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All Journal Journal of Fuzzy Systems and Control (JFSC)
Thanh-Binh Nguyen
Ho Chi Minh City University of Technology and Engineering (HCM-UTE)
Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Intelligent Control for 2D-Crane System Trung-Son Huynh; Dang-Khoa Dinh; Trong-Bang Tran; Huu-Loc Dang; Dinh-Nguyen-Phuc Le; Hung-Thinh Bui; Hoang-Lam Le; Thanh-Binh Nguyen; Van-Hiep Nguyen; Le-Nhat-Minh Nguyen; Thien-Quoc Dang; Ngoc-Hung Nguyen; Thi-Ngoc-Thao Nguyen; Huynh-Duc Pham; Xuan-Tien Nguyen; Van-Dong-Hai Nguyen
Journal of Fuzzy Systems and Control Vol. 4 No. 1 (2026): Vol. 4 No. 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i1.350

Abstract

This paper presents an Intelligent Learning-based Control approach for a 2D Crane System, aiming to evaluate the learning capability of various intelligent techniques based on a baseline Fuzzy Logic Controller (FLC). The initial fuzzy controller is designed for position and sway control, while Genetic Algorithm (GA), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) are employed in simulation to retrain and enhance its performance. Comparative results show that intelligent learning methods can significantly improve system response, reduce overshoot, and increase robustness compared to the original fuzzy controller. Moreover, an experimental setup using the baseline FLC is implemented to verify the practical effectiveness of the fuzzy control approach on a real 2D crane system. The findings highlight the potential of intelligent learning techniques for future real-time implementation.
A Study of a Laser Engraving System Based on a Cartesian Robot with Image Processing Thai-Duong Hoang; Manh-Dung Nguyen; Chi-Phat Pham; Thi-Ngoc-Thao Nguyen; Tan-Phat Nguyen; Anh-Son Tran; Quang-Thuan Le; Phuoc-Thinh Dang; Thai-Hiep Nguyen; Quang-Tung Trinh; Hoai-Bao-Nhan Nguyen; Huu-Nhan Nguyen; Thanh-Binh Nguyen
Journal of Fuzzy Systems and Control Vol. 4 No. 1 (2026): Vol. 4 No. 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i1.361

Abstract

Traditional CNC laser engraving systems often face limitations in flexibility, requiring manual alignment and pre-defined G-code files. This paper proposes an advanced automated laser engraving system based on a 3-axis Cartesian robot that bridges the gap between industrial control reliability and modern computer vision. The core novelty of this research lies in the seamless integration of a Mitsubishi Q03UDE Programmable Logic Controller (PLC) with a Python-based image processing framework. By utilizing the OpenCV library for real-time edge detection and trajectory generation, the system can autonomously identify object positions and convert complex patterns into precise motion commands. Communication is established via the MC Protocol over Ethernet, ensuring high-speed data synchronization between the vision system and the servo-driven hardware. Experimental results demonstrate that the proposed system achieves high precision in engraving, significantly reduces setup time by eliminating manual calibration, and maintains the robust stability required for industrial environments. This approach provides a scalable solution for intelligent manufacturing and personalized production.
Driver Drowsiness Detection and Warning System Using Computer Vision and Neural Networks on Embedded Platforms Chi-Phat Pham; Quang Tran; Binh-Hau Nguyen; Van-Dong-Hai Nguyen; Thi-Hong-Lam Le; Ngoc-Hung Nguyen; Van-Hiep Nguyen; Thanh-Binh Nguyen; Thi-Ngoc-Thao Nguyen; Hoang-Lam Le
Journal of Fuzzy Systems and Control Vol. 4 No. 2 (2026): Vol. 4 No. 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i2.372

Abstract

Driver drowsiness is one of the leading causes of traffic accidents worldwide. Traditional monitoring approaches, such as vehicle-based parameter analysis or physiological signal measurement, often require intrusive sensors or deep access to vehicle systems. To overcome these limitations, this paper proposes a real-time driver drowsiness detection and warning system using computer vision combined with a neural network classifier on an embedded platform. Facial landmarks are extracted using the dlib 68-point model, and the Eye Aspect Ratio (EAR) is computed to evaluate eye-closure behavior. A deep neural classifier is trained on eye-state and temporal EAR sequences collected from 25 subjects to classify normal and drowsy conditions. The system is deployed on a Raspberry Pi 3 B+ embedded platform, integrated with an Arduino-based alarm module to deliver audio–visual alerts when drowsiness is detected. Experimental results demonstrate a training accuracy of 98.4% and a testing accuracy of 92.8% with real-time performance of 15–20 FPS under daylight conditions, stable performance in real time, and feasibility for installation in passenger cars, trucks, and buses. The proposed method contributes a low-cost, efficient, and deployable solution for reducing road accidents with a focus on lightweight embedded implementation.
Co-Authors Anh-Son Tran Binh-Hau Nguyen Chi-Phat Pham Dang-Khoa Dinh Dinh-Nguyen-Phuc Le Hoai-Bao-Nhan Nguyen Hoang-Lam Le Hung-Thinh Bui Huu-Loc Dang Huu-Nhan Nguyen Huynh-Duc Pham Le-Nhat-Minh Nguyen Manh-Dung Nguyen Ngoc-Hung Nguyen Phuoc-Thinh Dang Quang Tran Quang-Thuan Le Quang-Tung Trinh Tan-Phat Nguyen Thai-Duong Hoang Thai-Hiep Nguyen Thi-Hong-Lam Le Thi-Ngoc-Thao Nguyen Thien-Quoc Dang Trong-Bang Tran Trung-Son Huynh Van-Dong-Hai Nguyen Van-Hiep Nguyen Xuan-Tien Nguyen