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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 4 (2024)" : 24 Documents clear
Advanced Threat Detection Using Soft and Hard Voting Techniques in Ensemble Learning Jabbar, Hanan Ghali
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.v5i4.22005

Abstract

This study addresses the challenge of detecting network intrusions by exploring the efficacy of ensemble learning methods over traditional machine learning models. The problem of network security is exacerbated by sophisticated cyber-attack techniques that standard single model approaches often fail to counter effectively. Our solution employs a robust ensemble methodology to improve detection rates. The research contribution lies in the comparative analysis of individual machine learning models—K-Nearest Neighbors (KNN), Decision Trees (DT), and Gradient Boosting (GB)—against ensemble methods employing soft and hard voting classifiers. This study is one of the first to quantify the performance gains of ensemble methods in the context of network intrusion detection. Our methodological approach involves applying these models to the WSNBFSF dataset, which consists of traffic types including normal operations and various attacks. Performance metrics such as accuracy, precision, recall, and F1-score are calculated to assess the effectiveness of each model. The ensemble methods combine the strengths of individual models using voting systems, which are tested against the same metrics. Results indicate that while individual models like DT and GB achieved near-perfect accuracy scores (99.95% and 99.9%, respectively), the ensemble models performed even better. The soft voting classifier achieved an accuracy of 99.967%, and the hard voting classifier reached 100%, demonstrating their superior capability in network traffic classification and intrusion detection. In conclusion, the integration of ensemble methods significantly enhances the detection accuracy and reliability of network intrusion systems. Future research should explore additional ensemble techniques and consider scalability and class imbalance issues to further refine the efficacy of intrusion detection systems.
Tracking Control for Affine Time-Varying Nonlinear Systems with Bounds Nguyen, Nam H.; Vu, Tung X.; Nguyen, Hung V.
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.v5i4.22077

Abstract

In practice, there exist systems with high nonlinearity and time-varying functions. Time-varying nonlinear systems (TVNS) present inherent challenges due to their high nonlinearity and time-varying nature, especially when unknown input disturbance and model uncertainties occur. In this work, a class of single input single output (SISO) uncertain affine TVNS is considered for tracking controller design in the presence of unknown disturbance, in which both the disturbance and model uncertainties are assumed to be bounded. Based on these bounds, a tracking controller will be proposed for first-order uncertain TVNS with unknown input disturbance, and then it is extended for second-order uncertain affine TVNS with unknown input disturbance. Unlike other existing works, the proposed controller does not use fuzzy systems, neural networks or any adaptive mechanism to cope with uncertainties and disturbances. It only uses the bounds of disturbance and model uncertainties, the information of tracking error to compute the control signal, and Lyapunov stability theory is applied to analyze stability of the closed-loop system. In addition, the convergence rate of tracking error can be adjusted by tuning parameters. Some numerical simulations with a first-order system and a model of inverted pendulum are given to verify the developed controller. These systems are uncertain and disturbed by unknown external signals and the proposed controller does not know this information but the tracking error still converges to a small circle containing the origin. The proposed controller can be extended for higher-order systems or MIMO systems such as robotic manipulators.
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.

Page 3 of 3 | Total Record : 24

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