An unsupervised learning method with convolutional auto-encoder for vessel trajectory similarity computation
TLDR
Wang et al. as mentioned in this paper proposed an unsupervised learning method which automatically extracts low-dimensional features through a convolutional auto-encoder (CAE), which can learn the lowdimensional representations of informative trajectory images.About:
This article is published in Ocean Engineering.The article was published on 2021-04-01 and is currently open access. It has received 56 citations till now. The article focuses on the topics: Unsupervised learning & Trajectory.read more
Citations
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Deep Learning-Powered Vessel Trajectory Prediction for Improving Smart Traffic Services in Maritime Internet of Things
TL;DR: Wang et al. as discussed by the authors proposed an AIS data-driven trajectory prediction framework, whose main component is a long short term memory (LSTM) network, embedded into the LSTM network to guarantee high-accuracy vessel trajectory prediction.
Journal ArticleDOI
MVFFNet: Multi-view feature fusion network for imbalanced ship classification
TL;DR: A multi-view feature fusion network (MVFFNet) is proposed to achieve accurate ship classification with imbalanced data and consistently outperforms other competing methods in terms of classification accuracy and robustness.
Journal ArticleDOI
STMGCN: Mobile Edge Computing-Empowered Vessel Trajectory Prediction Using Spatio-Temporal Multigraph Convolutional Network
TL;DR: Wang et al. as discussed by the authors proposed a spatio-temporal multigraph convolutional network (STMGCN)-based trajectory prediction framework using the mobile edge computing (MEC) paradigm.
Journal ArticleDOI
Random vector functional link neural network based ensemble deep learning for short-term load forecasting
TL;DR: In this paper , the authors proposed a novel ensemble deep Random Vector Functional Link (edRVFL) network for electricity load forecasting, where hidden layers are randomly initialized and kept fixed during the training process.
Journal ArticleDOI
Unsupervised hierarchical methodology of maritime traffic pattern extraction for knowledge discovery
TL;DR: Wang et al. as discussed by the authors developed a novel unsupervised methodology for feature extraction and knowledge discovery based on automatic identification system (AIS) data, allowing for seamless knowledge transfer to support trajectory data mining.
References
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Jiuxiang Gu,Zhenhua Wang,Jason Kuen,Lianyang Ma,Amir Shahroudy,Bing Shuai,Ting Liu,Xingxing Wang,Gang Wang,Jianfei Cai,Tsuhan Chen +10 more
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