cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Iswanto
Contact Email
-
Phone
+628995023004
Journal Mail Official
jrc@umy.ac.id
Editorial Address
Kantor LP3M Gedung D Kampus Terpadu UMY Jl. Brawijaya, Kasihan, Bantul, Yogyakarta 55183
Location
Kab. bantul,
Daerah istimewa yogyakarta
INDONESIA
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 30 Documents
 1   2   3 
Search results for , issue "Vol. 5 No. 5 (2024)" : 30 Documents clear
The Role of Occasional Assessment of Sensor Performance for Improved Subsea Search Efficiency Yetkin, Harun
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.v5i5.22298

Abstract

This study addresses the subsea search performance of an autonomous underwater vehicle equipped with a search sensor and an environment characterization sensor. The performance of the search sensor is assumed to be dependent on characteristics of the local environment, and thus sensor performance in some locations can be different than in other locations. For the case that the agent is able to occasionally characterize the environment, and therefore estimate the performance of its search sensor, we describe a method for selecting when and where to characterize the environment and when and where to search in order to maximize overall search effectiveness. Our work accounts for false positives, false negatives and uncertainty in the performance of the search sensor that varies geographically. We show that effort applied to characterizing the environment, and therefore the performance of the search sensor, can improve search performance. We derive a utility function that is used to compute the best path and when to switch between the tasks of search and environmental characterization. The objective of the subsea search mission is to maximize the probability of attaining a desired level of risk reduction, and we terminate the search mission as soon as it is found that the desired risk reduction cannot be attained. To the best of our knowledge, this is the first study that addresses the problem of attaining a desired level of risk and stopping the mission when the desired risk is found to be unachievable. Through numerical illustrations, we show realistic scenarios where the findings of this study can be useful to improve search effectiveness and attain the desired level of risk where the standard exhaustive search techniques will fail to achieve.
Cancer Treatment Precision Strategies Through Optimal Control Theory Abougarair, Ahmed J.; Oun, Abdulhamid A.; Sawan, Salah I.; Abougard, T.; Maghfiroh, H.
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.v5i5.22378

Abstract

Lung cancer is a highly heterogeneous disease, with diverse genetic, molecular, and cellular drivers that can vary significantly between individual patients and even within a single tumor. Though combination therapy is becoming more common in the treatment of cancer, it can be challenging to predict how various treatment modalities will interact and what negative effects they may have on a patient's health, such as increased gastrointestinal toxicities, or neurological problems.   This paper aims to regulate immunity to tumor therapy by utilizing optimal control theory (OCT). This research suggests a malignant tumor model that can be regulated with a combination of immunological, vaccine, and chemotherapeutic therapy. The optimal control variables are employed to support the best possible treatment plan with the fewest potential side effects by reducing the production of new tumor cells and keeping the number of normal cells above the average carrying capacity. Also, the study addresses patient heterogeneity, individual variations in tumor biology, and immune responses for both young and old cancer patients. Finding the right doses for a treatment that works is the main goal. To do this, we conducted a comparative analysis of two optimum control approaches: the Single Network Adaptive Critic (SNAC) approach, which directly applies the notion of reinforcement learning to the essential conditions for optimality and the Linear Quadratic Regulator (LQR) methodology. Although the study's results show the promise of precision treatment plans, a number of significant obstacles must be overcome before these tactics can be successfully applied in clinical settings. It will be necessary to make considerable adjustments to the healthcare system's infrastructure in order to successfully offer personalized treatment regimens. This includes enhanced interdisciplinary care coordination methods, safe data management systems.
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.
Human Activity Recognition Using Accelerometer & Gyroscope Smartphone Sensor by Extract Statistical Features Abdullah, Muthana Hmod; Ahmed, M. A.
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.v5i5.22443

Abstract

Understanding behavioral patterns and forecasting the bodily motions of persons heavily relies on detecting human activities. This has profound ramifications in several domains, including healthcare, sports, and security. This study sought to identify and classify 18 human actions recorded by 90 people using smartphone sensors using the KU-HAR dataset. The primary aim of this study is to examine statistical features such as (mean, mod, entropy, max, median …etc.) derived from time-domain sensory data collected using accelerometers and gyroscopes. Activity detection utilizes many machine learning methods such as Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LG), Naïve Bayes (NB), and AdaBoost. The RF model achieves the highest overall accuracy of 99%. While the DT model gets 95%, SVM receives 93%, and the KNN gets 82%. At the same time, the other model didn’t get good results. The research is evaluated using accuracy, recall, precision, and f1-scor. The research contribution is to extract the statistical feature from the raw file of the sensor to create a new dataset. This research recommends employing statistical features in time series. Future research is recommended to solve misclassification in certain activities, which could be achieved using feature selection to reduce the number of features.
Explainable Ensemble Learning Models for Early Detection of Heart Disease Laftah, Raed Hassan; Al-Saedi, Karim Hashim Kraidi
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.v5i5.22448

Abstract

Coronary diseases (CVD) are a major global health concern, and timely, accurate diagnosis is crucial for effective treatment and management. As machine learning is, has steadily been on the improvement way, and it's there where we find the transformative potential for enhancing the diagnostic accuracy for their predictive accuracy using the Local Interpretable Model-agnostic Explanations technique to ensure the explainability of our models. With the advancement of machine learning, we aim to enhance diagnostic accuracy by developing a high-precision prediction tool for heart disease using various ML models. We utilized a Kaggle dataset to implement several ML models, including Random Forest, Gradient Boosting, CatBoost, K-Nearest Neighbor, Naive Bayes, Support Vector Machine, and AdaBoost, with appropriate data preprocessing. The soft voting ensemble method, combining various models, achieved a notable 98.54% accuracy and 99% precision, recall, and f1-score, with Random Forest, CatBoost, and the Voting Classifier outperforming others. These results indicate that our model is highly reliable and sets a new standard for CVD prediction. Future research should focus on validating this model with larger datasets and exploring deep learning approaches.
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.
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.
ROS-based Multi-Robot System for Efficient Indoor Exploration Using a Combined Path Planning Technique Sandanika, Wanni Arachchige Heshani; Wishvajith, Supun Hansaka; Randika, Sahan; Thennakoon, Deshitha Adeeshan; Rajapaksha, Samantha Kumara; Jayasinghearachchi, Vishan
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.v5i5.22494

Abstract

This study introduces an innovative combined system utilizing the Robot Operating System (ROS) to enhance multi-robot systems for comprehensive coverage in indoor settings. The research emphasizes integrating diverse robotics technologies, such as map partitioning, path planning, and adaptive task allocation, to boost deployment and coordination for localization and navigation. The system uses occupancy grid maps for effective map partitioning and employs a market-based algorithm for adaptive task distribution. A hybrid path planning approach, merging Boustrophedon Traversing Coverage (BTC) and Spiral Traversing Coverage (STC), ensures complete area coverage while reducing redundancy. During thorough testing, our system showed coverage efficiencies between 94% and 98% in different layouts and conditions, with task completion rates as high as 19.6% per minute, highlighting its ability to effectively handle and adjust to various indoor environments. Additionally, dynamic robot deployment in response to environmental changes has led to enhanced operational efficiency and flexibility. The initial results are promising, though future research will focus on incorporating dynamic obstacle management and path planning to boost the system's robustness and adaptability. This study paves the way for further exploration and development of advanced path-planning algorithms to enhance the performance and usability of multi-robot systems in dynamic environment applications.
Optimizing Network Security with Machine Learning and Multi-Factor Authentication for Enhanced Intrusion Detection Mahmood, Rafah Kareem; Mahameed, Ans Ibrahim; Lateef, Noor Q.; Jasim, Hasanain M.; Radhi, Ahmed Dheyaa; Ahmed, Saadaldeen Rashid; Tupe-Waghmare, Priyanka
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.v5i5.22508

Abstract

This study examines the utilization of machine learning methodologies and multi-factor authentication (MFA) to bolster network security, specifically targeting network intrusion detection. We analyze the way in which the integration of these technologies effectively tackles existing security concerns and constraints. The research highlights the importance of incorporating energy conservation and environmental impact reduction into security solutions, in addition to traditional cryptography and biometric methods. In addition, we tackle the limitations of centralized systems, such as vulnerabilities to security breaches and instances of system failures. The study examines different security models, encompassing categories, frameworks, consensus protocols, applications, services, and deployment goals in order to determine their impact on network security. In addition, we offer a detailed comparison of seven machine learning models, showcasing their effectiveness in enhancing network intrusion detection and overall security. The objective of this study is to provide in-depth understanding and actionable suggestions for utilizing machine learning with MFA (Multi-Factor Authentication) to enhance network defensive tactics.
Integrated Deep Hybrid Learning Model Upon Spinach Leaf Classification and Prediction with Pristine Accuracy Elumalai, Meganathan; Fernandez, Terrance Frederick
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.v5i5.22546

Abstract

Over the years, Agriculture has been a mainstay of life for Indians and about half the working population of Tamil Nadu. Spinach is an integral part of everyone’s meal and its nutrient content is higher than other veggies. The nutrients are unique for varied varieties so there is a dire need to classify them and thus to predict them. Furthermore, exactitude prediction leads to easy detection of spinach leaves. In this work, we selected 5 varieties of spinach leaves populated under a huge dataset. We implemented the same employing a Deep Hybrid approach which is a fusion of conventional Machine Learning with state-of-the-art Deep Learning using Orange toolkit. Out of the plethora of these AI Domaine approaches, four classifiers, such as Support Vector Machine (SVM), k- Nearest Neighbour(kNN), Random Forest (RF), and Neural Network (NN) were chosen and implemented. Existing methods using these algorithms have achieved promising results, with individual accuracies of 98.80% (RF), 98.20% (KNN), 99.9% (NN), and 99.60% (SVM). However, the IDHLM aims to surpass these individual performances by integrating them into a cohesive framework. This approach leverages each algorithm's complementary strengths to achieve even higher classification accuracy. The abstract concludes by highlighting the potential of the IDHLM for achieving pristine accuracy in spinach leaf classification.

Page 2 of 3 | Total Record : 30

 1   2   3 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol. 6 No. 5 (2025) Vol. 6 No. 4 (2025) Vol. 6 No. 2 (2025) Vol. 6 No. 1 (2025) Vol 5, No 6 (2024) Vol. 5 No. 6 (2024) Vol. 5 No. 5 (2024) Vol 5, No 5 (2024) Vol 5, No 4 (2024) Vol 5, No 3 (2024) Vol 5, No 2 (2024) Vol 5, No 1 (2024) Vol 4, No 6 (2023) Vol 4, No 5 (2023) Vol 4, No 4 (2023) Vol 4, No 3 (2023) Vol 4, No 2 (2023) Vol 4, No 1 (2023) Vol 3, No 6 (2022): November Vol 3, No 5 (2022): September Vol 3, No 4 (2022): July Vol 3, No 3 (2022): May Vol 3, No 2 (2022): March Vol 3, No 1 (2022): January Vol 2, No 6 (2021): November (Forthcoming Issue) Vol 2, No 6 (2021): November Vol 2, No 5 (2021): September Vol 2, No 5 (2021): September (Forthcoming Issue) Vol 2, No 4 (2021): July Vol 2, No 4 (2021): July (Forthcoming Issue) Vol 2, No 3 (2021): May Vol 2, No 3 (2021): May (Forthcoming Issue) Vol 2, No 2 (2021): March (Forthcoming Issue) Vol 2, No 2 (2021): March Vol 2, No 1 (2021): January Vol 1, No 6 (2020): November Vol 1, No 5 (2020): September Vol 1, No 4 (2020): July Vol 1, No 3 (2020): May Vol 1, No 2 (2020): March Vol 1, No 1 (2020): January More Issue