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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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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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Applying a computational intelligence method to predict the rehabilitation treatment for females with lateral patellar displacement

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Performance Enhancement and Health 2018年第1期6卷 36-42页

作者： Karimzadehfini, Atiye Zolaktaf, Vahid Mahdavinejad, Reza Department of Sports Injuries and Corrective Exercises University of Isfahan Isfahan Iran

A lateral positioned patella has long been regarded as a major contributing factor in the development of patella femoral pain (PFP). Despite extensive research, there is still little consensus as to the most effective treatment strategy for the management of patients with lateral patellar displacement (LPD). computational intelligence methods are proving useful aids to physicians and other medical staff, improving objectivity when making diagnostic and treatment decisions and reducing the time to make decisions. This study describes an adaptive network-based fuzzy inference system (ANFIS) used to build a model for the indirect prediction of rehabilitation treatment outcomes for females with LPD from only demographic and clinical characteristics. The prediction abilities offered using two ANFIS models are presented using data from 48 female patients referred to rehabilitation clinics of Isfahan Ayatollah Kashani and Al Zahra hospitals, Iran. The results indicate that the ANFIS model has strong potential to improve indirect prediction of rehabilitation treatment for females with LPD with a high degree of accuracy and robustness. © 2017

关键词： Adaptive network-based fuzzy inference system fuzzy c-means clustering method Lateral patellar displacement Rehabilitation treatment Subtractive clustering method

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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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fuzzy rule base model for oil wells efficiency estimation 12th

Fuzzy rule base model for oil wells efficiency estimation

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12th International conference on Application of fuzzy Systems and Soft computing (IcAFS)

作者： Ramazanov, Dj. I. Jabiyeva, A. J. Amirguliyev, V. X. Baku Elect Distribut Grid Bakikhanov Str 13 AZ-1065 Baku Azerbaijan Azerbaijan State Oil & Ind Univ 20 Azadlig Ave AZ-1010 Baku Azerbaijan CNCoLTD Deputy 9th Akhmedli Str Baku 28 Azerbaijan

In this paper a fuzzy expert system is used for determining amount of efficiency of the oil wells from energy consumption point of view. Knowledgebase is extracted from data by using fuzzy c-means method based clustering. Knowledgebase is realized in environment Expert system shell ESPLAN. Experimental results have demonstrated efficiency of the proposed method and its advantages as compared to the existing classical methods. (c) 2016 Published by Elsevier B.V.

关键词： fuzzy expert system fuzzy c-means clustering method oil-water factor

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fuzzy Rule Base Model for Oil Wells Efficiency Estimation

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Procedia computer Science 2016年 102卷 198-201页

作者： Dj.I. Ramazanov A.J. Jabiyeva V.X. Amirguliyev Baku Electric Distribution Grid Bakikhanov str. 13 AZ 1065 Baku Azerbaijan Azerbaijan State Oil and Industry University 20 Azadlig Ave. AZ1010 Baku Azerbaijan CNCo.LTD Deputy 9-th Akhmedli str. 28 Baku Azerbaijan

关键词： fuzzy expert system fuzzy c-means clustering method oil-water factor

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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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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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Specifying collective Adaptive Situations in cross cultural Game towards Validation of Agent-Based Social Simulation

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SIcE Journal of control, Measurement, and System Integration 2013年第2期6卷 117-123页

作者： Keiki Takadama Yuya Ushida Department of Informatics Graduate School of Informatics and Engineering The University of Electro-Communications NTT Data Corporation

This paper focuses on a collective adaptive situation as an complex phenomena in social dynamics and aims at specifying its situation towards a validation of agent-based social simulations. For this purpose, the fuzzy c-means clustering (FcM) method is applied into the experimental data of both the human subject experiments and the agent-based social simulations in the cross-cultural game, Barnga, and compare these results from the viewpoint of a collective adaptive situation. The analysis of these data has revealed the following implications: (1) the collective adaptive situation can be specified by the FcM method; and (2) the classified results of both of the experimental data are close to each other, indicating that agent-based social simulations can be validated from the viewpoint of a collective adaptive situation.

关键词： agent-based simulation validation collective adaptive situation fuzzy c-means clustering method Barnga

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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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