Kamarulzaman Kamarudin
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Kamarulzaman Kamarudin
Official Name
Kamarudin, Kamarulzaman
Alternative Name
Kamarudin, K.
Kamaruddin, Kamarulzaman
Main Affiliation
Scopus Author ID
55193266400
Researcher ID
DVV-8479-2022

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Deep Neural Network for Localizing Gas Source Based on Gas Distribution Map

2022-01-01 , Zaffry Hadi Mohd Juffry , Kamarulzaman Kamarudin , Abdul Hamid Adom , Mao X. , Latifah Munirah Kamarudin , Ammar Zakaria , Syed Muhammad Mamduh Syed Zakaria , Abdulnasser Nabil Abdullah

The dynamic characteristic of gas dispersal in uncontrolled environment always leads to inaccurate gas source localization prediction from gas distribution map. Gas distribution map is a representation of the gas distribution over an environment which helps human to observe the concentration of harmful gases at a contaminated area. This paper proposes the utilization of Deep Neural Network (DNN) to predict the gas source location in a gas distribution map. DNN learns from the previous gas distribution map data and patterns to generate a model that is able predict location of gas source. The results indicate that DNN is able to accurately predict the location within the range of 0.8 to 2 m from the actual gas source. This finding shows that DNN has a high potential for utilization in gas source localization application.

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Improved mobile robot based gas distribution mapping through propagated distance transform for structured indoor environment

2020-05-18 , Visvanathan R. , Kamarulzaman Kamarudin , Syed Muhammad Mamduh Syed Zakaria , Toyoura M. , Ali Yeon A.S. , Ammar Zakaria , Latifah Munirah Kamarudin , Mao X. , Shazmin Aniza Abdul Shukor

Mobile robot carrying gas sensors have been widely used in mobile olfaction applications. One of the challenging tasks in this research field is Gas Distribution Mapping (GDM). GDM is a representation of how volatile organic compound is spatially dispersed within an environment. This paper addresses the effect of obstacles towards GDM for indoor environment. This work proposes a solution by improvising the Kernel DM + V technique using propagated distance transform (DT) as the weighing function. Since DT computations are CPU heavy, parallel computing, using Compute Unified Device Architecture (CUDA) available in Graphics Processing Unit (GPU), is used to accelerate the DT computation. The proposed solution is compared with the Kernel DM + V algorithm, presenting that the proposed method drastically improves the quality of GDM under various kernel sizes. The study is also further extended towards the effect of obstacles on gas source localization task. The outcome of this work proves that the proposed method shows better accuracy for GDM estimation and gas source localization if obstacle information is considered.

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Publication

Deep Neural Network for Localizing Gas Source Based on Gas Distribution Map

2022-01-01 , Zaffry Hadi Mohd Juffry , Kamarulzaman Kamarudin , Abdul Hamid Adom , Mao X. , Latifah Munirah Kamarudin , Ammar Zakaria , Syed Muhammad Mamduh Syed Zakaria , Abdullah A.N.

The dynamic characteristic of gas dispersal in uncontrolled environment always leads to inaccurate gas source localization prediction from gas distribution map. Gas distribution map is a representation of the gas distribution over an environment which helps human to observe the concentration of harmful gases at a contaminated area. This paper proposes the utilization of Deep Neural Network (DNN) to predict the gas source location in a gas distribution map. DNN learns from the previous gas distribution map data and patterns to generate a model that is able predict location of gas source. The results indicate that DNN is able to accurately predict the location within the range of 0.8 to 2 m from the actual gas source. This finding shows that DNN has a high potential for utilization in gas source localization application.

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