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Title: 國際標準S-124航行警告資料服務之設計與試驗
Design and Trial of S-124 Navigational Warning Data Service Provision
Authors: Chu, Ying-Jui
Contributors: NTOU:Department of Communications Navigation and Control Engineering
Keywords: S-100;S-124;深度學習;資料探勘;長短期神經網路;文字萃取
S-100;S-124;Deep Learning;Data Mining;LSTM;Word Extraction
Date: 2019
Issue Date: 2020-07-09T02:52:25Z
Abstract: ⾃國際海事組織(International Maritime Organization)定義 e 化航⾏及各國政 府的推動,船舶航儀已然從個別電⼦化朝向整合船橋系統並與岸基海事服務智慧 化整合發展。為減少船舶上⼈員作業繁雜負荷,須藉由整合船舶航儀將相關資訊 同時顯⽰於螢幕,並透過⾃動化分析使船員能夠輕易獲取解讀資訊。為避免資料 整合時數位化資料因不同區域造成的格式差異問題使資料交換困難⽽訂定共同 資料結構 。 S-100 是 ⼀ 由 國際海道測量組織 (International Hydrographic Organization) 所訂定新⼀代共通海 測資料模型(Universal Hydrographic Data Model),提供更為彈性且更豐富的資料框架,擴展⽀援圖像及網格類型資料、無 限制編碼格式、基於網⾴的服務等。 本論⽂以基於 S-100 資料模型之 S-124 航⾏警告資料服務為研究⽬標,就傳 統國際航⾏警告電傳(NAVTEX)航⾏警告電⽂與 S-124 資料交換標準草案之間的 雙向解析轉換進⾏設計與試驗。研究過程使⽤⾃然語⾔處理及深度學習⽅法解析 NAVTEX 航⾏警告電⽂結構,通過⾃動萃取相關⽂字類型演算法,建構 S-124 資 料標準之 XML 檔案及可視化地理資訊系統(GIS)⽂件。並試驗 NAVTEX 資料轉 換 S-124 標準格式資料之成功率及使⽤ S-124 標準資料重新建⽴符合 NAVTEX 航⾏警告格式電⽂之適⽤性。
As e-Navigation is defined by International Maritime Organization and promoted by governments, shipborne equipment are becoming not only electronic but also enhanced via digitalization and integration. In order to reduce the workload and make the information easily interpreted by mariners, relevant data from various equipment are integrated and shown on monitor screen. The common data structure is need to be established to avoid the difficulty of data exchange due to the format differences caused by different regions. S-100 is a new universal hydrographic data model setting by the International Hydrographic Organization which provides more flexible and extensible framework and supports the use of imagery, gridded data, unlimited encoding formats, web-based services. This thesis takes the S-124 navigational warning data service based on the S-100 data model as the research objective, designs and experiments the two-way analytical conversion between the traditional NAVTEX navigational warning message and the draft S-124 data exchange standard. The research analyzes the NAVTEX navigational warning message structure by natural language processing and deep learning method, and builds the XML file of the S-124 data standard as well as visualized GIS file through algorithms that automatically extract relevant text types. Then experiments are performed to accessthe accuracy of converting NAVTEX data to S-124 standard format data and the applicability of using S-124 standard data to generate the navigational warning message conforming to the NAVTEX format.
URI: http://ethesys.lib.ntou.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=G0010667010.id
Appears in Collections:[通訊與導航工程學系] 博碩士論文

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