The prediction of roll motion in unmanned surface vehicles (USVs) is vital for marine safety and the efficiency of USV operations. However, the USV roll motion at sea is a complex time-varying nonlinear and non-stationary dynamic system, which varies with time-varying environmental disturbances as well as various sailing conditions. The conventional methods have the disadvantages of low accuracy, poor robustness, and insufficient practical application ability. The rise of deep learning provides new opportunities for USV motion modeling and prediction. In this paper, a data-driven neural network model is constructed by combining a convolution neural network (CNN) with long short-term memory (LSTM) for USV roll motion prediction. The CNN is used to extract spatially relevant and local time series features of the USV sensor data. The LSTM layer is exploited to reflect the long-term movement process of the USV and predict roll motion for the next moment. The fully connected layer is utilized to decode the LSTM output and calculate the final prediction results. The effectiveness of the proposed model was proved using USV roll motion prediction experiments based on two case studies from “JingHai-VI” and “JingHai-III” USVS of Shanghai University. Experimental results on a real data set indicated that our proposed model obviously outperformed the state-of-the-art methods.
Glycosylation
is an effective solution for peptide drug modification
to overcome limitations such as instability under physiological conditions
and lack of receptor selectivity. Disclosed herein is a facile enzymatic
modular assembly strategy for the (semi)preparative-scale synthesis
of active glycopeptides. Sialyl Lewis x (sLex) oligosaccharides
with E-selectin-targeting activity can be conjugated with peptides
through multistep enzymatic reactions. The antiangiogenic peptide
ES2 and anticancer peptide citropin 1.1 were successfully modified
without loss of their biological activities. The half-lives of the
glycopeptides were approximately 64-fold (ES2-sLex) and
28-fold (citropin-sLex) higher than that of the unmodified
peptides in human serum.
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