Learning Spatial-Temporal Features of Fiber-Optical Data With Multi-scale Double Dynamic Network for Human Intrusion Detection-Reference-Cited by-同舟云学术

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Learning Spatial-Temporal Features of Fiber-Optical Data With Multi-scale Double Dynamic Network for Human Intrusion Detection

Published:2023 Issue: Volume:72 Page:1-11
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Zhao Shuo¹^ORCID,Guo Zhongwen¹^ORCID,Cheng Xu²^ORCID,Jiang Sining¹^ORCID,Zhao Wenchang¹^ORCID,Wang Hao³^ORCID

Affiliation:

1. Department of Computer Science and Technology, Ocean University of China, Qingdao, China

2. School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China

3. School of Cyber Engineering, Xidian University, Xi’an, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/10147841.pdf?arnumber=10147841

Reference39 articles.

1. A Novel DAS Signal Recognition Method Based on Spatiotemporal Information Extraction With 1DCNNs-BiLSTM Network

2. Fully convolutional networks for semantic segmentation

3. An event recognition method for fiber distributed acoustic sensing systems based on the combination of MFCC and CNN

4. Vehicle Detection and Classification Using Distributed Fiber Optic Acoustic Sensing

5. SNR Enhancement of Far-End Disturbances on Distributed Sensor Based on Phase-Sensitive Optical Time-Domain Reflectometry

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