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All Journal Journal of Fuzzy Systems and Control (JFSC) Control Systems and Optimization Letters
Binh-Hau Nguyen
Posts and Telecommunications Institute of Technology (PTIT)
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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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.
PLC-Based Trajectory Control of an Omnidirectional Mecanum-Wheel AGV Using Visual–Inertial Tracking and Grid-Based A* Path Planning Binh-Hau Nguyen; Hang-Ri Nguyen; Phuoc-Duy Nguyen; Trung-Kien Pham; Dang-Khoa Tran; Quoc-Trung Nguyen; Tu-Duc Nguyen; Thi-Ngoc-Thao Nguyen; Thi-Ngoc-Hieu Phu; Hoang-Lam Le; Hoang-Phuc Le; Cong-Tan Bien
Control Systems and Optimization Letters Vol 4, No 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i2.318

Abstract

This paper presents a PLC-based omnidirectional Automated Guided Vehicle (AGV) using four Mecanum wheels for indoor material transportation. The system integrates a Mitsubishi Q-series PLC for motion execution, an Intel RealSense T265 visual–inertial camera for relative pose tracking, and a Python-based supervisory interface running on a laptop. The AGV operates in a known static workspace represented by a predefined grid map, where each cell corresponds to 0.1 m in the real environment. Static obstacles, pickup points, and drop-off points are manually defined before operation. The A* algorithm generates collision-free waypoint paths, which are converted into wheel velocity commands through the inverse kinematic model and transmitted to the PLC via Ethernet-based MC-Protocol. Experimental tests at a commanded speed of 0.2 m/s show that the prototype can perform eight-directional motion and complete predefined pickup–delivery tasks. The main limitations include tracking drift of the T265 camera, lighting sensitivity, wheel slippage on tiled floors, and communication latency. Future work will focus on sensor fusion, real-time obstacle detection, and fail-safe mechanisms.
Real-Time Trajectory Tracking Control of a DC Motor Using a Self-Tuning Regulator with Online Parameter Estimation Quang-Thien Nguyen; Hoang-Linh Le; Anh-Huy Nguyen; Duc-Anh-Quan Nguyen; Van-Dong-Hai Nguyen; Minh-Tam Nguyen; Van-Hiep Nguyen; Thanh-Binh Nguyen; Phuong-Quang Nguyen; Thi-Hong-Lam Le; Binh-Hau Nguyen; Dinh-Minh Vu
Control Systems and Optimization Letters Vol 4, No 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i1.270

Abstract

An adaptive Self-Tuning Regulator (STR) is developed for DC motor control to address performance degradation caused by load disturbances and parameter uncertainties. The method combines online system identification using recursive least squares (RLS) with automatic controller retuning in discrete time. The motor dynamics are continuously estimated and used to update the controller parameters through a pole-placement (or minimum-variance) design, thereby maintaining the desired closed-loop response without manual gain adjustment. The STR is implemented in real time and tested under speed reference changes and varying load torque. Results confirm that the proposed approach enhances tracking performance and disturbance rejection compared with conventional fixed-gain control, making it suitable for practical DC drive systems operating under changing conditions.
Co-Authors Anh-Huy Nguyen Chi-Phat Pham Cong-Tan Bien Dang-Khoa Tran Dinh-Minh Vu Duc-Anh-Quan Nguyen Hang-Ri Nguyen Hoang-Lam Le Hoang-Linh Le Hoang-Phuc Le Minh-Tam Nguyen Ngoc-Hung Nguyen Phuoc-Duy Nguyen Phuong-Quang Nguyen Quang Tran Quang-Thien Nguyen Quoc-Trung Nguyen Thanh-Binh Nguyen Thi-Hong-Lam Le Thi-Ngoc-Hieu Phu Thi-Ngoc-Thao Nguyen Trung-Kien Pham Tu-Duc Nguyen Van-Dong-Hai Nguyen Van-Hiep Nguyen