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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA)
Muneer, Amgad
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Automotive Engineering Computer Science & IT Electrical & Electronics Engineering

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Design and implementation of automatic painting mobile robot Muneer, Amgad; Dairabayev, Zhan
IAES International Journal of Robotics and Automation (IJRA) Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v10i1.pp68-74

Abstract

Wall painting is a repetitive, stressful, and hazardous process that makes it an ideal automation case. In the automotive industry, painting had been automated but not yet for the construction industry. However, there is a strong need for a mobile robot that can move to paint residential interior walls. In this study, we aim to design and implement an automatic painting mobile robot. The conceptual design of the proposed wall painting robot consisting paint mechanism with a spray gun and ultrasonic sensor. The spray gun is attached to a pulley mechanism that has linear motion. The ultrasonic sensor is used to detect the spray gun when it reached a certain limit. The DC motor rotates clockwise and counterclockwise based on the ultrasonic sensor condition made. The experimental results indicate that the robot was able to paint the walls smoothly vertically, and horizontally. The spraying gun structure's speed is at a tolerable speed of 0.07 m/s, which could be increased, but to provide high-quality painting without any gaps, the current speed was selected as the most suitable, without any harm to the working process.
Short term residential load forecasting using long short-term memory recurrent neural network Muneer, Amgad; Ali, Rao Faizan; Almaghthawi, Ahmed; Taib, Shakirah Mohd; Alghamdi, Amal; Abdullah Ghaleb, Ebrahim Abdulwasea
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5589-5599

Abstract

Load forecasting plays an essential role in power system planning. The efficiency and reliability of the whole power system can be increased with proper planning and organization. Residential load forecasting is indispensable due to its increasing role in the smart grid environment. Nowadays, smart meters can be deployed at the residential level for collecting historical data consumption of residents. Although the employment of smart meters ensures large data availability, the inconsistency of load data makes it challenging and taxing to forecast accurately. Therefore, the traditional forecasting techniques may not suffice the purpose. However, a deep learning forecasting network-based long short-term memory (LSTM) is proposed in this paper. The powerful nonlinear mapping capabilities of RNN in time series make it effective along with the higher learning capabilities of long sequences of LSTM. The proposed method is tested and validated through available real-world data sets. A comparison of LSTM is then made with two traditionally available techniques, exponential smoothing and auto-regressive integrated moving average model (ARIMA). Real data from 12 houses over three months is used to evaluate and validate the performance of load forecasts performed using the three mentioned techniques. LSTM model has achieved the best results due to its higher capability of memorizing large data in time series-based predictions.
Co-Authors Abdullah Ghaleb, Ebrahim Abdulwasea Alghamdi, Amal Ali, Rao Faizan Almaghthawi, Ahmed Dairabayev, Zhan Taib, Shakirah Mohd