Association for Scientic Computing Electronics and Engineering (ASCEE): Open Journal Systems
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Detection of Sealing Defects in Canned Sardines Using Local Binary Pattern and Perceptron Techniques for Enhanced Quality Control
Full text linkIn the canned sardine production industry, sealing issues often arise due to various factors, such as the quantity of fish in the can or improper calibration of the sealing machine. These sealing defects can result in poorly sealed cans that may explode and contaminate an entire production batch, leading to significant financial losses and damage to the company's reputation. This study proposes an advanced and reliable method for classifying fish can images to detect potential defects, such as sealing issues, which are critical to maintaining quality standards in the canning industry. Our classification method utilizes the Local Binary Patterns (LBP) algorithm for feature extraction across the entire dataset of images. The extracted features are then processed using a Perceptron classifier to identify poorly sealed cans. This approach achieved a precision score of 0.85, demonstrating its effectiveness. Additionally, our analysis revealed that LBP significantly contributes to improving classification accuracy. By automating and enhancing the quality assurance process, this method provides the canning industry with a robust tool for ensuring high product standards, minimizing errors, and increasing efficiency in production lines
Design of a Small Wind Turbine Emulator for Testing Power Converters Using dSPACE 1104
Full text linkInterest in wind turbine emulators (WTE) has increased due to the growing need for wind power generation as a low-maintenance, more effective substitute for conventional models. This paper presents the design of a small WTE utilizing a dSPACE 1104 system. The setup includes a DC motor, driven by a buck converter, coupled to a permanent magnet synchronous generator, all managed through a hardware-in-the-loop configuration using the dSPACE 1104 board. The DC motor simulates the rotational motion generated by wind energy, accurately replicating the characteristics of an actual WT. This control system enables the simulation of various wind speeds and torque values in MATLAB/Simulink software, providing a valuable tool for analyzing and developing power converters. The results obtained confirmed the effectiveness of the proposed emulator, as the experimental outcomes closely matched the theoretical calculations
Euler-Maclaurin Method for Approximating Solutions of Initial Value Problems
Full text linkThis work is dedicated to advancing the approximation of initial value problems through the introduction of an innovative and superior method inspired by the Euler-Maclaurin formula. This results in a higher-order implicit corrected method that outperforms Taylor’s and Runge–Katta’s methods in terms of accuracy. We derive an error bound for the Euler-Maclaurin higher-order method, showcasing its stability, convergence, and greater efficiency compared to the conventional Taylor and Runge-Katta methods. To substantiate our claims, numerical experiments are provided, highlighting the exceptional efficiency of our proposed method over the traditional well-known methods
Comparative Analysis of 1D – CNN, GRU, and LSTM for Classifying Step Duration in Elderly and Adolescents Using Computer Vision
Full text linkDeveloping a classification system that can predict the onset of neurodegenerative diseases or gait-related disorders in elders is vital for preventing incidents like falls. Early detection allows reduction in symptoms and treatment cost for the elderly. In this study, step duration data from five healthy adolescents with age range of 23 – 29 years old and five healthy elderly individuals with age range of 71 – 77 years old were sourced from PhysioNet. To ensure proper training of the deep learning models, synthetic data was generated from the original dataset using a noise jittering technique with random noise of a range between -0.01 and 0.01 added to the original data. Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and 1D Convolutional Neural Network (1D-CNN) are used for training the data since the data is available in the form time series data. LSTM and GRU are advanced forms of Recurrent Neural Network (RNN) while 1D – CNN can capture temporal dependencies in sequential data. 1D – CNN has the advantages over GRU and LSTM of being more robust to noise and can capture complex patterns behind the data. These methods will be compared in terms of processing time and accuracy. Results show that 1D – CNN outperforms both LSTM and GRU with accuracy of 1.000 in less than 60 seconds. The novelty and contribution of this research shows that healthy old people and healthy young people can be classified with deep learning. Further direction of the research can explore the deep learning in classification of Parkinson’s disease
Fractional Approach to Two-Group Neutron Diffusion in Slab Reactors
Full text linkThe two-energy neutron diffusion model in slab reactors characterizes neutron behavior across two energy groups: fast and thermal. Fast neutrons, generated by fission, decelerate through collisions, transitioning into thermal neutrons. This model employs diffusion equations to compute neutron flux distributions and reactor parameters, thereby optimizing reactor design and safety to ensure efficient neutron utilization and stable, sustained nuclear reactions. The primary objective of this research is to explore both analytical and numerical solutions to the two-energy neutron diffusion model in slab reactors. Specifically, we will utilize the Laplace transform method for an analytical solution of the two-energy neutron diffusion model. Subsequently, employing the Caputo differentiator, we transform the original neutron diffusion model into its fractional-order equivalents, yielding the fractional-order two-energy group neutron diffusion model in slab reactors. To address the resulting fractional-order system, we develop a novel approach aimed at reducing the 2β-order system to a β-order system, where β ∈ (0, 1]. This transformed system is then solved using the Modified Fractional Euler Method (MFEM), an advanced variation of the fractional Euler method. Finally, we present numerical simulations that validate our results and demonstrate their applicability
A Novel Hybrid Backstepping and Fuzzy Control for Three Phase Induction Motor Drivers
Full text linkHigh-performance control using three-phase Induction Motors (IM) is increasingly required in industrial applications. However, due to the nonlinear structure and the continuous impact of issues such as load disturbances and motor parameter variations, traditional control techniques cannot achieve the desired high-performance drive system. In this paper, a new hybrid control scheme combining Backstepping (BS) with fuzzy logic (FL) control for the outer speed control loop to enhancing Field Oriented Control (FOC) vector control performance of the SPIM drives, is proposed. Different from the BS control strategies that have been proposed in the control of IM drive systems before, this paper proposes to use FL control theory to continuously update the coefficients appearing in the virtual control vectors extracted from the traditional BS control technique according to the input error of the system. This contributes to improving the performance of the drive system, enhancing the stability and adaptability of the drive system. Lyapunov stability theory is used to design the drive system to ensure the stability of the overall system. The proposed speed control strategy is validated through Matlab-Simulink. The simulation results show that: first, the proposed control strategy provides fast speed response, and the convergence capability of the drive system remains in an optimal state during transient modes without causing overshoot. Second, the drive system operates stably over the long term under load disturbances
Enhanced Voltage Regulation of Buck Converter-Fed DC Motors Using Fuzzy Logic Control Under Dynamic Load Conditions
Full text linkBuck converters are widely employed in power electronics for efficient DC voltage regulation, particularly in applications such as motor drives and embedded systems. However, conventional control methods, such as PID, often exhibit limitations including significant voltage ripple, overshoot, and sluggish dynamic response under varying load conditions. This study introduces a fuzzy logic controller (FLC) integrated into a buck converter system to address these challenges through adaptive and nonlinear control. The research contribution is the design and simulation of an FLC-based voltage regulation strategy that enhances output stability and improves transient performance in DC motor applications. The proposed buck converter operates in continuous conduction mode and consists of an IGBT switch, inductor, diode, and filter capacitor. The FLC employs voltage deviation and its rate of change as input variables and utilizes a 25-rule Mamdani fuzzy inference system to modulate the duty cycle in real time. Simulated in MATLAB Simulink with a dynamic DC motor load, the FLC demonstrates superior control characteristics over the PID controller. Most notably, voltage ripple is reduced by over 65%, leading to improved voltage stability and reduced fluctuations. The FLC also exhibits faster settling behavior and better handling of dynamic load variations, confirming its effectiveness in nonlinear and time-varying systems. Future work will focus on hardware validation, hybrid control integration, and deployment in renewable energy and electric vehicle systems to improve adaptability and real-world performance
Fault Detection and Identification Scheme for Boost Converter for Hybrid Vehicles
Full text linkIn a wide range of applications, such as smart buildings, electric vehicles, hybrid systems, and renewable energy, dc dc converters are crucial. The dc dc converters have many topologies, and the boost converter is one of the most important. The problem. The boost converter is connected to other sensitive devices and components, so any fault in the Boost converter will lead to a system issue, which may cause catastrophic damage to humans and related devices. These faults include parameter degradation of passive components, open switch failure, and sensors failures. Goal. The development of a fault detection and identification scheme for a dc-dc boost converter is the main goal of this study. Therefore, it is essential to make sure that the converters are safe from malfunctions and that there are no major accidents or disasters in order for them to carry out their vital jobs. Methodology. The scheme covers a wide range of potential faults, such as parametric degradation of passive components, open switch fault, and sensors failures. We created the scheme as a structured algorithm based on residuals between observers and measurements from the sensors, residuals between open switch fault signature and measurements from the sensors, residuals between assumed values of the sensors and real measurements, and carefully considered thresholds to compare these residuals with. Results. Simulations were used to assess the proposed scheme. The results show the effectiveness of the scheme in detecting and identifying faults quickly and accurately. The originality. of this work lies in the creation of a fault detection and identification scheme using luenberger observers and specific thresholds without the need for additional sensors or devices
The Utilization of a TSR-MPPT-Based Backstepping Controller and Speed Estimator Across Varying Intensities of Wind Speed Turbulence
Full text linkBecause wind systems are so prevalent in the electrical grid, an innovative control method can significantly increase the productivity of permanent magnet synchronous generators (PMSG). A wind power generation system's maximal power point (MPP) tracking control approach is presented in this paper. The nonlinear backstepping controller, which is robust to parameter uncertainty, is used in this work to enhance the tip speed ratio approach. To lower the system's equipment and maintenance costs, we suggested utilizing a speed estimator. As a novel addition to the backstepping controller development, the suggested estimator is a component of the backstepping controller development. The control and system organization approaches are presented. Lyapunov analysis is used to guarantee the stability of the controller. To assess the suggested approach, step change and varying wind speed turbulence intensities are employed. The results expose the great efficiency of the proposed method in both tracking MPP and calculating generator speed. The proposed control strategy and structure are validated by MATLAB simulations
Function Approximation Technique-based Adaptive Force-Tracking Impedance Control for Unknown Environment
Full text linkAn accurate force-tracking in various applications may not be achieved without a complete knowledge of the environment parameters in the force-tracking impedance control strategy. Adaptive control law is one of the methods that is capable of compensating parameter uncertainties. However, the direct application of this technique is only effective for time-invariant unknown parameters. This paper presents a Function Approximation Technique (FAT)-based adaptive impedance control to overcome uncertainties in the environment stiffness and location with consideration of the approximation error in the FAT representation. The target impedance for the control law have been derived for unknown time-varying environment location and constant or time-varying environment stiffness using Fourier Series. This allows the update law to be derived easily based on Lyapunov stability method. The update law is formulated based on the force error feedback. Simulation results in MATLAB environment have verified the effectiveness of the developed control strategy in exerting the desired amount of force on the environment in x-direction, while precisely follows the required trajectory along y-direction, despite the constant or time-varying uncertainties in the environment stiffness and location. The maximum force error for all unknown environment tested has been found to be less than 0.1 N. The test outcomes for various initial assumption of unknown stiffness between 20000N/m to 120000N/m have shown consistent and excellent force tracking. It is also evident from the simulation results that the proposed controller is effective in tracking time-varying desired force under the limited knowledge of the environment stiffness and location