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Akademický článek

Distribution of Air pollutant Concentration and Ammonium conversion rate in Livestock Areas: Focusing on Boryeong

Autor: Jaeeun Kim, Siyoung Seo, Saem-Ee Woo, Jun-Yeob Lee, Yeon-A Lee, Junsu Park

Publikováno v: 대한환경공학회지, Vol 46, Iss 10, Pp 559-569 (2024)

Objectives The association between the generation of fine particulate matter (PM2.5) and ammonia is well-established, highlighting the importance of managing ammonia as a primary means to reduce particulate matter. However, domestic research on ammon

Externí odkaz: https://doaj.org/article/58b236447cd14df5a72fb45e812c74f5

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Akademický článek

Prediction of Complex Odor from Pig Barn Using Machine Learning and Identifying the Influence of Variables Using Explainable Artificial Intelligence

Autor: Do-Hyun Lee, Sang-Hun Lee, Saem-Ee Woo, Min-Woong Jung, Do-yun Kim, Tae-Young Heo

Publikováno v: Applied Sciences, Vol 12, Iss 24, p 12943 (2022)

Odor is a very serious problem worldwide. Thus, odor prediction research has been conducted consistently to help prevent odor. Odor substances that are complex odors are known, but complex odors and odor substances do not have a linear dependence. In

Externí odkaz: https://doaj.org/article/37b5eea839f64e86992e580675460196

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Akademický článek

Evaluation of Odor Prediction Model Performance and Variable Importance according to Various Missing Imputation Methods

Autor: Do-Hyun Lee, Saem-Ee Woo, Min-Woong Jung, Tae-Young Heo

Publikováno v: Applied Sciences, Vol 12, Iss 6, p 2826 (2022)

The aim of this study is to ascertain the most suitable model for predicting complex odors using odor substance data that has a small number of data and a large number of missing data. First, we compared the data removal and imputation methods, and t

Externí odkaz: https://doaj.org/article/835daf4d96d14d1c85cb540fcb949f9c

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