Results 81 to 90 of about 142,397 (310)

A review on the applications of graph neural networks in materials science at the atomic scale

Materials Genome Engineering Advances
In recent years, interdisciplinary research has become increasingly popular within the scientific community. The fields of materials science and chemistry have also gradually begun to apply the machine learning technology developed by scientists from ...
Xingyue Shi, Linming Zhou, Yuhui Huang, Yongjun Wu, Zijian Hong   +4 more
doaj   +1 more source

Classification of 3D CAD Models considering the Knowledge Recognition Algorithm of Convolutional Neural Network

Advances in Multimedia, 2022
In order to improve the classification effect of the 3D CAD model, this paper combines the knowledge recognition algorithm of convolutional neural network to construct the 3D CAD model classification model.
Weiwei Wang, Dandan Sun
doaj   +1 more source

A Comprehensive Spatio-Temporal Model for Subway Passenger Flow Prediction

ISPRS International Journal of Geo-Information, 2022
Accurate subway passenger flow prediction is crucial to operation management and line scheduling. It can also promote the construction of intelligent transportation systems (ITS).
Zhihao Zhang, Yong Han, Tongxin Peng, Zhenxin Li, Ge Chen   +4 more
doaj   +1 more source

Processing of Incomplete Images by (Graph) Convolutional Neural Networks [PDF]

, 2020
We investigate the problem of training neural networks from incomplete images without replacing missing values. For this purpose, we first represent an image as a graph, in which missing pixels are entirely ignored. The graph image representation is processed using a spatial graph convolutional network (SGCN) -- a type of graph convolutional networks ...
Tomasz Danel, Marek Smieja, Lukasz Struski, Przemyslaw Spurek, Lukasz Maziarka   +4 more
openaire   +3 more sources

Polynomial-based graph convolutional neural networks for graph classification

Machine Learning, 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Pasa L., Navarin N., Sperduti A.
openaire   +1 more source

Transducers Across Scales and Frequencies: A System‐Level Framework for Multiphysics Integration and Co‐Design

Advanced Materials Technologies, EarlyView.
Transducers convert physical signals into electrical and optical representations, yet each mechanism is bounded by intrinsic trade‐offs across bandwidth, sensitivity, speed, and energy. This review maps transduction mechanisms across physical scale and frequency, showing how heterogeneous integration and multiphysics co‐design transform isolated ...
Aolei Xu, Mengyao Xiao, Zhouli Sui, Yinpeng Wang, Daoye Zheng, Yuxin Liu, Luwei Wang, Huajun Liu, Chengkuo Lee   +8 more
wiley   +1 more source

A Network Scanning Organization Discovery Method Based on Graph Convolutional Neural Network

Information
With the quick development of network technology, the number of active IoT devices is growing rapidly. Numerous network scanning organizations have emerged to scan and detect network assets around the clock.
Pengfei Xue, Luhan Dong, Chenyang Wang, Cheng Huang, Jie Wang   +4 more
doaj   +1 more source

Vision‐Augmented Wearable Interfaces: Bioinspired Approaches for Realistic AI‐Human‐Machine Interaction

Advanced Materials Technologies, EarlyView.
This review presents recent progress in vision‐augmented wearable interfaces that combine artificial vision, soft wearable sensors, and exoskeletal robots. Inspired by biological visual systems, these technologies enable multimodal perception and intelligent human–machine interaction.
Jihun Lee, Gwuidong Ryu, Kisoo Kim, Feng Xu, Won Gu Lee   +4 more
wiley   +1 more source

The combination model of CNN and GCN for machine fault diagnosis.

PLoS ONE, 2023
Learning powerful discriminative features is the key for machine fault diagnosis. Most existing methods based on convolutional neural network (CNN) have achieved promising results.
Qianqian Zhang, Caiyun Hao, Zhongwei Lv, Qiuxia Fan   +3 more
doaj   +1 more source

Robust Spatial Filtering With Graph Convolutional Neural Networks [PDF]

IEEE Journal of Selected Topics in Signal Processing, 2017
Convolutional Neural Networks (CNNs) have recently led to incredible breakthroughs on a variety of pattern recognition problems. Banks of finite impulse response filters are learned on a hierarchy of layers, each contributing more abstract information than the previous layer. The simplicity and elegance of the convolutional filtering process makes them
Felipe Petroski Such, Shagan Sah, Miguel Domínguez, Suhas Pillai, Chao Zhang, Andrew Michael, Nathan D. Cahill, Raymond W. Ptucha   +7 more
openaire   +2 more sources