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Cited 2 time in webofscience Cited 2 time in scopus
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Probability distribution for size and mass of a nodule in the KR5 area for the development of a manganese nodule miner

Authors
Kim, SaekyeolCho, Su-gilLim, WoochulLee, Tae HeePark, SanghyunHong, SupKim, Hyung-WooMin, Cheon-HongChoi, Jong-SuKo, Young-TakChi, Sang-Bum
Issue Date
1-1월-2019
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Keywords
Akaike information criterion; Clarion-Clipperton fracture zone; Copula; Deep-sea manganese nodule miner; Joint probability density function; Seafloor manganese nodules
Citation
OCEAN ENGINEERING, v.171, pp 131 - 138
Pages
8
Journal Title
OCEAN ENGINEERING
Volume
171
Start Page
131
End Page
138
URI
https://www.kriso.re.kr/sciwatch/handle/2021.sw.kriso/360
DOI
10.1016/j.oceaneng.2018.10.041
ISSN
0029-8018
Abstract
In this paper, probability distribution for the size and mass of seafloor manganese nodules is investigated to improve the reliability of a pick-up device in a deep-sea manganese nodule miner. Because the size and mass of the manganese nodules are strongly correlated, statistical models for the size and mass cannot be estimated independently. In order to consider the correlation between the size and mass in a statistical model, the joint probability density function (PDF) is estimated by using copula. This method requires the estimation of the marginal distributions and copula for the two correlated environmental variables. However, this is significantly difficult when there is no prior knowledge of the two physical properties. The proposed method, which employs the Akaike information criterion to select the fittest marginal distributions and copula, provides a systematic procedure to determine a statistical model of correlated environmental variables without any prior knowledge of their distributions. To demonstrate the effectiveness of the proposed method, the joint PDF for the size and mass of manganese nodules is modeled by using the multivariate normal distribution and the proposed method. It was found that the proposed method provides more accurate and reliable estimation results for the two correlated environmental variables.
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