Abdul Hamid Adom
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Abdul Hamid Adom
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Adom, Abdul Hamid
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Scopus Author ID
6506600412

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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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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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A Review on the efficiency and accuracy of localization of moisture distributions sensing in agricultural silos

2019-12-03 , Almaleeh A.A. , Ammar Zakaria , Zakaria S.M.M.S. , Latifah Munirah Kamarudin , Mohd Hafiz Fazalul Rahiman , Sukor A.S.A. , Yuzairi Abdul Rahim , Abdul Hamid Adom

The moisture distribution in the silos depends upon various seeds parameters such as type and size of seeds, amount of storage, external weather, and storage period as well as structural and environmental factors. It is very difficult to predict moisture distribution in silos effectively while taking all the above aspects into consideration. This study aims to investigate the efficiency and accuracy of localization of moisture distributions sensing in agricultural silo. The work is mainly focussed on three major elements: Radio Frequency (RF), tomographic imaging and classification process using machine learning. In particular, RF-based signal and volume tomographic images are used to predict the moisture distribution. Furthermore, computational intelligence techniques such as artificial neural network (ANN) is applied to develop models based on previous data. The generalization of these models towards new set of data is discussed in making sure a successful application of a model. A detailed study of the relative performance of computational intelligence techniques has been carried out based on different statistical performance criteria.

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