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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 24 Documents
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Search results for , issue "Vol 5, No 2 (2024)" : 24 Documents clear
Power Management and Voltage Regulation in DC Microgrid with Solar Panels and Battery Storage System Mutlag, Ashraf Abdualateef; Abd, Mohammed Kdair; Shneen, Salam Waley
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.v5i2.20581

Abstract

Photovoltaics are one of the most important renewable energy sources to meet the increasing demand for energy. This led to the emergence of Microgrid s, which revealed a number of problems, the most important of which is managing and monitoring their operation, this research contributes mainly by using a maximum power tracking algorithm Which depends on artificial neurons and integrating it with a proposed algorithm for energy management in Standalone DC Microgrid, in order to control the distribution of power and maintain the DC bus voltage level.  Maximum Power Point Tracking (MPPT) algorithm based on ANN+PID is used. Where ANN tracks the maximum power point by estimating the reference voltage using real-time data such as temperature and solar radiation. The PI controller reduces the error between the measured voltage and the reference voltage and makes the necessary adjustments in order to control the boost converter connected to the photovoltaic panels. While the process of controlling the DC bus voltage level is done by controlling the battery charging and discharging process through the power management algorithm and controlling the Bidirectional converter switches according to the battery’s state of charge. The simulation results obtained by used MATLAB Simulink are shown that the used MPPT algorithm achieved the maximum power with the least amount of fluctuation, the method's efficiency was 99.92%, and its accuracy was 99.85%, as well as the success of the power management algorithm controlling the battery charging/discharging process and maintaining the DC voltage level at the specified value in different operating scenarios.
Ovarian Tumors Detection and Classification on Ultrasound Images Using One-stage Convolutional Neural Networks Le, Van-Hung; Pham, Thi-Loan
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.v5i2.20589

Abstract

Currently, the advent of CNN (Convolutional Neural Network) has brought very convincing results to computer vision problems. One-stage CNNs are a suitable choice for research and development to have an overview of the current results of the process of detecting and classifying OTUM from ovarian ultrasound images. In this paper, we have performed a comprehensive study on one-stage CNNs for the problem of detecting and classifying OTUM on ovarian ultrasound images. The OTUM datasets we tested were two popular OTUM datasets: OTU and USOVA3D. The one-stage CNNs we tested and evaluated belong to the YOLO (You Only Look Once) family (YOLOv5, YOLOv7, YOLOv8 variations, and YOLO-NAS), and the SSD (Single Shot MultiBox Detector) family (VGG16-SSD, Mb1-SSD, Mb1-SSDLite, Sq-SSD-Lite, and Mb2-SSD-Lite). The results of detecting OTUM (with or without OTUM on ovarian ultrasound images) are high (with Mb1-SSD of Acc = 98.90%, P = 98.58%, R = 98.9% on “USOVA3D 2D f r1 80 20” set; with Mb2-SSD-Lite of Acc = 97.87%, P = 97.16%, R = 97.87% on “USOVA3D 2D f r2 80 20” set). The results of detecting and classifying OTUM into 8 classes are low (the highest is Acc = 92.04%, P = 74.81%, R = 92.04% on the OTU-2D dataset). Regarding computation time, CNNs of the YOLO family have faster computation times than networks of the SSD family. The above results show that the problem of classifying ovarian tumors on ultrasound images still contains many challenges that need to be resolved in the future.
Enhancing Pulmonary Disease Classification in Diseases: A Comparative Study of CNN and Optimized MobileNet Architectures Mohammed, Omar Nadhim
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.v5i2.21422

Abstract

Background: Deep learning technologies, especially Convolutional Neural Networks (CNNs), are revolutionizing the field of medical imaging by providing advanced tools for the accurate classification of pulmonary diseases from chest X-ray (CXR) images. In our study, we employed both traditional CNN models and MobileNet architectures to classify various chest diseases using CXR images. Initially, a conventional CNN model was utilized to estab- lish a baseline accuracy. Subsequently, we adopted MobileNet, known for its efficiency in processing image data, to enhance classification performance. To further optimize the system, we applied Energy Valley Optimization (EVO) for hyperparameter tuning. The baseline CNN model achieved an accuracy of 85.91%. The implementation of MobileNet significantly improved this metric, reaching a pre-optimization accuracy of 93.30%. Post-EVO optimization, the accuracy was further enhanced to 94.18%. Comparative analysis of accuracy, precision, recall, F1-score, and ROC curves was conducted to illustrate the impact of hyperparameter tuning on model performance in medical diagnostics. Our findings demonstrate that while standard CNNs provide a solid foundation for CXR image classification, the integration of MobileNet architectures and EVO for hyperparameter adjustment significantly boosts diagnostic accuracy. This advancement in automated medical image analysis could potentially transform the landscape of pulmonary disease diagnosis, offering a more robust framework for accurate and efficient patient care.
Analysis of Problems and Prospects for Improving Automatic Control Systems of Interconnected Electric Drives Nalibayev, Nurgali; Kozhageldi, Bolat; Omarov, Zhaksylyk; Zhanpeiissova, Aizhan; Tashimbetov, Murat
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.v5i2.20711

Abstract

The aim of this study was to analyse the problems and prospects for improving automatic control systems of interconnected electric drives. Various methods, including analytical, classification, functional, statistical, and synthesis, were used to provide recommendations for error correction in the design processes of these systems and to detail their functioning. The study revealed the peculiarities and differences of automatic control systems of interconnected electric drives. The study analysed the errors made during the operation of these systems and the reasons for their occurrence. It also identified uncertainties in the development process and their impact on the functioning of the systems. The mechanism's efficiency, development, and complexity in different spheres were analysed. The text also considered issues related to estimating the operation of systems, limitations during operation, and the influence of limitations on results. Recommendations for promoting more effective regulation have been provided. The research showed that these systems play a crucial role in complex technological processes. The results have vague practical implications for developing the mechanism of automatic control systems for interconnected electric drives to apply and influence a certain device. In conclusion, the study analysed the problems and prospects for improving automatic control systems for interconnected electric drives.
Solvability and Weak Controllability of Fractional Degenerate Singular Problem Fatma, Achab; Batiha, Iqbal; Imad, Rezzoug; Takieddine, Oussaeif; Ouannas, Adel
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.v5i2.20474

Abstract

In this paper, our objective is to investigate the unique solvability and the weak controllability of the fractional degenerate and singular problem. The energy inequality method is gives a sufficient conditions for the existence and the uniqueness of the strong solution of our problem. This problem is ill-posed in the sense of Hadamard. To address this, we attempt regularization through a fractional Tikhonov regularization method, which not only establishes weak controllability but also provides a full characterization of the optimal control.
Improving the Efficiency of Open Cathode PEM Fuel Cell Through Hydrogen Flow Control Using Wavelet-Clipping
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.v5i2.21227

Abstract

Open cathode proton exchange membrane fuel cells (OC-PEMFC) are devices that produce electrical energy through an electrochemical reaction between hydrogen and oxygen gas. Rapid load changes often lead to fluctuations in the flow of hydrogen entering the OC-PEMFC system. Increased load directly correlates with higher hydrogen gas consumption. However, if there is a delay in adjusting the gas flow rate to changes in load, it can trigger fluctuations in the amplitude and frequency of the output voltage. This fluctuation ultimately disrupts the stability of the power supply to the load, and reducing efficiency. Therefore, this paper presents a novel hybrid system that integrates wavelet and clipping techniques to regulate a more stable hydrogen flow, enhancing efficiency and accuracy under constant load conditions. A wavelet control system is used to mitigate noise, coupled with amplitude limitation through clipping techniques. This control system is implemented in OC-PEMFC model that is validated with experimental data. The performance analysis of this hybrid system reveals a 1.95 % increase in efficiency and attains high accuracy, as evidenced by a low ISE value of 0.028 during interference.
Model Predictive Control in Hardware in the Loop Simulation for the OnBoard Attitude Determination Control System Irwanto, Herma Yudhi; Yusgiantoro, Purnomo; Sahabuddin, Zainal Abidin; Bura, Romie O.; Artono, Endro; Hakim, Arif Nur; Nuryadi, Ratno; Andiarti, Rika; Mariani, Lilis
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.v5i2.21613

Abstract

Rocket flight tests invariably serve a purpose, one of which involves area monitoring or aerial photography. Consequently, the rocket necessitates the installation of a camera that remains consistently oriented toward the Earth's surface throughout its trajectory. Thus, ensuring the rocket's stability and preventing any rotation becomes imperative. To achieve this, the Onboard Attitude Determination Control System (OADCS) was researched and developed, fully controlled by NI myRIO with Labview as the programming language, ensures the rocket's attitude control and maintains a rolling angle of 0 degrees during flight. The MyRIO oversees the retrieval of attitude and position data from the X-Plane flight simulator, offering feedback through actuator control. The development of the OADCS proceeded incrementally through stages utilizing the Software in the Loop Simulation (SILS) and Hardware in the Loop Simulation (HILS) techniques, to ensure the verification of the system's functionality before its application to the rocket for real flight testing. In the OADCS control scheme, Model Predictive Control (MPC) is chosen, and it is compared with a PID controller to serve as a benchmark for processing speed. Because the rocket's flight time is short and its speeds of up to Mach 4. The simulation results indicate that MPC can halt the rocket's rotation 12 times more rapidly than PID control. Additionally, the MPC's ability to maintain a zero-degree rotation can persist throughout the rocket's flight time. Employing SILS and HILS enhances the OADCS rocket development process by incorporating MPC, which holds promise for application in real rockets.
Optimizing Latent Space Representation for Tourism Insights: A Metaheuristic Approach Win, Thinzar Aung; Sunat, Khamron
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.v5i2.21419

Abstract

In the modern digital era, social media platforms with travel reviews significantly influence the tourism industry by providing a wealth of information on consumer preferences and behaviors. However, these textual reviews' complex and varied nature poses analytical challenges. This research employs advanced Natural Language Processing (NLP) techniques to process and analyze vast amounts of travel data efficiently, tackling the challenges posed by the diverse and detailed content in the tourism field. We have developed an innovative text clustering methodology that combines BERT's deep linguistic analysis capabilities (Bidirectional Encoder Representations from Transformers) with the thematic organization strengths of LDA (Latent Dirichlet Allocation). This hybrid model, further refined with the dimensionality reduction capabilities of ELM-AE and the optimization precision of PPSO (Phasor Particle Swarm Optimization), yields concise, contextually enriched text representations. Such refined data representations enhance the accuracy of K-means clustering, facilitating nuanced topic identification within the complex domain of travel reviews. This approach streamlines feature extraction and ensures rapid training and minimal loss, underscoring the model's effectiveness in distilling and reconstructing textual features. Our application of this hybrid LDA-BERT model to analyze TripAdvisor reviews of Thailand's shopping destinations reveals meaningful insights, significantly aiding in understanding customer experiences. Despite its contributions, this study acknowledges limitations, including biases in user-generated content and the intricacies of accurately interpreting sentiments and contexts within reviews. This research marks a significant step forward in utilizing NLP for tourism industry analysis, providing a pathway for future investigations to build upon.
Sorting Line Assisted by A Robotic Manipulator and Artificial Vision with Active Safety Mogro, María F.; Jácome, Fausto A.; Cruz, Guillermo M.; Zurita, Jonathan R.
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.v5i2.20327

Abstract

This article presents the design, implementation and evaluation of an object classification and manipulation system in industrial environments by integrating artificial vision and a MELFA RV-2SDB robotic manipulator. The central problem lies in the need to achieve rapid and accurate classification of objects for palletizing, while ensuring the safety of operators. To address this challenge, a machine vision system based on Logitech C920 HD Pro cameras and force and torque sensors was used on the robotic manipulator. The methodology focused on the use of object and person detection algorithms, as well as direct and inverse kinematics to calculate adaptive movements of the manipulator. The experiments covered evaluation of the system's accuracy and efficiency under various lighting and environmental conditions, as well as testing people detection and geometric shape classification. The results indicated that the system allowed precise and efficient manipulation, adapting in real time to the position and characteristics of the detected objects. The conclusions highlighted the effectiveness of the system in improving productivity and safety in collaborative industrial environments, highlighting the importance of integrating cutting-edge technologies to address automation challenges in the industry.
Reliable Wireless Sensor Network Planning with Multipath Topology through Relay Placement Optimization Amron, Kasyful; Kusumawinahyu, Wuryansari M; Anam, Syaiful; Mahmudy, Wayan F
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.v5i2.19921

Abstract

Recent developments in Wireless Sensor Networks (WSN) focus on scalability and reliability. This research addresses the challenge of improving reliability in WSNs through optimal relay placement and multipath topology design. A heuristic method with a Multi-Objective Optimization (MOO) approach is proposed to solve this problem. The proposed method integrates a modified Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) characteristics. The hybrid approach aims to minimize the number of relays and associated communication costs while maintaining network reliability. The method encodes relay positions and quantities into GA chromosomes that are updated by mutation, crossover, and PSO-inspired particle motion. Simulations are performed in a simplified square area with twenty randomly placed sensors, a hundred and thirty-two arranged relays, and a single sink node. As a result, the simulation generated two multipath topologies that offer unique advantages. The first emphasizes relay efficiency (61 relays, with 2096 costs), while the second ensures lower communication costs (64 relays, 1832 costs). Comparisons with alternative algorithms, including Dijkstra, A-star, GA, and PSO, prove the superiority of the proposed approach. The optimum results obtained with a composition of 95% GA and 5% PSO, outperform alternative algorithms in terms of relay efficiency and communication cost. This research contributes to the field by providing a robust solution for designing reliable multipath WSNs with a minimum number of relays.

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