Zhe Xiao

TL;DR: The development of maritime traffic research in pattern mining and traffic forecasting affirms the importance of advanced maritime traffic studies and the great potential in maritime traffic safety and intelligence enhancement to accommodate the implementation of the Internet of Things, artificial intelligence technologies, and knowledge engineering and big data computing solution.

TL;DR: A multi-regime vessel trajectory reconstruction model is proposed through three-steps processing, including (i) outliers removal, (ii) ship navigational state estimation and (iii) vessel trajectory fitting, which allows for ship trajectory reconstruction in different navigation states, namely hoteling, maneuvering, and normal-speed sailing.

TL;DR: A novel knowledge assisted methodology for maritime traffic forecasting based on a vessel’s waterway pattern and motion behavior that is capable of accurately predicting maritime traffic 5, 30, and 60 min ahead, while its computation can be efficiently completed in milliseconds for single vessel prediction.

TL;DR: Wang et al. as mentioned in this paper developed and tested retinal photograph-based deep learning algorithms for detection of myopic macular degeneration and high myopia, using a total of 226,686 retinal images.

TL;DR: 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.