[논문]가스 사용 환경에서의 위험 상황 인지를 위한 딥러닝 예측모델 개발

강병준; 조현찬

[국내논문] 가스 사용 환경에서의 위험 상황 인지를 위한 딥러닝 예측모델 개발
Development of a Deep Learning Prediction Model to Recognize Dangerous Situations in a Gas-use Environment 원문보기

반도체디스플레이기술학회지 = Journal of the semiconductor & display technology, v.21 no.1, 2022년, pp.132 - 135

강병준 (한국기술교육대학교 대학원 전기전자통신공학과) , 조현찬 (한국기술교육대학교 전기전자통신공학부)

Abstract ▼ AI-Helper

Recently, with the development of IoT communication technology, products and services that detect and inform the surrounding environment under the name of smart plugs are being developed. In particular, in order to prepare for fire or gas leakage accidents, products that automatically close and warn when abnormal symptoms occur are used. Most of them use methods of collecting, analyzing, and processing information through networks. However, there is a disadvantage that it cannot be used when the network is temporarily in a failed state. In this paper, sensor information was analyzed using deep learning, and a model that can predict abnormal symptoms was learned in advance and applied to MCU. The performance of each model was evaluated by developing firmware that can judge and process on its own regardless of network and applying a predictive model to the MCU after 3 to 120 seconds.

Keyword

참고문헌 (10)

Hong Suk il, "Smart Home Technology Trend", The Journal of The Korean Institute of Communication Sciences, Vol. 37(11), pp.28-35, 2020.
Yeon Ho Chu, Young Kyu Choi, "A Deep Learning based IOT Device Recognition System", Journal of the Semiconductor & Display Technology. Vol. 18(2), pp.1-5, 2019.
Gyou-tae Park, Eun-jung Kim, In-chan Kim and Kie-sik Kim, "Development and Field Test of a Smart-home Gas Safety Management System", Journal of the Korean Institute of Gas, Vol. 15(6), pp.128-135, 2012.
Young Gyu Choi, "A Study on the Development of Low Power Automatic ON/OFF Valve System for Gas Leak Detection", Journal of Korea Institute of Information, Electronics, and Communication Technology, Vol. 14(5), pp.369-374, 2021.

원문보기 상세보기
Jeong-Hwan Kim, Gong-Myeong Lee, Taesam Kang, and Jungkeun Park, "Development of a Small Smart Plug for Remote Monitoring and Control of Temperature and Humidity", Journal of Institute of Control, robotics and Systems, Vol. 27(11), pp.878-882, 2021.

상세보기
Jun-Yong Park, Chun-Kyong Lee and Tae-Keun Park, "A Case Study of Measuring and Analyzing Electric Energy Usage in University Facilities Using Smart Plug", Journal of the Architectural Institute of Korea Structure & Construction, Vol. 34(9), pp. 27-34, 2018.

원문보기 상세보기
Byeong-Gyu Choe, Min-Chang Cha and Jin-Jun Kim, "Study on the Current Safety Management Satatus and Safety Improvement of Gas Valve", Journal of the Korean Institute of Gas, Vol. 20(5), 2016.
Jong-Bae Hwang, Se-Chang Jang, Jae-Won Jang, KiHyun Kim and Jae-Cheol Ha, "Anomaly detection on ICS(Industrial Control System) using deep learning method", Journal of the Korea Academia-Industrial cooperation Society, Vol. 23(1), pp. 24-32, 2022.

상세보기
Chang-Woo Son, Sang-Bae Lee, "The research of Automatic classification of Products Using Smart Plug by Artificial Intelligence Technique", Joural of the Korea Institute of Information and Communication Engineering, Vol. 22(6). pp.842-848, 2018.
Pau, D., Lattuada, M., Loro, F., De Vita, A., & Licciardo, G. D. " Comparing industry frameworks with deeply quantized neural networks on microcontrollers", 2021 IEEE International Conference on Consumer Electronics (ICCE), pp. 1-6, 2021.

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