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71 articles for “model robustness”
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DC Motor Control using Deep Reinforcement Learning for Enhanced Robustness and Precision
Abstract: DC motors remain the workhorse of industrial automation and mobile robotics, but achieving simultaneous high-speed transient response and negligible steady-state error under variable load conditions continues to challenge classical Proportional-Integral-Derivative (PID) controllers. These model-dependent systems often require extensive tuning and struggle to maintain optimal performance when confronted with parametric uncertainties, non-linear friction, or sudden voltage fluctuations. This study presents a novel, model-free control paradigm utilizing Deep Reinforcement Learning (DRL)—specifically, a …
Published in International Journal of Electrical Machine Analysis and Design · Vol. 3, Issue 2, 2025 · pp. 22–29 Read article
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Thermal Effects in High-Power Laser Systems: Modeling and Mitigation
Abstract: High-power laser systems are increasingly employed in industrial manufacturing, defense, medical procedures, and scientific research due to their ability to deliver high energy density with excellent spatial coherence. However, the performance and reliability of these systems are significantly influenced by thermal effects arising from optical absorption, non-radiative recombination, and inefficient heat dissipation within laser gain media and optical components. These thermal phenomena lead to adverse effects such as thermal lensing, …
Published in International Journal of Manufacturing and Production Engineering · Vol. 3, Issue 2, 2025 · pp. 9–13 Read article
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Land surface dynamics: A Multiphysics Approach to Modeling Mass Transport
Abstract: Land surface dynamics are governed by complex interactions among hydrological, atmospheric, and geomorphological processes that collectively drive the transport of mass across terrestrial environments. Traditional modeling approaches often isolate individual mechanisms, limiting their ability to capture the coupled feedbacks that shape landscape evolution. This study presents a multiphysics framework for modeling mass transport on land surfaces, integrating fluid flow, sediment transport, heat exchange, and chemical reactions within a unified computational …
Published in International Journal of Land · Vol. 2, Issue 2, 2025 · pp. 31–36 Read article
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Reaching Conditions for Discrete-time Sliding Mode Control: Analysis and Design
Abstract: Sliding mode control (SMC) is a robust control method widely used in engineering due to its ability to handle uncertainties and disturbances effectively. In discrete-time sliding mode control (DSMC), system trajectories are constrained to sliding surfaces in the state space, leading to improved performance and stability. This paper provides an overview of DSMC, focusing on its applications, advantages, and limitations. It discusses the use of DSMC in various engineering fields …
Published in International Journal of Electrical Machine Analysis and Design · Vol. 2, Issue 1, 2024 · pp. 7–14 Read article
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Performance Forecasting in Solo Sports: Leveraging Psychometrics, Economic Analysis, and Cultural Insights for Predictive Excellence
Abstract: The science of performance forecasting in solo sports has transcended traditional metrics, embracing a multidisciplinary approach. This paper explores the integration of psychometrics, economic analysis, and cultural insights to predict athletic success. By leveraging psychological profiling, economic factors, and cultural dynamics, this study aims to establish a comprehensive model for forecasting athlete performance with heightened accuracy. In the realm of solo sports, where individual prowess dictates success, traditional performance forecasting …
Published in Recent Trends in Sports · Vol. 1, Issue 1, 2024 · pp. 35–44 Read article
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Implementing Machine Learning in Data Classification
Abstract: Data classification forms an essential aspect of artificial intelligence (AI) and soft computing, helping a great deal in the transformation of raw data into knowledge that forms the basis of numerous applications, such as fraud detection, medical diagnostics, and natural language processing. This study discusses the challenges and the state of the art in data classification, as far as scalability, noise handling, and feature selection optimization are concerned. It gives …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 2, 2025 · pp. 15–22 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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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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A GIS and MCDA Framework for Land Suitability Analysis in Sustainable Agriculture
Abstract: This study examines land suitability for sustainable agriculture by integrating Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) as tools to optimize agricultural planning. Sustainable agricultural practices are increasingly critical to meet global food demands without depleting essential land resources. This research focuses on assessing specific parameters that determine the agricultural viability of land, including soil characteristics, topography, climatic conditions, and water availability. These criteria are integral to understanding …
Published in International Journal of Land · Vol. 1, Issue 2, 2024 · pp. 30–34 Read article
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Simulation and Analysis of Battery Pack Using the Multi Scale Multi-Domain Battery Model
Abstract: The creation of sophisticated simulation models has been made necessary by the need for reliable and effective battery packs in energy storage systems and electric vehicles. This study focuses on the simulation and analysis of battery packs using a multi-scale multi-domain battery model. The model enables a thorough knowledge of battery pack behavior across a range of operating situations by integrating the electricity, thermal, and mechanical domains. Multi-scale modeling bridges …
Published in International Journal of Electro-Mechanics and Material Behaviour · Vol. 2, Issue 2, 2024 · pp. 31–46 Read article
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Crystal Engineering Strategies for Tailoring Mechanical Properties of Structural Materials
Abstract: Crystal engineering has emerged as a promising approach for designing materials with tailored mechanical properties, enabling advancements in various fields such as aerospace, automotive, and construction. This review examines the diverse strategies employed in crystal engineering to manipulate the mechanical behavior of structural materials. One key strategy involves controlling the crystal structure at the atomic level through techniques such as alloying, doping, and phase transformations. Alloying introduces foreign atoms into …
Published in International Journal of Crystalline Materials · Vol. 1, Issue 1, 2024 · pp. 01–06 Read article
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Design and Implementation of a Four-Port DC to DC Converter for a Hybrid Energy System
Abstract: A four-port DC-DC converter has been developed for integrating a hybrid energy from renewable sources system into an AC microgrid. This converter is particularly suitable for AC microgrid applications that require system-level power management. It boasts a straightforward design, making it effective for interfacing sources with varying voltage and current characteristics. The converter is created to connect a battery bank, a photovoltaic (PV) panel, a wind turbine, and an inverter, …
Published in International Journal of Electro-Mechanics and Material Behaviour · Vol. 2, Issue 2, 2024 · pp. 47–60 Read article
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Problems and Difficulties in the Additive Manufacturing of Composites with Carbon Fiber Reinforcement for Medical Application
Abstract: Amputation is more prevalent than one may think due to an accident, war, or disease. Prosthetics and associated orthotics have a combined global market value of $2.8 billion. Carbon fibre has found a market due to increased demand for foot prostheses. Fused deposition modeling (FDM) is a rapidly advancing three-dimensional (3D) printing technique. PEEK (polyether-ether-ketone) is a biocompatible, high-performance polymer that could potentially be utilised as an orthopedic surgery implant …
Published in International Journal of Fracture Mechanics and Damage Science · Vol. 1, Issue 1, 2023 · pp. 8–12 Read article
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Spectral Intuitionistic Fuzzy Hypergraph Operators and Dominance Kernels for Resilient Discrete Network Design
Abstract: A new discrete-mathematical framework is developed for resilient network design on intuitionistic fuzzy hypergraphs, where uncertainty is explicitly represented through membership, non-membership, and hesitation degrees associated with both vertices and hyperedges. These three components are systematically integrated into an effective incidence operator that captures the underlying uncertain relationships within complex hypergraph structures. Based on this operator, both un-normalised and normalized Laplacian matrices are formulated to characterize the spectral properties and …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 41–48 Read article
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Deep Learning Enhanced Compressive Sensing for Wireless IoT Data Optimization and Weather Monitoring.
Abstract: This research explores the application of deep learning and compressive sensing in order to optimize data traffic in non-orthogonal multiple access (NOMA)-based wireless internet of things (IoT) networks and weather monitoring. Such a framework would be very effective and overcome pilot attacks and reconstruction losses for secure data transmission. In this regard, a strong communication model has been adopted based on power-domain NOMA for simultaneous wireless transmission by multiple IoT …
Published in International Journal of Satellite Remote Sensing · Vol. 2, Issue 2, 2024 · pp. 20–36 Read article
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A study in Leveraging Deep Learning and IoT Arrays for Dynamic, Hyper-Local Atmospheric Intelligence
Abstract: The critical demand for high-resolution, actionable atmospheric data is challenged by the high cost and sparse coverage of traditional regulatory monitoring stations. This paper explores the synergistic paradigm shift enabled by integrating low-cost, dense Internet of Things (IoT) sensor arrays with advanced Artificial Intelligence (AI) methodologies, specifically Deep Learning (DL) models. We address the primary limitations of low-cost sensors—inherent bias, sensitivity to environmental drift (temperature/humidity), and calibration inconsistency—by utilizing AI …
Published in International Journal of Atmosphere · Vol. 2, Issue 2, 2025 · pp. 50–62 Read article
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LLM-based Chatbot for Course-based Question Answering
Abstract: The “LLM-based Chatbot for Course-Based Question Answering” project addresses the pressing need for tailored and efficient learning tools in education. By using a state-of-the-art Large Language Model (LLM) with a diverse dataset, including textbooks, professor slides, and web scraping data, the chatbot offers accurate and contextually enriched responses to students' course-related queries. Using recent advances in language modeling, this work presents a Longformer-based Language Model (LLM) for constructing a smart …
Published in International Journal of Electronics Automation · Vol. 1, Issue 2, 2023 · pp. 30–41 Read article
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AI-Powered Emotion Recognition in Dog
Abstract: Understanding animal emotions is important for improving veterinary care, human animal interaction, and overall pet well-being. Inspired by previous research that utilized a modified EfficientNetB5 model for emotion classification in cats and dogs, our study builds upon this foundation with a focus on real-time emotion recognition in dogs. While earlier approaches achieved high accuracy using Dense Residual and Squeeze-and-Excitation blocks, they often lacked real-time applicability and were not optimized for …
Published in International Journal of Machine Systems and Manufacturing Technology · Vol. 4, Issue 1, 2026 · pp. 20–32 Read article
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A Study on “Clean" in Beauty: A Machine LearningApproach to Ingredient Transparency and ConsumerTrust
Abstract: The burgeoning "clean beauty" market, while driven by consumer demand for safer and more sustainable products, is plagued by ambiguous definitions and the pervasive challenge of "greenwashing". This ambiguity hinders informed consumer choices and complicates brand authenticity. This study addresses these complexities by developing a novel machine learning (ML) framework designed to objectively analyze cosmetic ingredient lists, classify products based on their "cleanliness" profile, and identify key ingredient attributes that …
Published in Recent Trends in Cosmetics · Vol. 3, Issue 1, 2026 · pp. 1–12 Read article
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Strategy for Improving Software Maintenance Using Machine Learning for Security Requirements: A Review
Abstract: Within the area of software technical education, the significance of software defect discovery has increased as a research focus to enhance program reliability. By maximizing testing resources and assisting developers in identifying potential problems using program defect predictions, program dependability is increased. Applying software engineering (SE) techniques to critical and intricate systems, like networking and security systems, is imperative. Traditional methods of predicting software maintainability have limitations, particularly in balancing …
Published in International Journal of Information Security Engineering · Vol. 2, Issue 2, 2024 · pp. 36–48 Read article