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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Shingo Yamaguchi
Yamaguchi University
Computer Science & IT Electrical & Electronics Engineering Engineering

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Pipe leakage detection system with artificial neural network Muhammad Iqmmal Rezzwan Radzman; Abd Kadir Mahamad; Siti Zarina Mohd Muji; Sharifah Saon; Mohd Anuaruddin Ahmadon; Shingo Yamaguchi; Muhammad Ikhsan Setiawan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp977-985

Abstract

This project aims to develop a system that can monitor to detect leaks in water distribution networks. It has been projected that leakage from pipelines may lead to significant economic losses and environmental damage. The loss of water from leaks in pipeline systems accounts for a large portion of the water supply. Pipelines are maintained throughout their lives span; however, it is difficult to avoid a leak occurring at some point. A tremendous amount of water could be saved globally if automated leakage detection systems were introduced. An embedded system that monitors water leaks can efficiently aid in water conservation. This project focuses on developing a real-time water leakage detection system using a few types of sensors: water flow rate sensor, vibration sensor, and water pressure sensor. The data from the sensors is uploaded and stored by the microcontroller (NodeMCU V3) to the database cloud (Google Sheets). The data that is stored in the database is analyzed by artificial neural network (ANN) by using Matlab software. An application is developed based on results from ANN training to detect the leakage event. Implementing the proposed system can increase operations efficiency, reduce delay times, and reduce maintenance costs after leaks are detected.
Cloud-based people counter Abd Kadir Mahamad; Sharifah Saon; Hamimi Hashim; Mohd Anuaruddin Ahmadon; Shingo Yamaguchi
Bulletin of Electrical Engineering and Informatics Vol 9, No 1: February 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (358.667 KB) | DOI: 10.11591/eei.v9i1.1849

Abstract

Emergence of Industry 4.0 in current economic trend promotes the usage of Internet of Things (IoT) in product development. Counting people on streets or at entrances of places is indeed beneficial for security, tracking and marketing purposes. The usage of cameras or closed-circuit television (CCTV) for surveillance purposes has emerged the need of tools for the digital imagery content analysis to improve the system. The purpose of this project is to design a cloud-based people counter using Raspberry Pi embedded system and send the received data to ThingSpeak, IoT platform. The initial stage of the project is simulation and coding development using OpenCV and Python. For the hardware development, a Pi camera is used to capture the video footage and monitor the people movement. Raspberry Pi acts as the microcontroller for the system and process the video to perform people counting. Experiment have been conducted to measure the performance of the system in the actual environment, people counting on saved video footage and visualized the data on ThingSpeak platform.
Internet of things (IoT) based traffic management & routing solution for parking space Zulnazim Dzulkurnain; Abd Kadir Mahamad; Sharifah Saon; Mohd Anuaruddin Ahmadon; Shingo Yamaguchi
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 1: July 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i1.pp336-345

Abstract

The idea of Internet of Things (IoT) based traffic management & routing solution for parking space is due to the vehicle parking has become major issue in urban areas. The growing number of vehicles has contributed to the traffic problem and vehicle parking issue nowadays. The main purpose of this project is to assist the user to locate the vacant parking space, which help to reduce time and fuel consumption on searching the parking space. This proposed system was used online system via website application, which assist people to find the available parking slot. In fact, the system counted the capacity of the available parking space and notified the user through the website application. Frankly, the system was equipped with an ultrasonic sensor, which acts as the detector that sent data to the microcontroller in order to update into UBIDOTS cloud server for data logger purposes. This system could lessen or solve the time management problem at the parking area, which user could save their time by checking the available parking slots in advance through the website application.
Feed forward neural network application for classroom reverberation time estimation Fathin Liyana Zainudin; Sharifah Saon; Abd Kadir Mahamad; Musli Nizam Yahya; Mohd Anuaruddin Ahmadon; Shingo Yamaguchi
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 1: July 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i1.pp346-354

Abstract

Acoustic problem is a main issues of the existing classroom due to lack of absorption of surface material. Thus, a feed forward neural network system (FFNN) for classroom Reverberation Time (RT) estimation computation was built. This system was developed to assist the acoustic engineer and consultant to treat and reduce this matter. Data was collected and computed using ODEON12.10 ray tracing method, resulting in a total of 600 rectangular shaped classroom models that were modeled with various length, width, height, as well as different surface material types. The system is able to estimate RT for 500Hz, 1000Hz, and 2000Hz. Using the collected data, FFNN for each frequency were trained and simulated separately (as absorption coefficients are frequency dependent) in order to find the optimum solution. The final system was validated and compared with the actual measurement value from 15 different classrooms in Universiti Tun Hussein Onn Malaysia (UTHM). The developed system show positive results with average validation accuracy of 94.35%, 95.91%, and 96.42% for 500Hz, 1000Hz, and 2000Hz respectively. 
Co-Authors Abd Kadir Mahamad Abd Kadir Mahamad Fathin Liyana Zainudin Hamimi Hashim Mohd Anuaruddin Ahmadon Muhammad Ikhsan Setiawan Muhammad Iqmmal Rezzwan Radzman Musli Nizam Yahya Sharifah Saon Sharifah Saon Siti Zarina Mohd Muji Zulnazim Dzulkurnain