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Development of a Deep Learning Prediction Model to Recognize Dangerous Situations in a Gas-use Environment  

Kang, Byung Jun (Department of Electrical, Electronics and Communication Engineering, Graduate School, Korea University of Technology and Education)
Cho, Hyun-Chan (Department of Electrical, Electronics and Communication Engineering, Korea University of Technology and Education)
Publication Information
Journal of the Semiconductor & Display Technology / v.21, no.1, 2022 , pp. 132-135 More about this Journal
Abstract
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.
Keywords
Predictive model; Gas valve; Deep learning; Gas Safety; Firmware;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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