| Project/Area Number |
07805089
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
船舶工学
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| Research Institution | Tokyo University of Mercantile Marine |
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Principal Investigator |
HAGIWARA Hideki Tokyo University of Mercantile Marine Chair of Information Systems Engineering Professor, 商船学部・情報システム設計工学講座, 教授 (30126338)
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| Co-Investigator(Kenkyū-buntansha) |
SHOJI Ruri Tokyo University of Mercantile Marine Chair of Information Systems Engineering A, 商船学部・情報システム設計工学講座, 助手 (50272729)
KUWASHIMA Susumu Tokyo University of Mercantile Marine Chair of Marine Science and Technology Pro, 商船学部・海洋工学講座, 教授 (30016943)
SUGISAKI Akio.M. Tokyo University of Mercantile Marine Chair of Information Systems Engineering P, 商船学部・情報システム設計工学講座, 教授 (20016926)
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| Project Period (FY) |
1995 – 1997
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| Project Status |
Completed (Fiscal Year 1997)
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| Budget Amount *help |
¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 1997: ¥400,000 (Direct Cost: ¥400,000)
Fiscal Year 1996: ¥500,000 (Direct Cost: ¥500,000)
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| Keywords | Neural network / Route selection / Upper-air circulation pattern / Simulation |
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| Research Abstract |
In this research, a new method of ship weather routing was developed using the neural network which is known as a powerful tool of pattern recognition. The proposed weather routing neural network consists of three layrs, i.e.input, hidden and output layrs. The 5-day mean 500hPa heights on the grid points covering the North Pacific Ocean for the first 5 days and the latter 5 days during the voyage were input to the neural network.
The teacher signals were produced by simulating the navigation of a container ship on the various routes from San Francisco to Tokyo using the analyzed wave data and calculating the passage times of these routes. A score of each route was then computed based on the passage time so as to range from 0.1 to 0.9. The highest score 0.9 and the lowest score 0.1 were allocated to the minimum time route and the maximum time route, respectively. These scores were used as the teacher signals.
To perform the learning of the proposed neural network, many successive two 5-day mean 500hPa height patterns during 5 winter seasons (1978-1983) were input to the network repeatedly, and the weights and threshold of each unit of the hidden and output layrs were modified so as to let the output signals from the network coincide with the teaching signals. After the completion of the learning, a new set of successive two 5-day mean 500hPa height patterns in the different winter seasons (1989-1991) were input to the network to verify the effectiveness of the network.
As a result, the output signals of a trained neural network coincided with the target signals, i.e.the scores of the routes calculated by the simulations, very well for most of the voyages. In conclusion, the proposed weather routing neural network could provide the optimum or sub-optimum routes for most of the voyages given the accurate successive two 5-day mean 500hPa height patterns.
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