Detection of Sealing Defects in Canned Sardines Using Local Binary Pattern and Perceptron Techniques for Enhanced Quality Control

In 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

Self-Motion Control Exoskeleton for Upper Limb Rehabilitation with Perceptron Neuron Motion Capture

Upper limb rehabilitation robot can facilitate patients to regain their original impaired arm function and reduce therapist’ workload. However, the patient does not have a direct control over his/ her arm movement, which may lead to discomfort or even injury. This paper focuses on the development of a self-motion rehabilitation robot using Perception Neuron motion capture, where the movement of the impaired arm imitates the motion of the healthy limb.  The Axis Neuron software receives the healthy upper limb’s motion data from Perception Neuron. Unity serves as the simulation engine software that provides a 3-dimensional animation. ARDUnity acts as the communication platform between Unity software with Arduino. Arduino code is generated using Wire Editor, which avoids the need of the programming to be written in C++ or C#. Finally, Arduino instructs the exoskeleton motors that are connected to the impaired arm to move, following the healthy joint’s motion. The forward kinematics analysis for the robotic exoskeleton has been carried out to identify its workspace. Hardware experimental tests on the elbow and wrist flexion/ extension have shown the root-mean-square errors (RMSE) between the healthy and impaired arms movement to be 1.5809○ and 12.1955○ respectively. The average time delay between the healthy and impaired elbow movement is 0.1 seconds. For the wrist motion, the time delay is 1 second. The experimental results verified the feasibility and effectiveness of the Perception Neuron in realizing the self-motion control robot for upper limb rehabilitation. The proposed system enables the patients to conduct the rehabilitation therapy in a safer and more comfortable way as they can directly adjust the speed or stop the movement of the affected limb whenever they feel pain or discomfort

Comparative Analysis of 1D – CNN, GRU, and LSTM for Classifying Step Duration in Elderly and Adolescents Using Computer Vision

Developing 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

Comparative Analysis of Yolov-8 Segmentation for Gait Performance in Individuals with Lower Limb Disabilities

This research aims to develop an example of gait pattern segmentation between normal and disabled individuals. Walking is the movement of moving from one place to another, where individuals with physical limitations on the legs have different walking patterns compared to individuals without physical limitations. This study classifies gait into three categories, namely individuals with assistive devices (crutches), individuals without assistive devices, and normal individuals. The study involved 10 subjects, consisting of 2 individuals with assistive devices, 3 individuals without assistive devices, and 5 normal individuals. The research process was conducted through three main stages, namely: image database creation, data annotation, and model training and segmentation using YOLOv8. YOLOv8-seg is the platform used to segment the data. The test results showed that the YOLOv8L-seg model achieved convergence value at the 23rd epoch with the 4th scenario in recognizing the walking patterns of the three categories. However, research on walking patterns of people with disabilities faces several obstacles, such as the lack of confidence or emotion of the subject during the data collection process, which is conducted at the location of the subject's choice. In addition, YOLOv8-seg showed consistent performance across the five models used, obtaining a maximum mAP50 value of 0.995 for mAP50 box and mAP50 mask

Improving TCP/AQM Network Stability Using BBO-Tuned FLC

One of the modern technologies used to improve the performance of various systems, including communications networks and the Internet, is the technology based on biogeography (BBO) that many researchers in the field of automation and control have shed light on. Fuzzy logic is one of the expert systems that has dealt with its use in control systems by many researchers within different applications. The current work has shed light on the mechanism of using The Biogeography Based-Optimization (BBO) technique for adjusting FLC parameters is called (BBO-FLC). The simulation was performed using Matlab program and the researchers adopted the technique as part of the stability of TCP network. The performance of the techniques used in the optimization process can be identified by comparing the results of each case, such as the proposed technique, with other types represented by the traditional control type Proportional–integral–derivative controller (PID). The possibility of using modern and intelligent optimization techniques for the optimal controller is tested using a tuning process for the parameters of the fuzzy type expert controller with the help of the biogeography-based optimization (BBO) technique. The contributions of the research are to verify the possibility of improving the performance by comparing the behavior of the system for the proposed test and simulation cases by obtaining the prescribed level and without exceeding the permissible values

Fractional Approach to Two-Group Neutron Diffusion in Slab Reactors

The 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

Function Approximation Technique-based Adaptive Force-Tracking Impedance Control for Unknown Environment

An 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

A Hybrid PSO-GCRA Framework for Optimizing Control Systems Performance

Optimization is essential for improving the performance of control systems, particularly in scenarios that involve complex, non-linear, and dynamic behaviors. This paper introduces a new hybrid optimization framework that merges Particle Swarm Optimization (PSO) with the Greater Cane Rat Algorithm (GCRA), which we call the PSO-GCRA framework. This hybrid approach takes advantage of PSO's global exploration capabilities and GCRA's local refinement strengths to overcome the shortcomings of each algorithm, such as premature convergence and ineffective local searches. We apply the proposed framework to a real-world load forecasting challenge using data from the Australian Energy Market Operator (AEMO). The PSO-GCRA framework functions in two sequential phases: first, PSO conducts a global search to explore the solution space, and then GCRA fine-tunes the solutions through mutation and crossover operations, ensuring convergence to high-quality optima. We evaluate the performance of this framework against benchmark methods, including EMD-SVR-PSO, FS-TSFE-CBSSO, VMD-FFT-IOSVR, and DCP-SVM-WO. Comprehensive experiments are carried out using metrics such as Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and convergence rate.  The proposed PSO-GCRA framework achieves a MAPE of 2.05% and an RMSE of 3.91, outperforming benchmark methods, such as EMD-SVR-PSO (MAPE: 2.85%, RMSE: 4.49) and FS-TSFE-CBSSO (MAPE: 2.98%, RMSE: 4.69), in terms of accuracy, stability, and convergence efficiency. Comprehensive experiments were conducted using Australian Energy Market Operator (AEMO) data, with specific attention to normalization, parameter tuning, and iterative evaluations to ensure reliability and reproducibility

Autonomous Driving Model with Collision Prediction for Urban and Extra-Urban Environments

This study introduces an architecture for an autonomous vehicle control system based on a collision detector and geometric modeling of trajectories. The goal is to develop a robust and reliable control model that can navigate metropolitan environments, often crowded with pedestrians and bicycles, as well as suburban areas, where traffic patterns can fluctuate. We have created a modular control unit that includes a collision predictor, which interacts closely with the decision module. The executed algorithm demonstrates the effectiveness of our system by ensuring the safety and comfort of the passengers. It can identify potential collisions from a distance and initiate braking preventively, following precise guidelines for deceleration and acceleration. To validate our methods, we are looking at simulations of realistic case studies. The research conducted underscores a crucial advancement in the development of safer and more flexible autonomous driving technologies

Two-Flexible-Link Manipulator Vibration Reduction Through Fuzzy-Based Position

The increasing demand for robotic applications has emphasized the need for advanced control strategies, particularly for flexible manipulators with lightweight links. These manipulators offer advantages such as reduced energy consumption, increased payload capacity, and precise high-speed operation but face challenges due to oscillations and delays caused by their flexibility. This study evaluates the performance of Fuzzy Logic Control (FLC) and Linear Quadratic Regulator (LQR) techniques for a Quanser two-link flexible manipulator, using quantitative metrics to compare their effectiveness. The LQR controller was implemented using state-space modeling, with weighting matrices Q and R tuned to achieve minimal overshoot and fast settling times. The FLC system employed five triangular membership functions for inputs and outputs, covering normalized ranges of [-1, 1] for angular errors and [-2.75, 2.75] for error rates, with a heuristic rule base designed to optimize performance. Simulations were conducted under step input conditions at target angles of 30° and 60°, with performance evaluated using vibration amplitude, settling time, steady-state error, and overshoot. Quantitatively, the LQR controller reduced vibration amplitudes to 5 radians for a 30° input and achieved settling times of approximately 2 seconds. For the same conditions, the FLC system reduced vibrations further to 4 radians, though with slightly longer settling times of around 2.3 seconds. At a 60° input, LQR vibrations peaked at over 10 radians, while FLC maintained peak vibrations at approximately 4 radians. These results highlight the FLC’s superior vibration suppression, particularly at higher input angles, albeit with marginally slower response times. However, the study was limited to idealized simulation conditions and requires further experimental validation. This research underscores the trade-offs between LQR’s precision and FLC’s adaptability, emphasizing the importance of parameter tuning and system modeling in achieving optimal performance for flexible manipulators