Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Chungeon Kim"'
Publikováno v:
Journal of Computational Design and Engineering. 9:793-804
This paper proposes a video-based SlowFast model that combines the SlowFast deep learning model with a new boundary box annotation algorithm. The new algorithm, namely the MTB (i.e., the ratio of the number of Moving object pixels To the number of Bo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2a5e6d90eb19416d4395c911b48077b2
https://doi.org/10.1093/jcde/qwac027
https://doi.org/10.1093/jcde/qwac027
Publikováno v:
Transactions of the Korean Society of Mechanical Engineers - A. 46:153-162
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::30a63ebea4620060d82c059021087d87
https://doi.org/10.3795/ksme-a.2022.46.2.153
https://doi.org/10.3795/ksme-a.2022.46.2.153
Publikováno v:
Structural Health Monitoring. 21:2354-2369
Pipelines in critical engineered facilities, such as petrochemical and power plants, conduct important roles of fire extinguishing, cooling, and related essential tasks. Therefore, failure of a pipeline system can cause catastrophic disaster, which m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::430a61ef125c2f415d47281516eda258
https://doi.org/10.1177/14759217211056557
https://doi.org/10.1177/14759217211056557
Autor:
Chungeon Kim, Hyunseok Oh
Publikováno v:
Smart Structures and NDE for Energy Systems and Industry 4.0.
This paper presents a genetic-algorithm-based optimization method to design a sensor network for pipeline fracture detection in petrochemical plants. To avoid catastrophic consequences, it is desirable to detect any pipeline fractures as early as pos
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::384db5fab213362d8f1c7a3b7c98f69f
https://doi.org/10.1117/12.2511637
https://doi.org/10.1117/12.2511637