A Mixture-Gas Edge-Computing Multisensor Device With Generative Learning Framework
Jeonghoon Cho, You Jang Pyeon, Yeong Min Kwon, Yonggi Kim, Junyeong Yeom, Myeong Woo Kim, Chan Sam Park, Inho Kim, Yoon‐Sik Lee, Jae Joon Kim · IEEE Sensors Journal · 2024
This paper presents a mixture-gas detectable edge-computing device with a generative learning framework for selectivity and accuracy. Mixture-gas detection capability is enabled through two proposed schemes of temperature modulation and cross-iterative-tuning artificial neural network (CIT-ANN). Their related computations are facilitated inside the edge device level, applying analog normalization concepts in the readout integrated circuit (ROIC). This proposed edge platform provides generative training data for mixture-gas detection, allowing much less empirical data for its learning process, especially under mixture gas environment. An edge-computing IoT device prototype was manufactured based on a fabricated ROIC and in-house metal-oxide-semiconductor sensor arrays embedding heater modulation function. Under mixture-gas experiments of NO2and CO gases, the proposed CIT-ANN together with the heater modulation demonstrated 44% higher recognition performance than in the conventional ANN. The proposed generative learning method showed higher relative label coincidence, achieving 17% higher correlation with real training data than in the conventional mathematical interpolation method.