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고층 건물 화재 관련 R&D 위상 분석 및 신기술 탐색 연구
심위(We Shim),최재경(Jaekyung Choi),정현상(Hyunsang Chung),허요섭(Yoseob Heo),서성호(Seongho Seo) 한국산업융합학회 2020 한국산업융합학회 논문집 Vol.23 No.2
Due to the development of the urban economy, high-density buildings and skyscrapers have continued to increase in order to alleviate high population densities and to make efficient use of urban space. However, a fire in a high-rise building is a disaster that can lead to massive casualties and property damage because of the difficulty of firefighting and escaping. Various studies have been conducted on these high-rise buildings because they are sympathetic to these difficulties all over the world. In this paper, trends of researches and technologies related to fire in high-rise buildings are analyzed synthetically through thesis and patent data. In other words, we explored the trends of various studies that have been carried out so far through the thesis, and performed technical monitoring on actual implemented technology and newly implemented technologies through patent data. Through this research, we have studied the present and the future of technology for high-rise building fire.
장문수 ( Moonsoo Jang ),허요섭 ( Yoseob Heo ),정현상 ( Hyunsang Chung ),박소영 ( Soyoung Park ) 한국산업융합학회 2021 한국산업융합학회 논문집 Vol.24 No.3
With global warming and pollution problems, accurate forecasting of the harmful gases would be an essential alarm in our life. In this paper, we forecast the emission of the five gases(SOx, NO<sub>2</sub>, NH<sub>3</sub>, H<sub>2</sub>S, CH<sub>4</sub>) using the time series model of ARIMA, the learning algorithms of Random forest, and LSTM. We find that the gas emission data depends on the short-term memory and behaves like a random walk. As a result, we compare the RMSE, MAE, and MAPE as the measure of the prediction performance under the same conditions given to three models. We find that ARIMA forecasts the gas emissions more precisely than the other two learning-based methods. Besides, the ARIMA model is more suitable for the real-time forecasts of gas emissions because it is faster for modeling than the two learning algorithms.
머신러닝 앙상블을 활용한 공압기의 전력 효율 최적화 시뮬레이션
김주헌 ( Juhyeon Kim ),장문수 ( Moonsoo Jang ),최지은 ( Jieun Choi ),허요섭 ( Yoseob Heo ),정현상 ( Hyunsang Chung ),박소영 ( Soyoung Park ) 한국산업융합학회 2023 한국산업융합학회 논문집 Vol.26 No.6
This study delves into methods for enhancing the power efficiency of air compressor systems, with the primary objective of significantly impacting industrial energy consumption and environmental preservation. The paper scrutinizes Shinhan Airro Co., Ltd.'s power efficiency optimization technology and employs machine learning ensemble models to simulate power efficiency optimization. The results indicate that Shinhan Airro's optimization system led to a notable 23.5% increase in power efficiency. Nonetheless, the study's simulations, utilizing machine learning ensemble techniques, reveal the potential for a further 51.3% increase in power efficiency. By continually exploring and advancing these methodologies, this research introduces a practical approach for identifying optimization points through data-driven simulations using machine learning ensembles.