Zobrazeno 1 - 10 of 10 pro vyhledávání: '"Sang-Ik Suh"'
1
Akademický článek
Assessing the Performance of Deep Learning Algorithms With and Without Transfer Learning in Similar or Identical Wastewater Treatment Processes
Autor: Jaeil Kim, Sang-Ik Suh, Yongtae Ahn, Seo Jin Ki
Publikováno v: 대한환경공학회지, Vol 46, Iss 3, Pp 111-117 (2024)
This study assessed the feasibility of transfer learning from one wastewater treatment process to another using two popular deep learning algorithms. Specifically, convolutional neural network (CNN) and long short-term memory (LSTM), which consisted
Externí odkaz: https://doaj.org/article/441cb172cb9e4dfeb663441fed56d7a4
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2
Akademický článek
Development and application of an advanced algorithm for environmental risk assessment and safety management of pesticide residues in agricultural soils: Monitoring of currently used pesticide in upland soils
Autor: Sang-Ik Suh, In-Seong Hwang, Hyo-Sub Lee, Seo-Jin Ki, Min-Ho Son
Publikováno v: Environment International, Vol 190, Iss , Pp 108865- (2024)
This study conducted the development of an advanced risk assessment algorithm system and safety management strategies using pesticide residue monitoring data from soils. To understand the status of pesticide residues in agricultural soils, monitoring
Externí odkaz: https://doaj.org/article/f3b1689a31614c7892d5fcf967525b20
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3
Akademický článek
Assessing the Performance of a Long Short-Term Memory Algorithm in the Dataset with Missing Values
Autor: Hyun-Geoun Park, Sang-Ik Suh, Gyeong Cheol Jo, Jinuk Jang, Seo Jin Ki
Publikováno v: 대한환경공학회지, Vol 44, Iss 12, Pp 636-642 (2022)
This study was conducted to assess the performance of a long short-term memory algorithm (LSTM), which was suitable for time series prediction, in the multivariate dataset with missing values. The full dataset for the adopted LSTM model was prepared
Externí odkaz: https://doaj.org/article/4d6f040328954f638b46a350541fe3f5
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9
Opportunities and challenges of machine learning in bioprocesses: Categorization from different perspectives and future direction
Autor: Seung Ji, Lim, Moon, Son, Seo Jin, Ki, Sang-Ik, Suh, Jaeshik, Chung
Publikováno v: Bioresource Technology. 370:128518
Recent advances in machine learning (ML) have revolutionized an extensive range of research and industry fields by successfully addressing intricate problems that cannot be resolved with conventional approaches. However, low interpretability and inco
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b5d680d5954ccd99be693c3aa524d097
https://doi.org/10.1016/j.biortech.2022.128518
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10
Assessing the Performance of Deep Learning Algorithms for Short-Term Surface Water Quality Prediction
Autor: Heelak Choi, Eun Jin Han, Su-Hee Kim, Seo Jin Ki, Sang-Ik Suh
Publikováno v: Sustainability, Vol 13, Iss 10690, p 10690 (2021)
Sustainability
Volume 13
Issue 19
This study aimed to investigate the applicability of deep learning algorithms to (monthly) surface water quality forecasting. A comparison was made between the performance of an autoregressive integrated moving average (ARIMA) model and four deep lea
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::24cc16013cb774b35659f1b61a5aa8e1
https://www.mdpi.com/2071-1050/13/19/10690
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