Abstract | ||
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Ship-motion prediction is very useful for several naval operations such as aircraft landing, cargo transfer, off-loading of small boats, and ship "mating" between a big transport ship and some small ships. The prediction information is extremely useful in sea states above 3. Five to ten seconds of ship motion prediction can give the operator ample time to avoid serious collisions. The paper summarizes the development of a high performance ship-motion prediction algorithm using minor component analysis (MCA). Simulation results show that this method can predict ship motion a long time ahead with consistent accuracy. That is, the prediction error is almost the same for the 5 second and 20 second predictions. Other conventional algorithms, such as neural networks (NN), autoregressive methods (AR), and Wiener prediction, were also studied for comparative purposes. |
Year | DOI | Venue |
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2004 | 10.1109/ICASSP.2004.1327063 | ICASSP '04). IEEE International Conference |
Keywords | Field | DocType |
autoregressive processes,neural nets,prediction theory,ships,statistical analysis,20 sec,5 sec,Wiener prediction,aircraft landing,autoregressive methods,cargo transfer,minor component analysis,naval operations,neural networks,prediction error,sea states,ship-motion prediction,small boat off-loading | Autoregressive model,Mean squared prediction error,Computer science,Aircraft landing,Algorithm,Automation,Operator (computer programming),Motion prediction,Artificial neural network,Minor component analysis | Conference |
Volume | ISSN | ISBN |
5 | 1520-6149 | 0-7803-8484-9 |
Citations | PageRank | References |
4 | 1.03 | 8 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
---|---|---|---|
George Zhao | 1 | 4 | 1.03 |
Roger Xu | 2 | 111 | 14.71 |
Chiman Kwan | 3 | 4 | 1.03 |