Chào mừng đến với Tạp chí Khoa học Đại học Công Thương ✨ Chính thức được cấp phép hoạt động Tạp chí Khoa học Đại học Công Thương (HUIT Journal of Science), với chỉ số p-ISSN 3030-4113 và e-ISSN 3030-413X, đã chính thức được Bộ Thông tin và Truyền thông cấp phép hoạt động báo chí in và tạp chí điện tử vào ngày 06 tháng 03 năm 2024.
Trường Đại học Công Thương Thành phố Hồ Chí Minh
A SINGLE-STAGE DIFFERENTIAL BOOST DC-AC INVERTER WITH SIMPLIFIED CONTROL FOR ON-BOARD ELECTRIC VEHICLE CHARGERS
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.
EXPERIMENTAL EVALUATION OF CLASSICAL BACKSTEPPING AND RBF NEURAL NETWORK BASED BACKSTEPPING ON AN INVERTED PENDULUM SYSTEM
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.
METACOGNITIVE REGIME-SWITCHED SPIKING NEURAL NETWORK WITH CONTRASTIVE MEMORY CONSOLIDATION
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.
ROBUST MODBUS-RTU COMMUNICATION DESIGN FOR SAFETY-CRITICAL COMPRESSOR VFD CONTROL IN SUB-ZERO COLD STORAGE
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.
A DEEP LEARNING APPROACH FOR DRIVER FATIGUE DETECTION
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.
A LIGHTWEIGHT MACHINE LEARNING APPROACH FOR ALCOHOL CONCENTRATION ESTIMATION USING AN ESP32 AND MEMS GAS SENSOR
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.
COMPARATIVE ANALYSIS OF LOSS FUNCTIONS FOR IMAGE-TEXT MATCHING UNDER NOISY CORRESPONDENCE
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.
Similarity-based Negative Log-Likelihood
MACHINE LEARNING-BASED DRIVETRAIN FAULT CLASSIFICATION IN NEW ENERGY VEHICLES
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.
OPTICAL WIRELESS POWER TRANSFER BETWEEN OBLIQUE PLANES OF TRANSMITTERS AND RECEIVERS
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.
ADAPTIVE COLOR SORTING ROBOT APPLYING MACHINE LEARNING ON EMBEDDED SYSTEMS
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.
A PRACTICAL APPROACH TO ELECTRONIC COMPONENT DETECTION ON PCBs USING YOLOv11 DEEP LEARNING MODEL
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.
LOAD POWER PREDICTION IN SMART SOLAR MICROGRID BASED GATED RECURRENT UNIT
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.
TRAJECTORY OPTIMIZATION OF ROBOTS VIA MODEL PREDICTIVE CONTROL AND REINFORCEMENT LEARNING
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.
RESEARCH ON A HAND GESTURE RECOGNITION MODEL TO OPTIMIZE ROBOTIC HAND CONTROL
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.
DEEP LEARNING-BASED VISION SYSTEM FOR AUTOMATED NIPA PALM FLESH SEPARATOR USING YOLOv11 AND OPC-UA INTEGRATION
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.
AI-BASED FILTERING OF VESSEL-INDUCED NOISE IN SIPHON-TYPE RIVER WATER LEVEL MEASUREMENTS AT PHU AN HYDROLOGICAL STATION
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.
OPTIMIZING UAV PID FLIGHT CONTROLLERS WITH REINFORCEMENT LEARNING: A TD3 ALGORITHM APPROACH
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.
A REVIEW OF UNDERWATER ACOUSTIC TARGET RECOGNITION IN THE ERA OF ARTIFICIAL INTELLIGENCE
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.
UNDERSTANDING WHOLESALE CLIENT BEHAVIOR AT A VIETNAMESE GARMENT OUTSOURCING FACTORY
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.
ORITRIP: ENHANCING PERSON RE-IDENTIFICATION WITH ORIENTATION-AWARE TRIPLET LOSS
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.
COMPUTER VISION BY YOLOs FOR DETECTION OF MAJOR DISEASES IN VIETNAMESE CUSTARD APPLE USING FRUIT IMAGERY
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.
ADAPTIVE VIRTUAL SYNCHRONOUS MACHINE PARAMETER TUNING FOR REGFM-B1 GRID-FORMING INVERTERS UNDER CURRENT SATURATION CONSTRAINTS
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.
LOW-COST MONITORING SYSTEM FOR EARLY FAULT DETECTION IN 3S LITHIUM-ION BATTERIES
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.
AN ADAPTIVE NEURAL PI CONTROL FRAMEWORK USING DEEP REINFORCEMENT LEARNING FOR HIGH-PERFORMANCE PMSM SPEED DRIVES
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.
ENERGY-AWARE PATH PLANNING FOR A QUADCOPTER USING GENERALIZED PARTICLE SWARM OPTIMIZATION
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.
GENERALIZED FAULT TOLERANT CONTROL METHOD FOR DUAL THREE-PHASE PMSM USING PERMUTATION REDUCED-ORDER MATRIX
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.
ELECTROMAGNETIC VIBRATION ANALYSIS OF POWER TRANSFORMER WINDINGS: VERIFICATION OF STRUCTURAL MODELS BASED ON ANALYTICAL MODELING
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.
APPLICATION OF DC-DC CONVERTER IN CONTROLLING THE ELECTRIC DRIVE SYSTEM OF THE СБЩ-250MH ROTARY BLAST HOLE DRILL
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.
RESEARCH ON METHODS TO IMPROVE THE TORQUE CHARACTERISTICS OF SWITCHED RELUCTANCE MOTORS
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.
ANALYSIS OF FLUX BARRIER EFFECTS ON MAGNETIC FLUX DISTRIBUTION AND TORQUE CHARACTERISTICS IN SPOKE-TYPE INTERIOR PERMANENT MAGNET SYNCHRONOUS MOTORS
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.