臺灣博碩士論文加值系統

漁船監控系統(Vessel Monitoring System; VMS)對於船隻的監控、管制與偵察(Monitoring, Control and Surveillance; MCS)是一項很有用的工具，它能夠幫助海岸巡防單位更有效率的執行海岸巡邏工作。當各國、各區域漁業管理組織廣泛應用VMS，有越來越多的研究與應用集中在挖掘VMS的資料庫，以從VMS資料庫中挖掘出更多更有用的資訊，並運用這些資訊增進VMS的效益與實用性，我們稱之為資料探勘。在文中，會特別介紹資料探勘中的群聚技術演算法。
本篇論文最初的目的是為了找出在方圓3海浬範圍內，至少包含3艘船隻的船隻密集區域，為達此目的，我們使用了資料探勘中，以密度為基礎的群聚演算法－DBSCAN(Density Based Spatial Clustering of Applications with Noise) [1]。研究並發展DBSCAN應用於台灣的船隻VMS，以找出船隻的密集區域。論文中，我們探討了DBSCAN群聚分類所需花費的時間成本與分類的準確性，並嘗試使用不同的方法改進DBSCAN群聚分類效能以及解決問題的整體準確性，如估測船隻的即時位置、使用切割資料表或建立鄰近點資料表等。最後，並利用密集區域偵測系統所得的結果，應用於非法運搬船與平行船隻問題的偵測上，並討論判別出運搬船與平行船隻的準確性與實用性。

Fishing Vessel Monitoring System (VMS) is an effective tool of fisheries monitoring, control and surveillance measures to counter over-fishing. It can also help the coast guard to safeguard vessels more efficiently. As VMS is widely implemented, more and more efforts focus on mining the VMS database to discover knowledge and clues that would further enhance the benefits. This thesis is focused on data mining VMS database with clustering technology developed for and implemented into the VMS of Taiwan. The initial request form the Fisheries Administration was to constantly identify wherever there are at least three fishing vessels within 3 nautical miles of range. The proposed solution was based on DBSCAN [1] clustering algorithm. The performances in accuracy and run-time were evaluated and improved with vessel position prediction, partitioning of datasets, data structure and algorithm design. With the promising results, this solution has been recognized by the fisheries management and VMS operation experts to be of many extended use in VMS.
Finally, this Density Area Detection System was applied to the detection of at-sea transshipment and parallel-track vessels. Then, the performance in accuracy and practicability would be discussed.

誌謝 I
摘要 III
ABSTRACT IV
目錄 V
圖目錄 VIII
表目錄 X
第一章 緒論 - 1 -
1.1 研究背景 - 1 -
1.2 研究動機 - 3 -
1.3 論文架構 - 4 -
第二章 研究方法與文獻回顧 - 6 -
2.1 文獻回顧 - 6 -
2.2 群聚技術演算法 - 7 -
2.2.1 切割式群聚演算法 - 7 -
2.2.2 格子基礎群聚演算法 - 9 -
2.2.3 密度基礎群聚演算法 - 11 -
2.3 文獻探討 - 14 -
2.4 研究方法 - 17 -
第三章 目標資料庫分析與模擬 - 21 -
3.1 空間資料庫 - 21 -
3.1.1 沿近海VMS測試資料庫 - 21 -
3.1.2 遠洋VMS測試資料庫 - 23 -
3.1.3 參考比較用的均勻分佈資料庫 - 25 -
3.2 異常之資料篩選偵測 - 27 -
3.3 船位估測 - 29 -
3.3.1 船位估測之需求 - 29 -
3.3.2 船位估測之實作 - 30 -
3.3.3 船位估測的效能 - 32 -
第四章 系統設計、實作與效能改進 - 36 -
4.1 程式實作 - 36 -
4.2 程式效能改進 - 41 -
4.3 群聚分類結果 - 46 -
4.4 整體應用管理系統設計 - 49 -
第五章 延伸應用之評估 - 57 -
5.1 偵測運搬船的應用 - 57 -
5.1.1 偵測運搬船的問題 - 57 -
5.1.2 偵測運搬船的結果 - 60 -
5.2 偵測平行船隻的應用 - 63 -
5.2.1 偵測平行船隻的問題 - 63 -
5.2.2 偵測平行船隻的結果 - 64 -
第六章 結論與未來發展 - 68 -
參考文獻 - 70 -

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