determinations of pollutants in river sediments from the Tamil Nadu state. The Vellar River covers a total length of about 200 km and flows into the Bay of Bengal. Sediment samples were collected at different sediment depths (up to 90 cm) from 12 sites to investigate their magnetic properties (27 samples) and the contents of trace elements (21 out of 27 samples) along the river;as well as to perform magnetic studies for various grain size fractions (16 sub samples). The magnetic results of magnetic susceptibility and remanent magnetizations suggest that the magnetic signal of these sediments is controlled by ferrimagnetic minerals magnetite-like minerals and a minor contribution of antiferromagnetic carriers (such as hematite minerals). Detailed studies of selected samples showed a higher magnetic concentration in finer grain-sized fractions and a slightly different magnetic mineralogy. Magnetic concentration-dependent parameters evidenced high values, which, together with the background values, allowed us to identify magnetic enhancement at some sites. The Pearson correlation and multivariate statistical studies (Principal component Analysis, canonical correlation Analysis) supported the relationship between the magnetic and chemical variables;in particular, magnetic susceptibility, anhysteretic and isothermal remanent magnetization are closely correlated to co, cr, cu, Fe, V, Zn, and the pollution load index. In addition, Principal coordinate Analysis and fuzzy c-means cluster analysis allowed us to make a classification and to perform a magnetic-chemical characterization of the data into four groups, thereby identifying critical (possibly polluted) sites from the Vellar River.

关键词： Environmental magnetism Magnetic susceptibility fuzzy c-means clustering method Pollution River sediments

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Magnetic studies and elemental analysis of river sediments: a case study from the Ponnaiyar River (Southeastern India)

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ENVIRONMENTAL EARTH ScIENcES 2013年第1期70卷 201-213页

作者： chaparro, Marcos A. E. Suresh, G. chaparro, Mauro A. E. Ramasamy, V. Sinito, Ana M. Consejo Nacl Invest Cient & Tecn Inst Fis Arroyo Seco UNCPBA RA-1033 Buenos Aires DF Argentina Arulmigu Meenakshi Amman Coll Engn Dept Phys Vadamavandal 604410 Tamil Nadu India Consejo Nacl Invest Cient & Tecn Inst Multidisciplinario Ecosistemas & Desarrollo RA-1033 Buenos Aires DF Argentina Annamalai Univ Dept Phys Annamalainagar 608002 Tamil Nadu India

The Ponnaiyar River is one of the largest rivers of the Tamil Nadu state (India), flowing a distance of 430 km from its point of origin to the sea. This work contributes with new data of magnetic and elemental composition of river sediments, and improves the knowledge obtained by preliminary and previous studies of rivers from Southeastern India. Magnetic susceptibility, anhysteretic and isothermal remanent magnetization and chemical determinations (major and trace metals) were measured. Magnetic results reveal the predominance of magnetite-like mineral with magnetic grain size variations along the river and in depth. Most of the uppermost samples have the major presence of trace metals and higher values of magnetic concentration. Magnetic and chemical variables were also analysed as potential pollution indicators using multivariate statistical techniques: canonical correlation and fuzzy c-means clustering analyses, which confirmed the existence of relationships, but not in a simple way, between magnetic and chemical variables. Furthermore, fuzzy analysis allows classifying the data in different well-differentiated groups regarding the trace metal load, concentration and feature-dependent parameters. The most polluted samples show high concentration of trace elements and magnetic carriers, softer and coarser magnetic minerals;on the contrary, the unpolluted samples (from the deepest sediments) have the opposite characteristics.

关键词： Environmental magnetism Magnetic susceptibility fuzzy c-means clustering method Pollution River sediments

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Estimation of P- and S-wave impedances using Bayesian inversion and adaptive neuro-fuzzy inference system from a carbonate reservoir in Iran

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NEURAL cOMPUTING & APPLIcATIONS 2018年第11期29卷 1059-1072页

作者： Karimpouli, Sadegh Fattahi, Hadi Univ Zanjan Min Engn Grp Fac Engn Zanjan Iran Arak Univ Technol Dept Min Engn Arak Iran

P- and S-wave impedances are accounted as two significant parameters conventionally inverted from seismic amplitudes for evaluation of gas and oil reservoirs. They may not be the final goal of interpretation studies;however, they play an important role in many methods such as reservoir characterization, rock physical modeling, geostatistical simulation, fluid detection. Bayesian inversion is a conventional method used by many researchers and even by industry to invert these parameters. To compare this method with intelligent methods, the adaptive network-based fuzzy inference system (ANFIS) was utilized to construct a model for the prediction of P- and S-wave impedances. Two ANFIS models were implemented, subtractive clustering method (ScM) and fuzzy c-means clustering method. The prediction capabilities offered by ANFIS models were shown by using field data obtained from a carbonate reservoir in Iran. Unlike other studies, input parameters, in this study, are pre-stack seismic data and attributes, while the P- and S-wave impedances are the output parameters in all methods. Mean square error was used for comparison of the performance of those models. The obtained results show that the ANFIS-ScM model generates the best indirect estimation of P- and S-wave impedances with high degree of accuracy and robustness.

关键词： carbonate reservoir P- and S-wave impedances ANFIS Subtractive clustering method fuzzy c-means clustering method Bayesian inversion

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Spinning reserve scheduling in power systems containing wind and solar generations

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ELEcTRIcAL ENGINEERING 2021年第5期103卷 2507-2526页

作者： Ghaedi, Amir Gorginpour, Hamed Islamic Azad Univ Dept Elect Engn Dariun Branch Dariun Iran Persian Gulf Univ Engn Dept Mahini AvePOB *** Bushehr Iran

Wind and solar as two renewable energy resources are largely used to generate clean and sustainable energy in the power systems. To integrate these renewable energies in the power system, different aspects of the power system such as reliability and operation are affected that must be investigated. It is due to the variation in the generated power of these resources that are arisen from the variation in the wind speed and solar radiation. To model the uncertainty nature of large-scale wind and photovoltaic farms in the operation studies of the power system, an appropriate multistate reliability model is developed for the wind and photovoltaic farms considering both failure of main components and variation in the wind speed and solar radiation. To determine the optimum number of states in the reliability model of wind and photovoltaic farms, XB index is calculated and based on the fuzzy c-means clustering technique the transition rates among different states are obtained. To calculate the transition rates among different states, the fuzzy numbers are used that results in the accurate and reduced reliability model for wind and photovoltaic farms. To determine the probability of different states in the reliability model of wind and photovoltaic farms in the operation studies, matrix multiplication technique is utilized to determine important indices of the power system such as the unit commitment risk. In this paper, the amount of required spinning reserve of a power system containing wind and solar generation units can be determined based on the reliability criterion. The proposed technique is applied to the two reliability test systems including RBTS and IEEE-RTS and the reliability-based operation indices of these systems are calculated to present the effectiveness of the proposed analytical method.

关键词： Operating studies Photovoltaic (PV) farm Spinning reserve Uncertainty Wind farm XB index fuzzy c-means clustering method

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A hybrid thermal error modeling method of heavy machine tools in z-axis

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INTERNATIONAL JOURNAL OF ADVANcED MANUFAcTURING TEcHNOLOGY 2015年第1-4期80卷 389-400页

作者： Wang, Liping Wang, Haitong Li, Tiemin Li, Fengchun Tsinghua Univ Inst Mfg Engn Dept Mech Engn Beijing 100084 Peoples R China

Thermal error is one of the major contributors to dimensional errors of machine tools. In this paper, a hybrid thermal error modeling method is proposed to forecast the thermal expansion of a heavy boring and milling machine tool in z-axis. In this model, the thermal error in z-axis is calculated as the expansion of a one-dimensional rod. The inputs of the model are temperatures of spindle motor, hydrostatic bearing and environment, selected through fuzzy c-means (FcM) clustering method. The temperatures of the nodes in the rod are solved by Thomas algorithm and the coefficients of the model are optimized by genetic algorithm (GA). The accuracy of the proposed model is compared with that of the output error model and stepwise regression model, and the performance of the proposed model is better in accuracy. In addition, a compensation system is developed based on the proposed model to reduce the thermal error in z-axis.

关键词： Thermal error modeling Heavy machine tools fuzzy c-means clustering method Thomas algorithm Genetic algorithm

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Resilient and sustainable supply chain design: sustainability analysis under disruption risks

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INTERNATIONAL JOURNAL OF PRODUcTION RESEARcH 2018年第17期56卷 5945-5968页

作者： Jabbarzadeh, Armin Fahimnia, Behnam Sabouhi, Fatemeh Iran Univ Sci & Technol Dept Ind Engn Tehran Iran Univ Sydney Business Sch ITLS Sydney NSW Australia

Resilience to disruptions and sustainability are both of paramount importance to supply chains. However, the interactions between the two have not been thoroughly explored in the academic *** attempt to contribute to this area by presenting a hybrid methodology for the design of a sustainable supply network that performs resiliently in the face of random disruptions. A stochastic bi-objective optimisation model is developed that utilises a fuzzy c-means clustering method to quantify and assess the sustainability performance of the suppliers. The proposed model determines outsourcing decisions and resilience strategies that minimise the expected total cost and maximise the overall sustainability performance in disruptions. Important managerial insights and practical implications are obtained from the model implementation in a case study of plastic pipe industry.

关键词： resilience sustainability disruption supply chain design sustainability performance scoring stochastic programming fuzzy c-means clustering method supplier selection

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