HUIT Journal of Science
type: channel
description: Welcome to HUIT Journal of Science ✨ Officially Licensed The HUIT Journal of Science, bearing p-ISSN 3030-4113 and e-ISSN 3030-413X, was officially licensed by the Ministry of Information and Communications for print and electronic publication on March 6th, 2024.
publisher: Trường Đại học Công Thương Thành phố Hồ Chí Minh
language: en
publication name: HUIT Journal of Science
issn: 3030-4113
items: A SINGLE-STAGE DIFFERENTIAL BOOST DC-AC INVERTER WITH SIMPLIFIED CONTROL FOR ON-BOARD ELECTRIC VEHICLE CHARGERS
items: EXPERIMENTAL EVALUATION OF CLASSICAL BACKSTEPPING AND RBF NEURAL NETWORK BASED BACKSTEPPING ON AN INVERTED PENDULUM SYSTEM
items: METACOGNITIVE REGIME-SWITCHED SPIKING NEURAL NETWORK WITH CONTRASTIVE MEMORY CONSOLIDATION
items: ROBUST MODBUS-RTU COMMUNICATION DESIGN FOR SAFETY-CRITICAL COMPRESSOR VFD CONTROL IN SUB-ZERO COLD STORAGE
items: A DEEP LEARNING APPROACH FOR DRIVER FATIGUE DETECTION
items: A LIGHTWEIGHT MACHINE LEARNING APPROACH FOR ALCOHOL CONCENTRATION ESTIMATION USING AN ESP32 AND MEMS GAS SENSOR
items: COMPARATIVE ANALYSIS OF LOSS FUNCTIONS FOR IMAGE-TEXT MATCHING UNDER NOISY CORRESPONDENCE
items: MACHINE LEARNING-BASED DRIVETRAIN FAULT CLASSIFICATION IN NEW ENERGY VEHICLES
items: OPTICAL WIRELESS POWER TRANSFER BETWEEN OBLIQUE PLANES OF TRANSMITTERS AND RECEIVERS
items: ADAPTIVE COLOR SORTING ROBOT APPLYING MACHINE LEARNING ON EMBEDDED SYSTEMS
items: A PRACTICAL APPROACH TO ELECTRONIC COMPONENT DETECTION ON PCBs USING YOLOv11 DEEP LEARNING MODEL
items: LOAD POWER PREDICTION IN SMART SOLAR MICROGRID BASED GATED RECURRENT UNIT
items: TRAJECTORY OPTIMIZATION OF ROBOTS VIA MODEL PREDICTIVE CONTROL AND REINFORCEMENT LEARNING
items: RESEARCH ON A HAND GESTURE RECOGNITION MODEL TO OPTIMIZE ROBOTIC HAND CONTROL
items: DEEP LEARNING-BASED VISION SYSTEM FOR AUTOMATED NIPA PALM FLESH SEPARATOR USING YOLOv11 AND OPC-UA INTEGRATION
items: AI-BASED FILTERING OF VESSEL-INDUCED NOISE IN SIPHON-TYPE RIVER WATER LEVEL MEASUREMENTS AT PHU AN HYDROLOGICAL STATION
items: OPTIMIZING UAV PID FLIGHT CONTROLLERS WITH REINFORCEMENT LEARNING: A TD3 ALGORITHM APPROACH
items: A REVIEW OF UNDERWATER ACOUSTIC TARGET RECOGNITION IN THE ERA OF ARTIFICIAL INTELLIGENCE
items: UNDERSTANDING WHOLESALE CLIENT BEHAVIOR AT A VIETNAMESE GARMENT OUTSOURCING FACTORY
items: ORITRIP: ENHANCING PERSON RE-IDENTIFICATION WITH ORIENTATION-AWARE TRIPLET LOSS
items: COMPUTER VISION BY YOLOs FOR DETECTION OF MAJOR DISEASES IN VIETNAMESE CUSTARD APPLE USING FRUIT IMAGERY
items: ADAPTIVE VIRTUAL SYNCHRONOUS MACHINE PARAMETER TUNING FOR REGFM-B1 GRID-FORMING INVERTERS UNDER CURRENT SATURATION CONSTRAINTS
items: LOW-COST MONITORING SYSTEM FOR EARLY FAULT DETECTION IN 3S LITHIUM-ION BATTERIES
items: AN ADAPTIVE NEURAL PI CONTROL FRAMEWORK USING DEEP REINFORCEMENT LEARNING FOR HIGH-PERFORMANCE PMSM SPEED DRIVES
items: ENERGY-AWARE PATH PLANNING FOR A QUADCOPTER USING GENERALIZED PARTICLE SWARM OPTIMIZATION
items: GENERALIZED FAULT TOLERANT CONTROL METHOD FOR DUAL THREE-PHASE PMSM USING PERMUTATION REDUCED-ORDER MATRIX
items: ELECTROMAGNETIC VIBRATION ANALYSIS OF POWER TRANSFORMER WINDINGS: VERIFICATION OF STRUCTURAL MODELS BASED ON ANALYTICAL MODELING
items: APPLICATION OF DC-DC CONVERTER IN CONTROLLING THE ELECTRIC DRIVE SYSTEM OF THE СБЩ-250MH ROTARY BLAST HOLE DRILL
items: RESEARCH ON METHODS TO IMPROVE THE TORQUE CHARACTERISTICS OF SWITCHED RELUCTANCE MOTORS
items: ANALYSIS OF FLUX BARRIER EFFECTS ON MAGNETIC FLUX DISTRIBUTION AND TORQUE CHARACTERISTICS IN SPOKE-TYPE INTERIOR PERMANENT MAGNET SYNCHRONOUS MOTORS

A SINGLE-STAGE DIFFERENTIAL BOOST DC-AC INVERTER WITH SIMPLIFIED CONTROL FOR ON-BOARD ELECTRIC VEHICLE CHARGERS
type: item
description: This paper proposes a single-stage DC-AC boost inverter topology for low-voltage DC sources, addressing the limitations of conventional two-stage converters that utilize separate boost and inversion stages, which result in increased complexity, cost, and power losses. The proposed topology achieves simultaneous DC voltage boosting and AC inversion within a single power conversion stage. Detailed analysis of the operating principles and switching states is presented, leading to the development of a comprehensive boost inverter model that incorporates capacitor voltage and inductor current controllers. Performance validation is conducted through simulations under varying input voltage and load conditions, demonstrating robust voltage boosting capability and stable sinusoidal output. Additionally, a low-power hardware prototype is implemented using a TMS320F28379D microcontroller for digital control. Experimental results confirm the inverter's capabilities in voltage boosting, reliable inversion, stable output regulation, and generation of sinusoidal waveforms with low total harmonic distortion.
subject: Electricity - Electronics - Automation
  topic: section
subject: DC–AC conversion
  topic: keywords
subject: Differential boost inverter
  topic: keywords
subject: Output voltage control
  topic: keywords
subject: Single-phase inverter
  topic: keywords
subject: TMS320F28379D
  topic: keywords
creator: Trong-Tri Nguyen
creator: Quoc-Anh Le
creator: Hoang-Vu Nguyen
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-12
publication date: 2026-06-12
starting page: 527
ending page: 527
doi: 10.62985/j.huit_ojs.vol26.no2E.425

EXPERIMENTAL EVALUATION OF CLASSICAL BACKSTEPPING AND RBF NEURAL NETWORK BASED BACKSTEPPING ON AN INVERTED PENDULUM SYSTEM
type: item
description: This paper presents an experimental comparison between the classical Backstepping controller and an improved Backstepping design incorporating Radial Basis Function (RBF) neural networks on an inverted pendulum system. While the conventional Backstepping method ensures stability based on a known mathematical model, its performance is sensitive to parameter uncertainties and unmodeled nonlinearities. The RBF-enhanced Backstepping controller addresses these limitations by compensating for unknown dynamics through real-time neural network approximation. Experimental results on an STM32F4 embedded platform indicate that the Backstepping-RBF controller achieves faster stabilization, reduced oscillations, and smoother control signals compared with the classical Backstepping method.
subject: Electricity - Electronics - Automation
  topic: section
subject: Backstepping
  topic: keywords
subject: RBF neural networks
  topic: keywords
subject: nonlinear control
  topic: keywords
subject: inverted pendulum
  topic: keywords
creator: Khuong Huynh Van
creator: Anh Nguyen The
creator: Xuan Bui Thanh
creator: Thang Doan Cong
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 516
ending page: 516
doi: 10.62985/j.huit_ojs.vol26.no2E.424

METACOGNITIVE REGIME-SWITCHED SPIKING NEURAL NETWORK WITH CONTRASTIVE MEMORY CONSOLIDATION
type: item
description: Continual learning in Spiking Neural Networks (SNNs) remains a significant challenge due to the plasticity-stability dilemma, often leading to catastrophic forgetting. In this paper, we propose a novel computational architecture titled Metacognitive Regime-Switched SNN (MRS-SNN), which integrates a metacognitive control mechanism into a Complementary Learning Systems framework. The core of our architecture is the Regime-Switching Markov (RMS) controller, acting as a metacognitive agent [8] that monitors prediction errors in real-time to modulate gating signals. This allows the system to autonomously switch between a "Plasticity" mode (upon novelty detection) and a "Stability" mode (for familiar data). To address the cold-start problem and weight saturation inherent in noisy neuromorphic data, we introduce a Contrastive Memory Consolidation learning rule. This rule combines Gated Trace Learning with Subtractive Normalization, enabling sharp, one-shot feature extraction. Experimental validation on the N-MNIST dataset confirms that MRS-SNN maintains superior reliability across sequential tasks, with accuracy metrics reaching 83% for old tasks and 99% for new tasks (Global Avg: ~93%). Furthermore, through a simulated Wake-Sleep cycle, the system successfully consolidates memories from a short-term store (artificial Hippocampus) to a long-term store (artificial Neocortex), offering a promising solution for self-adaptive Edge AI systems.
subject: Electricity - Electronics - Automation
  topic: section
subject: Spiking neural networks
  topic: keywords
subject: metacognition
  topic: keywords
subject: regime-switching
  topic: keywords
subject: continual learning
  topic: keywords
subject: memory consolidation
  topic: keywords
subject: N-MNIST
  topic: keywords
creator: Van Nhan Tran
creator: Thi Hong Minh Bui
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 506
ending page: 506
doi: 10.62985/j.huit_ojs.vol26.no2E.423

ROBUST MODBUS-RTU COMMUNICATION DESIGN FOR SAFETY-CRITICAL COMPRESSOR VFD CONTROL IN SUB-ZERO COLD STORAGE
type: item
description: This paper presents a robust Modbus-RTU communication design for safety-critical variable-frequency drive (VFD) control in an industrial sub-zero cold storage refrigeration system. The target platform is a Siemens S7-1200 PLC with RTD temperature acquisition and an RS-485 Modbus-RTU network interfacing VFD-driven compressors over a 100-meter shielded cable installation. The proposed design integrates (i) deterministic polling and command scheduling under a fixed 250 ms communication cycle, (ii) bounded retry with exception-aware error handling distinguishing four fault types, and (iii) a watchdog-based safe-fallback stra tegy with temperature-threshold control to preserve thermal safety during communication degradation. A communication state machine is formulated to classify failures into transient timeouts, CRC errors, bus-off conditions, and Modbus exception responses, each mapped to a specific mitigation and recovery procedure. In addition, practical RS-485 physical-layer configurations-including baud rate selection (9600 bps), parity settings (8N1), termination (120Ω), biasing, bus topology, and grounding-are investigated through design analysis and trade-off evaluation to determine optimal parameters for frame error minimization and recovery performance. Field deployment observations, including cable disconnection, power supply disturbances, and electromagnetic interference scenarios, are presented to evaluate detection latency, recovery time, and temperature stability. Operational experience since November 2025 indicates that the proposed communication framework improves resilience and supports stable refrigeration operation with temperature control within ±2°C during transient industrial disturbances.
subject: Electricity - Electronics - Automation
  topic: section
subject: Modbus-RTU
  topic: keywords
subject: safety-critical control
  topic: keywords
subject: VFD compressor
  topic: keywords
subject: RS-485 communication
  topic: keywords
subject: industrial refrigeration
  topic: keywords
subject: fault tolerance
  topic: keywords
creator: Doan Cong Thang
creator: Nguyen Thanh Thao
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 491
ending page: 491
doi: 10.62985/j.huit_ojs.vol26.no2E.422

A DEEP LEARNING APPROACH FOR DRIVER FATIGUE DETECTION
type: item
description: Driver fatigue is a major contributing factor to traffic accidents worldwide, motivating the development of reliable, non-invasive, and real-time monitoring systems. This paper presents a vision-based driver fatigue detection system built upon the YOLO (You Only Look Once) deep learning framework. The proposed system integrates face detection and eye-state classification (open/closed) to infer driver fatigue from monocular video streams captured by a standard camera. Compared with traditional fatigue detection approaches based on physiological signals or vehicle dynamics, the proposed method requires no wearable sensors and exhibits low deployment cost and high practical applicability. YOLO is adopted as the core detection model due to its anchor-free design, efficient feature extraction, and fast inference speed. A customized dataset is constructed by combining public datasets with self-collected images, augmented through random background fusion, scaling, and rotation to enhance robustness and generalization. Experimental results demonstrate that the proposed system achieves high detection accuracy and stable real-time performance under typical driving conditions. The results indicate that the proposed approach is suitable for real-world driver monitoring applications and can serve as a foundation for more advanced fatigue detection systems.
subject: Electricity - Electronics - Automation
  topic: section
subject: Driver fatigue detection
  topic: keywords
subject: deep learning
  topic: keywords
subject: eye state recognition
  topic: keywords
subject: computer vision
  topic: keywords
subject: YOLO
  topic: keywords
creator: Li Zhenyang
creator: Nguyen Dinh Hoa
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 481
ending page: 481
doi: 10.62985/j.huit_ojs.vol26.no2E.421

A LIGHTWEIGHT MACHINE LEARNING APPROACH FOR ALCOHOL CONCENTRATION ESTIMATION USING AN ESP32 AND MEMS GAS SENSOR
type: item
description: This paper presents a low-cost alcohol concentration measurement system based on an ESP32 microcontroller and a GM-302B MEMS alcohol gas sensor. The sensor output is sampled in analog mode to capture the time-domain response to ethanol vapor. From this response, a set of six features is extracted, including peak value, peak time, response slope, area under the curve, ambient temperature, and humidity. Alcohol concentration is estimated using a lightweight regression model implemented as a small fully connected neural network with two hidden layers. The trained model is converted to TensorFlow Lite for Microcontrollers and embedded directly into the ESP32 firmware using a TinyML framework, enabling on-device real-time inference without external computation. Experimental results indicate that the proposed system provides a practical and accessible solution for fermentation monitoring and educational research, offering an effective alternative to conventional alcohol measurement instruments.
subject: Electricity - Electronics - Automation
  topic: section
subject: Lightweight machine learning
  topic: keywords
subject: MEMS
  topic: keywords
subject: TinyML
  topic: keywords
subject: alcohol concentration estimation
  topic: keywords
creator: Minh-Hai Le
creator: Duc-Toan Nguyen
creator: Huu-Phuc Dang
creator: Song-Toan Tran
creator: Phuong-Thao Cao
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 471
ending page: 471
doi: 10.62985/j.huit_ojs.vol26.no2E.420

COMPARATIVE ANALYSIS OF LOSS FUNCTIONS FOR IMAGE-TEXT MATCHING UNDER NOISY CORRESPONDENCE
type: item
description: Image–Text Matching (ITM) plays an important role in vision–language applications such as cross-modal retrieval. However, real-world datasets often contain noisy correspondence, where image–text pairs are incorrectly aligned or only partially related, which can degrade model performance. In this paper, we conduct a comparative analysis of common loss functions for ITM under noisy conditions, including Triplet Loss and InfoNCE. We further introduce a new objective, Similarity-based Negative Log-Likelihood (SNLL), which formulates image–text alignment as a probabilistic binary classification over all pairwise similarities. Experiments on the MS-COCO dataset under different noise levels show that while all methods perform similarly on clean data, SNLL achieves more stable training and higher retrieval performance when noise increases, demonstrating stronger robustness to noisy correspondence.
subject: Electricity - Electronics - Automation
  topic: section
subject: Text-to-Image
  topic: keywords
subject: Cross-Modality
  topic: keywords
subject: Noisy Correspondence
  topic: keywords
subject: Contrastive objectives
  topic: keywords
subject: Similarity-based Negative Log-Likelihood
  topic: keywords
creator: Tam T. Ngo
creator: Anh V. Nguyen
creator: Hoa N. Nguyen
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 463
ending page: 463
doi: 10.62985/j.huit_ojs.vol26.no2E.419

MACHINE LEARNING-BASED DRIVETRAIN FAULT CLASSIFICATION IN NEW ENERGY VEHICLES
type: item
description: New Energy Vehicles (NEVs) represent an important direction in the development of sustainable transportation. The drivetrain of NEVs operates under changing electrical, mechanical, and environmental conditions, which can affect system stability and component integrity. Faults in the motor, inverter, and battery can interrupt power delivery, reduce operational continuity, and create risks for vehicle performance. This study presents an evaluation of machine learning methods for classifying drivetrain operating states using a structured dataset that contains measurements of voltage, current, motor speed, temperature, vibration, ambient temperature, and humidity. Three ensemble tree-based machine learning models, Random Forest, XGBoost, and CatBoost, are developed to classify four labeled states: normal condition, motor fault, inverter fault, and battery fault. Each model is trained and tested using common performance indicators, including accuracy, precision, recall, F1 score, and computation time. The experimental results demonstrate that ensemble learning models achieve high and stable classification performance across all drivetrain operating conditions, with XGBoost exhibiting the best balance between accuracy and computational efficiency. To enhance practical applicability, a real-time fault diagnosis demonstration is implemented, confirming the model's suitability for onboard systems. The findings indicate that machine learning can support automatic identification of drivetrain faults and contribute to diagnostic procedures, maintenance planning, and system reliability improvement in NEVs applications.
subject: Electricity - Electronics - Automation
  topic: section
subject: New Energy Vehicles Drivetrain
  topic: keywords
subject: Fault Classification
  topic: keywords
subject: XGBoost
  topic: keywords
subject: CatBoost
  topic: keywords
subject: Random Forest
  topic: keywords
creator: Long Giang Nguyen Van
creator: Trung Kien Nguyen
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 452
ending page: 452
doi: 10.62985/j.huit_ojs.vol26.no2E.418

OPTICAL WIRELESS POWER TRANSFER BETWEEN OBLIQUE PLANES OF TRANSMITTERS AND RECEIVERS
type: item
description: This paper studies an optical wireless power transfer (OWPT) system, in which the transmitter is a set of LEDs belonging to a plane and the receiver is a small solar panel assumed to move within another plane that is not parallel with the transmitter plane. Owing to the nonlinearity and non-convexity of the DC gain of the OWPT efficiency from each LED to the solar panel, the transmission efficiency of the overall OWPT system is also a nonlinear and non-convex function of the transmitting distances. It is therefore challenging to determine the optimal relative position between the LEDs and the solar panel such that the OWPT efficiency of the whole system is maximal. Some initial results on this problem will be presented in current research with simulations and demonstrated with realistic experiments.
subject: Electricity - Electronics - Automation
  topic: section
subject: Optical wireless power transfer
  topic: keywords
subject: LEDs
  topic: keywords
subject: solar cells
  topic: keywords
subject: nonlinear optimization
  topic: keywords
subject: non-convex optimization
  topic: keywords
creator: Hoa Dinh Nguyen
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 441
ending page: 441
doi: 10.62985/j.huit_ojs.vol26.no2E.417

ADAPTIVE COLOR SORTING ROBOT APPLYING MACHINE LEARNING ON EMBEDDED SYSTEMS
type: item
description: Automation in manufacturing relies heavily on accurate product classification. However, affordable color sensors often suffer from interference caused by ambient lighting changes, leading to errors in traditional systems based on fixed thresholds. This study presents an adaptive sorting robot arm integrated with a Programmable Logic Controller and an embedded microcontroller. The novelty of this research lies in the implementation of the K Nearest Neighbors machine learning algorithm directly on the microcontroller to process color data. Instead of using fixed logic, the system features a calibration mode where it learns reference color features in the current environment. During operation, the algorithm calculates the Euclidean distance between the detected object and the learned samples to perform classification. Experimental results demonstrate that this approach significantly improves recognition accuracy under varying lighting conditions compared to methods using static logic, offering a robust and economical solution for industrial automation.
subject: Electricity - Electronics - Automation
  topic: section
subject: Adaptive Color Sorting
  topic: keywords
subject: K-Nearest Neighbors
  topic: keywords
subject: Embedded Machine Learning
  topic: keywords
subject: PLC
  topic: keywords
subject: Industrial Automation
  topic: keywords
creator: Dan-Huy Truong
creator: Hien-Nam Phan Phuc
creator: Thanh-Tuan Nguyen
creator: Xuan-Huy Nguyen
creator: Mong-Fong Horng
creator: Chin-Shiuh Shieh
creator: Thanh-Lam Nguyen
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 432
ending page: 432
doi: 10.62985/j.huit_ojs.vol26.no2E.416

A PRACTICAL APPROACH TO ELECTRONIC COMPONENT DETECTION ON PCBs USING YOLOv11 DEEP LEARNING MODEL
type: item
description: In the field of PCB manufacturing, Automated Optical Inspection (AOI) is critical for defect detection. However, traditional methods often fail under varying lighting and complex background conditions. To overcome these issues, this paper presents a deep learning-based approach using the YOLOv11 model for robust electronic component detection. We establish a complete workflow for identifying components such as resistors, capacitors, and ICs, validated through a custom dataset. A key contribution of this work is the comprehensive evaluation of the YOLOv11 architecture specifically optimized for PCB component identification. Validated through a custom dataset, the YOLOv11 model achieved a robust accuracy with an mAP@0.5 of 97.5% on standard hardware. This study demonstrates the feasibility of utilizing YOLOv11 for high-precision Automated Quality Control (QC) in PCB manufacturing. This study demonstrates the feasibility of YOLOv11 in industrial environments and provides a framework for future optimization studies.
subject: Electricity - Electronics - Automation
  topic: section
subject: YOLOv11
  topic: keywords
subject: Automated Optical Inspection (AOI)
  topic: keywords
subject: Electronic Component Detection
  topic: keywords
subject: Printed Circuit Board (PCB)
  topic: keywords
creator: Xuan-Huy Nguyen
creator: Khai Hoan Nhu
creator: Xuan-Giang Nguyen
creator: Thanh-Tuan Nguyen
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 426
ending page: 426
doi: 10.62985/j.huit_ojs.vol26.no2E.415

LOAD POWER PREDICTION IN SMART SOLAR MICROGRID BASED GATED RECURRENT UNIT
type: item
description: Accurate load power prediction plays a critical role in smart solar microgrids by enabling efficient energy management and optimal power flow control. This paper proposes a load power forecasting approach based on the Gated Recurrent Unit (GRU) neural network to address the nonlinear and time-dependent characteristics of microgrid load profiles. The proposed GRU model is optimized using a grid search strategy to systematically determine key hyperparameters, thereby enhancing prediction accuracy and model stability. Historical load and operational data from a smart solar microgrid are utilized for model training and validation. To demonstrate the effectiveness of the proposed approach, the GRU model is compared with a traditional Recurrent Neural Network (RNN). Experimental results indicate that the GRU-based model consistently outperforms the RNN in terms of evaluation metrics, including loss, mean absolute error (MAE), and mean absolute percentage error (MAPE). The findings confirm that the optimized GRU model provides an effective and reliable tool for load power prediction, supporting intelligent control and energy management in smart solar microgrid systems.
subject: Electricity - Electronics - Automation
  topic: section
subject: Load power prediction
  topic: keywords
subject: gated recurrent unit
  topic: keywords
subject: smart solar microgrid
  topic: keywords
subject: recurrent neural network
  topic: keywords
creator: Thuc-Minh Bui
creator: Huong Le Thi
creator: Trang Nguyen Thi Thu
creator: Van Pham Thi
creator: Thao Nguyen Da
creator: Phuong Nguyen Thanh
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 415
ending page: 415
doi: 10.62985/j.huit_ojs.vol26.no2E.414

TRAJECTORY OPTIMIZATION OF ROBOTS VIA MODEL PREDICTIVE CONTROL AND REINFORCEMENT LEARNING
type: item
description: Trajectory optimization for industrial robots remains a critical challenge due to complex kinematic constraints, environmental disturbances, and strict real-time performance requirements in modern automation systems. This study proposes a hybrid control framework that combines Model Predictive Control (MPC) with Reinforcement Learning (RL) to generate efficient and robust robot trajectories. MPC is employed to perform short-horizon optimization under explicit system constraints, ensuring precise and feasible motion planning, while a Reinforcement Learning strategy based on Proximal Policy Optimization (PPO) is integrated to learn long-term adaptive policies capable of compensating for uncertainties such as sensor noise and variable payload conditions. The synergy between MPC and RL enables improved motion accuracy, faster response, and reduced energy consumption. Simulation and experimental results validate the effectiveness of the proposed approach, demonstrating notable performance improvements over conventional control strategies. Future work will focus on real-time embedded deployment of the framework for intelligent manufacturing applications.
subject: Electricity - Electronics - Automation
  topic: section
subject: Model Predictive Control
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Proximal Policy Optimization
  topic: keywords
creator: Le Anh Hao
creator: Vi-Do Tran
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 407
ending page: 407
doi: 10.62985/j.huit_ojs.vol26.no2E.413

RESEARCH ON A HAND GESTURE RECOGNITION MODEL TO OPTIMIZE ROBOTIC HAND CONTROL
type: item
description: Achieving accurate and responsive control of robotic hands remains a major challenge in human-machine interaction, particularly for applications requiring high dexterity such as prosthetic control and rehabilitation systems. Surface electromyography (sEMG) offers a non-invasive solution for capturing human motion intent; however, its nonlinear, non-stationary nature and susceptibility to noise significantly degrade recognition performance when using conventional methods. This paper proposes a robust sEMG-based hand gesture recognition framework that integrates systematic signal preprocessing, including noise filtering, normalization, and temporal segmentation, with a hybrid deep learning architecture. The proposed model combines a one-dimensional convolutional neural network (1D-CNN) for effective spatial and time–frequency feature extraction with a long short-term memory (LSTM) network to capture long-term temporal dependencies in multi-channel sEMG signals. Six fundamental hand gestures such as Rock, Paper, Scissors, Pointing, Hand Flexion, and Hand Relaxation are evaluated using a custom annotated sEMG dataset. Experimental results demonstrate that the proposed hybrid 1D-CNN–LSTM model achieves consistently high classification accuracies of 96.0% and 0.96 weighted F1-score, outperforming conventional 1D-CNN-based approaches relying solely on time-domain features. Furthermore, the proposed framework exhibits low computational complexity and strong inter-subject generalization, indicating its suitability for real-time robotic and prosthetic hand control applications.
subject: Electricity - Electronics - Automation
  topic: section
subject: Hand gesture recognition
  topic: keywords
subject: Surface electromyography (sEMG)
  topic: keywords
subject: 1D-CNN
  topic: keywords
subject: EMG classification
  topic: keywords
creator: Bui Thi Ngoc Han
creator: Vi-Do Tran
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 394
ending page: 394
doi: 10.62985/j.huit_ojs.vol26.no2E.412

DEEP LEARNING-BASED VISION SYSTEM FOR AUTOMATED NIPA PALM FLESH SEPARATOR USING YOLOv11 AND OPC-UA INTEGRATION
type: item
description: In line with the trend of sustainable development and the efficient exploitation of natural resources, the demand for processed products derived from agricultural produce is steadily increasing. With advancements in technology, the application of machinery and automation equipment in production has become an essential requirement to enhance productivity and product quality. In this study, the application of the YOLOv11 deep learning model combined with a Mitsubishi FX5U Programmable Logic Controller (PLC) via an OPC Unified Architecture (OPC-UA) server is proposed for a nipa palm flesh separation machine. The image of the nipa palm flesh is fed through the YOLOv11 model for analysis and quality assessment (immature, acceptable, mature). Based on the model's results, the corresponding signal is transmitted to the PLC to control the actuators that perform the separation of the coconut flesh. An OPC-UA intermediate server is used to enable two-way data exchange between the Python program on the computer and the PLC, while also facilitating easy expansion and integration with a monitoring system. The training results of the nipa palm flesh recognition model using YOLOv11 achieved a mAP50 of up to 97.3%. A multi-threading algorithm in Python allows for the simultaneous processing of coconut flesh quality recognition and data exchange with the PLC through the OPC-UA server. The research findings contribute to the construction and development of industrial systems integrated with image processing technology.
subject: Electricity - Electronics - Automation
  topic: section
subject: Nipa palm
  topic: keywords
subject: PLC
  topic: keywords
subject: OPC-UA server
  topic: keywords
subject: YOLOv11
  topic: keywords
creator: Song-Toan Tran
creator: Truc-Ly Le
creator: Phuc-Hau Nguyen
creator: Thien-Nhan Mai
creator: Quoc-Kien Lam
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 384
ending page: 384
doi: 10.62985/j.huit_ojs.vol26.no2E.411

AI-BASED FILTERING OF VESSEL-INDUCED NOISE IN SIPHON-TYPE RIVER WATER LEVEL MEASUREMENTS AT PHU AN HYDROLOGICAL STATION
type: item
description: Accurate river water level observations are fundamental for hydrological analysis, tidal assessment, and flood forecasting. At the Phu An Hydrological Station, water level measurements recorded by a siphon-type gauge system are frequently contaminated by high-frequency oscillations caused by vessel traffic on the river. These vessel-induced disturbances introduce significant noise into the observed time series, leading to errors in identifying tidal extrema and reducing the reliability of water level data, particularly under increasing hydrodynamic variability associated with climate change an artificial intelligence (AI)-based filtering framework to mitigate vessel-induced noise in river water level observations. The proposed approach integrates signal preprocessing and deep learning techniques to model the intrinsic temporal dynamics of natural river water levels while suppressing non-hydrological fluctuations. Specifically, a deep learning model is trained to learn the smooth and continuous behavior of water level variations driven by tides and river flow, enabling the reconstruction of a standardized and noise-free water level hydrograph from raw observations affected by vessel-induced disturbances. The experimental results demonstrate that the proposed AI-based method effectively reduces high-frequency noise and significantly improves data stability without distorting the underlying hydrological signal. The corrected water level series shows improved accuracy in capturing tidal peaks and troughs, thereby improving the reliability of hydrological observations at Phu An Station. The proposed methodology provides a practical and scalable solution for improving water level measurements at river stations influenced by intensive vessel traffic and offers strong support for hydrological forecasting, climate change impact assessment, and sustainable water resource management.
subject: Electricity - Electronics - Automation
  topic: section
subject: River water level monitoring
  topic: keywords
subject: Water level noise filtering
  topic: keywords
subject: Vessel-induced disturbance
  topic: keywords
subject: Time-series analysis
  topic: keywords
subject: Tidal water level
  topic: keywords
subject: Hydrological data quality
  topic: keywords
creator: Le Van Sang
creator: Le Van Phan
creator: Le Do Van Bang
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 374
ending page: 374
doi: 10.62985/j.huit_ojs.vol26.no2E.410

OPTIMIZING UAV PID FLIGHT CONTROLLERS WITH REINFORCEMENT LEARNING: A TD3 ALGORITHM APPROACH
type: item
description: This paper proposes a reinforcement learning-based approach to optimize PID control parameters for a quadcopter unmanned aerial vehicle (UAV), employing the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The model of the UAV is derived considering nonlinearity, exterior disturbances, and strong coupling. The learning agent adaptively updates the PID gains using a reward function that optimizes tracking deviations and excessive control effort. Simulation and comparisons with other methods indicate that the TD3-tuned scheme delivers fast trajectory tracking while enhancing stability, adaptability, and control robustness.
subject: Electricity - Electronics - Automation
  topic: section
subject: Reinforcement learning
  topic: keywords
subject: UAV
  topic: keywords
subject: PID controller
  topic: keywords
subject: learning-based control
  topic: keywords
creator: Minh Tu Nguyen
creator: Van Truong Hoang
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 363
ending page: 363
doi: 10.62985/j.huit_ojs.vol26.no2E.407

A REVIEW OF UNDERWATER ACOUSTIC TARGET RECOGNITION IN THE ERA OF ARTIFICIAL INTELLIGENCE
type: item
description: Underwater Acoustic Target Recognition (UATR) is a critical technology for maritime domain awareness, naval defense, and oceanographic monitoring. With the increasing shift of naval operations toward complex littoral environments, conventional passive sonar systems that rely heavily on human operators and classical signal processing methods are encountering substantial difficulties. These challenges stem primarily from low signal-to-noise ratios (SNR), multipath propagation effects, and highly non-stationary background interference. This paper presents a comprehensive review of the evolution of UATR technologies, progressing from traditional physics-based approaches such as LOFAR and DEMON to advanced deep learning frameworks. We critically analyze the limitations of conventional machine learning techniques (e.g., SVM and HMM) and evaluate how modern neural network architectures - including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), attention mechanisms, Transformers, and Generative Adversarial Networks (GANs) - have significantly enhanced feature extraction and classification performance in underwater environments. Furthermore, this review addresses key contemporary challenges, including data scarcity, model interpretability, adversarial vulnerability, and the deployment of efficient models on resource-constrained edge devices such as sonobuoys. By synthesizing recent advancements, we highlight existing research gaps and propose future directions to develop more robust, intelligent, and practical underwater acoustic surveillance systems.
subject: Electricity - Electronics - Automation
  topic: section
subject: Passive sonar
  topic: keywords
subject: deep learning
  topic: keywords
subject: feature extraction
  topic: keywords
subject: adversarial robustness
  topic: keywords
subject: edge computing
  topic: keywords
creator: Van Vuong Vu
creator: Chi Hieu Ta
creator: Truong Giang Bui
creator: Ngoc Dong Nguyen
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 353
ending page: 353
doi: 10.62985/j.huit_ojs.vol26.no2E.406

UNDERSTANDING WHOLESALE CLIENT BEHAVIOR AT A VIETNAMESE GARMENT OUTSOURCING FACTORY
type: item
description: To increase sales, businesses need to understand customer behavior in both wholesale and retail markets. This includes considering crucial factors like product type, timing, and location. This study, based on data from a Vietnamese garment outsourcing factory, reveals distinct differences in consumer behavior between the country's wholesale and retail sectors. The findings allowed us to categorize customers into two groups: Key accounts, who contribute over 50% of sales value and all other customers. The research also examines the unique needs of each group and proposes effective strategies for engaging with them. Ultimately, this study introduces a new approach for those working in Vietnam's wholesale and retail industries.
subject: Electricity - Electronics - Automation
  topic: section
subject: Consumer behavior
  topic: keywords
subject: outsourcing company
  topic: keywords
subject: Pearson correlation coefficient
  topic: keywords
subject: wholesale buyer
  topic: keywords
creator: Lu Thi Kim Phung
creator: Nguyen Duy Dat
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 343
ending page: 343
doi: 10.62985/j.huit_ojs.vol26.no2E.405

ORITRIP: ENHANCING PERSON RE-IDENTIFICATION WITH ORIENTATION-AWARE TRIPLET LOSS
type: item
description: Person re-identification faces real challenges when the same person appears in different poses and camera angles across multiple views. While standard triplet loss functions are effective for learning features that distinguish between individuals, they struggle with capturing spatial orientation details. This creates problems when the system needs to match the same person who appears at different angles or in different poses. This study presents an orientation-aware triplet loss function that incorporates human body orientation information during training. The approach builds on conventional triplet loss by adding orientation constraints that capture the geometric relationships between different poses and viewpoints of the same person. This orientation-aware mechanism can maintain consistent feature representations across varying body orientations while keeping the ability to distinguish between different individuals. Thorough evaluations on three popular person re-identification datasets showed performance gains when integrating the orientation-aware triplet loss with state-of-the-art algorithms. The results demonstrated better matching accuracy compared to methods using traditional triplet loss. In addition, the orientation-aware approach tackles key limitations in existing person re-identification systems by modeling the geometric properties of human subjects during feature learning. This research contributes to advancing person re-identification technology by providing a more effective loss function that better captures the inherent spatial characteristics of human appearance across different orientations.
subject: Electricity - Electronics - Automation
  topic: section
subject: Human body orientation
  topic: keywords
subject: metric learning
  topic: keywords
subject: person re-identification
  topic: keywords
subject: triplet loss
  topic: keywords
subject: viewpoint variation
  topic: keywords
creator: Trinh Quoc Nguyen
creator: Syahid Al Irfan
creator: Oky Dicky Ardiansyah Prima
creator: Tista Pal
creator: Nguyen Gia Minh Thao
creator: Dung Thi My Nguyen
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 332
ending page: 332
doi: 10.62985/j.huit_ojs.vol26.no2E.404

COMPUTER VISION BY YOLOs FOR DETECTION OF MAJOR DISEASES IN VIETNAMESE CUSTARD APPLE USING FRUIT IMAGERY
type: item
description: Amid Vietnam’s rapid digital transformation in agriculture, the integration of artificial intelligence (AI) technologies for identifying, monitoring, and predicting crop pests and diseases has emerged as an inevitable trend. Custard apple (Annona squamosa L.), a high-value fruit crop, suffers significant losses due to prevalent diseases, including Anthracnose, Black Canker, Diplodia rot, Leaf Spot, and Mealybug infestations. This paper proposes a robust computer vision framework that leverages state-of-the-art YOLO (You Only Look Once)-series object detection models to enable early and accurate detection of major diseases on custard apple leaves and fruits. The proposed system is designed for practical deployment through mobile and edge-device applications in the future, empowering farmers with timely, actionable diagnostics to minimize yield losses. Furthermore, we systematically evaluate and benchmark the performance of the latest YOLO variants (YOLOv8, YOLOv11, and YOLOv12) on a large-scale, real-world custard apple disease dataset collected under diverse field conditions in Vietnam. This work not only delivers a high-performance, deployable solution but also lays the groundwork for establishing a comprehensive digital database to advance AI-driven agricultural research in Vietnam.
subject: Electricity - Electronics - Automation
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: computer vision
  topic: keywords
subject: YOLO
  topic: keywords
subject: object detection
  topic: keywords
subject: custard apple diseases
  topic: keywords
creator: Tri Nhut Do
creator: Quoc Tuan Vo
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 321
ending page: 321
doi: 10.62985/j.huit_ojs.vol26.no2E.403

ADAPTIVE VIRTUAL SYNCHRONOUS MACHINE PARAMETER TUNING FOR REGFM-B1 GRID-FORMING INVERTERS UNDER CURRENT SATURATION CONSTRAINTS
type: item
description: The increasing penetration of inverter-based resources has significantly reduced effective system inertia, posing serious challenges to frequency stability in weak power systems. While virtual synchronous machine (VSM) control provides synthetic inertia, conventional fixed-parameter designs suffer from an inherent trade-off between frequency nadir and transient settling speed. Furthermore, many optimization-based tuning methods disregard the non-linear operational limits of power semiconductors, leading to non-smooth search spaces and sub-optimal local equilibria. This paper proposes a hardware-constrained adaptive VSM control framework compliant with the reference explicit grid-forming model B1 (REGFM-B1) standard, in which virtual inertia and damping parameters are optimized using a particle swarm optimization (PSO) algorithm with a time-varying inertia weight (TVIW) strategy. The inertia weight is gradually reduced from 0.9 to 0.4, enabling effective global exploration despite saturation-induced discontinuities while ensuring accurate local convergence. Nonlinear current-limiting constraints are explicitly embedded into the optimization process to guarantee physically realizable solutions. The proposed method is validated through MATLAB simulations on a 10 MVA grid-forming battery energy storage system under ten disturbance scenarios with short-circuit ratios ranging from 1.5 to 3.0. Results show a 98.1% improvement in frequency nadir and a 60.0% reduction in settling time compared with fixed-parameter benchmarks. Moreover, the proposed strategy achieved an average ITAE reduction of 7.52% relative to standard PSO and 6.44% compared to modern adaptive PSO (APSO) variants, while strictly maintaining a 1.2 pu current limit.
subject: Electricity - Electronics - Automation
  topic: section
subject: Virtual synchronous machine
  topic: keywords
subject: grid-forming inverter
  topic: keywords
subject: REGFM-B1
  topic: keywords
subject: time-varying Inertia weight
  topic: keywords
subject: particle swarm optimization
  topic: keywords
subject: current saturation
  topic: keywords
subject: weak power system
  topic: keywords
creator: Pham Hong Thanh
creator: Le Van Dai
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 305
ending page: 305
doi: 10.62985/j.huit_ojs.vol26.no2E.402

LOW-COST MONITORING SYSTEM FOR EARLY FAULT DETECTION IN 3S LITHIUM-ION BATTERIES
type: item
description: Lithium-ion batteries are widely used in two-wheel electric vehicles; however, safety risks such as cell imbalance, abnormal current behavior, and localized overheating remain critical concerns during operation. This study experimentally investigates early fault indicators in a 3S lithium-ion battery pack using multi-parameter electrical measurements. Individual cell voltages and pack current were acquired using calibrated instruments under both constant and dynamic load conditions to ensure reliable charging-discharging datasets. The results show that abnormal behaviors, including accelerated voltage drop, cell voltage divergence, and irregular current fluctuations can be detected several seconds before reaching unsafe thresholds. Based on these findings, a low-cost embedded monitoring architecture is proposed for future real-time applications. The system integrates an ESP32 microcontroller, resistor-divider circuits for cell voltage measurement, a bidirectional current sensor, and temperature sensors for continuous monitoring and early fault detection. With its simple design, low cost, and adaptability to both laboratory and practical conditions, the proposed approach offers a promising solution to enhance battery safety in affordable two-wheel electric vehicles.
subject: Electricity - Electronics - Automation
  topic: section
subject: Lithium-ion battery
  topic: keywords
subject: dynamic discharge load
  topic: keywords
subject: early fault detection
  topic: keywords
subject: cell-level monitoring
  topic: keywords
subject: low-cost battery system
  topic: keywords
creator: Chuyen Lam Quang
creator: Hung Nguyen Huy
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 294
ending page: 294
doi: 10.62985/j.huit_ojs.vol26.no2E.401

AN ADAPTIVE NEURAL PI CONTROL FRAMEWORK USING DEEP REINFORCEMENT LEARNING FOR HIGH-PERFORMANCE PMSM SPEED DRIVES
type: item
description: High-performance PMSM speed control remains challenging due to parameter uncertainties, nonlinear dynamics, and load disturbances. Although conventional PI controllers are widely adopted, their fixed gains limit adaptability under varying operating conditions. To overcome this, this paper proposes an adaptive DRL-based neural PI control framework for PMSM drives. The method integrates a DDPG algorithm with an RBF neural network for continuous online gain tuning. Furthermore, an anti-windup mechanism and a reference model–based augmentation are incorporated to ensure closed-loop stability under actuator saturation. The learning objective is formulated to optimize tracking accuracy, transient performance, and disturbance rejection. Simulation results demonstrate that the proposed framework significantly outperforms fixed-gain PI controllers, minimizing settling time, overshoot, and steady-state error under ideal physical constraints. These findings confirm the framework's robustness and efficiency for advanced motor drives.
subject: Electricity - Electronics - Automation
  topic: section
subject: Permanent magnet synchronous motor
  topic: keywords
subject: adaptive PI control
  topic: keywords
subject: deep reinforcement learning
  topic: keywords
subject: DDPG
  topic: keywords
subject: neural networks
  topic: keywords
creator: Do Trung Khanh Cong
creator: Vo Ngoc Vinh
creator: Nguyen Minh Tam
creator: Le Tien Loc
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 279
ending page: 279
doi: 10.62985/j.huit_ojs.vol26.no2E.400

ENERGY-AWARE PATH PLANNING FOR A QUADCOPTER USING GENERALIZED PARTICLE SWARM OPTIMIZATION
type: item
description: This paper presents an integrated framework for energy-aware path planning of a quadcopter unmanned aerial vehicle (UAV). The ultimate objective is to generate a trajectory that simultaneously satisfies path-length constraints, collision avoidance, and mission goals while accounting for vehicle energy consumption. The path-planning objectives are combined into a multi-objective cost function to discover optimal solutions. The generalized particle swarm optimization (GEPSO) is then applied to manage the overall cost, considering real-world applications. Simulation and experimental results exhibit that the proposed framework achieves smoother trajectories and significant reductions in overall energy expenditure compared to energy-ignored planning strategies.
subject: Electricity - Electronics - Automation
  topic: section
subject: Path planning
  topic: keywords
subject: Particle swarm optimization
  topic: keywords
subject: Generalized PSO
  topic: keywords
subject: Obstacle avoidance
  topic: keywords
subject: Quadcopter
  topic: keywords
creator: Van Truong Hoang
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 267
ending page: 267
doi: 10.62985/j.huit_ojs.vol26.no2E.399

GENERALIZED FAULT TOLERANT CONTROL METHOD FOR DUAL THREE-PHASE PMSM USING PERMUTATION REDUCED-ORDER MATRIX
type: item
description: This paper proposes a generalized fault-tolerant control strategy for dual three-phase permanent-magnet synchronous machines (DT-PMSMs) based on a Permutation Reduced-Order Matrix Fault-Tolerant Control (PROM-FTC) method. The approach analytically characterizes the current relationships under any single open-phase fault by introducing a permutation matrix. This enables the automatic and accurate computation of the reference currents without relying on lookup tables or if–else structures. Regarding objective function, the stator copper loss minimization strategy is used to find the optimal currents. The proposed method is validated in MATLAB/Simulink through a sequence of six individual open-phase faults. Simulation results demonstrate that PROM-FTC maintains constant electromagnetic torque during all faulty cases. Additionally, an analysis of stationary current trajectories is conducted to characterize the behavior of each fault scenario, resulting in an effective approach for fault detection and localization. These results confirm the efficacy and generality of the proposed PROM-FTC strategy for DT-PMSMs.
subject: Electricity - Electronics - Automation
  topic: section
subject: Fault-tolerant control
  topic: keywords
subject: PMSM
  topic: keywords
subject: dual three-phase
  topic: keywords
subject: loss minimization
  topic: keywords
subject: electrical drive
  topic: keywords
creator: Pham Le Tam
creator: Nguyen Ngac Ky
creator: Phan Quoc Dung
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 256
ending page: 256
doi: 10.62985/j.huit_ojs.vol26.no2E.398

ELECTROMAGNETIC VIBRATION ANALYSIS OF POWER TRANSFORMER WINDINGS: VERIFICATION OF STRUCTURAL MODELS BASED ON ANALYTICAL MODELING
type: item
description: Excessive vibration in power transformers is a critical issue that compromises mechanical integrity, may result in insulation failure, and generates significant acoustic noise. Consequently, establishing a reliable method to quantify and predict these vibrations during the design phase is essential. This paper proposes a rigorous multiphysics simulation framework to analyze the vibration characteristics of a 40 MVA transformer winding. To validate the accuracy of the numerical approach, the study employs a systematic verification process based on analytical modeling. Initially, a fundamental beam model is utilized as a benchmark. The natural frequencies calculated via analytical solutions are compared with simulation results to confirm the correctness of mesh settings and boundary conditions. This agreement supports the assumption that the winding material behaves within the linear elastic region under harmonic excitations. Subsequently, the electromagnetic force distribution acting on the winding is computed using Finite Element Analysis. These forces are then applied to the verified structural model to determine the winding's natural frequencies and forced vibration response. The results demonstrate that verifying numerical models against analytical theory provides a robust foundation for assessing transformer vibration risks.
subject: Electricity - Electronics - Automation
  topic: section
subject: Power transformer
  topic: keywords
subject: electromagnetic vibration
  topic: keywords
subject: multiphysics simulation
  topic: keywords
subject: analytical verification
  topic: keywords
creator: Khanh Le-Tran
creator: Cuong Bui-Cong
creator: Tu Nguyen-Ngoc
creator: Nghia Pham-Dai
creator: Huan Pham-Duy
creator: Tuan Phung-Anh
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 244
ending page: 244
doi: 10.62985/j.huit_ojs.vol26.no2E.397

APPLICATION OF DC-DC CONVERTER IN CONTROLLING THE ELECTRIC DRIVE SYSTEM OF THE СБЩ-250MH ROTARY BLAST HOLE DRILL
type: item
description: Power electronics is currently extensively applied in the area of electric drive control with high efficiency. This paper presents the research on replacing the speed control method for the СБЩ-250MH rotary blast hole drill from a rectifier-motor system to a control method utilizing a DC-DC power converter. The operational results of the drive system using the new method are simulated via MATLAB- SIMULINK software, and simulation results indicate that the system utilizing the DC-DC converter fully meets the operational quality requirements of the rotary blast hole drill, providing flexible voltage regulation, guaranteed continuous current, and rapid speed stabilization. Furthermore, the new solution simplifies the control circuit structure, reduces maintenance costs, and enhances overall energy efficiency. This research opens up prospects for the widespread application of DC-DC converters in replacing the legacy drive systems of rotary drill used specifically in the mineral mining sector generally in industrial electric drive systems.
subject: Electricity - Electronics - Automation
  topic: section
subject: Rotary blast hole drill
  topic: keywords
subject: Electric drive system
  topic: keywords
subject: DC-DC converter
  topic: keywords
subject: Rectifier
  topic: keywords
creator: Thuy Nguyen Thi
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 232
ending page: 232
doi: 10.62985/j.huit_ojs.vol26.no2E.396

RESEARCH ON METHODS TO IMPROVE THE TORQUE CHARACTERISTICS OF SWITCHED RELUCTANCE MOTORS
type: item
description: Switched reluctance motor (SRM) is a motor with many outstanding advantages, with simple structure, low manufacturing cost because the rotor has no winding and permanent magnets. Therefore, SRM is increasingly widely used in industries, especially with the trend of saving energy and the strong development of vehicles with electric motors as today. The article proposes a solution to improve the torque of reluctance motors by examining the influence of the motor's magnetic poles or combining it with the magnetic circuit optimization method; Applying MATLAB simulink and Ansys maxwell software to evaluate the results.
subject: Electricity - Electronics - Automation
  topic: section
subject: Switched reluctance motor
  topic: keywords
subject: SRM
  topic: keywords
subject: torque of SRM
  topic: keywords
subject: Ansys Maxwell
  topic: keywords
creator: Van Khuyen Dang
creator: Van Nam Tran
creator: Van Hien Tran
creator: Van Long Bui
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 224
ending page: 224
doi: 10.62985/j.huit_ojs.vol26.no2E.395

ANALYSIS OF FLUX BARRIER EFFECTS ON MAGNETIC FLUX DISTRIBUTION AND TORQUE CHARACTERISTICS IN SPOKE-TYPE INTERIOR PERMANENT MAGNET SYNCHRONOUS MOTORS
type: item
description: This paper investigates how multiple flux barriers (FBs) affect the magnetic flux distribution and torque performance of Spoke-Type Interior Permanent Magnet Synchronous (SIPM) motors for electric vehicle (EV) applications. A 5-kW conventional SIPM motor serves as the baseline for comparison. The study first explores how multiple FBs influence the magnetic flux density, air-gap flux density, cogging torque, and inductance characteristics (Ld and Lq) under both no-load and load conditions. Applying a positive Id current lead to flux enhancement, increasing the effective PM flux density in the rotor. This results in higher torque output and reduces the risk of irreversible magnet demagnetization. Integrating multiple FBs into the rotor core yields significant performance improvements over a wide speed range. All findings are validated through finite-element method (FEM) simulations.
subject: Electricity - Electronics - Automation
  topic: section
subject: Flux Intensifying (FI)
  topic: keywords
subject: Permanent Magnet
  topic: keywords
subject: Multiple Flux Barriers
  topic: keywords
subject: Spoke-type
  topic: keywords
creator: Anh Thanh Huynh
creator: Viet-Vu Do
creator: Minh-Hoc Duong Le
creator: Min-Fu Hsieh
rights: Copyright (c) 2026 HUIT Journal of Science
date: 2026-06-11
publication date: 2026-06-11
starting page: 213
ending page: 213
doi: 10.62985/j.huit_ojs.vol26.no2E.394