BuiltNet: Graph based Spatio-Temporal Indoor Thermal Variation Detection-Reference-Cited by-同舟云学术

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BuiltNet: Graph based Spatio-Temporal Indoor Thermal Variation Detection

Published:2021-12 Issue: Volume: Page:
ISSN:
Container-title:2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)
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Author:

Khan Naima,Roy Nirmalya

Funder

U.S. Army

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9679834/9679948/09680202.pdf?arnumber=9680202

Reference16 articles.

1. Detecting concrete abnormality using time-series thermal imaging and supervised learning;cheng;arXiv preprint arXiv 1804 05809,2018

2. A graph-based approach to detect spatiotemporal dynamics in satellite image time series

3. Detection of spatio-temporal evolutions on multi-annual satellite image time series: A clustering based approach

4. T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction

5. Semi-supervised classification with graph convolutional networks;kipf;arXiv preprint arXiv 1609 02907,2016

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Unsupervised Spatio-Temporal Anomalous Thermal Behavior Monitoring of Inside-Built Environments;2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT);2023-06

2. Towards IoT-assisted Non-Intrusive Monitoring of Thermal Variation on Building Envelope;2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops);2023-03-13

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