Modeling of an Intelligent Cooking Gas Leakage Detection System Using Convolutional Neural Network
Authors: Nnenna, H. N., Ozoemena, P. C., Odo, H. U.
Journal: American Journal of Applied Sciences and Engineering (AJASE), ISSN 2766-7596
Citation: AJASE 3(5), 2022-10-07.
DOI: 10.70878/ajase.2022.a6d18b48
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Type: Original Research
Abstract
This paper presents the modeling of an intelligent cooking gas leakage detection system using convolutional neural network (CNN). The study reviewed many relevant literatures which shows that cooking gas accident have remained a major cause of fire outbreak at home and has remained a major challenge over the years. This problem was addressed in this research using cooking gas data collection with FLIR GF346 gas camera and then trained with a convolutional neural network algorithm modeled using architectural diagram, self-defining equations and then implemented using Simulink. The result when tested showed high gas leakage detection accuracy of 99%.
Keywords
Cooking Gas, Convolutional Neural Network (CNN), Leakage Detection System, Authorship: Nnenna, H. N., Ozoemena, P. C., Odo, H. U. | FULL PDF
Full Text
This paper presents the modeling of an intelligent cooking gas leakage detection system using convolutional neural network (CNN). The study reviewed many relevant literatures which shows that cooking gas accident have remained a major cause of fire outbreak at home and has remained a major challenge over the years. This problem was addressed in this research using cooking gas data collection with FLIR GF346 gas camera and then trained with a convolutional neural network algorithm modeled using architectural diagram, self-defining equations and then implemented using Simulink. The result when tested showed high gas leakage detection accuracy of 99%.