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Journal of Robotics and Control (JRC)
Published by Universitas Muhammadiyah Yogyakarta
ISSN : 27155056     EISSN : 27155072     DOI : https://doi.org/10.18196/jrc
Core Subject : Science, Engineering,
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering
Journal of Robotics and Control (JRC) is an international open-access journal published by Universitas Muhammadiyah Yogyakarta. The journal invites students, researchers, and engineers to contribute to the development of theoretical and practice-oriented theories of Robotics and Control. Its scope includes (but not limited) to the following: Manipulator Robot, Mobile Robot, Flying Robot, Autonomous Robot, Automation Control, Programmable Logic Controller (PLC), SCADA, DCS, Wonderware, Industrial Robot, Robot Controller, Classical Control, Modern Control, Feedback Control, PID Controller, Fuzzy Logic Controller, State Feedback Controller, Neural Network Control, Linear Control, Optimal Control, Nonlinear Control, Robust Control, Adaptive Control, Geometry Control, Visual Control, Tracking Control, Artificial Intelligence, Power Electronic Control System, Grid Control, DC-DC Converter Control, Embedded Intelligence, Network Control System, Automatic Control and etc.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 708 Documents
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Balancing Inventory Management: Genetic Algorithm Optimization for A Novel Dynamic Lot Sizing Model in Perishable Product Manufacturing Leuveano, Raden Achmad Chairdino; Asih, Hayati Mukti; Ridho, Muhammad Ihsan; Darmawan, Dhimas Arief
Journal of Robotics and Control (JRC) Vol 4, No 6 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i6.20667

Abstract

In Indonesia, the significant role of perishable products in food wastage has placed the country fourth globally in household food waste. Managing inventory for such products, with their short shelf life and stringent safety standards, emphasizes the need for efficient lot sizing planning. This study introduces a novel Dynamic Lot-Sizing (DLS) model, addressing perishable products and inventory constraints across multiple products, periods, and varying demands. The model aims to optimize production quantity and binary production, minimizing overall system costs. Employing a Genetic Algorithm (GA), this research solves the DLS model under constrained and unconstrained inventory capacities. Real-case data from a bread manufacturing company validates the model, while sensitivity analysis examines perishability's impact on the solution and model performance. The DLS-GA model not only reduces system costs but also effectively considers product perishability, offering optimal production plans.
Robust DeepFake Face Detection Leveraging Xception Model and Novel Snake Optimization Technique Al-Qazzaz, Ahmed SAAD; Salehpour, Pedram; Aghdasi, Hadi S.
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22473

Abstract

DeepFake technology has created an existential crisis around authenticity in digital media with the ability to create nearly imperceptible forgeries on a massive scale, such as impersonating public figures for nefarious reasons like misinformation campaigns, harassment, and fraud. In this thesis, a model Xception is combined with the Snake optimization technique to ensure efficient and accurate detection of ADOR in practice. The former is deep CNN architecture Xception which exploits depthwise separable convolutions to perform efficient feature extraction, and the latter is a novel snake optimization that borrows lessons from real-life predatory snakes to dynamically adapt parameters for better exploration of search space while avoiding local optima. The combined modality is systematically evaluated using multiple challenging DeepFake video datasets and shows significant improvement. A comparison of performance with other methods showed that a mean accuracy, precision, recall, and F1-score was 98.53% for the Snake-optimized Xception model while outperformed some state-of-the-art approaches and traditional Xception itself. This helps in reducing missing of misdetection and reduction of false positives, helping achieve a tool that is highly effective for digital media forensics. Such discoveries open the door for this method to unlock new levels of digital content integrity, necessary in media verification and legal evidence authentication, as well as assist individuals dealing with fake news or videos attempting identity theft online. This research highlights the strong efficacy of coupling the Xception model with Snake optimization for DeepFake detection; thus, establishes a new state-of-the-art and will inspire future studies and applications to protect genuineness in digital media.
The Impact of Simplifications of the Dynamic Model on the Motion of a Six-Jointed Industrial Articulated Robotic Arm Movement Fazilat, Mehdi; Zioui, Nadjet
Journal of Robotics and Control (JRC) Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i1.20263

Abstract

This research investigates the impact of model simplification on the dynamic performance of an ABB IRB-140 six-jointed industrial robotic arm, concentrating on torque prediction and energy consumption. The entire mathematical model of forward, reverse, differential kinematics, and dynamic model proposed based on the technical specifications of the arm, and to obtain the center of the mass and inertia matrices, which are essential components of the dynamic model, Utilizing Solidworks, we developed three CAD/CAM models representing the manipulator with varying detail levels, such as simplified, semi-detailed, and detailed. Our findings indicate minor differences in the model's torque and energy consumption graphs. The semi-detailed model consumed the most energy, except for joint 1, with the detailed model showing a 0.53% reduction and the simplified model a 6.8% reduction in energy consumption. Despite these variations, all models proved effective in predicting the robot's performance during a standard 30-second task, demonstrating their adequacy for various industrial applications. This research highlights the balance between computational efficiency and accuracy in model selection. While the detailed model offers the highest precision, it demands more computational resources, which is suitable for high-precision tasks. In discrepancy, simplified, less precise models offer computational efficiency, making them adequate for specific scenarios. Our study provides critical insights into selecting dynamic models in industrial robotics. It guides the optimization of performance and energy efficiency based on the required task precision and available computational resources. This comprehensive comparison of dynamic models underscores their applicability and effectiveness in diverse industrial settings.
AI-based Bubbles Detection in the Conformal Coating for Enhanced Quality Control in Electronics Manufacturing Zouhri, Nizar; Mourabit, Aimad El; Abidine, Alaoui Ismaili Zine El
Journal of Robotics and Control (JRC) Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i2.20441

Abstract

This research pioneers the application of artificial intelligence (AI) methodologies—machine learning, deep learning, hybrid models, transfer learning, and edge AI deployment—in enhancing bubble detection within conformal coatings, a critical as- pect of electronics manufacturing quality control. By addressing the limitations of traditional detection methods, our work offers a novel approach that significantly improves automation, accuracy, and speed, thereby ensuring the reliability of electronic assemblies and contributing to economic and safety benefits. We navigate through the challenges of creating diverse datasets, system robustness, and the imperative for industry-wide standardization, proposing strategies for overcoming these obstacles. Our findings highlight the transformative impact of AI on quality control processes, demonstrating substantial advancements in detection capabilities. Furthermore, we advocate for future research, development, and collaboration to extend these AI-driven improvements across the manufacturing spectrum. This study underscores the potential of AI to revolutionize electronics manufacturing, emphasizing the need for continued innovation and standardization to realize safer, more efficient, and cost-effective production methodologies.
Using Grey Wolf Optimization Algorithm and Whale Optimization Algorithm for Optimal Sizing of Grid-Connected Bifacial PV Systems Hadi, Husam Ali; Kassem, Abdallah; Amoud, Hassan; Nadweh, Safwan; Ghazaly, Nouby M.
Journal of Robotics and Control (JRC) Vol 5, No 3 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i3.21777

Abstract

The shift towards renewable energies is driven by the shortage of fossil fuels for electricity generation and the associated harmful impacts. Grid-connected PV systems are a reliable and effective choice for power production across different uses, making them a key player in the global renewable energy landscape. Consequently, the careful selection of components for these systems is a crucial and widely studied aspect in this area of research. This paper introduced using gray wolf optimization algorithm GWO whale optimization algorithm WOA for determining the optimal number of grid - connected bifacial photovoltaic PV systems in Babylon Hilla. The considered factors included available space, desired energy production, radiation, dihedral factor, budget constraints, and grid connectivity requirements. The mathematical formulation of the problem and implementation details of the algorithms are presented. In addition, two cases studied are performed one for a residential area, and the other for a single house. The results demonstrated the efficiency and effectiveness of both algorithms in identifying optimal solutions for determining the size of systems in the area under study. However, the WOA surpassed the GWO in meeting the optimization criteria. The proper selection of these systems resulted in higher power generation, lower costs, improved energy management, and the advancement of sustainable solar energy solutions.
Real-Time Optimal Switching Angle Scheme for a Cascaded H-Bridge Inverter using Bonobo Optimizer Taha, Taha A.; Wahab, Noor Izzri Abdul; Hassan, Mohd Khair; Zaynal, Hussein I.; Taha, Faris Hassan; Hashim, Abdulghafor Mohammed
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.21701

Abstract

This study demonstrates a novel method for using the Bonobo Optimizer (BO) to selective harmonic elimination in a cascaded H-Bridge Multilevel Inverter (CHB-MLI) running on solar power. The primary objective is to calculate, in real time, the optimal switching angles for eliminating low-order harmonics while maintaining a constant output voltage despite variations in the input voltage. To prove that the BO algorithm works, tests were done on a three-phase, seven-level CHB-MLI that compared it to other evolutionary algorithms like the genetic algorithm (GA) and particle Swarm optimization (PSO). An adaptive BO-Artificial neural network (BO-ANN) based system was developed to compute real-time switching angles and applied to a 7-level CHB-MLI. The results demonstrate that the BO algorithm is the most accurate and fastest evolutionary algorithm for calculating optimal switching angles. This study illustrates the BO algorithm's effective utilization in real-time harmonic elimination applications in CHB-MLI.
Optimizing Parameters for Earthquake Prediction Using Bi-LSTM and Grey Wolf Optimization on Seismic Data Shidik, Guruh Fajar; Pramunendar, Ricardus Anggi; Purwanto, Purwanto; Hasibuan, Zainal Arifin; Dolphina, Erlin; Kusumawati, Yupie; Sriwinarsih, Nurul Anisa
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.22199

Abstract

Earthquakes pose a significant threat to societies worldwide, underscoring the urgent need for advanced prediction technologies. This study introduces an optimization technique aimed at reducing the error rate in earthquake prediction by selecting the most suitable parameters for a Bi-LSTM (Bidirectional Long Short-Term Memory) model. Despite Bi-LSTM's promising outcomes, variations in parameters can impact performance, necessitating careful parameter selection. This research employs Grey Wolf Optimization (GWO) to optimize parameters and evaluates its effectiveness against other group optimization approaches to identify the most efficient parameters for earthquake prediction. Additionally, a multiple input multiple output (MIMO) architecture is implemented to enhance prediction accuracy. The evaluation results demonstrate that GWO outperforms other optimization techniques, achieving a reduced loss score of 0.364. The ANOVA method yields a p-value approaching 0, indicating statistical significance. This study contributes to the development of early warning systems for earthquake disasters by emphasizing the importance of parameter optimization in earthquake prediction and showcasing the effectiveness of Bi-LSTM and GWO methodologies.
Design and Simulation of an Analog Robust Control for a Realistic Buck Converter Model Mohammed, Ibrahim Khalaf; Khalaf, Laith Abduljabbar
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22408

Abstract

The simplicity and cost of the control systems used in power converters are an urgent aspect. In this research, a simple and low cost voltage regulation system for a Buck converter system operating in uncertain conditions is provided. Using an electronic PID controller technique, the feedback control scheme of the presented Buck converter is carried out. Matlab software used a simulation environment for the proposed analog PID-based Buck converter scheme. The PID controller is easily implementable since it is built with basic and conventional electronic components like a resistor, capacitor and op-amp. The system simulation has high reliability as it is implemented using the Simscape package. The Simscape components used to build the converter system are modeled effectively taking into consideration including the practical factors such as internal resistance, tolerance and parasitic elements. This procedure certainly enhances the reliability of the simulation findings as the working conditions of the simulated system become more closer to the real-world conditions. Particle Swarm Optimization (PSO) is employed to properly optimize tune the PID gains. The regulation process of the PID control scheme is assessed under voltage and load disturbances in order to explore the robustness of the Buck converter performance. The findings from the system simulation, under the uncertainties, show largest rise time and settling time of 20 ms and 25 ms respectively, zero overshoot and minimum steady state error response, except at load disturbance case there is a fluctuation of 1 V. Consequently, It can be said that the proposed Buck converter based on analog PID controller can be used efficiently in the industrial and power applications.
Towards Controlling Mobile Robot Using Upper Human Body Gesture Based on Convolutional Neural Network Fuad, Muhammad; Umam, Faikul; Wahyuni, Sri; Fahriani, Nuniek; Nurwahyudi, Ilham; Darwaman, Mochammad Ilham; Maulana, Fahmi
Journal of Robotics and Control (JRC) Vol 4, No 6 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i6.20399

Abstract

Human-Robot Interaction (HRI) has challenges in investigation of a nonverbal and natural interaction. This study contributes to developing a gesture recognition system capable of recognizing the entire human upper body for HRI, which has never been done in previous research. Preprocessing is applied to improve image quality, reduce noise and highlight important features of each image, including color segmentation, thresholding and resizing. The hue, saturation, value (HSV) color segmentation is executed by utilizing blue color backdrop and additional lighting to deal with illumination issue. Then thresholding is performed to get a black and white image to distinguish between background and foreground. The resizing is completed to adjust the image to match the size expected by the model. The preprocessed data image is used as input for gesture recognition based on Convolutional Neural Network (CNN). This study recorded five gestures from five research subjects in difference gender and body posture with total of 450 images which divided into 380 and 70 images for training and testing respectively. Experiments that performed in an indoor environment showed that CNN achieved 92% of accuracy in the gesture recognition. It has lower level of accuracy compare to AlexNet model but with faster training computation time of 9 seconds. This result was obtained by testing the system over various distances. The optimal distance for a camera setting from user to interact with mobile robot by using gesture was 2.5 m. For future research, the proposed method will be improved and implemented for mobile robot motion control.
Advanced Ensemble Deep Learning Framework for Enhanced River Water Level Detection: Integrating Transfer Learning Tawfeeq, Nisreen; Harbi, Jameelah
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22291

Abstract

The precise monitoring and prediction of river water levels are crucial for effective environmental management, flood prevention, and ensuring water security. This paper introduces an advanced deep learning framework that utilizes an ensemble of state-of-the-art neural networks, namely InceptionV3, VGG16, Xception, MobileNet, and ResNet152, to enhance the accuracy of water level detection from river imagery. The proposed system integrates these models through a robust ensemble methodology that leverages hard voting to improve predictive performance and reliability. Through rigorous preprocessing, including normalization, resizing, and augmentation, alongside strategic transfer learning, the framework achieves an impressive accuracy of 99.5833%, precision of 99.5929%, recall of 99.5762%, and an F1 score of 99.5838%. The ensemble approach not only addresses the variability in image data but also ensures robustness against overfitting and data imbalances. Furthermore, the application of Gradient-weighted Class Activation Mapping (Grad-CAM) enhances the interpretability of the model's decisions, facilitating trust and transparency in its predictions. This study not only demonstrates the potential of ensemble deep learning in hydrological applications but also sets the stage for future enhancements such as real-time processing and integration into comprehensive flood management systems. Future research will explore scalability, the incorporation of additional predictive variables, and the expansion of the model to include real-time monitoring capabilities, aiming to provide a more dynamic tool for disaster readiness and environmental conservation.

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