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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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Hyperparameter Tuning Impact on Deep Learning Bi-LSTM for Photovoltaic Power Forecasting Sutarna, Nana; Tjahyadi, Christianto; Oktivasari, Prihatin; Dwiyaniti, Murie; Tohazen, Tohazen
Journal of Robotics and Control (JRC) Vol 5, No 3 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i3.21120

Abstract

Solar energy is one of the most promising renewable energy sources that can reduce greenhouse gas emissions and fossil fuel dependence. However, solar energy production is highly variable and uncertain due to the influence of weather conditions and environmental factors. Accurate forecasting of photovoltaic (PV) power output is essential for optimal planning and operation of PV systems, as well as for integrating them into the power grid. This study develops a deep learning model based on Bidirectional Long Short-Term Memory (Bi-LSTM) to predict short-term PV power output. The main objective is to examine the effect of hyperparameter tuning on the forecasting accuracy and the actual PV output power. The main contribution is identifying the optimal combination of hyperparameters, namely the optimizer, the learning rate, and the activation function, for the PV output. The dataset consists of 143786 observations from sensors measuring solar irradiation, PV surface temperature, ambient temperature, ambient humidity, wind speed, and PV power output for 50 days in Bandung, Indonesia. The data is preprocessed by smoothing and splitting into training (70%, 35 days), validation (15%, 7.5 days), and testing (15%, 7.5 days) sets. The Bi-LSTM model is trained and tested with two optimizers: Adam and RMSprop, and three activation functions: Tanh, ReLU, and Swish, with different learning rates. The results indicate that the optimal performance is obtained by the Bi-LSTM model with Adam optimizer, learning rate of 〖1e〗^(-4), and Tanh activation function. This model has the lowest MAE of 0.002931070979684591, the lowest RMSE of 0.008483537231080387, and the highest R-squared of 0.9988813964105624 when tested with the validation dataset and requires 93 epochs to build. The model also performs well on the test dataset, with the lowest MAE of 0.002717077964916825, the lowest RMSE of 0.007629486798682186, and the highest R-squared of 0.9992563395109665. This study concludes that hyperparameter tuning is a vital step in developing the Bi-LSTM model to improve the accuracy of PV output power prediction.
Improving CBIR Techniques with Deep Learning Approach: An Ensemble Method Using NASNetMobile, DenseNet121, and VGG12 Sadiq, Shereen Saleem
Journal of Robotics and Control (JRC) Vol 5, No 3 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i3.21805

Abstract

In the evolving field of Content-Based Image Retrieval (CBIR), we introduce a novel approach that integrates deep learning models—NASNetMobile, DenseNet121, and VGG16—with ensemble methods to enhance retrieval accuracy and relevance. This study uniquely combines NASNetMobile's adaptability, DenseNet121's feature extraction, and VGG16's robustness through hard and soft voting techniques, aiming to effectively bridge the semantic gap in CBIR systems. Our comparative analysis against existing CBIR algorithms using diverse online datasets demonstrates superior performance, with our approach achieving up to 98% in accuracy, precision, recall, and F1-score, thereby redefining performance benchmarks. This advancement proves particularly impactful in medical imaging and surveillance, where precise image retrieval is crucial. Our research contributes to CBIR by (1) demonstrating the integrated deep learning ensemble's ability to narrow the semantic gap and (2) providing a comparative performance analysis, underscoring our method's improvement over current technologies. The combination of these models marks a significant step forward in CBIR, offering a more accurate and efficient solution for image retrieval challenges.
Visual Slam and Visual Odometry Based on RGB-D Images Using Deep Learning: A Survey Le, Van-Hung
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.v5i4.22061

Abstract

Visual simultaneous localization and mapping (Visual SLAM) based on RGB-D images includes two main tasks: building an environment map and simultaneously tracking the location/motion trajectory of the image sensor, or called visual odometry (VO). Visual SLAM and VO are used in many applications as robot systems, autonomous mobile robots, supporting systems for the blind, human-machine interaction, industry, etc. With the strong development of deep learning (DL), it has been applied and brought impressive results when building Visual SLAM and VO from image sensor data (RGB-D images). To get the overall picture of the development of DL applied to building Visual SLAM and VO systems. At the same time, the results, challenges, and advantages of DL models to solve Visual SLAM and VO problems. In this paper, we proposed the taxonomy to conduct a complete survey based on three methods from RGB-D images: (1) using DL for the modules (depth estimation, optical flow estimation, visual odometry, mapping, and loop closure detection) of the Visual SLAM and VO framework; (2) using DL modules to supplement (feature extraction, semantic segmentation, pose estimation, map construction, loop closure detection, others module) to Visual SLAM and VO framework; (3) using end-toend DL to build Visual SLAM and VO systems. The studies were surveyed based on the order of methods, datasets, and evaluation measures, the detailed results according to datasets are also presented. In particular, the challenges of studies using DL to build Visual SLAM and VO systems are also analyzed and some of our further studies are also introduced.
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.
Evaluating the Battery Management System's Performance Under Levels of State of Health (SOH) Parameters Amifia, Lora Khaula; Kamali, Muhammad Adib
Journal of Robotics and Control (JRC) Vol 4, No 6 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i6.20401

Abstract

Batteries in electric vehicles are the primary focus battery health care. The Battery Management System (BMS) maintains optimal battery conditions by evaluating the system's Htate of health (SOH). SOH identification can recommend the right time to replace the battery to keep the electric vehicle system working optimally. With suitable title and accuracy, the battery will avoid failure and have a long service life. This research aims to produce estimates and identify SOH parameters so that the performance of the battery management system increases. The central parameter values obtained are R0, Rp, and Cp based on Thevenin battery modeling. Then, to get good initialization and accurate results, the parameter identification is completed using an adaptive algorithm, namely Coulomb Counting and Open Circuit Voltage (OCV). The two algorithms compare the identification results of error, MAE, RSME, and final SOH. The focus of this research is to obtain data on estimation error values along with information regarding reliable BMS performance. The performance of the current estimation algorithm is known by calculating the error, which is presented in the form of root mean square error (RMSE) and mean absolute error (MAE). The SOH estimation results using Coulomb Counting have a better error than OCV, namely 1.770%, with a final SOH value of 17.33%. The Thevenin battery model can model the battery accurately with an error of 0.0451%.
Leveraging a Two-Level Attention Mechanism for Deep Face Recognition with Siamese One-Shot Learning Albayati, Arkan Mahmood; Chtourou, Wael; Zarai, Faouzi
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.v5i1.20135

Abstract

Discriminative feature embedding is used for largescale facial recognition. Many image-based facial recognition networks use CNNs like ResNets and VGG-nets. Humans prioritise different elements, but CNNs treat all facial pictures equally. NLP and computer vision use attention to learn the most important part of an input signal. The inter-channel and inter-spatial attention mechanism is used to assess face image component significance in this study. Channel scalars are calculated using Global Average Pooling in face recognition channel attention. A recent study found that GAP encodes low-frequency channel information first. We compressed channels using discrete cosine transform (DCT) instead of scalar representation to evaluate information at frequencies other than the lowest frequency for the channel attention mechanism. Later layers can acquire the feature map after spatial attention. Channel and spatial attention increase CNN facial recognition feature extraction. Channel-only, spatial-only, parallel, sequential, or channel-after-spatial attention blocks exist. Current face recognition attention approaches may be outperformed on public datasets (Labelled Faces in the Wild).
Design of PID, IMC and IMC based PID Controller for Hydro Turbine Power System of Non-minimum Phase Dynamics Bhuran, Supriya Y.; Jadhav, Sharad P.
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.v5i2.21342

Abstract

The primary objective of this paper is to design and assess the performance of conventional Proportional Integral Derivative (PID), Internal Model Controller (IMC), and IMCbased PID controllers tailored for Hydro Turbine Power Systems (HTPS) exhibiting Non-Minimum Phase (NMP) dynamics. The focus is on overcoming the limitations of existing approaches in handling such complex system dynamics. Existing literature underscores the difficulty of crafting controllers for such systems. The current study represents a sincere endeavour to design and evaluate the performance of conventional Proportional Integral and Derivative (PID), Internal Model Controller (IMC), and IMCbased PID controllers tailored for HTPS characterized by NMP behaviour. The design case study and simulations were conducted using MATLAB and Simulink. The closed-loop responses of HTPS with PID, IMC, and IMC-PID are presented, and the controller performances are scrutinized in both time and frequency domains. To validate the effectiveness of the controllers, performance indices such as Integrated Squared Error (ISE), Integrated Absolute Error (IAE), Integrated Time-weighted Absolute Error (ITAE), Integrated Time Squared Error (ITSE) are calculated, as well as control efforts are calculated using 2-norm and infinity-norms. These performance indices and control effort norms offer a comprehensive evaluation of the controllers’ performance in terms of minimizing error, handling system dynamics, and optimizing control effort across different time scales. Analysing these metrics aids in selecting and refining controllers for optimal performance in HTPS with NMP behaviour. Our findings illustrate that IMCbased PID controllers exhibit superior performance compared to conventional PID controllers in effectively handling the NonMinimum Phase (NMP) dynamics of Hydro Turbine Power Systems (HTPS). This superiority is substantiated by enhanced performance indices, including reductions in ISE, IAE, ITSE, and ITAE.
A Systematic Literature Review of Performance Hospital Supply Chain Management Louah, Soulaiman; Sarir, Hicham; Kriouich, Mohamed
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.v5i2.21541

Abstract

Over the last few decades, globalization has driven up the demand for hospital Supply Chain Management (SCM) with the goal of bio-medical development and improving performance. This review aims to offer both a qualitative and quantitative comprehension of the hospital SCM re-search field's overall developmental trend. By using the methodology science mapping approach are visualize the organization of academic knowledge, 87 significant papers, that were published between 2002 and 2023 in total due to their importance in recent years, were located, expanded upon, and summarized. Bibliographic analysis for under-standing the global research state and academic develop-ment was performed on visualized statistics can help identi-fy trends in data about co-occurring keywords, interna-tional cooperation, journal allocation/co-citation, and view clusters of study subjects based on this five categorization, 22 sub-branches in total of hospital SCM identification and topical discussion of knowledge were conducted, namely (i) technologies; (ii) planning; (iii) supply chain field in hospi-tals; (iv) logistics and (v) environmental. Lastly, suggestions for future study directions and current knowledge gaps were made due to constraints of international cooperation and insufficient platforms to quickly advance innovation technology research. The results contribute to a methodical intellectual representation of the current state of hospital SCM research. Furthermore, it offers heuristic ideas to practitioners and researchers to control the quality of de-veloped healthcare and logistics services.
Ophthalmic Diseases Classification Based on YOLOv8 Khalaf, Ahmed Tuama; Abdulateef, Salwa Khalid
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.v5i2.21208

Abstract

With the rising prevalence of retinal diseases, identifying eye diseases at an early stage is crucial for effective treatment and prevention of irreversible blindness. But Ophthalmologists face challenges in detecting subtle symptoms that may indicate the presence of a disease before it progresses to an advanced stage Among these challenges, eye diseases can present with a wide range of symptoms, and some conditions may share similar signs. To solve these difficulties, in the research proposed YOLOV8(You Only Look Once) Lightweight Self-Attention model to classify seven different retinal diseases. In this regard, the dataset that have been used in this study contains 5787 images from three different sources (Roboflow, Kaggle and Medical Clinics) were included in the seven classes of Glaucoma, Age-related Macular Degeneration (AMD), Cataract, Diabetic retinopathy (DR), and Retinal Vein Occlusion, which comprises of Branch Retinal Vein Occlusion (BRVO) and Central Retinal Occlusion (CRVO) and normal.  As a results, the model has proven excellent performance in its classification ability. Boasting an average classification accuracy of 94% across the seven disease with precsition 96.2%, recall 96.6%and f1 score was 96.3% At the time of training it was 0.6 Houres(H). When compaired with Resnet50, VGG16 results underscore the model’s superior performance in precision and computational efficiency compared. The algorithm's evaluation reveals its superiority when compared to earlier pertinent research, making it a trustworthy method for classifying retinal illnesses.
Adaptive Vector Field Histogram Plus (VFH+) Algorithm using Fuzzy Logic in Motion Planning for Quadcopter Mohammed, Khitam; Aliedani, Ali; Al-Ibadi, Alaa
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.v5i2.21540

Abstract

This work introduces the adaptive version of the vector field histogram plus (VFH+) motion planning algorithm, which is designed for unmanned aerial vehicles, particularly quadcopters, to enhance its performance in navigation tasks. The method suggests incorporating fuzzy control to adaptively modify the VFH+ look-ahead distance parameter by analysis continuous environmental and motion conditions. Simulation tests were completed using different scenarios that varied in obstacle quantity, density, distribution, and size and waypoint quantity. Simulation results showed the successful outcomes of this strategy in enhancing quadcopter motion performance in various contexts. The results indicated notable enhancements in obstacle avoidance, smoother motion trajectories, and decreased travel time compared to the traditional VFH+ method. One of the most important aspects of creating real-time motion planning systems is handling uncertainty. This is accomplished by incorporating a fuzzy system knowledge base for automatic algorithmic modification into the planning process and employing advanced motion-planning techniques. The adaptive algorithm improves the quadcopter's ability to deal with high uncertainty levels by incorporating fuzzy logic for dynamic parameter adjustment, allowing for accurate and efficient navigation in various environments, even in uncertain conditions.

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