cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Hari Maghfiroh
Contact Email
jfsc.journal@gmail.com
Phone
-
Journal Mail Official
jfsc.journal@gmail.com
Editorial Address
Jl. Empu Sedah No. 12, Pringwulung, Condongcatur, Kec. Depok, Kabupaten Sleman, Daerah Istimewa Yogyakarta 55281, Indonesia
Location
Kab. sleman,
Daerah istimewa yogyakarta
INDONESIA
Journal of Fuzzy Systems and Control (JFSC)
Published by Peneliti Teknologi Teknik Indonesia
ISSN : -     EISSN : 29866537     DOI : https://doi.org/10.59247/jfsc.v1i1.24
Core Subject : Science, Engineering,
Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering
Journal of Fuzzy Systems and Control is an international peer review journal that published papers about Fuzzy Logic and Control Systems. The Journal of Fuzzy Systems and Control should encompass original research articles, review articles, and case studies that contribute to the advancement of the theory and application of fuzzy systems and control, and their integration with other technologies, such as artificial intelligence, machine learning, and optimization.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 85 Documents
 5   6   7   8   9 
An Adaptive-Weighted Ensemble of CNNs, RNNs, and Vision Transformers for Multi-Modal Neuroimaging in Amyotrophic Lateral Sclerosis Diagnosis Asuai , Clive Ebomagune
Journal of Fuzzy Systems and Control Vol. 3 No. 3 (2025): Vol. 3 No. 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v3i3.338

Abstract

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder that presents significant diagnostic challenges due to its heterogeneous clinical manifestations and symptom overlap with other neurological conditions. Early and accurate diagnosis is critical for initiating timely interventions and improving patient outcomes. Traditional diagnostic approaches rely heavily on clinical expertise and manual interpretation of neuroimaging data, such as structural MRI, diffusion tensor imaging (DTI), and functional MRI (fMRI), which are inherently time-consuming and prone to interobserver variability. Recent advances in artificial intelligence (AI) and deep learning (DL) have demonstrated potential for automating neuroimaging analysis, yet existing models often suffer from limited generalizability across modalities and datasets. To address these limitations, we propose a Transformer-augmented deep learning ensemble framework for automated ALS diagnosis using multi-modal neuroimaging data. The proposed architecture integrates convolutional neural networks (CNNs), recurrent neural networks (RNNs), and vision transformers (ViTs) to leverage the complementary strengths of spatial, temporal, and global contextual feature representations. An adaptive weighting-based fusion mechanism dynamically integrates modality-specific outputs, enhancing the robustness and reliability of the final diagnosis. Comprehensive preprocessing steps, including intensity normalization, motion correction, and modality-specific data augmentation, are employed to ensure cross-modality consistency. Evaluation on a curated multi-modal ALS neuroimaging dataset demonstrates the superior performance of the proposed model, achieving a classification accuracy of 99.2%, sensitivity of 98.7%, specificity of 99.5%, F1-score of 98.9%, and an AUC-ROC of 0.997. These results significantly outperform baseline CNN models and highlight the potential of transformer-augmented ensembles in complex neurodiagnostic applications.
Study, Design, Modeling, Simulation, and Control Analysis of DC-DC Power Converters Shneen , Salam Waley
Journal of Fuzzy Systems and Control Vol. 3 No. 3 (2025): Vol. 3 No. 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v3i3.345

Abstract

Researchers are interested in studying power electronics converters because of their importance in many fields and applications, including industrial, agricultural, and domestic applications. Power electronics converters are relatively economical compared to conventional converters. In this work, the researchers present research contributions, including a study on how to design a DC-DC converter from a constant electrical quantity at the converter's input to a variable quantity depending on the load requirements associated with the converter's output. As another research contribution, the researchers are working on building a model of a DC-DC converter. The third contribution is conducting tests using the model and simulating the converter using the engineering computer program MATLAB. Performance is evaluated, ways to improve the converter's operation are identified, and its behavior is analyzed during the transient and steady-state operation periods. Power electronics converters are used to increase the voltage, called a boost converter. There is a type used to decrease the voltage, called a buck converter. Another type combines both states, depending on the system requirements, called a buck-boost converter. Tests are conducted to identify how the converter can be used to meet the load requirements associated with the converter output. They also identify how to control system state changes during operation and how to counter fluctuations resulting from various factors. To adequately cover the load, efforts are made to regulate and improve the performance of the converter by regulating the electrical power to suit this. The converter design is developed to provide the required voltage and current for efficient operation.
Enhanced Disturbance Estimation for Tracking Control of Nonlinear Systems Using Adaptive Fuzzy Finite-Time Observers Hoang Duc Long; Duc , Vu Xuan
Journal of Fuzzy Systems and Control Vol. 3 No. 3 (2025): Vol. 3 No. 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v3i3.348

Abstract

Accurate estimation of unknown and time-varying disturbances is essential for achieving high-performance control of nonlinear systems. This paper investigates the design and comparative evaluation of finite-time disturbance observers with different gain adaptation mechanisms. First, a conventional fixed-gain finite-time disturbance observer and a linearly adaptive finite-time disturbance observer are presented. Then, an adaptive finite-time disturbance observer based on fuzzy logic control is developed to automatically adjust observer gains according to the disturbance estimation error and its rate of change, thereby reducing gain sensitivity and improving transient performance. Finite-time stability of the closed-loop system is rigorously analyzed using Lyapunov theory, and sufficient conditions for convergence are derived. Extensive simulation studies on a nonlinear system subject to high-frequency time-varying disturbances demonstrate the effectiveness of the proposed approach. Quantitative results show that the adaptive finite-time disturbance observer based on fuzzy logic control reduces tracking error and disturbance estimation root mean square error by more than 75% compared with the conventional finite-time disturbance observer and by over 50% compared with the linearly adaptive observer, while yielding smoother control inputs. These results confirm that the adaptive finite-time disturbance observer based on fuzzy logic control significantly enhances robustness and estimation accuracy, making the proposed observer suitable for practical nonlinear control applications under severe disturbance conditions.
Study, Design, Modeling, Simulation, and Control Analysis of AC-AC Power Converters Shneen , Salam Waley
Journal of Fuzzy Systems and Control Vol. 3 No. 3 (2025): Vol. 3 No. 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v3i3.347

Abstract

The current study aims to provide an analysis of the performance of the AC power converter by constructing a simulation model. A preliminary simulation model is designed to identify the behavior of open-loop and closed-loop systems. To improve the converter's performance, pulse width modulation (PWM) technology and a conventional controller are used to control the converter's output voltage and frequency. An AC converter varies electrical quantities to suit the load requirements and the available power source. Converters can be used in lighting circuits to control the intensity of lighting and to control the rotational speed of electric motors, such as single-phase induction motors. The power electronics converter model is an AC voltage control unit type, built using electronic switches, which are semiconductor devices such as thyristors and transistors (IGBT, MOSFET). The input terminal of the converter is connected to a constant voltage and frequency AC power supply, while the output terminal is connected to an AC load, controlled by the root mean square value of the AC voltage. The output of the converter can be controlled by regulating the operating periods of the electronic switches, depending on the type and method of connecting the switches, whether full wave or half wave, with regulated periods. The study presents a test of AC converter, and through simulation results, it is shown that the converter's performance can be improved using pulse width modulation technology and a conventional PID controller. Modeling a single-phase AC transformer system using a thyristor as an electronic switch. The system model consists of a 100V, 50Hz single-phase power supply connected in series with a transformer containing two thyristor switches connected in parallel. The transformer output is connected to a load with a resistance of 10 ohms. Tests were proposed using single-phase converter simulation models, where the switching angle of the electronic thyristor was changed from 10 to 90 degrees in 10-degree increments. The simulation results showed that the converter's output voltage could be changed by changing the switching angle, with the change being inverse; that is, increasing the switching angle leads to a decrease in the converter's output voltage.
Design and Implementation of an IoT-Enabled Autonomous Fire-Fighting Robot Using Vision-Based Fire Detection Nguyen, Hoang-Thong; Nguyen, Quoc-Thuan; Tran, Phuoc-Dat; Nguyen, Quang-Khai; Le, Thi-Hong-Lam; Nguyen, Le-Minh-Kha; Nguyen, Van-Hiep; Nguyen, Thanh-Binh; Nguyen, Ngoc-Hung; Nguyen, Thi-Ngoc-Thao; Phung, Son-Thanh; Le, Hoang-Lam; Nguyen, Thanh-Toan; Nguyen, Hai-Thanh
Journal of Fuzzy Systems and Control Vol. 3 No. 3 (2025): Vol. 3 No. 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This paper presents the design and implementation of an IoT-enabled autonomous fire-fighting mobile robot for early hazard detection, remote monitoring, and emergency response. The proposed system integrates real-time deep learning–based fire detection using a YOLO model with fire and gas sensor–based monitoring for IoT-based alert transmission and SLAM-based environmental visualization to form a multifunctional robotic platform capable of performing a sequence of tasks from detection and warning to initial fire response. The robot is capable of autonomous movement with obstacle avoidance, while a 2D SLAM-based mapping module is employed to provide environmental visualization for monitoring and decision support. A mobile application enables remote supervision and control, and real-time alerts are delivered through an IoT platform to enhance situational awareness. Experimental results show that the proposed system achieves a fire detection and response success rate of approximately 70%, with reliable fire recognition and fast response time under indoor testing conditions. The developed robot demonstrates strong potential as a practical solution for improving safety and supporting early-stage fire response in residential and industrial environments.

Page 9 of 9 | Total Record : 85

 5   6   7   8   9 

Filter by Year

2023 2025


Reset
Filter By Issues
All Issue Vol. 3 No. 3 (2025): Vol. 3 No. 3 (2025) Vol. 3 No. 2 (2025): Vol. 3, No. 2, 2025 Vol. 3 No. 1 (2025): Vol. 3, No. 1, 2025 Vol. 2 No. 3 (2024): Vol. 2, No. 3, 2024 Vol. 2 No. 2 (2024): Vol. 2, No. 2, 2024 Vol. 2 No. 1 (2024): Vol. 2, No. 1, 2024 Vol. 1 No. 3 (2023): Vol. 1, No. 3, 2023 Vol. 1 No. 2 (2023): Vol. 1, No. 2, 2023 Vol. 1 No. 1 (2023): Vol. 1, No. 1, 2023