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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
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.
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.
Soft Actuator Based on a Novel Variable Stiffness Compound Extensor Bending-Pneumatic Artificial Muscle (CEB-PAM): Design and Mathematical Model Al-Mayahi, Wafaa; Al-Fahaam, Hassanin
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.v5i2.21127

Abstract

Soft robots have gained prominence in various fields in recent years, particularly in medical applications such as rehabilitation, due to their numerous advantages. The primary building blocks of a soft robot are often pneumatic artificial muscles (PAM). The Extensor PAM (EPAM), including Extensor Bending PAM (EB-PAM), is characterized by its low stiffness, and because stiffness is important in many robotic applications, for example, in rehabilitation, the degree of disability varies from one person to another, such as spasticity, weakness, and contracture. Therefore, it was necessary to provide an actuator with variable stiffness whose stiffness can be controlled to provide the appropriate need for each person, this study presents a new design for the EB-PAM that combines the EB-PAM and contractor PAM (CPAM), It has higher stiffness than traditional EPAM, A stiffness of over 850 N/m was achieved, whereas EB-PAM only reached a stiffness of less than 450 N/m, it is also possible to change its stiffness at a specific bending angle. It is also possible to obtain fixed stiffness at different angles.  A mathematical model was developed to calculate the output force of the new muscle by calculating its size and the pressure applied to it and comparing the model with experimental results. The mathematical model was enhanced by calculating the wasted energy consumed by the actuator before the bladder begins to expand, and also by calculating the thickness of the bladder and the sleeve. To make the muscle lighter, cheaper, and work under low pressures, balloons were used in manufacturing, offering practical advantages for soft robotic applications.
Review of Intelligent and Nature-Inspired Algorithms-Based Methods for Tuning PID Controllers in Industrial Applications Patil, Ramakant S; Jadhav, Sharad P.; Patil, Machhindranath D.
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.v5i2.20850

Abstract

PID controllers can regulate and stabilize processes in response to changes and disturbances. This paper provides a comprehensive review of PID controller tuning methods for industrial applications, emphasizing intelligent and nature-inspired algorithms. Techniques such as Fuzzy Logic (FL), Artificial Neural Networks (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS) are explored. Additionally, nature-inspired algorithms, including evolutionary algorithms like Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO), Simulated Annealing (SA), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Cuckoo Search (CS), Harmony Search (HS), and Grey Wolf Optimization (GWO), are examined. While conventional PID tuning methods are valuable, the evolving landscape of control engineering has led to the exploration of intelligent and nature-inspired algorithms to further enhance PID controller performance in specific applications. The study conducts a thorough analysis of these tuning methods, evaluating their effectiveness in industrial applications through a comprehensive literature review. The primary aim is to offer empirical evidence on the efficacy of various algorithms in PID tuning. This work presents a comparative analysis of algorithmic performance and their real-world applications, contributing to a comprehensive understanding of the discussed tuning methods. Findings aim to uncover the strengths and weaknesses of diverse PID tuning methods in industrial contexts, guiding practitioners and researchers. This paper is a sincere effort to address the lack of specific quantitative comparisons in existing literature, bridging the gap in empirical evidence and serving as a valuable reference for optimizing intelligent and nature-inspired algorithms-based PID controllers in various industrial applications. Keywords— PID controller; Intelligent and Nature-Inspired Algorithms; Fuzzy Logic; Artificial Neural Network; Adaptive NeuroFuzzy Inference System; Genetic Algorithm; Particle Swarm Optimization; Differential Evolution; Ant Colony Optimization; Simulated Annealing; Artificial Bee Colony; Firefly Algorithm; Cuckoo Search; Harmony Search; Grey Wolf Optimization.

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