Results 41 to 50 of about 38,896 (265)
Future Internet, 2021 Using optical motion capture and wearable sensors is a common way to analyze impaired movement in individuals with neurological and musculoskeletal disorders.
Ibsa K. Jalata +4 more
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Graph Learning-Convolutional Networks
CoRR, 2018 Recently, graph Convolutional Neural Networks (graph CNNs) have been widely used for graph data representation and semi-supervised learning tasks. However, existing graph CNNs generally use a fixed graph which may be not optimal for semi-supervised learning tasks.
Bo Jiang 0002 +3 more
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IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022 Investigating neural mechanisms of anesthesia process and developing efficient anesthetized state detection methods are especially on high demand for clinical consciousness monitoring.
Kun Chen +5 more
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Upright Adjustment With Graph Convolutional Networks
2020 IEEE International Conference on Image Processing (ICIP), 2020 We present a novel method for the upright adjustment of 360 images. Our network consists of two modules, which are a convolutional neural network (CNN) and a graph convolutional network (GCN). The input 360 images is processed with the CNN for visual feature extraction, and the extracted feature map is converted into a graph that finds a spherical ...
Raehyuk Jung, Sungmin Cho, Junseok Kwon
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Graph-Time Convolutional Neural Networks
2021 IEEE Data Science and Learning Workshop (DSLW), 2021 Spatiotemporal data can be represented as a process over a graph, which captures their spatial relationships either explicitly or implicitly. How to leverage such a structure for learning representations is one of the key challenges when working with graphs. In this paper, we represent the spatiotemporal relationships through product graphs and develop
Isufi, E. (author) +1 more
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Leak Detection in Water Supply Network Using a Data-Driven Improved Graph Convolutional Network
IEEE Access, 2023 Due to the complex correlation within data collection, it is a challenging task to detect leakage in the water supply network. The Graph Convolutional Network (GCN) has recently gained significant attention in correlation research. However, most existing
Suisheng Chen +4 more
doaj +1 more source
Advanced Functional Materials, EarlyView.Thermally oxidized MoS2‐based radio‐frequency switches enable a multifunctional platform that unifies broadband RF switching and in‐memory computation. The device achieves a cutoff frequency of 33.2 THz with high energy efficiency and supports hardware‐aware signal processing.
Juho Son +5 more
wiley +1 more source
Sports behavior analysis technology based on GCN and domain knowledge graph
Discover ComputingTo improve the performance of sports behavior recognition, the spatial temporal graph convolutional network is introduced to analyze the spatial temporal features of sports behavior, achieving accurate action recognition. In the experimental results, the
Jiaojiao Hu, Shengnan Ran
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BMC Bioinformatics, 2023 Background Drug-target interaction (DTI) prediction plays an important role in drug discovery and repositioning. However, most of the computational methods used for identifying relevant DTIs do not consider the invariance of the nearest neighbour ...
Peng Chen, Haoran Zheng
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Advanced Healthcare Materials, EarlyView.Human periosteum‐derived cell spheroids bioprinted at high density within a hyaluronic acid matrix promote fusion and hypertrophic cartilage formation in vitro. Early encapsulation enhances spheroid interaction and matrix maturation, generating scalable cartilage templates intended for endochondral bone regeneration.
Ane Albillos Sanchez +6 more
wiley +1 more source