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.
Trường Đại học Công Thương Thành phố Hồ Chí Minh
RECOVERY OF LIGNIN FROM COCONUT COIR (Cocos nucifera L.) BY REUSABLE DEEP EUTECTIC SOLVENTS: CHARACTERIZATION AND POTENTIAL APPLICATION IN GAUZE FABRIC COATING
This study investigated lignin recovery from coconut coir using a choline chloride–oxalic acid deep eutectic solvent and evaluated the solvent reusability and potential coating application. The results showed that the lignin recovery yield reached 18.70% (w/w) based on the dry weight of biomass when using a solvent system composed of choline chloride (ChCl) and oxalic acid (OA) at a molar ratio of 6:4, at a reaction temperature of 100 °C for 3 hours. The reusability of the DES was also evaluated through ten consecutive extraction cycles and showed that the lignin recovery yield did not change significantly. The recovered lignin exhibited a purity of nearly 90% and showed antioxidant activity with an IC50 value of 6.02 μg/mL. In addition, the lignin/DES dispersion system exhibited antibacterial activity against two bacterial strains, including Staphylococcus aureus and Escherichia coli with inhibition zones diameter of 39 mm and 32 mm, respectively. Furthermore, this study preliminarily demonstrated the potential application of lignin as a coating material for gauze fabric through the lignin/DES dispersion system under conditions of 2 hours of immersion time and lignin/DES dispersion at a concentration of 0.1% (w/w), achieving a weight gain percentage of 53.90% (w/w). The results of this study demonstrate that DES is potentially more sustainable than conventional solvent systems for lignin recovery from coconut coir. Moreover, the findings highlight the potential application of lignin in the development of antibacterial coating materials and other value-added products derived from agricultural residues, in line with the principles of the circular economy and green chemistry.
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IMPROVING FUZZY CLUSTERING WITH MULTI-VERSE OPTIMIZER FOR CLASSIFYING RESPIRATORY DISEASES: INFLUENZA, COVID-19, AND PNEUMONIA
Accurately classifying respiratory diseases such as influenza, COVID-19, and pneumonia is crucial for diagnosis and treatment. However, due to the similarity in symptoms, grouping patients into distinct categories presents significant challenges. This study proposes an improvement to the fuzzy clustering algorithm by integrating the Multi-Verse Optimizer (MVO) to optimize cluster center positions, enhancing clustering quality and reducing misclassification among disease groups. By combining MVO's global optimization capability with the FCM method, the model achieves more precise disease cluster separation. Experimental results demonstrate that the proposed method outperforms traditional FCM, significantly improving clustering accuracy and disease group differentiation. This approach has the potential to be applied in supporting respiratory disease diagnosis, contributing to increased efficiency in medical practice.
THREE-STAGE SOLID STATE TRANSFORMER (SST)
In recent years, the complexity of power grid systems has increased due to the increasing penetration of renewable energy and distributed generation sources. Increasing complexity requires new methods to quickly manage changing sources and loads. This article presents the basics of bidirectional AC-AC power electronics converters called semiconductor transformers. Semiconductor transformers typically consist of three main conversion stages AC-DC, DC-DC, and DC-AC; at the heart of which is a high-power DC-DC converter, operating at high frequencies to significantly reduce the size, overall mass, and cost of the converter.
MODEL PREDICTIVE CONTROL FOR 3-LEVEL T-TYPE INVERTER GRID CONNECTED FOR SOLAR SYSTEM
This article presents model predictive control technique for a grid-connected 3-phase 3-level T-type inverter for solar system. First, the DC-DC conversion stage using the maximum power point tracing technique is presented. Next, model predictive control method with the goal of current tracking and balancing the capacitor voltages is introduced. In this control strategy, the reference current is taken from the active current and reactive current through the transformation from dq to αβ coordinate. The effectiveness of the control method is demonstrated through simulations on Matlab/ Simulink software. The power transmitted to the grid is maintained stable corresponding to the maximum power point of the PV array, with power factor at 1 under different heat radiation conditions. The grid current response quickly reaches the reference value. The voltage of the capacitors is maintained in a well-balanced.
RESEARCH TO EVALUATE THE PERCEPTION AND BEHAVIOR OF BUSINESS HOUSEHOLDS IN DA LAT CITY ABOUT GENERATION AND REDUCTION OF PLASTIC WASTE
To sustainably develop the tourism industry, Da Lat city needs to change the awareness and behavior of local people and tourists in issues related about environmental protection. In particular, the issue of minimizing plastic waste at business households needs special attention to protect the public health. Based on an actual survey of 200 business households in Da Lat city, it shows that 100% of business households use disposable plastic products, the most used is plastic bags and 87% of plastic waste has not been classified at source. 90% of surveyed households are aware of the harmful effects of plastic waste but still use it due to convenience and low prices. The average score of the Likert scale shows that business households choose the solution to implement waste separation at source and use alternative products "often" but choose "rarely" for propaganda activities. Thereby, the study proposes solutions to reduce plastic waste including improving propaganda effectiveness, using alternative products, classifying and collecting to recycle plastic waste.
ISOLATION, SELECTION AND IDENTIFICATION OF OF BACTERIA WITH CELLULOSE AND PROTEIN-DEGRADING POTENTIAL FROM ORGANIC WASTE IN CHAU THANH – KIEN GIANG
Using bacteria is one of the environmentally friendly waste treatment methods for ensuring the sustainability of the ecological environment. This study aimed to isolate bacteria with cellulose- and protein- degrading potential from organic waste sources. Organic waste samples were collected from markets in Chau Thanh district, Kien Giang province. The results showed that 38 bacterial strains were isolated. Of these, 9 strains were capable of decomposing protein and cellulose. The two strains with the best ability to simultaneously decompose protein and cellulose on agar plates were GT5 (18.1 mm and 9.75 mm) and NT4 (15.5 mm and 8.00 mm), respectively. Molecular identification of two strains based on16S rRNA sequence showed that GT5 was 99.87% homogeneous to Bacillus cereus; NT4 was 99.86% homogeneous to Proteus mirabilis.
STUDY ON SELECTION AND CULTIVATION OF Bacillus sp. FOR GAMMA-AMINOBUTYRIC ACID SYNTHESIS IN TOFU SOUR LIQUID
Gamma-aminobutyric acid (GABA) is a non-protein amino acid that plays an important role in the nervous system of organisms. The application of microorganisms to produce GABA from by-products is also considered a sustainable and effective production method. The study was conducted to select Bacillus strains capable of synthesizing GABA and to investigate the culture conditions, such as the carbon source, nitrogen source, and pH of the environment suitable for the selected Bacillus strains in the liquid waste of tofu. The results indicate that Bacillus sp. HV5 had the highest GABA biosynthesis ability in liquid waste of tofu when supplemented with 1% (w/v) starch, 1% (w/v) peptone, and 1% (w/v) MSG, pH 7. After 72 h of fermentation under shaking at 200 rpm at 35 ℃, the GABA content reached 1.288 ± 0.089 mg/mL.
EVALUATION OF ANTI-MELANOGENIC ACTIVITY OF PHYCOCYANIN EXTRACTED FROM Arthrospira platensis
Phycocyanin (CPC) is a powerful antioxidant protein extracted from Arthrospira platensis. This study extracted cosmetics-grade CPC from Arthrospira platensis and determined its melanin-inhibitory effect in vitro. Cell walls were disrupted by the freezing-thawing cycles in 0.1% or 1% CaCl2 solution, and then CPC was purified by ammonium sulfate to reach the cosmetic grade. In vitro cytotoxicity of CPC was evaluated in L-929 cells. The anti-melanogenic effect of CPC was performed in B16F10 cells. The B16F10 cells were cultured in the cell culture medium containing CPC at 1-10 µg/mL concentrations for 48 hours. Melanin content was determined by the ratio of OD420/mg protein. The results showed the optimized extraction method was the combination of 3 freezing-thawing cycles in 1% CaCl2 and two-step precipitation by ammonium sulfate (1st 20% and 2nd 30%) with the purity was 2.411 ± 0.090 and concentration was 3.84 ± 0.07 mg/mL. CPC is safe for L-929 cells, and IC50 was 1405 ± 16 µg/mL. CPC inhibited melanin synthesis in B16F10 cells at 5 µg/mL, and the melanin content decreased from 0.086 ± 0.012 to 0.022 ± 0.002. Conclusion: obtained cosmetic-grade CPC, and the CPC reduced melanin content at 5 µg/mL.
THE EFFECT OF COBALT 60 GAMMA IRRADIATION ON SEED GERMINATION AND GROWTH OF Abelmoschus sagittifolius
Physical mutagenesis is an effective mutational breeding method for improving various morphological characteristics of agricultural and medicinal crops. In this study, seeds of Abelmoschus sagittifolius were irradiated with gamma rays at doses ranging from 50 to 300 Gy to evaluate the effects of gamma irradiation on germination percentage, and morphological mutations. The results showed that the germination rate increased at irradiation doses from 150 to 200 Gy, and the highest seedling survival rate from 90 days post-germination of seeds was observed at 150 Gy. Additionally, several morphological mutations were observed in the leaf and floral structures of irradiated A. sagittifolius. These mutations included aberrant leaf lobation, wrinkling and curling at the petal edges, and white streaks on the petals. The frequency of morphological mutations increased with higher irradiation intensity, with numerous mutations observed in the dose range of 150 Gy to 250 Gy. These results provide an initial scientific basis for creating mutation resources for the selection and development of mutant lines in A. sagittifolius.
STUDY ON SYNTHESIS OF NANOGEL MATERIAL ENCAPSULATED CLOVE BUD EXTRACT (Syzygium aromaticum L.)
Nanogels encapsulating clove bud extract were fabricated using β-cyclodextrin (CD) and alginate (alg) via electrostatic interactions between the Ca²⁺-crosslinked alginate matrix and CD. The structures of hollow nanogel (CD/alg) and nanogel carrying clove bud extract (DH@CD/alg) were analyzed by infrared spectroscopy. The morphology, particle size, dispersion in water and stability of nanogel with different clove bud extract ratios were analyzed by TEM, DLS and Zeta potential measurement techniques. Nanoparticles prepared under the optimal formulation conditions were predominantly spherical and exhibited a fairly uniform size distribution within the range of 40–100 nm. The nanogel dispersions showed high colloidal stability in aqueous solution, with zeta potential values ranging from -45.6 to -68.8 mV.
ULTRASOUND-ASSISTED EXTRACTION OF ANTIOXIDANT COMPOUNDS FROM LA GIANG (Aganonerion polymorphum Pierre ex Spire)
Aganonerion polymorphum Pierre ex Spire, commonly known as lá giang, is an indigenous medicinal plant that contains various bioactive compounds, notably total polyphenols (TPC) and total flavonoids (TFC), which exhibit potential antioxidant activity. However, research on ultrasound-assisted extraction of bioactive compounds from A. polymorphum, and the influence of factors like drying temperature, extraction solvents, ultrasonic temperature, and duration, remains limited. Therefore, this study was conducted to evaluate the effects of these parameters on the yield of TPC and TFC from A. polymorphum using UAE. The experimental design included the following conditions: drying temperatures of 45; 50; 55; 60; and 65 °C; extraction with a 1:10 (w/v) solvent-to-sample ratio using water and ethanol at concentrations of 50%; 70%; and 90%; and ultrasound treatment at a frequency of 37 kHz, under temperatures of 28 ± 0.5; 40; 50; and 60 °C for durations of 5; 10; 15; and 20 minutes. The analytical parameters included the quantification of TPC (expressed as µg gallic acid equivalents per 100 g dry weight [dw]), TFC (expressed as µg quercetin equivalents per 100 g dw), and antioxidant activity measured by IC₅₀ values. The results indicated that a drying temperature of 55 °C was optimal for preserving bioactive compounds in A. polymorphum. The most effective extraction solvent was 70% ethanol. Maximum extraction efficiency for both TPC and TFC was achieved at 50 °C ultrasonic treatment for 10 minutes, yielding 456.57 ± 2.34 (µgGAE/100 g dw) and 138.21 ± 1.37 (µgQE/100 g dw), respectively. The extract also demonstrated notable antioxidant activity, with an IC₅₀ value of 207.50 ± 3.74 µg/mL.
EXTRACTION OF CHITIN FROM Sesarma mederi SHELLS USING CHEMICAL TREATMENT
The shell of Sesarma mederi, a byproduct of fermented crab (mam ba khia) production, is rich in minerals and proteins, requiring proper processing to obtain high-purity chitin. This study examines the effectiveness of hydrochloric acid (HCl) in the demineralization step of chitin extraction. The results show that optimal mineral removal is achieved using a 5% HCl solution with a material-to-solvent ratio of 1:30 (w/v), at 60°C for 3 hours. After demineralization, the sample underwent NaOH treatment to remove residual proteins, yielding chitin with a mineral content below 1.0% and a purity of approximately 97.66%. Structural analysis using Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM) confirmed the efficient removal of minerals and proteins. The extracted chitin was identified as predominantly α-chitin. This study highlights the potential of conventional chemical processing for chitin recovery from Sesarma mederi shells, offering a sustainable approach to utilizing seafood byproducts and expanding their applications in industrial and biotechnological fields.
EXTRACTING SAPONINS FROM Phyllanthus amarus WITH MICROWAVE AND ENZYME ASSISTED EXTRACTION
Natural herbal sources have long been recognized as vast reservoirs of bioactive compounds with diverse biological activities. Among these, saponins have emerged as a prominent subclass, garnering significant attention in modern medicine for their positive physiological effects. Given its proven pharmacological values, Phyllanthus amarus has been widely utilized in traditional medicine with immense therapeutic potential. To maximize these biological benefits, the present study evaluates the impact of various pretreatment methods-including microwave, enzyme, and a microwave-enzyme combination-on the extraction efficiency of saponins from P. amarus. Using microwave-assisted extraction (MAE), a saponin yield of 29.08 ± 0.79 mg/g CK was achieved at 360 W for 90 s. In contrast, enzyme-assisted extraction (EAE) yielded 23.42 ± 1.09 mg/g CK under the optimal conditions of 0.8% (v/w) enzyme concentration, pH 5.5, and 90 min incubation period. And for the microwave-assisted enzymatic extraction, the saponins content obtained was 34.75 ± 1.05 mg/g CK under the condition of power 270 W for 60 s.
Ho Chi Minh City University of Industry and Trade
DEEP EUTECTIC SOLVENT EXTRACTION FOR ASTAXANTHIN FROM GIANT TIGER PRAWN (Penaeus monodon) SHRIMP SHELLS
Astaxanthin, a powerful antioxidant compound commonly used in the food and pharmaceutical industries, was the main focus of this study. The research aimed to evaluate a new method that combines deep eutectic solvent and citric acid for extracting astaxanthin from shrimp waste. The study compared the astaxanthin levels and antioxidant effects of this approach with the traditional ethanol extraction method. The results showed that the highest astaxanthin content (0.311±0.004 mg/g) was achieved by using a mix of choline chloride – glycerol (ChCl-Gl) at a 1:2 molar ratio with an additional 5% citric acid (CG2-5%), extracted at 40 ºC for 3 hours. This led to astaxanthin levels 1.3 times higher than those obtained through ethanol extraction. Additionally, the CG2-5% treatment involving choline chloride – glycerol (ChCl-Gl) and 5% citric acid resulted in cell wall deformation, facilitating a more efficient extraction of astaxanthin, as shown by SEM results. The astaxanthin extracts obtained with CG2-5% demonstrated superior antioxidant properties compared to the ethanol-extracted sample, highlighting the environmentally friendly aspect of the CG2-5% solvent for recovering astaxanthin from shrimp waste.
EFFECTS OF PROCESSING CONDITIONS ON γ-AMINOBUTYRIC ACID (GABA) ACCUMULATION IN GERMINATED HYACINTH BEAN (Lablab purpureus (L.) Sweet) TEA
This study aimed to investigate the effects of processing conditions, including soaking, germination, exogenous γ-aminobutyric acid (GABA) supplementation, and ultrasound treatment, on GABA content in germinated hyacinth bean (Lablab purpureus (L.) Sweet) tea. The results indicated that all processing factors significantly affected GABA accumulation. The optimal soaking condition was identified at 35 °C for 9 h, while germination at 35°C for 24 h yielded the highest GABA content (140.28 mg/100g dry weight). Exogenous GABA supplementation significantly enhanced GABA levels, with an optimal concentration of 10 mM. In addition, ultrasound treatment at 2.4 W/g for 15 min was found to be the most effective among the tested conditions. The observed increase followed by a decrease in GABA content suggests a balance between stimulation of GABA biosynthesis and its degradation under processing-induced stress. These findings demonstrate that hyacinth bean is a promising raw material for the development of GABA-enriched germinated tea.
OPTIMIZATION OF SAPONIN EXTRACTION FROM Ageratum conyzoides L. LEAVES USING MICROWAVE-ASSISTED EXTRACTION
Ageratum conyzoides L. is a valuable medicinal plant widely used in traditional medicine. Among its parts, leaves are particularly rich in saponins, a class of bioactive natural compounds known for their diverse pharmacological properties and potential industrial applications. This study aimed to determine the parameters affecting saponin extraction from A. conyzoides L. leaves using solvent-assisted extraction, ultrasound-assisted extraction and microwave-assisted extraction. The results showed that among the three extraction methods, solvent-assisted extraction (28.04 ± 1.15 mgOAE/gck), ultrasound-assisted (36.46 ± 0.36 mgOAE/gck), and microwave-assisted extraction yielded saponin contents of (43.50 ± 1.31 mgOAE/gck), indicating that the microwave-assisted method was the most efficient. With the above conditions, the optimal microwave method obtained the maximum saponin content of 45.45 mgOAE/gck when the raw solvent /material ratio was 21.35/1 v/w with a microwave power of 270.96 W and a microwave time of 65.34 seconds. In addition, SEM micrographs showed changes in the powder’s cellular structure after extraction under optimized conditions, compared with the raw material. Furthermore, FT-IR analysis confirmed functional groups characteristic of saponin compounds in the A. conyzoides L. leaves extract. These results provide a scientific basis for exploiting saponins from A. conyzoides L. leaves, contribute to the effective utilization of locally abundant natural resources in Vietnam, and demonstrate potential applications in pharmaceutical and functional food product development. Therefore, the extraction of saponins from A. conyzoides L. leaves is considered essential, as it provides scientific and practical value while supporting the pursuit of sustainable development objectives.
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.