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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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Enhancement of Underwater Video through Adaptive Fuzzy Weight Evaluation Sonawane, Jitendra; Patil, Mukesh; Birajdar, Gajanan K
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.v5i2.20496

Abstract

Underwater video enhancement plays a critical role in improving the visibility and quality of underwater imagery, which is essential for various applications such as marine biology, underwater archaeology, and offshore inspection. In this article, we present a novel approach for enhancing underwater videos. Our method employs fuzzy logic and a unique fuzzy channel weight coefficient to effectively address challenges in underwater imaging. The method aims to improve the perceptual quality of underwater videos by enhancing contrast, reducing noise, and increasing overall image clarity. The key component in our approach is the integration of fuzzy logic based channel weight coefficient which is adaptively selected to enhance the video frames. The fuzzy channel weight coefficient-based method assigns weights to different color channels in a manner that optimally addresses the underwater imaging conditions. To evaluate the performance of our fuzzy enhancement algorithm, we conducted experiments on the Fish4Knowledge database, a widely used benchmark dataset for underwater video analysis. We quantitatively assessed the improvement in video quality using various metrics, including Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error (RMSE), Structural Similarity Index (SSIM), and entropy. Our results demonstrate that the proposed fuzzy logic-based enhancement method outperforms existing techniques in terms of video quality enhancement and underwater image correction in terms of PSNR, RMSE and SSIM.
Using Learning Focal Point Algorithm to Classify Emotional Intelligence Sakhi, Abdelhak; Mansour, Salah-Eddine; Sekkaki, Abderrahim
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.v5i1.20895

Abstract

Recognizing the fundamental role of learners' emotions in the educational process, this study aims to enhance educational experiences by incorporating emotional intelligence (EI) into teacher robots through artificial intelligence and image processing technologies. The primary hurdle addressed is the inadequacy of conventional methods, particularly convolutional neural networks (CNNs) with pooling layers, in imbuing robots with emotional intelligence. To surmount this challenge, the research proposes an innovative solution—introducing a novel learning focal point (LFP) layer to replace pooling layers, resulting in significant enhancements in accuracy and other vital parameters. The distinctive contribution of this research lies in the creation and application of the LFP algorithm, providing a novel approach to emotion classification for teacher robots. The results showcase the LFP algorithm's superior performance compared to traditional CNN approaches. In conclusion, the study highlights the transformative impact of the LFP algorithm on the accuracy of classification models and, consequently, on emotionally intelligent teacher robots. This research contributes valuable insights to the convergence of artificial intelligence and education, with implications for future advancements in the field.
Unveiling the Advancements: YOLOv7 vs YOLOv8 in Pulmonary Carcinoma Detection Elavarasu, Moulieswaran; Govindaraju, Kalpana
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.v5i2.20900

Abstract

In this work, precision and recall measures are used to assess the performance of YOLOv7 and YOLOv8 models in identifying pulmonary carcinoma on a distinct collection of 700 photos. The necessity of early disease detection is increasing, thus choosing a reliable object detection model is essential. The goal of the research is to determine which model works best for this purpose, taking into account the unique difficulties that pulmonary cancer presents. The work makes a contribution to the field by showcasing the improvements made to YOLOv8 and underlining how well it detects both benign and malignant. YOLOv7 and YOLOv8 were used to independently train custom models using the pulmonary carcinoma dataset. The models' performance was measured using precision, recall, and mean average precision measures, which allowed for a comprehensive comparison examination. When it came to precision (58.2%), recall (61.2%), and mean average precision at both the 0.5:0.95 (33.3%) and 0.5 (53.3%) criteria, YOLOv8 outperformed YOLOv7. The 3.0% accuracy gain highlights YOLOv8's improved capabilities, especially in identifying small objects. YOLOv8's enhanced accuracy can be attributed to the optimisation of the detection process through its anchor-free design. According to this study, YOLOv8 is a more reliable model for pulmonary carcinoma identification than YOLOv7. The results indicate that YOLOv8 is the better option because of its higher recall, precision, and enhanced capacity to detect smaller objects—all of which are critical for early illness detection in medical imaging.
Stock Price Forecasting with Multivariate Time Series Long Short-Term Memory: A Deep Learning Approach Furizal, Furizal; Ritonga, Asdelina; Ma’arif, Alfian; Suwarno, Iswanto
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.v5i5.22460

Abstract

Stocks with their inherent complexity and dynamic nature influenced by a multitude of external and internal factors, play a crucial role in investment analysis and trend prediction. As financial instruments representing ownership in a company, stocks not only reflect the company's performance but are also affected by external factors such as economic conditions, political climates, and social changes. In a rapidly changing environment, investors and analysts continuously develop models and algorithms to aid in making timely and effective investment decisions. This study applies a Sequential model to predict stock data using a LSTM neural network. The model consists of a single hidden LSTM layer with 200 units. The LSTM layer, the core element of this model, enables it to capture temporal patterns and long-term relationships within the data. The training and testing data were divided into 80% for training and 20% for testing. The Adam optimizer was chosen to optimize the model's learning process, with a learning rate of 0.001. Dropout techniques were applied to reduce overfitting, with a dropout rate of 0.4, along with batch normalization and ReLU activation functions to enhance model performance. Additionally, callback mechanisms, including ReduceLROnPlateau and EarlyStopping, were used to optimize the training process and prevent overfitting. The model was evaluated using MAE and MSE metrics on training, testing, and future prediction data. The results indicate that the model achieved high accuracy, with an MAE of 0.0142 on the test data. However, future predictions showed higher MAE values, suggesting room for improvement in long-term forecasting. The model's ability to accurately predict future stock closing prices can assist investors in making informed investment decisions.
Formation Control of Multiple Unmanned Aerial Vehicle Systems using Integral Reinforcement Learning Dang, Ngoc Trung; Duong, Quynh Nga
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23505

Abstract

Formation control of Unmanned Aerial Vehicles (UAVs), especially quadrotors, has many practical applications in contour mapping, transporting, search and rescue. This article solves the formation tracking requirement of a group of multiple UAVs by formation control design in outer loop and integral Reinforcement Learning (RL) algorithms in position sub-system. First, we present the formation tracking control structure, which uses a cascade description to account for the model separation of each UAV. Second, based on value function of inner model, a modified iteration algorithm is given to obtain the optimal controller in the presence of discount factor, which is necessary to employ due to the finite requirement of infinite horizon based cost function. Third, the integral RL control is developed to handle dynamic uncertainties of attitude sub-systems in formation UAV control scheme with a discount factor to be employed in infinite horizon based cost function. Specifically, the advantage of the proposed control is pointed out in not only formation tracking problem but also in the optimality effectiveness. Finally, the simulation results are conducted to validate the proposed formation tracking control of a group of multiple UAV system.
Enhancing IoT Security: A Deep Learning and Active Learning Approach to Intrusion Detection Mahdi, Hawraa Fadel; Khadhim, Ban Jawad
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.v%vi%i.22292

Abstract

In response to the escalating demand for robust security solutions in increasingly complex Internet of Things (IoT) networks, this study introduces an advanced Intrusion Detection System (IDS) leveraging both deep learning and active learning techniques. This research addresses the unique challenges posed by IoT environments, such as limited resources and diverse network components, which traditional security measures fail to adequately protect. Employing a BiLSTM model integrated with an active learning strategy, our approach achieved impressive results, including precision, recall, and F1-scores close to 1, and a total accuracy of 0.99. The inclusion of active learning enables the IDS to focus on the most informative data subsets, enhancing processing efficiency and reducing computational demands essential for IoT contexts. This method demonstrates significant promise for detecting sophisticated cyber threats and providing an effective tool for real-world applications. The performance of the proposed model has been rigorously validated on well-established cybersecurity datasets and through simulations in an IoT network environment, confirming its scalability and efficiency. Future work will address potential limitations such as computational demands and adaptability to diverse IoT device architectures, ensuring broader applicability and robustness of the IDS in varied IoT scenarios.
Active Vibration Isolation using Tilt Horizontal Coupling Immune Inertial Double Link Sensor for Low Frequency Applications Nair, Vishnu G.; Hegde, Navya Thirumaleswar; V., Dileep M.
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.22595

Abstract

Addressing the challenge of horizontal tilt coupling is crucial for using inertial sensors in precise applications, such as seismology and seismic isolation, including gravitational wave detection. Researchers have proposed various design solutions, with the Double Link (DL) sensor standing out for its sim- plicity, precision, and effectiveness. This paper explores the use of the DL sensor in an active vibration isolation system. We evaluated different control algorithms, including Proportional- Integral-Derivative (PID), Linear Quadratic Regulator (LQR), Linear Quadratic Gaussian (LQG), and H-infinity. Simulations conducted in the Simscape environment showed that the H-infinity controller performed best, achieving a significant reduction in vibration. While the current study is based on simulations, future work will focus on experimental validation to confirm the system’s practical applicability and robustness in real-world scenarios. Our results demonstrate the potential of the DL sensor and LQG controller to enhance vibration isolation in low-frequency applications. Additionally, we conducted a detailed literature review on various methods used in similar applications. This review highlights alternative approaches, such as other sensor designs and control strategies, and discusses their advantages and limitations.
A Scoping Review on Unmanned Aerial Vehicles in Disaster Management: Challenges and Opportunities Nair, Vishnu G.; D'Souza, Jeane Marina; C. S., Asha; Rafikh, Rayyan Muhammad
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.22596

Abstract

Unmanned Aerial Vehicles (UAVs), or drones, have recently become transformative tools in disaster management. This paper provides an overview of the role of drones in dis- aster response and recovery, covering natural disasters such as earthquakes, floods, and wildfires, as well as man-made incidents like industrial accidents and humanitarian crises. UAVs offer advantages including rapid data collection, real-time situational awareness, and improved communication capabilities. Notable examples include the use of drones in the 2015 Nepal earthquake for mapping and search operations, and during the 2017 Hurricane Harvey for flood assessment and resource distribution. Advanced technologies further enhance drone capabilities; AI algorithms were used in the 2019 Mozambique cyclone to prioritize rescue operations, while thermal sensors located survivors in the 2018 Mexico earthquake. Despite these benefits, challenges such as privacy concerns, regulatory issues, and community acceptance remain. For instance, privacy issues arose during Hurricane Harvey due to aerial surveillance, and regulatory barriers delayed responses in the 2018 Indonesia earthquake. Ethical dilemmas also surface, such as balancing response urgency with privacy rights and ensuring equitable access to UAV services. The paper discusses potential solutions, including establishing privacy protocols, engaging communities, and streamlining regulations. Collaboration between government agencies, NGOs, and the private sector is essential to develop standardized protocols and enhance community acceptance. By integrating AI, machine learning, and advanced sensors, drones can significantly improve disaster response efficiency. In conclusion, drones play a pivotal role in revolutionizing disaster management strategies. Ongoing advancements in drone technology offer unprecedented opportunities to enhance disaster response, ultimately mitigating human suffering and preserving critical infrastructure. This paper reviews the role of drones in disaster response and recovery efforts, covering various disaster types including natural and man-made incidents.
Towards Resilient Machine Learning Models: Addressing Adversarial Attacks in Wireless Sensor Network Shihab, Mustafa Abdmajeed; Marhoon, Haydar Abdulameer; Ahmed, Saadaldeen Rashid; Radhi, Ahmed Dheyaa; Sekhar, Ravi
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.v5i5.23214

Abstract

Adversarial attacks represent a substantial threat to the security and reliability of machine learning models employed in wireless sensor networks (WSNs). This study tries to solve this difficulty by evaluating the efficiency of different defensive mechanisms in minimizing the effects of evasion assaults, which try to mislead ML models into misclassification. We employ the Edge-IIoTset dataset, a comprehensive cybersecurity dataset particularly built for IoT and IIoT applications, to train and assess our models. Our study reveals that employing adversarial training, robust optimization, and feature transformations dramatically enhances the resistance of machine learning models against evasion attempts. Specifically, our defensive model obtains a significant accuracy boost of 12% compared to baseline models. Furthermore, we study the possibilities of combining alternative generative adversarial networks (GANs), random forest ensembles, and hybrid techniques to further boost model resilience against a broader spectrum of adversarial assaults. This study underlines the need for proactive methods in preserving machine learning systems in real-world WSN contexts and stresses the need for continued research and development in this quickly expanding area.
A Comprehensive Review of AI and Deep Learning Applications in Dentistry: From Image Segmentation to Treatment Planning Nambiar, Rajashree; Nanjundegowda, Raghu
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23056

Abstract

Deep learning leverages multi-layered neural networks to analyze intricate data patterns, offering advancements beyond traditional methods. This review paper explores the significant impact of deep learning on diagnostic and treatment processes across various dental specialties. In restorative dentistry, deep learning algorithms enhance the detection of dental caries and optimize the design of restorations. Orthodontics benefits from automated cephalometric analysis and personalized treatment planning. Periodontics utilizes deep learning for accurate diagnosis and classification of periodontal diseases, as well as monitoring disease progression. In endodontics, these technologies improve root canal detection and treatment outcome predictions. Prosthodontics and oral surgery leverage deep learning for precise prosthesis design and surgical planning, enhancing patient-specific care. Despite the promising advancements, challenges such as data quality, model interpretability, and regulatory issues persist. To solve these problems and get the most out of deep learning in dentistry, the review stresses the need for ongoing research and collaboration between different fields. In our review, we discuss significant deep learning models such as Convolutional Neural Networks (CNNs) and their applications in dentistry, including tooth segmentation, lesion detection, and orthodontic treatment planning. We also examine the use of Generative Adversarial Networks (GANs) for generating synthetic data to enhance training datasets. This paper reviews recent research to provide a comprehensive overview of how deep learning is transforming dentistry, leading to improved patient outcomes, diagnostic accuracy, and treatment efficiency. The advancements in AI and 3D imaging herald a future of automated, high-quality dental diagnostics and treatments.

Page 52 of 71 | Total Record : 708

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