« Previous AbstractWorkplace field testing of the pressure drop of particulate respirators using welding fumes    Next AbstractSample stacking with in-column silylation for less volatile polar compounds using GC-MS »

Micromachines (Basel)

Title:		Optimization of Machine Learning in Various Situations Using ICT-Based TVOC Sensors
Author(s):		Cho JH; Lee H;
Address:		"School of Electronic Engineering, Soongsil University, Seoul 06978, Korea"
Journal Title:		Micromachines (Basel)
Year:		2020
Volume:		20201210
Issue:		12
Page Number:		-
DOI:		10.3390/mi11121092
ISSN/ISBN:		2072-666X (Print) 2072-666X (Electronic) 2072-666X (Linking)
Abstract:		"A computational framework using artificial intelligence (AI) has been suggested in numerous fields, such as medicine, robotics, meteorology, and chemistry. The specificity of each AI model and the relationship between data characteristics and ground truth, allowing their guidance according to each situation, has not been given. Since TVOCs (total volatile organic compounds) cause serious harm to human health and plants, the prevention of such damages with a reduction in their occurrence frequency becomes not an optional process but an essential one in manufacturing, as well as for chemical industries and laboratories. In this study, with consideration of the characteristics of the machine learning technique and ICT (information and communications technology), TVOC sensors are explored as a function of grounded data analysis and the selection of machine learning models, determining their performance in real situations. For representative scenarios, considering features from an ICT semiconductor sensor and one targeting TVOC gas, we investigated suitable analysis methods and machine learning models such as LSTM (long short-term memory), GRU (gated recurrent unit), and RNN (recurrent neural network). Detailed factors for these machine learning models with respect to the concentration of TVOC gas in the atmosphere are compared with original sensory data to obtain their accuracy. From this work, we expect to significantly minimize risk in empirical applications, i.e., maintaining homeostasis or predicting abnormal situations to construct an opportune response"
Keywords:		Gru Lstm Rnn Tvoc measurement applications prediction sensory data reliability;
Notes:		"PubMed-not-MEDLINECho, Jae Hyuk Lee, Hayoun eng No. HI19C1032, Development of autonomous defense-type security technology and management system for strengthening cloud-based CDM security/IITP/ Switzerland 2020/12/17 Micromachines (Basel). 2020 Dec 10; 11(12):1092. doi: 10.3390/mi11121092"