Results 111 to 120 of about 46,986 (307)

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Biofeedback and Feedforward in Telerobotics Control

, 1994
Biofeedback and Feedforward in Telerobotics ...
COSMI, Francesca, A. Rovetta, L. Termite, L. Molinari Tosatti   +3 more
core  

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Accelerating Primary Screening of USP8 Inhibitors from Drug Repurposing Databases with Tree‐Based Machine Learning

Advanced Intelligent Discovery, EarlyView.
This study introduces a tree‐based machine learning approach to accelerate USP8 inhibitor discovery. The best‐performing model identified 100 high‐confidence repurposable compounds, half already approved or in clinical trials, and uncovered novel scaffolds not previously studied. These findings offer a solid foundation for rapid experimental follow‐up,
Yik Kwong Ng, Sharon Ai Er Lam, Wei Chun Lim, Hannah Hui Min Toh, Shi Jun Ang   +4 more
wiley   +1 more source

A Parallel GRU-Transformer Neural Network Approach for Feedforward Compensation in Precision Motion Platform Control

IEEE Access
This paper proposes a feedforward compensation strategy based on Parallel GRU-Transformer neural network to address the issues of large tracking errors and insufficient stability of multi degree of freedom precision motion platforms in complex dynamic ...
Yi-Min Wang, Jun-Jie Wang, Ji-Teng Yang, Bin Liu, Shi Bu, Xi Chen, Jian-Ping Xing   +6 more
doaj   +1 more source

Smart Flexible Tactile Sensors: Recent Progress in Device Designs, Intelligent Algorithms, and Multidisciplinary Applications

Advanced Intelligent Discovery, EarlyView.
Flexible tactile sensors have considerable potential for broad application in healthcare monitoring, human–machine interfaces, and bioinspired robotics. This review explores recent progress in device design, performance optimization, and intelligent applications. It highlights how AI algorithms enhance environmental adaptability and perception accuracy
Siyuan Wang, Ming Dong, Rongrong Bao, Caofeng Pan   +3 more
wiley   +1 more source

A Multi-Resonant based reference feedforward adaptive voltage control for Grid-Forming inverter in island mode to compensate system uncertain and harmonic distortions

International Journal of Electrical Power & Energy Systems
This paper investigates a novel adaptive voltage control over a three-phase grid-forming (GFM) inverter. The proposed voltage controller includes two function parts: power control input and signal control input. The former improves dynamic performance by
Renzhi Huang, Zaijun Wu, Xueyong Xu, Xiangjun Quan, Mingfei Li, Zichen Wang, Mengnan Liu   +6 more
doaj   +1 more source

Tuning rules for feedforward control from measurable disturbances combined with PID control a review

Feedforward control can be considered as the most well-known control approach to deal with measurable disturbances. It started to be used almost 100 years ago, and since then it is being used in most industrial processes.
Hägglund, T.,, Guzmán, J. L.,, Lund University.   +2 more
core   +1 more source

Feedforward motion control: from batch-to-batch learning to online parameter estimation

, 2019
Feedforward control is essential in highperformance motion control. The aim of this paper is to develop a unified framework for automatic feedforward optimization from both batch-wise data sets as well as real-time data.
Witvoet, Gert, Witvoet, G Gert, Noud Mooren, Oomen, Tom, Oomen, TAE Tom, Mooren, Noud, Mooren, NFM, Gert Witvoet, Tom Oomen   +8 more
core   +1 more source

Modelling of methanol synthesis in a network of forced unsteady-state ring reactors by artificial neural networks for control purposes [PDF]

, 2004
A numerical model based on artificial neural networks (ANN) was developed to simulate the dynamic behaviour of a three reactors network (or ring reactor), with periodic change of the feed position, when low-pressure methanol synthesis is carried out.
MANCA, DAVIDE, A. A. BARRESI, Fissore, Davide, Barresi, Antonello, Manca, D., Manca D., D. FISSORE   +6 more
core   +1 more source