Journal of Fuzzy Logic and Modeling in Engineering最新文献

{"title":"Industry 4.0 road mapping: A fuzzy linguistic approach","authors":"Kerem Elibal, Eren Özceylan","doi":"10.2174/2666294901666210713142801","DOIUrl":"https://doi.org/10.2174/2666294901666210713142801","url":null,"abstract":"\u0000\u0000The industry 4.0 transition is becoming crucial for organizations. The literature reviewed showed that whilst there are many studies on industry 4.0 assessment that help organizations evaluate their current state, limited studies exist for road-mapping activities.\u0000\u0000\u0000\u0000\u0000The main aim of this study is to construct a model that leads organizations to their fourth industrial revolution transition. Companies, especially small and medium-sized ones (SMEs), need clear, agile, and efficient road maps because of their limited resources. Lack of a procedure that guides organizations in the right way is the motivation of this study.\u0000\u0000\u0000\u0000\u0000A linguistic fuzzy inference system is used in this study. Concepts are determined, and relations between concepts with if-then rules have been constructed according to the expert opinion. MATLAB R2015a is used for the inference system.\u0000\u0000\u0000\u0000\u0000 An exemplary case is considered, and the results show that the inference system can provide company-specific roadmaps. To which extend an industry 4.0 concept should be taken into account for a company can be seen with the proposed method.\u0000\u0000\u0000\u0000\u0000The proposed method showed that specific and agile roadmaps could be obtained. Because of the dependency of expert opinion for the fuzzy rule base, different methods for obtaining rules and relations may be a future research direction.\u0000\u0000","PeriodicalId":436903,"journal":{"name":"Journal of Fuzzy Logic and Modeling in Engineering","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115629336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Robust Fuzzy Decision Making on Global Warming","authors":"Kousik Bhattacharya, S. Kumar De, P. Nayak","doi":"10.2174/2666294901999201222150703","DOIUrl":"https://doi.org/10.2174/2666294901999201222150703","url":null,"abstract":"\u0000\u0000In this article we develop a global warming indicator model under fuzzy system. It is the light of\u0000sun that environmental pollution is responsible for the cause and immediate effect of global warming. Limited amount of\u0000oxygen in the air, continuous decrease of fresh water volume, more especially the amount of drinking water and the rise of\u0000temperature in the globe are the major symptoms (variants) of global warming. Thus, to capture the facts we need to\u0000develop a mathematical model which has not yet been developed by the earlier researchers.\u0000\u0000\u0000\u0000 An efficient literature survey has been done over the three major parameters of the environment namely\u0000oxygen, fresh water and surface temperature exclusively. In fact we have accumulated 150 years-data structure for these\u0000major components and have analyzed them under fuzzy system so as to develop an efficient global warming indicator\u0000model.\u0000\u0000\u0000\u0000 First of all, we gave few definitions on fuzzy set. Utilizing the data set we have constructed appropriate\u0000membership functions of the three major components of the environment. Then applying goal programming problem, we\u0000have constructed a fuzzy global warming indicator (GWI) model subject to some goal constraints with respective priority\u0000vectors (Scenario 1 and Scenario 2). An extension has also been included for multi-valued goal programming problem and\u0000numerical illustrations have been done with the help of LINGO software.\u0000\u0000\u0000\u0000 Numerical study reveals that the GWI takes maximum and minimum values in a decreasing manner as time\u0000increases. It is seen that for scenario 1, the global environmental system will attain its stability after 30 years by degrading\u000031% of GWI with respect to present base line. For scenario 2, after the same time the global environmental system will\u0000attain its stability quite slowly by degrading 28% of GWI with respect to present base line.\u0000\u0000\u0000\u0000 Here we have studied a mathematical model of global warming first time using fuzzy system. No other\u0000mathematical models have been existed in the literature. Thus, the basic novelty lies in a robust decision-making approach\u0000which shows the expected time of extinction of major species in this world. However, extensive study on data analytics\u0000over major environmental components can tell the stability of the global warming indicator and hence the future fate of\u0000the globe also.\u0000","PeriodicalId":436903,"journal":{"name":"Journal of Fuzzy Logic and Modeling in Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128500029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A New Cutset-type Kernelled Possibilistic C-Means Clustering Segmentation Algorithm Based on SLIC Super-pixels","authors":"Jiu-lun Fan, Haiyan Yu, Yang Yan, Mengfei Gao","doi":"10.2174/2666294901666210105141957","DOIUrl":"https://doi.org/10.2174/2666294901666210105141957","url":null,"abstract":"\u0000\u0000The kernelled possibilistic C-means clustering algorithm (KPCM) can effectively cluster hyper-sphere data\u0000with noise and outliers by introducing the kernelled method to the possibilistic C-means clustering (PCM) algorithm.\u0000However, the KPCM still suffers from the same coincident clustering problem as the PCM algorithm due to the lack of\u0000between-class relationships. Therefore, this paper introduces the cut-set theory into the KPCM and modifies the\u0000possibilistic memberships in the iterative process. Then a cutset-type kernelled possibilistic C-means clustering (CKPCM) algorithm is proposed to overcome the coincident clustering problem of the KPCM. Simultaneously a adaptive\u0000method of estimating the cut-set threshold is also given by averaging inter-class distances. Additionally, a cutset-type\u0000kernelled possibilistic C-means clustering segmentation algorithm based on the SLIC super-pixels (SS-C-KPCM) is also\u0000proposed to improve the segmentation quality and efficiency of the color images. Several experimental results on artificial\u0000data sets and image segmentation simulation results prove the excellent performance of the proposed algorithms in this\u0000paper.\u0000","PeriodicalId":436903,"journal":{"name":"Journal of Fuzzy Logic and Modeling in Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1969-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129108817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}