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研究生中文姓名:沈冠宇
研究生英文姓名:Shen, Kuan-Yu
中文論文名稱:深度學習應用於AIS狀態辨識
英文論文名稱:AIS State Recognition with Deep Learning
指導教授姓名:張淑淨
口試委員中文姓名:教授︰王和盛
業界委員︰邱永芳
教授︰張淑淨
學位類別:碩士
校院名稱:國立臺灣海洋大學
系所名稱:通訊與導航工程學系
學號:10667020
請選擇論文為:應用型
畢業年度:108
畢業學年度:107
學期:
語文別:中文
論文頁數:45
中文關鍵詞:船舶自動辨識系統資料探勘深度學習漁船行為錨泊
英文關鍵字:Automatic Identification System(AIS)Data MiningDeep LearningFishing ActivityAnchoring
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過去的研究中,判斷船舶是否處於航行、錨泊或捕魚狀態等,大多依據船對地面的速度(Speed over ground,SOG)當作參考指標辨識船舶狀態。國際法規的規定和自願裝設下,越來越多船舶裝設船舶自動辨識系統(Automatic Identification System,AIS),AIS提供巨量的船舶軌跡和眾多的船舶特徵,可以使用AIS提供的特徵來提高偵測船舶行為的機率。
本論文使用台灣沿岸AIS動態船位資料,預先選擇與船舶狀態行為有相關的屬性,採用深度學習方法,建構多層雙向長短期記憶網路模型,分別對拖網、延繩釣和曳繩釣三種漁船作業行為以及貨輪的錨泊行為進行預測。
In past researches, the identification of the ship’s state was mainly based on the ship’s speed over ground (SOG) to determine whether the ship is in a state of navigation or mooring or fishing. Under the provisions of international regulations and voluntary installations, more and more ships are equipped with an automatic identification system (AIS). AIS provides a huge number of ship trajectories and numerous ship features. Features provided by AIS can be used to increase the probability of detecting ship behavior.
This paper uses AIS dynamic data along the coast of Taiwan and selects attributes related to ships' behavior. Deep learning is used to build multi-layer bi-directional long short-term memory networks. Finally, the fishing activities of three types as well as the anchoring activity of cargo ships are predicted.
致謝 I
摘要 II
ABSTRACT III
圖目次 VI
表目次 IX
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 論文架構 2
第二章 背景知識與文獻探討 3
2.1. 船舶自動辨識系統及研究應用 3
2.2. 漁船作業行為偵測 6
2.3. 貨輪錨泊狀態偵測 8
第三章 研究方法 9
3.1 研究流程 9
3.2 資料預處理 10
3.2.1 資料清理 10
3.2.2 特徵選取 13
3.3 沿近海捕魚方式 15
3.3.1 拖網 15
3.3.2 延繩釣 17
3.3.3 曳繩釣 19
3.4 錨泊狀態 21
3.5 深度學習模型 23
3.5.1 感知器(Perception) 23
3.5.2 循環神經網路(Recurrent Neural Networks,RNNs) 23
3.5.3 雙向循環神經網路(Bi-directional RNNs,BRNNs) 24
3.5.4 長短期記憶網路(Long Short Term Memory Networks,LSTMs) 25
3.6 模型架構 26
第四章 研究結果與討論 27
4.1 捕魚行為準確率分析 27
4.1.1 訓練和驗證捕魚模型 27
4.1.2 捕魚模型與SOG分類比較 32
4.1.3測試捕魚模型 33
4.2 錨泊行為準確率分析 37
4.2.1 訓練與驗證錨泊模型 37
4.2.2 錨泊監測系統架構 40
第五章 結論與展望 43
參考資料 44

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[15] 袁家偉,船舶軌跡錨泊狀態之自動化偵測,國立臺灣海洋大學通訊與導航工程學系碩士論文,2016年8月
[16] 邱永芳、張淑淨、黃茂信,結合動態船舶與環境資訊之綠色航路智慧領航計畫(4/4),交通部運輸研究所,2017年3月
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