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All Journal ELKHA : Jurnal Teknik Elektro Makara Journal of Technology
Baihaqi, Muhammad Yeza
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Humanoid Robot Application as COVID-19 Symptoms Checker Using Computer Vision and Multiple Sensors Baihaqi, Muhammad Yeza; Vincent, Vincent; Simatupang, Joni Welman
ELKHA : Jurnal Teknik Elektro Vol. 13 No. 2 October 2021
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/elkha.v13i2.47213

Abstract

Novel Corona Virus (nCoV) infects human’s respiratory system. It spreads easily when an infected person makes a close contact with other people. To prevent its massive spread, it is necessary to ensure anyone coming to a certain place is not being infected. The symptoms include high body temperature (≥37.5°C) and low oxygen saturation level (≤95%). This day, most places only check the human body temperature. Thus, the authors are interested to make an attempt to design a system that is able to measure both human body temperature and oxygen saturation level. This work also applies the 7-DoF Upper-Body of Humanoid Robot to prevent virus spread from and to the employee. The system will detect the coming of visitors by using face detection. It requires 7.24 seconds to detect the visitor without a mask, and 1.26 second when the visitor wears a mask. The body temperature measurement was done using GY-906 temperature sensor with an error of 0.51%. For the oxygen saturation level measurement, MAX30100 pulse oximeter module was applied and showed an error of 0.78%. In addition, the upper-body of humanoid robot will perform some gestures to instruct the visitors in every process of the system. The implemented 7-DoF upper-body of humanoid robot has 93.33% gesture comprehension rate. In conclusion, the overall system has been tested and showed success rate up to 75%.
Direct Control Strategy using Polynomial Fuzzy-Based Adaptive Fractional Order PID Controller Rospawan, Ali; Angelina, Clara Lavita; Samsuri, Faisal; Baihaqi, Muhammad Yeza; Halawa, Edmun; Munajat, Muhammad; Vincent, Vincent; Setiyadi, Surawan; Purnama, Irwan; Simatupang, Joni Welman
Makara Journal of Technology Vol. 29, No. 2
Publisher : UI Scholars Hub

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Abstract

This paper presents a novel direct control strategy using a polynomial fuzzy neural network-based adaptive fractional order proportional integral derivative (PFNN-AFOPID) controller for nonlinear and time-varying systems. The proposed approach integrates the enhanced flexibility of fractional order calculus PID with the superior nonlinear approximation capabilities of polynomial fuzzy models, enabling dynamic adjustment of all control parameters without requiring precise mathematical modeling of system dynamics. By extending traditional PID control with fractional-order operations, the controller achieves improved frequency response and robustness against disturbances. Experimental validation on a DC motor position control system demonstrates significant performance improvements. Compared to traditional PID, the proposed PFNN-AFOPID achieved a performance improvement of 53.69% in RMSE, 78.56% in ISE, 69.92% in IAE, and 83.98% in ITAE. When compared to the existing fuzzy neural network-based adaptive PID (FNN-APID), our approach delivered improvements of 21.06% in RMSE, 28.79% in ISE, 5.69% in IAE, and 32.86% in ITAE. These results confirm the superior capability of the proposed approach in handling system nonlinearities while maintaining precise control under varying operational conditions, without requiring prior system dynamics knowledge or extensive offline training.
Co-Authors Angelina, Clara Lavita Halawa, Edmun Joni Welman Simatupang Munajat, Muhammad Purnama, Irwan Rospawan, Ali Samsuri, Faisal Setiyadi, Surawan Vincent Vincent, Vincent