CO Multi-Forecasting Model for Indoor Health and Safety Management in Smart Home

Chih-Yuan Chang,
Kuei-Sheng Ko,
Sy-Jye Guo,
San-Shan Hung,
Yi-Ting Lin,

Abstract


The integrated application of the Internet of Things and artificial intelligence (AIoT) is key during the developmental process from a smart home to smart city and the design of sensors for collecting various types of data is the foundation for establishing the entire AIoT. In this study, we placed our self-designed carbon monoxide (CO) sensor into a 1:10 ratio acrylic house model and simulated three types of CO hazard scenarios. The results comparing 10 cases were used to establish an innovative CO Multi-Forecasting Model (CMFM). The CMFM is suitable for application in the semi-supervised learning - based AIoT. In addition to having a 3-7 times better safety warning time compared to that of commercially available CO sensors, our CO sensor also possesses a health warning function for indoor air quality.


Citation Format:
Chih-Yuan Chang, Kuei-Sheng Ko, Sy-Jye Guo, San-Shan Hung, Yi-Ting Lin, "CO Multi-Forecasting Model for Indoor Health and Safety Management in Smart Home," Journal of Internet Technology, vol. 21, no. 1 , pp. 273-284, Jan. 2020.

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