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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 99 Documents
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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 | DOI: 10.59247/jfsc.v3i3.354

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.
Intelligent Control for 2D-Crane System Trung-Son Huynh; Dang-Khoa Dinh; Trong-Bang Tran; Huu-Loc Dang; Dinh-Nguyen-Phuc Le; Hung-Thinh Bui; Hoang-Lam Le; Thanh-Binh Nguyen; Van-Hiep Nguyen; Le-Nhat-Minh Nguyen; Thien-Quoc Dang; Ngoc-Hung Nguyen; Thi-Ngoc-Thao Nguyen; Huynh-Duc Pham; Xuan-Tien Nguyen; Van-Dong-Hai Nguyen
Journal of Fuzzy Systems and Control Vol. 4 No. 1 (2026): Vol. 4 No. 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i1.350

Abstract

This paper presents an Intelligent Learning-based Control approach for a 2D Crane System, aiming to evaluate the learning capability of various intelligent techniques based on a baseline Fuzzy Logic Controller (FLC). The initial fuzzy controller is designed for position and sway control, while Genetic Algorithm (GA), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) are employed in simulation to retrain and enhance its performance. Comparative results show that intelligent learning methods can significantly improve system response, reduce overshoot, and increase robustness compared to the original fuzzy controller. Moreover, an experimental setup using the baseline FLC is implemented to verify the practical effectiveness of the fuzzy control approach on a real 2D crane system. The findings highlight the potential of intelligent learning techniques for future real-time implementation.
Development of an Automated PCB Inspection, Error Statistics, and Classification System Truong-Nguyen Phan; Thi-Ngoc-Tram Tran; Thanh-Viet Ho; Binh-Hau Nguyen; Minh-Tri Hoang; Hai-Nam Tran; Nhat-Nam Nguyen; Nguyen-Cong-Anh Tran; Le-Huu-Tri Do; Thi-Ngoc-Thao Nguyen; Nam-Long Tran; Duong-Thuan Nguyen; Van-Huy Le; Van-Tuan Nguyen; Huynh-Anh-Tuan Pham
Journal of Fuzzy Systems and Control Vol. 4 No. 1 (2026): Vol. 4 No. 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i1.353

Abstract

In the electronics manufacturing industry, Printed Circuit Boards are critical to electronic devices, and their quality directly affects product performance and reliability. Common assembly defects, such as missing components, misalignment, or wrong parts, must be detected promptly to reduce waste and maintain reputation. In Vietnam, PCB inspection is largely manual, limiting speed, accuracy, and consistency. The system integrates a YOLOv5-based machine vision module for detecting missing and misaligned components, a Siemens S7-1200 PLC for controlling an XY gantry and conveyor system, and a web interface for real-time monitoring. The primary contributions include: a fully integrated cyber-physical prototype suitable for educational and small-scale industrial use; a novel method for component misalignment detection using fiducial-based relative positioning; and seamless communication between vision, control, and HMI modules. Experimental results on two common PCB types, L298N and ULN2003, demonstrate a classification and error detection accuracy of up to 93%. The system achieves a throughput suitable for laboratory and small-batch production, with a positioning accuracy of ±0.5 mm. The system aims to achieve high accuracy, fast processing, and practical applicability in production lines.
Study, Design, Modeling, Simulation, and Control Analysis of Single-Phase Rectifier AC-DC Power Converters Salam Waley Shneen
Journal of Fuzzy Systems and Control Vol. 4 No. 1 (2026): Vol. 4 No. 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i1.356

Abstract

Many researchers are interested in studying and analyzing electronic power systems due to their importance in providing the electrical quantities needed to meet load requirements. It's worth noting that there are different types of power sources, including direct current (DC) sources such as DC generators, batteries, or solar power, and alternating current (AC) sources such as diesel generators, wind power, and the main grid. Loads vary in that they require DC power, such as lighting, electric motors, and electronic devices. The most widely available power systems are single-phase AC systems, which can power loads of the same type. However, when DC loads, such as electric motors, are present, a rectifier is required to convert the DC current to AC. A rectifier consists of semiconductors such as diodes, thyristors, and transistors, and its output can be controlled by adjusting the operating period of the switches. This study aims to explore the differences between a single-phase half-wave rectifier and a full-wave rectifier. It presents a simulation model of a single-phase rectifier to conduct proposed tests to understand the system's behavior and analyze the simulation results to verify the rectifier's effectiveness in converting alternating current (AC) voltage to direct current (DC) voltage. The results confirm the convertibility capability, making the rectifier one of the important converters that can be used to supply DC loads with electrical power.
Exploring Interval-Valued Fermatean Neutrosophic Tactics for Empowering AI-Driven Financial Risk Frameworks: Compliance Automation, Fraud Detection, and Beyond Aiman Ishtiaq; Khadija Tul Kubra; Anns Uzair; Muhammad Talha Azhar
Journal of Fuzzy Systems and Control Vol. 4 No. 1 (2026): Vol. 4 No. 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i1.357

Abstract

The financial risk evaluation process, which includes the investigation of the risks related to loans, funding, and trading activities in economic decisions, is greatly utilized in advanced financial systems. However, nowadays in an inconsistent, fast, and digitalized world, conventional risk models are inadequate, particularly when there is ambiguity, inconsistency, or incomplete information provided in the data. In such scenarios, the interference of Artificial Intelligence (AI) is playing a crucial role. Not only are the large data sets dealt with by AI, but also the decision-making processes can be enhanced. Conventional mathematical tools cannot analyze the discipline whose limits are complex, ambiguous, and indeterminate. In this research article, to recognize and analyze these mathematical disciplines, Interval-Valued Fermatean Neutrosophic Numbers (I-VFNNs) are used, an argumentation of modern fuzzy logic. I-VFNNs are particularly mapped out for such circumstances where the information in the data contains uncertainty, ambiguity, or inconsistency. We have used Interval-Valued Fermatean Neutrosophic Numbers (I-VFNNs), an extension of modern fuzzy logic, to identify and analyze these mathematical constraints. In this article, firstly, a list of essential aspects is composed that are affected by Artificial Intelligence in financial risk models, like fraud detection and prevention, stress testing and scenario simulation, automation of regulatory compliance, behavioral risk analysis, enhanced predictive accuracy, dynamic risk modeling, and real-time risk monitoring, etc. All these components we visualized as mathematical disciplines, which are non-probabilistic, irregular, and multifaceted in nature. With the help of I-VFNNs, we portrayed these disciplines in the guise of numbers and demonstrated their influence, intensity, and indeterminacy in accordance with the impact of Artificial intelligence. The results demonstrate that I-VFNNs not only composed ambiguity superiorly, but also refined the disparity between various risk factors. In general, not only are modern ways to constructively assess financial risk models based on Artificial Intelligence (AI) developed, but new approaches in the inspection of indeterminate data using I-VFNNs are furnished in this study. By virtue of this model, in financial organizations, better decisions can be made, accelerate the recognition of risks, and put down the right consideration even in uncertain conditions. In the future, this model can also contribute to other innovative research areas such as international financial policies, large investments, and insurance institutions.
Design and Simulation of DC-AC Power Converters Salam Waley Shneen
Journal of Fuzzy Systems and Control Vol. 4 No. 1 (2026): Vol. 4 No. 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i1.360

Abstract

Currently, various types of electronic power converters are used in many fields and industrial applications, one of the most important of which is industrial power converters. They are an essential part of electrical power systems in generating stations, distribution systems, and transmission systems, through their connection to the main grid and sub-grids. Electronic power converters are used in energy generation systems from sustainable development sources, and clean energy is a good example of this. The functions of electronic power converters vary according to the need for them, including changing the type of voltage, current, and frequency, which are electrical quantities. Electronic power converters can be classified into buck, step-down, boost, and step-up. The inverter is one of the types of electronic power converters that works to convert and change the converter input from direct current to a converter output with an alternating voltage. The input of the converter is connected to a DC source such as batteries, solar power, or a DC generator, while the output of the converter supplies AC loads such as a single-phase or three-phase induction motor. The inverter can be single-phase or three-phase, depending on the load to be supplied, and it can be a half-wave or full-wave bridge. The study contributes to implementing and setting appropriate design steps, in addition to building a simulation model to determine the system's behavior during a specific operating period.

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