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52 articles for “Fuzzy Systems”
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Advancements in Multiple-Input DC–DC Converters for Hybrid Renewable Energy Systems: Topologies, Control Strategies, and Applications
Abstract: Multiple-input DC to DC converters (MICs) are essential components in hybrid energy systems, enabling efficient management of diverse energy inputs from renewable sources such as solar photovoltaic (PV) panels and wind turbines. These converters facilitate the seamless integration of variable power outputs, addressing the intermittent nature of renewable energy through advanced power electronics. This paper provides a comprehensive review of the topologies, control strategies, and applications of MICs within renewable …
Published in Trends in Electrical Engineering · Vol. 15, Issue 2, 2025 · pp. 10–17 Read article
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Critical Review on Multifunctional Polymer Composites for Weight Reduction and AI Based Battery Thermal Management in Electric Vehicles
Abstract: The rapid growth of electric vehicles (EVs) has intensified the need for advanced materials and intelligent control systems capable of improving energy efficiency, driving range, thermal safety, and overall vehicle sustainability. This paper presents a critical review of multifunctional polymer composites and artificial intelligence-based battery thermal management systems (AI-BTMS) for next-generation EV applications. Polymer composites reinforced with carbon fibers, graphene, boron nitride, nanoclays, and carbon nanotubes offer significant advantages over …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 603–619 Read article
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Design and Implementation of Intelligent Obstacle Avoiding Robot
Abstract: The Intelligent Obstacle Avoiding Robot is an autonomous robotic system designed to navigate safely through unknown or congested environments by detecting and avoiding obstacles in real time. This robot integrates sensor modules, embedded control systems, and intelligent decision-making algorithms to achieve smooth and collision-free movement. Ultrasonic, infrared, or LiDAR-based sensors are used to continuously measure the distance between the robot and surrounding objects. The sensor data is processed by a …
Published in Journal of Mechatronics and Automation · Vol. 13, Issue 1, 2026 · pp. 1–6 Read article
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Deep Reinforcement Learning-Based Intelligent Energy Management Strategy for Battery–Supercapacitor Hybrid Energy Storage Systems in Electric Vehicles
Abstract: As the number of EVs increases, smart solutions for energy management are needed that will optimize energy use, prolong battery life and boost vehicle performance. The application of conventional rule based and optimization-based Energy Management Strategies (EMS) for Battery–Supercapacitor Hybrid Energy Storage Systems (HESS) often leads to sub-optimal power management, supercapacitor mismatch and battery degradation when subjected to varying driving conditions. This study aims to design an intelligent energy management …
Published in International Journal of Advanced Control and System Engineering · Vol. 4, Issue 2, 2026 Read article
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Sliding Mode Controller-based Load Frequency Regulation for a Two-area Interrelated Power System
Abstract: This study presents the use of the sliding mode control approach for power systems load frequency regulation. A sliding mode load frequency controller is part of a two-area power system. Both reheat and non-reheat. These two regions each have a distribution of thermal turbines. Nonlinearities such as governor dead region and production rate limitations are included in the block diagram of a power plant model. Our control objective is to …
Published in International Journal of Advanced Control and System Engineering · Vol. 1, Issue 2, 2023 · pp. 8–13 Read article
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Intelligent Power Quality Enhancement Strategies for PV-Integrated Smart Distribution Networks: A State-of-the-Art Review
Abstract: The rapid integration of photovoltaic (PV) systems into modern power distribution networks has introduced significant challenges related to power quality. Issues such as voltage fluctuations, harmonic distortion, flicker, and reactive power imbalance arise due to the intermittent and nonlinear nature of solar energy generation. This paper presents a concise literature review of various power quality enhancement techniques employed in PV-integrated networks. Key approaches include the use of active power filters …
Published in International Journal of Electrical Power and Machine Systems · Vol. 4, Issue 1, 2026 · pp. 30–53 Read article
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Semiring-Weighted Automata and Recursive Path Counting for Multi-State Reliability in Discrete Infrastructures
Abstract: Multi-state infrastructures such as communication backbones, microgrids, warehouse routing systems, and sensor-actuator pipelines evolve through discrete event sequences rather than through a single binary "working/failed" transition. This paper develops a semiring-weighted automata framework for reliability analysis in which state changes, repair actions, and degraded operating modes are represented by weighted transitions on a finite automaton. A path valuation is defined over an additively idempotent reliability semiring and extended to a …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 13, Issue 2, 2026 · pp. 30–36 Read article
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Enhancing Power Quality and Harmonics Control via Integration of PV, Wind, and Battery Systems with Three-level Inverter in DC Micro grid Management
Abstract: The primary goal of this research is to develop a multi-level inverter-based energy management approach for a smart DC-micro grid. An electric micro grid is created in this project by combining photovoltaic (PV), wind, and batteries. Solar and wind power can be efficiently extracted, and the quality of power can be improved by using FOPID control for source-side converters (SSC) and load-side converters controlled by multilevel inverters. To make the …
Published in Journal of Power Electronics and Power Systems · Vol. 14, Issue 1, 2024 · pp. 18–27 Read article
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A Review on Two-Wheeled Self-Balancing Robot Using Spartan-3E FPGA for Sensor Fusion and Real-Time Motor Control
Abstract: Two-wheeled self-balancing robots (TWSBR) are a popular application of embedded control and robotics because they operate on the inverted pendulum concept, which is naturally unstable. The main objective of such robots is to continuously maintain balance by estimating the tilt angle and applying corrective motor action in real time. In most practical systems, low-cost inertial sensors such as accelerometers and gyroscopes are used for tilt measurement. However, accelerometer readings are …
Published in Journal of VLSI Design Tools and Technology · Vol. 16, Issue 1, 2026 · pp. 17–27 Read article
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Improving Dataset Integrity Through Automated Data Cleaning Techniques
Abstract: High-quality data is a fundamental requirement in data science for producing trustworthy analytical insights and effective machine learning models. Problems, including incomplete records, inconsistent entries, duplicate observations, and anomalous values, can severely reduce the accuracy and robustness of predictive systems. As modern datasets continue to expand in both volume and structural complexity, relying on manual data cleaning methods become time-consuming and error-prone, highlighting the growing importance of automated data preprocessing …
Published in International Journal of Data Structure Studies · Vol. 4, Issue 1, 2026 · pp. 40–45 Read article
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Efficient Energy Management using Artificial Intelligence (AI) and Machine Learning (ML) in Chemical Industry
Abstract: The globe is moving toward higher usage of renewable energy sources, particularly solar and wind energy, as a result of depleting fossil fuel supplies and growing environmental concerns. There are several forecasting methods available for effective wind energy utilization. This review uses algorithms for predicting solar and wind energy as well as artificial intelligence (AI) techniques. A wind-coal coupling energy system planning scheme was designed to lower the high energy …
Published in Journal of Modern Chemistry & Chemical Technology · Vol. 16, Issue 2, 2025 · pp. 33–50 Read article
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Fuzzy C-Means Clustering for Effective Segmentation and Classification of Brain Tumors in MRI Scans
Abstract: The paper discusses the importance of detecting and classifying brain tumors via MRI for effective treatment. It proposes a framework utilizing the Fuzzy C-means clustering algorithm for segmentation, demonstrating improved performance through real dataset validation. The model is trained on a large, annotated MRI dataset to identify and classify different tumor types, enabling machine learning-based classification into benign and malignant tumors. The MATLAB-based solution automates brain tumor feature extraction, aiding …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 2, 2024 · pp. 23–28 Read article