Title :
Humidity compensation by neural network for bad-smell sensing system using gas detector tube and built-in camera
Author :
Nakamoto, T. ; Ikeda, T. ; Hirano, H. ; Arimoto, T.
Author_Institution :
Grad. Sch. of Sci. & Eng., Tokyo Inst. of Technol., Tokyo, Japan
Abstract :
Cheap and rapid sensing system is required for detecting bad smell or VOC. Although a gas detector tube is known as simple gas detection method, its measurement process has not been automated. We studied an automated measurement system for gas detector tube using a built-in camera. Although the measurement was automated using our system, other problem was disclosed. Since a digital camera is sensitive to color change, the slight change due to humidity, which is not the problem for manual inspection, cannot be ignored in our system. Thus, the humidity sensor was added to the system. However, the simple compensation method such as linear regression etc did not work because the humidity influenced the data in a complicated manner. Then, MLP (multilayer perceptron) neural network was used for the humidity compensation. Both discoloration area and the humidity data were input to the neural network. As a result, the accurate concentration estimation was successfully performed.
Keywords :
cameras; electrical engineering computing; gas sensors; humidity sensors; multilayer perceptrons; automated measurement system; bad-smell sensing system; built-in camera; color change; concentration estimation; digital camera; discoloration area; gas detector tube; humidity compensation; humidity data; multilayer perceptron neural network; Charge coupled devices; Costs; Digital cameras; Gas detectors; Humidity; Inspection; Linear regression; Mobile handsets; Neural networks; Sensor systems;
Conference_Titel :
Sensors, 2009 IEEE
Conference_Location :
Christchurch
Print_ISBN :
978-1-4244-4548-6
Electronic_ISBN :
1930-0395
DOI :
10.1109/ICSENS.2009.5398164