Identification of marginal and joint CDFs using bivariate type I interval multiply censored data for RBDO of a pick-up device of a pilot mining robot SCIE SCOPUS

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Title
Identification of marginal and joint CDFs using bivariate type I interval multiply censored data for RBDO of a pick-up device of a pilot mining robot
Author(s)
Kim, Saekyeol; Cho, Su-gil; Lee, Tae Hee; Choi, Jong-Su; Park, Sanghyun; Hong, Sup; Kim, H.-W.; Min, Cheon-Hong; Ko, Youngtak; Chi, Sang Bum
KIOST Author(s)
Ko, Young Tak(고영탁)Chi, Sang Bum(지상범)
Alternative Author(s)
고영탁; 지상범
Publication Year
2021-04
Abstract
In this paper, joint probability distribution for the size and mass of deep-sea manganese nodules is investigated and reliability-based design optimization (RBDO) of a deep-sea pilot mining robot is performed. As the size and mass of the manganese nodules are strongly correlated and their data are given as bivariate type I interval multiply censored data, a new statistical modeling method should be developed to deal with these issues. However, this is significantly difficult as the conventional methods cannot resolve these issues and there is no prior knowledge of the two physical properties. The proposed method, which employs the multinomial distribution to define the likelihood function and the Akaike information criterion to select the fittest marginal distribution and copula, provides a systematic approach to find the joint probability distribution using the type I interval multiply censored data. To demonstrate the accuracy and effectiveness of the proposed method, two numerical examples are tested. Then, the RBDO of the pilot mining robot is performed using the joint probability distribution resulted from the proposed method.
ISSN
1615-147X
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/41327
DOI
10.1007/s00158-020-02828-5
Bibliographic Citation
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, v.63, pp.1989 - 2007, 2021
Publisher
SPRINGER
Keywords
Bivariate type I interval multiply censored data; Reliability-based design optimization; Joint probability density function; Deep-sea manganese nodules; Pilot mining robot; Copula; Akaike information criterion
Type
Article
Language
English
Document Type
Article; Early Access
Publisher
SPRINGER
Related Researcher
Research Interests

deep sea mineral resources,deep sea sediments,심해저 광물자원,심해저 퇴적물,심해 환경

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