A novel modular ANN architecture for efficient monitoring of gases/odours in real-time. (27th April 2018)

• Record Type: Journal Article
• Authors: Mishra, A; Rajput, N S
• Is Part Of: Materials research express. Volume 5:Number 4(2018)
• Publication Date: 2018-04-27
• Subjects: data processing -- electronic nose -- gas classification -- artificial neural network; Materials science -- Research -- Periodicals; Materials science -- Periodicals; 620.11
• DOI: 10.1088/2053-1591/aabe09
• Languages: English
• ISSNs: 2053-1591

Abstract

Abstract: Data pre-processing is tremendously used for enhanced classification of gases. However, it suppresses the concentration variances of different gas samples. A classical solution of using single artificial neural network (ANN) architecture is also inefficient and renders degraded quantification. In this paper, a novel modular ANN design has been proposed to provide an efficient and scalable solution in real–time. Here, two separate ANN blocks viz. classifier block and quantifier block have been used to provide efficient and scalable gas monitoring in real—time. The classifier ANN consists of two stages. In the first stage, the Net 1 -NDSRT has been trained to transform raw sensor responses into corresponding virtual multi-sensor responses using normalized difference sensor response transformation (NDSRT). These responses have been fed to the second stage (i.e., Net 2 -classifier ). The Net 2 -classifier has been trained to classify various gas samples to their respective class. Further, the quantifier block has parallel ANN modules, multiplexed to quantify each gas. Therefore, the classifier ANN decides class and quantifier ANN decides the exact quantity of the gas/odor present in the respective sample of that class.