.png){kind=icon}
返回
DeepOcean: A General Deep Learning Framework for Spatio-Temporal Ocean Sensing Data Prediction
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.2990939.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The emerging Internet of Underwater Things (IoUT) and deep learning technologies are combined to provide a novel, intelligent, and efficient data processing and analyzing schema, which facilitates the sensing and computing abilities for the smart ocean. The underwater acoustic (UWA) communication network is an essential part of IoUT. The thermocline, in which temperature and density change drastically, affects the connectivity and communication performance between IoUT nodes, as well as the network topologies. In this paper, we propose DeepOcean, a deep learning framework for spatio-temporal ocean sensing data prediction, which consists of a generative module and a prediction module. We implement the generative module with a multi-layer perceptron (MLP) to capture the spatial dependencies and construct high-resolution data based on sparse observations. The prediction module is implemented with our proposed Multivariate Convolutional LSTM (MVC-LSTM) neural network, which captures both the spatio-temporal dependencies and the interactions of different oceanographic features for prediction. We evaluate the effectiveness of DeepOcean with Argo data, where the proposed framework outperforms fifteen state-of-art baselines in terms of accuracy.
Keyword:
Internet of Underwater Things (IoUT)
deep learning
spatio-temporal prediction
multivariate convolutional LSTM (MVC-LSTM) neural network
thermocline
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
.png){kind=icon}
Spatio-Temporal Graph Deep Neural Network for Short-Term Wind Speed Forecasting用于短期风速预测的时空图深度神经网络
PREAI
分享收藏Underwater Acoustic Communication Channels: Propagation Models and Statistical Characterization
PREAI
分享收藏
查看更多内容