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AI 기반 신재생에너지 해수담수화 융합시스템을 위한 예측진단시스템 구축 방안
오효근(Hyogeun Oh),박인규(Ingyu Park),이지하(Ziha Lee),민준기(Joonki Min),홍희기(Hiki Hong) 대한설비공학회 2019 설비공학 논문집 Vol.31 No.12
Recently, the desalination technology market has been growing rapidly because of the lack of water resources. However, it is consuming considerable power and heat. Thus, research is underway on the development of the HCPVT desalination system using renewable energy. And the development of the predictive diagnosis system is necessary to improve operational efficiency. In this study, research methods for establishing a more rational AI-based diagnostic system were presented by analyzing the method of research for AI-based diagnosis system studied in advance. Additionally, this study includes a plan for establishing a predictive diagnosis system for AI-based new and renewable energy desalination convergence system by identifying the components of each HCPVT desalination system equipment and presenting the connection conceptual diagram and ANN model.
Smart O&M을 위한 RS485 통신 기반 태양열 모니터링 시스템
오효근(Hyogeun Oh),홍희기(Hiki Hong) 대한설비공학회 2021 대한설비공학회 학술발표대회논문집 Vol.2021 No.6
Solar thermal systems, which have been widely distributed in Korea since the 1990s, are not maintained and operated normally, leading to poor performance and failure. Recently, interest in smart O&M is growing to increase maintenance efficiency and reduce maintenance costs. In this study, a monitoring program for smart O&M is developed using an IoT smart solar thermal controller with RS485 communication and MFC. After setting COM port and baudrate, the request protocol is sent to the solar thermal controller, and the data is extracted from the response protocol, allowing monitoring of a total of 20 temperature, flow rate, and collection efficiency calculations and so on. At the same time, it measures every 10 seconds and saves the data. It is expected that predictive diagnosis technology can be applied by converting the data into a database and to help reduce maintenance costs.
학교 미세먼지 관리기술 개발을 위한 건물 DB 구축 및 코드체계 표준화 연구
한혜린(Hyerin Han),오효근(Hyogeun Oh),홍희기(Hiki Hong),민준기(Joonki Min) 대한설비공학회 2021 설비공학 논문집 Vol.33 No.2
The purpose of this study is to review, and categorize building information for the development of school particulate matter management technology. Therefore, in this study, we will divide the data into 7 categories (School Type, Area, External Environment, Internal Environment, School Building Status, Building Certification Status, Et Cetera) and subdivisions from 1,000 schools and organize the database by organizing data for a total of 38 detailed types. In this study, we present a method of outputting data according to 7 categories. Due to its easy syntax and high productivity, it outputs the desired data using Python, which is widely used in the recent use of big data. In order to output data efficiently, Pandas library for data processing and Matplotlib library for visualization are used. Through this, it becomes the basis for the development of a school particulate matter prediction model, and it can be used as reference data for establishing a database for the influence of particulate matter transmitted by each school and for preparing support measures using this.