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Introduction. Scalar control (SC) and field-oriented control (FOC) are widely used in sensorless induction motor (IM) drives for their balance of performance and cost. Among estimation techniques, the voltage-model (VM) ...

Introduction. Metaheuristic algorithms are effective for solving complex power system optimization problems characterized by nonlinearity, multimodality, and high dimensionality. Nature-inspired strategies based on adapt...

Robotic technologies are becoming more advanced and are becoming more popular. Requirements are high levels of accuracy and operational speed, such as in surgery and manufacturing. These targets were highly challenging a...

Soilless cultivation systems such as hydroponics rely on continuous and accurate monitoring of physicochemical parameters to maintain optimal plant growth conditions. In practice, however, hydroponic operations are const...

Reliable trajectory similarity estimation underpins applications such as map matching, mobility anomaly detection, and travel behavior analysis. However, traditional distance-based methods are computationally expensive a...

In response to the limitations of traditional planning methods for Autonomous Surface Vehicles (ASVs), which heavily rely on predefined map representations and neglect kinematic feasibility, this paper proposes a novel k...

The Global Flood Awareness System (GloFAS) is a promising flood-modelling tool available for most river basins worldwide. However, it contains inherent biases, notably linked to its spatial resolution. This study propose...

Emergency evasive maneuvers unfold in short near-limit windows in which combined-slip saturation, load transfer, and rapidly varying friction make onset-computation budget and model mismatch critical to collision-avoidan...

The purpose of this study is to justify the design parameters of spiral-vortex equipment ensuring energy-efficient desulfurization of flue gases from autonomous medium-capacity gas-piston power plants (GPPPs). The study ...

This paper proposes an innovative automated multiphysics Microelectromechanical systems (MEMS) co-optimization platform integrating the embedded reduced-order modeling offered by Coventor MEMS+ with MATLAB’s numerical ...

Recent advancements in helmet design demonstrate that mimicking natural models can enhance both aerodynamics and safety protection. Thus, this paper comprehensively reviews the bio-inspired helmet designs reported in the...

Abstract Broadband Power Line Communication (BB-PLC) technology enables data transmission for smart grid applications. Nevertheless, channel equalizers are required in the receiver to estimate and compensate for the nonl...

Abstract Machine learning surrogate models of Kohn-Sham Density Functional Theory Hamiltonians provide a powerful tool for accelerating the prediction of electronic properties of materials, such as electronic band struct...

Abstract Normally the physical dimensions of the taper transition that realizes the impedance matching between the impedance of the feeding line built in microstrip line technology and the impedance of the component buil...

Abstract In this paper, it is presented a general optimization methodology for improving empirical models predicting Okumura-Hata, Cost-231, ECC-33, and Egli using Genetic and Differential Evolution Algorithms. The metho...

Abstract Mechanical engineering structures and structural components are often subjected to cyclic thermomechanical loading which stresses their material beyond its elastic limits well inside the inelastic regime. Depend...

Climate change and human impact are increasing the frequency and severity of flooding, making real-time, accurate simulation of mountainous flood inundation an urgent necessity. In this study, we developed a proxy model ...

This article proposes a new method to solve non-linear magnetostatic problems applied to the modeling of electromagnetic devices. A reduced scalar potential formulation is presented and solved by a hybrid BEM-NRM. It has...

Reliable beat-level arrhythmia detection from electrocardiogram (ECG) signals is essential for continuous patient monitoring. However, many existing deep learning models remain computationally expensive and are sensitive...

Abstract Predictive atomistic simulations have propelled materials discovery, yet routine setup and debugging still demand computer specialists. This know-how gap limits the use of Integrated Computational Materials Engi...

This work presents and validates a direct analytical methodology for the parametric hydrodynamic design of Pelton rotors. The proposed approach combines a one-dimensional theoretical framework for defining global geometr...

"This thesis investigates the application of machine learning methods to the task of deforestation monitoring using time-series satellite data. The objective is to assess how different algorithmic approaches perform in d...