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General Information
    • ISSN: 2010-3700 (Online)
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.

IJMLC 2011 Vol.1(2): 176-184 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2011.V1.26

An Intelligent Data Analysis-Base: Evaluation of Nuclear Power Plants Output Flow

Azizul Azhar Ramli, Junzo Watada, and Witold Pedrycz

Abstract—Abstract In order to realize stable electricity generation, nuclear power plant (NPP) generators are evaluated in their performance of generated output power in term of quality and quantity. Therefore, the evaluation is realized on the basis of several influential factors, which have to be analyzed via the exploitation of heterogeneous data sets obtained from scattered locations and different types of sources. In this paper, we stress the pivotal role of extended fuzzy switching regression analysis in handling this type of data, which come from real world of the NPPs industry. The key objective of this study is to implement the enhancement of a convex hull approach in the fuzzy switching regression analysis process which can be viewed as an intelligent data analysis (IDA) approach. This approach is concerned with the effective combination of fuzzy sets theory with the analysis of large amounts of online data. For deploying the multisource data problem, the fuzzy switching repression analysis is developed as an IDA by enhancing a fuzzy regression analysis based on convex hull, specifically Beneath-Beyond algorithm. The selected IDA becomes a potential analysis vehicle to successfully reduce the computing time as well as minimize the computational complexity. It is shown that the proposed approach becomes an efficient vehicle for the evaluation of produced output flow by NPPs. The study offers an interesting and practically appealing alterative platform to evaluate the quality and quantity of produced output flow of NPPs.

Index Terms—Convex hull, Fuzzy switching regression, nuclear power plant.

A. A. Ramli. is with Graduate School of Information, Production and Systems (IPS), Waseda University, 2-7, Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka-ken, 808-0135 JAPAN (phone: +81-80-3981-9429; fax: +81-93-692-5179; e-mail: azizulazhar@ moegi.waseda.jp).
J. Watada, was with Graduate School of Information, roduction and Systems (IPS), Waseda University, 2-7, Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka-ken, 808-0135 JAPAN (e-mail: junzow@osb.att.ne.jp).
W. Pedrycz is with the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, CANADA T6G 2V4 and Systems Research Institute, Polish Academy of Sciences, Warsaw, POLAND (e-mail: pedrycz@ece.ualberta.ca).


Cite: Azizul Azhar Ramli, Member, IACSIT, Junzo Watada, and Witold Pedrycz, "An Intelligent Data Analysis-Base: Evaluation of Nuclear Power Plants Output Flow," International Journal of Machine Learning and Computing vol. 1, no. 2, pp. 176-184, 2011.

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