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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Indonesian Journal of Electrical Engineering and Computer Science
Mahdi Faramarzi
Universiti Teknologi Malaysia
Computer Science & IT

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Optical Tomography Sensor Configuration for Estimating the Turbidity Level of Water Mohd Taufiq Mohd Khairi; Sallehuddin Ibrahim; Mohd Amri Md Yunus; Mohd Najmi Mohd Sulaiman; Mahdi Faramarzi
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 3, No 3: December 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (689.388 KB) | DOI: 10.11591/ijict.v3i3.pp153-161

Abstract

This paper presents an investigation on an optical sensor configuration to estimate the turbidity level in a sample of water based on tomography technique. The optical sensors consist of infrared light - emitting diodes (LED) as transmitters and photodiodes as the receivers where the projections of the sensors are designed in fan beam mode. The promising results obtained from the analysis of light path detection demonstrated the accuracy of the proposed technique in estimating the turbidity level of water. The approach has potential to contribute and utilize for monitoring the quality level of water in water treatment industries.
Artificial Neural Network for Non-Intrusive Electrical Energy Monitoring System Khairell Khazin Kaman; Mahdi Faramarzi; Sallehuddin Ibrahim; Mohd Amri Md Yunus
Indonesian Journal of Electrical Engineering and Computer Science Vol 6, No 1: April 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v6.i1.pp124-131

Abstract

 This paper discusses non-intrusive electrical energy monitoring (NIEM) system in an effort to minimize electrical energy wastages. To realize the system, an energy meter is used to measure the electrical consumption by electrical appliances. The obtained data were analyzed using a method called multilayer perceptron (MLP) technique of artificial neural network (ANN). The event detection was implemented to identify the type of loads and the power consumption of the load which were identified as fan and lamp. The switching ON and OFF output events of the loads were inputted to MLP in order to test the capability of MLP in classifying the type of loads. The data were divided to 70% for training, 15% for testing, and 15% for validation. The output of the MLP is either ‘1’ for fan or ‘0’ for lamp. In conclusion, MLP with five hidden neurons results obtained the lowest average training time with 2.699 seconds, a small number of epochs with 62 iterations, a min square error of 7.3872×10-5, and a high regression coefficient of 0.99050.
Co-Authors Khairell Khazin Kaman Mohd Amri Md Yunus Mohd Najmi Mohd Sulaiman Mohd Taufiq Mohd Khairi Sallehuddin Ibrahim