A Multi-Channel Multi-Scale Spatiotemporal Convolutional Cross-Attention Fusion Network for Bearing Fault Diagnosis. [PDF]
Sensors (Basel)Li R +5 more
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STGATN: A novel spatiotemporal graph attention network for predicting pollutant concentrations at multiple stations. [PDF]
PLoS OneXu H, Song W, Qian L, Mei X, Zou G.
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Improved running gait parameter estimation from single foot-mounted IMU data based on refined event detection. [PDF]
Front Bioeng BiotechnolWu Y +6 more
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A modified flexible spatiotemporal data fusion model
Frontiers of Earth Science, 2020Remote sensing spatiotemporal fusion models blend multi-source images of different spatial resolutions to create synthetic images with high resolution and frequency, contributing to time series research where high quality observations are not available with sufficient frequency.
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Spatiotemporal Data Cleaning and Knowledge Fusion
2021Knowledge fusion aims to establish the relation-ship between heterogeneous ontology or heterogeneous instances. Data cleaning is one of the underlying key technologies supporting knowledge fusion. In this chapter, we give a brief overview of some important technologies of knowledge fusion and data cleaning. We first briefly introduce the motivation and
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Knowledge Fusion and Spatiotemporal Data Cleaning: A Review
2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC), 2020Knowledge fusion is aimed to establish the relationship between heterogeneous ontology or heterogeneous instances. Data cleaning is one of the key technologies for solving knowledge fusion problems. In this paper, we provide a brief survey of knowledge fusion and data cleaning.
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Abstract Spatiotemporal image fusion is a potential way to resolve the constraint between the spatial and temporal resolutions of satellite images and has been developed rapidly in recent years. However, two key challenges related to fusion accuracy remain: a) reducing the uncertainty of image fusion caused by sensor differences and b) addressing ...
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Small Low-Contrast Target Detection: Data-Driven Spatiotemporal Feature Fusion and Implementation
IEEE Transactions on Cybernetics, 2022Detecting small low-contrast targets in the airspace is an essential and challenging task. This article proposes a simple and effective data-driven support vector machine (SVM)-based spatiotemporal feature fusion detection method for small low-contrast targets.
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Deep learning spatiotemporal air pollution data in China using data fusion
Earth Science Informatics, 2020An efficient and effective spatiotemporal prediction algorithm for PM2.5 (i.e. particulate matter with a diameter of less than 2.5 micrometers) is urgently needed to study the distribution of PM2.5 over a continuous spatiotemporal domain, which not only helps to make scientific decisions on the prevention and control of PM2.5 pollution but also ...
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Fast spatiotemporal data fusion: merging LISS III with AWiFS sensor data
International Journal of Remote Sensing, 2014The high resolution of remote sensors has evolved to capture the fine details of the Earth’s surface features in remote-sensing (RS) data. There is a trade-off between this fine spatial resolution and the temporal resolution of global space-borne sensors.
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