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Wyszukujesz frazę "Chang, J. S." wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
AIS-Assisted Service Provision and Crowdsourcing of Marine Meteorological Information
Autorzy:
Chang, J. S.
Huang, C. H.
Chang, S. M.
Powiązania:
https://bibliotekanauki.pl/articles/116576.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:
crowdsourcing
meteorology
marine meteorological information
AIS-assisted service
Automatic Identification System (AIS)
typhoon forecast
Application-Specific Messages (ASM)
AIS-Weather project
Opis:
Ship navigation and operational activities at sea need meteorological and hydrographic information to enhance safety and efficiency. Besides observations collected from deployed data buoys, marine meteorological information services rely on weather observation reports from ships to increase the data density and thus service quality. Automatic Identification System (AIS), adopted internationally to facilitate ship-ship and ship-shore data exchange, has developed into communication links between ship/shore and buoys with many potential applications. This paper presents AIS applications designed and implemented for marine meteorological information services in a long-term project initiated by the Central Weather Bureau of Taiwan. Achievements of this initiative include: versatile service delivery via shore-based AIS network to shipborne application platforms such as smart phones, remote controllable moving weather data collection, and assisted sharing of weather observation reports from ships. Typhoon forecast and warning is one of the key applications implemented for this region.
Źródło:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2019, 13, 1; 63-67
2083-6473
2083-6481
Pojawia się w:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Study of Correlation between Fishing Activity and AIS Data by Deep Learning
Autorzy:
Shen, K. Y.
Chu, Y. J.
Chang, S. J.
Chang, S. M.
Powiązania:
https://bibliotekanauki.pl/articles/1841621.pdf
Data publikacji:
2020
Wydawca:
Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:
AIS Data
deep learning framework
learning methods
Recurrent Neural Network
(RNN)
Automatic Identification System
(AIS)
fishing operation
Opis:
Previous researches on the prediction of fishing activities mainly rely on the speed over ground (SOG) as the referential attribute to determine whether the vessel is navigating or in fishing operation. Since more and more fishing vessels install Automatic Identification System (AIS) either voluntarily or under regulatory requirement, data collected from AIS in real time provide more attributes than SOG which may be utilized to improve the prediction. To be specific, the ships' trajectory patterns and the changes in course become available and should be considered. This paper aims to improve the accuracy in the identification of fishing activities. First, we do feature extraction from the AIS data of coastal waters around Taiwan and build a Recurrent Neural Network (RNN) model. Then, the activity data of fishing vessels are divided into fishing and non-fishing. Finally, based on the testing by feeding various fishing activity data, we can identify the fishing status automatically.
Źródło:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2020, 14, 3; 527-531
2083-6473
2083-6481
Pojawia się w:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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