Results 311 to 320 of about 8,619,235 (382)

Dual-energy CT based mass density and relative stopping power estimation for proton therapy using physics-informed deep learning [PDF]

open access: greenPhysics in Medicine and Biology, 2022
Proton therapy requires accurate dose calculation for treatment planning to ensure the conformal doses are precisely delivered to the targets. The conversion of CT numbers to material properties is a significant source of uncertainty for dose calculation.
Chih-Wei Chang   +11 more
semanticscholar   +2 more sources

Applications of Physics-Informed Neural Networks in Power Systems - A Review

IEEE Transactions on Power Systems, 2023
The advances of deep learning (DL) techniques bring new opportunities to numerous intractable tasks in power systems (PSs). Nevertheless, the extension of the application of DL in the domain of PSs has encountered challenges, e.g., high requirement for ...
Bin Huang, Jianhui Wang
semanticscholar   +1 more source

Physics-Constrained Robustness Evaluation of Intelligent Security Assessment for Power Systems

IEEE Transactions on Power Systems, 2023
Machine learning (ML) algorithms have been widely developed to enable real-time security assessment for large-scale electricity grids. However, it also has been extensively recognized that the ML models are vulnerable to adversarial examples, which are ...
Zhenyong Zhang   +4 more
semanticscholar   +1 more source

USB-powered physics experiments

The Physics Teacher, 2020
Physics experiments powered by a universal serial bus (USB) connection are becoming increasingly common. With teachers going through so many batteries each year, USB-powered physics provides an economical and environmentally friendly alternative. In this article, the authors discuss several ways that USB power can provide a convenient way to do physics
James Lincoln, Anton Skorucak
openaire   +1 more source

A Physics-Guided Graph Convolution Neural Network for Optimal Power Flow

IEEE Transactions on Power Systems
The data-driven method with strong approximation capabilities and high computational efficiency provides a promising tool for optimal power flow (OPF) calculation with stochastic renewable energy.
Maosheng Gao   +3 more
semanticscholar   +1 more source

Toward Physics-Informed Machine-Learning-Based Predictive Maintenance for Power Converters—A Review

IEEE transactions on power electronics
Predictive maintenance for power electronic converters has emerged as a critical area of research and development. With the rapid advancements in deep-learning techniques, new possibilities have emerged for enhancing the performance and reliability of ...
Youssof Fassi   +3 more
semanticscholar   +1 more source

Physics-Guided Neural Network for Load Margin Assessment of Power Systems

IEEE Transactions on Power Systems
The power system load margin is an important index used in power system operation centers. Traditional load margin calculation involves solving a set of differential-algebraic equations where not all information is always available.
M. E. Bento
semanticscholar   +1 more source

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