International Journal of Electrical and Computer Engineering (IJECE)
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Experimental comparison of air, oil, and liquid nitrogen cooling media on the efficiency of a single-phase transformer
Get PDFTransformers are critical component in electric power system, where minimizing energy losses is essential for efficiency and reliability. While ideal transformers operate with zero losses, practical transformers dissipate energy through winding and core losses caused by resistive heating. This study investigates the impact of three cooling media with ambient air, mineral oil, and liquid nitrogen on the efficiency and thermal performance of a 1 kVA single phase copper wound transformer. The experiment applied a resistive load under each cooling condition, recording input and output parameters using a HIOKI power meter model PW3360. Thermal behavior was monitored using infrared thermography and thermocouples. Copper winding resistivity was evaluated using a four-point probe within a cryogenic magnet system. The results show that liquid nitrogen cooling significantly reduced copper resistivity due to low-temperature conditions, achieving a transformer efficiency of 89.9%. Oil cooling improved efficiency to 86.0%, compared to 80.7% with air cooling. Although liquid nitrogen provided the greatest efficiency enhancement, its practical use is limited due to handling complexity and cost. In contrast, oil cooling offers a more feasible and effective solution for improving transformer performance in real world applications. These finding provide valuable insight for optimizing transformer thermal management strategies in power systems
Microbubble size and rise velocity measurement in dissolved air flotation system
Get PDFWater reuse and resource recovery are priority environmental goals under increasing water scarcity and climate stress. Dissolved air flotation (DAF) is widely applied in municipal, industrial, and decentralized treatment trains because fine microbubbles (MB) enhance solids removal efficiency. Accurate, low-cost characterization of MB size and rise velocity is therefore valuable for process monitoring and optimization. This study develops and validates a smartphone-based, computer-vision pipeline for laboratory-scale DAF systems. After camera calibration and lens un-distortion, each video sequence (235 frames per run) is processed through grayscale conversion, median, Gaussian, and local-Laplacian filtering, gamma correction, and Otsu thresholding, followed by morphological refinement. Circular Hough transform then identifies MB candidates, providing their diameters and centroid locations. These detections are then linked frame-to-frame using a distance-gated nearest-neighbor tracker with dynamic memory allocation to accommodate new MBs under turbulent, bubble-clustering conditions. Rise velocity is computed from interframe centroid displacement and frame interval. The system reliably tracked up to 32 microbubbles simultaneously per video. Across four operating pressure/airflow combinations, mean MB diameters ranged 95.47–216.42 µm and mean rise velocities 9.40×10³–2.76×10⁴ µm/s. The approach is low cost, computationally lightweight, and suitable for rapid MB characterization to support DAF monitoring, optimization, and research
Autonomous mobile robot implementation for final assembly material delivery system
Get PDFThis study presents the development and implementation of an autonomous mobile robot (AMR) system for material delivery in a final assembly environment. The AMR replaces conventional transport methods by autonomously moving trolleys between the warehouse, production stations, and recycling areas, thereby reducing human intervention in repetitive logistics tasks. The proposed system integrates a laser-SLAM navigation approach, customized trolley design, RoboShop programming, and robot dispatch system coordination, enabling real-time route planning, obstacle detection, and material scheduling. Experimental validation demonstrated high accuracy in path following, with root mean square error values ranging between 0.001 to 0.020 meters. The AMR achieved an average travel distance of 118.81 meters and a cycle time of 566.90 seconds across three final assembly stations. The overall efficiency reached 57%, primarily due to reduced idle time and optimized material replenishment. These results confirm the feasibility of AMR deployment as a scalable and flexible intralogistics solution, supporting the transition toward Industry 4.0 smart manufacturing systems
An enhanced improved adaptive backstepping–second-order sliding mode hybrid control strategy for high-performance electric vehicle drives
Get PDFThis paper proposes an enhanced hybrid speed control strategy, termed improved adaptive backstepping–second-order sliding mode (IABSSOSM), for six-phase induction motor (SPIM) drives in electric vehicle (EV) propulsion systems. The proposed method combines the systematic design framework of Backstepping in the outer speed and flux loops with a second-order sliding mode controller in the inner current loop. An innovation of the approach is the integration of a variable-gain super-twisting algorithm (VGSTA), which dynamically adjusts the control effort based on disturbance levels, thereby minimizing chattering and enhancing robustness against system uncertainties. To further improve disturbance rejection, a predictive torque estimator is incorporated using a forward Euler discretization, enabling accurate torque prediction and proactive compensation. This hybrid structure significantly improves convergence speed, enhances reference speed tracking accuracy, and ensures fast and precise torque response, and its strong resilience to external load disturbances, system parameter variations enable stable and reliable operation under challenging conditions. The effectiveness of the proposed approach is validated through comprehensive simulations using the MATLAB/Simulink
Hybrid machine learning framework for chronic disease risk assessment
Get PDFChronic diseases like asthma, diabetes, stroke, and heart disease are the major causes of morbidity globally, which emphasizes the need for efficient predictive models to facilitate early detection and precautionary measures. Previous studies have used machine learning approaches for single-disease prediction, where models are designed for specific diseases, such as diabetes or heart disease. However, very few attempts have been made to develop unified frameworks for predicting multiple diseases simultaneously. This work presents a novel, unified framework using an ensemble of extreme gradient boosting classifier (XGBClassifier) and artificial neural networks (ANN) as individual classifiers to concurrently predict the risk of developing asthma, diabetes, stroke, and heart disease. This work follows a questionnaire-based approach that utilizes demographic, lifestyle, health metrics, symptoms and exposure-related data to create personalized risk assessments. The model achieves satisfactory accuracy rates of 95.82% for asthma, 96.68% for diabetes, 94.91% for stroke, and 94.52% for heart disease. The findings highlight how this novel hybrid model serves as an effective approach to tackle the intricate interactions between chronic ailments. The research also includes a user-friendly website that comprises a questionnaire and makes use of the best performing model to predict the probabilities of developing different diseases
Machine learning-based predictive maintenance framework for seismometers: is it possible?
Get PDFSeismometers are crucial in earthquake and tsunami early warning systems, since they record ground vibrations due to significant seismic events. The health condition of a seismometer is strongly related to the measurement of seismic data quality, making seismometer health condition maintenance critical. Predictive maintenance is the most advanced control or measurement system maintenance method, since it informs about the faults that have occurred in the system and the remaining lifetime of the system. However, no research has proposed a seismometer predictive maintenance framework. Thus, this article reviews general predictive maintenance methods and seismic data quality analysis methods to find the feasibility of developing a predictive maintenance framework for seismometers in seismic stations. Based on the review, it is found that such a framework can be built under particular challenges and requirements. Finally, machine learning is the best approach to build the classification and regression models in the predictive maintenance framework due to its robustness and high prediction accuracy
Design of a thermionic electron gun of 6 MeV linac by using neural network based surrogate model
Get PDFHigh performance electron guns are fundamental components in linear accelerators (linacs), directly influencing beam quality and downstream system efficiency. However, designing electron guns for applications such as a 6 MeV linac presents complex trade-offs between current, perveance, and beam emittance. Traditional simulation-driven optimization methods are computationally expensive and limit rapid prototyping. In this study, we develop a neural network-based surrogate model trained on CST Studio Suite simulation data to predict the electron gun's performance metrics. Our approach significantly accelerates the optimization process by providing real-time predictions of beam current and perveance across a wide design parameter space. The surrogate model achieves high prediction accuracy, with training and validation losses on the order of 10⁻⁷. Results demonstrate that neural network models can serve as reliable and efficient tools for electron gun design, offering considerable computational savings while maintaining accuracy. Future extensions include expanding the surrogate model to multi-objective optimization and incorporating thermal and mechanical effects into the design process
Optimizing hourly air quality index forecasting: a particle swarm optimization-enhanced hybrid approach combining convolutional and recurrent neural networks
Get PDFAir pollution is still a serious worldwide issue, and accurate air quality index (AQI) prediction is needed. This paper proposes a hybrid deep learning model integrating 1D convolutional neural networks (Conv1D) and long short-term memory (LSTM) networks, optimized with particle swarm optimization (PSO) to enhance AQI forecasting. The model was evaluated at six urban areas: Bandra, Thane, Mazgaon, Kurla, Nerul, and Malad, and compared with a single LSTM network. PSO adjusted hyperparameters like hidden units, batch size, epochs, and learning rate was used to improve predictive accuracy. The Conv1D+LSTM hybrid model drastically decreased RMSE by 49.19% (Bandra), 33.97% (Thane), 5.24% (Mazgaon), 20.52% (Kurla), 35.85% (Nerul), and 27.54% (Malad), and R² Score improvements up to 751.2%. Training logs indicated smoother convergence with loss decrease at faster rates compared to LSTM, showing better learning efficiency and generalization. By combining spatial and temporal feature extraction with automated hyperparameter tuning, this model captures sophisticated pollution patterns which increases the reliability of AQI prediction. Enhancements in the future can be adding regularization methods and more feature inputs to improve the accuracy
Stochastic planning of multi-bus hydrothermal systems using the scenario tree technique
Get PDFHydrothermal operation planning (HTOP) is a complex, large-scale optimal control problem. Traditionally, mathematical programming is used to solve it; however, metaheuristic techniques have emerged as an alternative approach. However, even in the context of current technological developments, the models developed to date generally require simplifications in the formulation. In particular, in medium-term planning, they have used a deterministic model or simplified transmission lines into a single bus. However, this approach leads to conservative and unrealistic solutions that may result in either oversizing or underutilization of resources. Therefore, this work proposes a methodology for incorporating uncertainties into the HTOP problem with a multi-bus topology. It was tested in a three-bus system, where linear functions are applied to simplify the production of hydroelectric plants and the cost of thermal units. The methodology incorporated well-established techniques in an implicit stochastic optimization (ISO) model, using a tree of 50 scenarios to model the hydrological series, which is solved with linear programming (LP). The results were validated with the costs of the 10000 generated series, showing an error of 5.07%. Additionally, the solutions were compared with an adapted metaheuristic technique for this problem to explore models applicable to more complex formulations
Study on the acceleration process of three-phase induction motors driving elevator loads
Get PDFThree-phase induction motor drive systems, especially in elevator applications and other precision motion systems, require optimized acceleration profiles to minimize vibrations and extend mechanical lifespan. Previous studies have primarily focused on fast speed response control but often overlooked the impact of jerk, which affects smoothness and operational safety. This paper proposes a combination of field-oriented control (FOC) and S-curve acceleration profiles to reduce jerk and improve motion quality. A dynamic model of the drive system is developed to simulate the acceleration process, demonstrating that the S-curve significantly reduces torque and current oscillations, thus enhancing stability. The S-curve trajectory generation algorithm is implemented and deployed on a field programmable gate array (FPGA) hardware platform. Experimental hardware results confirm that the generated speed control signals possess high resolution and fast response, making the method suitable for embedded control systems in elevator drives and other sensitive motion-control applications. This integrated approach not only addresses the limitations of previous methods but also provides a practical solution to improve comfort, safety, and durability in various electromechanical drive systems