Results 221 to 230 of about 2,086,846 (295)

Frequency‐Domain Subspace Identification for Noninvasive Transformer Winding Parameter Estimation and Fault Diagnostics

open access: yesInternational Journal of Circuit Theory and Applications, Volume 54, Issue 2, Page 658-677, February 2026.
Noninvasive frequency‐domain method estimates transformer winding parameters from terminal measurements, eliminating the need for design data. ABSTRACT A novel frequency‐domain subspace identification method enables precise estimation of transformer winding ladder network parameters directly from terminal measurements.
K. Lakshmi Prasanna   +3 more
wiley   +1 more source

The Influence of Big Data‐Driven Educational Technologies on College Teaching Development

open access: yesEngineering Reports, Volume 8, Issue 2, February 2026.
Exploring the impact of big data on college instructors: enhancing teaching, fostering professional growth, and addressing challenges in data adoption and privacy. ABSTRACT The rapid development of big data and mobile Internet technologies has significantly influenced the instructional growth of college instructors. This study investigated how big data
Ling Yu, Wenye Li, Ying Luo
wiley   +1 more source

Apparatus Design and Finite Element Modeling for Controlled‐Speed Nakazima Experiments

open access: yesEngineering Reports, Volume 8, Issue 2, February 2026.
This study has two primary aims: first, to design a scaled device with deformable tooling components that can conduct Nakazima experiments with a rotating tool, and second, to develop a 3D Finite Element model for scaled Nakazima‐test simulation considering deformable tooling components.
Radouane Benmessaoud
wiley   +1 more source

Unified Hysteresis Modeling via Physics‐Based Deep Learning and Data Augmentation

open access: yesEarthquake Engineering &Structural Dynamics, Volume 55, Issue 2, Page 379-396, February 2026.
ABSTRACT Deep learning‐based models have recently emerged as alternatives to traditional form‐constrained hysteresis models, including Bouc‐Wen class models, offering significant potential for unified hysteresis modeling to capture complex nonlinearities and diverse response patterns exhibited under stochastic excitations such as ground motions.
Jaehwan Jeon, Oh‐Sung Kwon, Junho Song
wiley   +1 more source

Seismic Structural Response and Loss Estimation for Dense Urban Districts Using Neural Network Parameterized Gaussian Process

open access: yesEarthquake Engineering &Structural Dynamics, Volume 55, Issue 2, Page 397-412, February 2026.
ABSTRACT Earthquakes pose a major threat to urban areas, causing fatalities, injuries, and significant economic losses. This study proposes a Gaussian process parametrized by deep neural networks (DNN–GP) as an efficient surrogate for assessing seismic losses of building structures at a regional scale.
Byeongseong Choi   +2 more
wiley   +1 more source

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