Results 111 to 120 of about 186,586 (268)

A Neural Network‐Based Self‐Sensing Embedded Position Control System for Shape Memory Alloy Wire Actuators

Advanced Intelligent Systems, EarlyView.
Shape memory alloy wires exhibit thermally induced phase changes that generate actuation strain and resistance variations enabling self‐sensing. However, hysteretic electromechanical behavior complicates accurate state estimation. This paper presents an artificial in‐based self‐sensing method to reconstruct SMA actuator position in real time, achieving
Krunal Koshiya, Gianluca Rizzello, Paul Motzki   +2 more
wiley   +1 more source

Output quality improvement for single‐phase inverter in V2G system

IET Power Electronics
In vehicle‐to‐grid (V2G) applications, a voltage source inverter (VSI) directly connects to a residential load or grid for DC/AC conversion and power flow control.
Yipei Wang, Ancheng Liu, A‐Rim Kwak, Sung‐Jun Park   +3 more
doaj   +1 more source

Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review

Advanced Intelligent Systems, EarlyView.
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy, Vitalii Kapitan, Andrey Ustyuzhanin   +2 more
wiley   +1 more source

Probabilistic Lesion Mapping to Optimize Thalamotomy Targets for Focal Hand Dystonia

Annals of Neurology, EarlyView.
Objective Focal hand dystonia (FHD) severely impairs task‐specific motor control, yet the optimal surgical target for stereotactic intervention remains uncertain. This study aimed to identify the precise thalamic lesion site associated with symptomatic improvement and to clarify its network connectivity. Methods We retrospectively analyzed 164 patients
Masahiko Nishitani, Shiro Horisawa, Konstantin Butenko, Hiroki Togo, Arun Garimella, William Drew, Melissa Chua, Esraa Shaban, Takakazu Kawamata, Takaomi Taira, Andreas Horn, Michael D. Fox, Takashi Hanakawa   +12 more
wiley   +1 more source

A Novel Deep Temporal Feature Enhanced Just‐in‐Time Learning Framework for Predicting Rare Earth Component Content

Asia-Pacific Journal of Chemical Engineering, EarlyView.
ABSTRACT Real‐time online detection of rare earth element component contents is a crucial link in ensuring the stable production of the rare earth extraction and separation industry and improving the quality of rare earth products. The traditional methods for predicting the content of rare earth element components based on just‐in‐time learning fail to
Zhaohui Huang, Liangfang Liao, Chunfa Liao, Hui Zhang, Tao Qi, Rongxiu Lu, Wenhao Dai   +6 more
wiley   +1 more source

Changes in Intrinsic Activity of the Primary Somatosensory Cortex Causally Explain Differences in Emotion Perception in Autism

Autism Research, EarlyView.
ABSTRACT Autism Spectrum Disorder (ASD) is characterized by certain difficulties in emotion‐related processing. Recent research using electroencephalography (EEG) to measure somatosensory evoked potentials during emotion perception has shown reduced embodiment of emotional expressions in autistic compared to neurotypical individuals, independently from
Martina Fanghella, Danai Dima, Dimitrios Pinotsis, Sebastian B. Gaigg, Beatriz Calvo‐Merino, Bettina Forster   +5 more
wiley   +1 more source

Artificial intelligence and machine learning‐assisted digital applications for biopharmaceutical manufacturing

Biotechnology Progress, EarlyView.
Abstract Artificial intelligence and automation are no longer just buzzwords in the biopharmaceutical industry. The manufacturing of a class of biologics, comprising monoclonal antibodies, cell therapies, and gene therapies, is far more complex than that of traditional small molecule drugs.
Shyam Panjwani, Hao Wei, John Mason
wiley   +1 more source

Data‐driven simulation of crude distillation using Aspen HYSYS and comparative machine learning models

The Canadian Journal of Chemical Engineering, EarlyView.
Integrated Aspen HYSYS–machine learning framework for predicting product yields and quality variables. Abstract Crude oil refining is a complex process requiring precise modelling to optimize yield, quality, and efficiency. This study integrates Aspen HYSYS® simulations with machine learning techniques to develop predictive models for key refinery ...
Aldimiro Paixão Domingos, Heleno Bispo, Thiago Vaz da Costa, Sérgio Mauro da Silva Neiro   +3 more
wiley   +1 more source

Dynamic geo‐hydrogeological monitoring‐driven situational awareness for real‐time floor water inrush risk prediction in deep mining

Deep Underground Science and Engineering, EarlyView.
The fused data extracted from the distributed monitoring system as the data basis, combined with dynamic geological data, are imported into a deep learning model. As the geological conditions of mining and excavation change, the risk of water inrush at the working face is retrieved in real time.
Yongjie Li, Huiyong Yin, Fangying Dong, Tao Wu, Chao Zhang   +4 more
wiley   +1 more source

Synergistic Enhancement of Modified‐PVDF Humidity Sensitivity via Chemical Adsorption‐Ionic Conductivity and its Application in Intelligent Powered Air‐Purifying Respirator

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
The study developed a PVDF/SiO2 humidity sensor with superhydrophobic properties and integrated the humidity sensor into the intelligent powered air‐purifying respirator (IPAPR) for breath monitoring. Coupled with the Gated Recurrent Unit (GRU) machine learning prediction algorithm, it enables feedforward control of the fan to achieve respiratory ...
Xinjian He, Hongwei Liu, Xianjie Xu, Xinkai Qi, Danhong Gao, Hanling Deng, Dusong Hou, Qianyao Lu, Lei Zhu, Sheng Huang   +9 more
wiley   +1 more source