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研究生:王博駿
研究生(外文):Bo-Jiun, Wang
論文名稱:應用分散式計算於智慧系統之研究
論文名稱(外文):Distributed Computing Techniques for Intelligent System
指導教授:郭忠義郭忠義引用關係
指導教授(外文):Jong-Yih, Kuo
口試委員:郭忠義王秉豐劉建宏李允中
口試委員(外文):Jong-Yih, KuoPing-Feng, WangChien-Hung, LiuJonathan Lee
口試日期:2018-07-04
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:資訊工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:中文
論文頁數:51
中文關鍵詞:多代理人分散式運算平行式運算
外文關鍵詞:Multi-AgentDecentralized computingParallel Computing
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平行運算與分散式運算的技術為巨量資料的分析、運算與儲存帶來新的發展契機,透過將巨量資料切割為較小的資料集,不僅可以降低資料的大小,亦可降低網路傳輸使用量及加速資料運算,透過多台分散式的個人電腦的計算能力來合力完成計算工作,以縮短整體計算時間,不僅可以達到降低成本的好處,亦可平衡計算時所需資源的負載及提升資料的運算速度。
本論文將使用平行運算、分散式運算與多代理人系統技術進行實驗,如:Hadoop MapReduce、Spark、JADE 與多執行緒等技術,將這些技術應用於智慧系統中進行計算平行化、計算分散化、運算情境設計及效能測試比對,進而提升智慧系統執行之效能。
The technology of parallel computing and decentralized computing bring new development opportunities for the analysis, calculation, and storage of big data. Splitting large amounts of data into smaller data sets can reduce the size of data, network transmission usage and speed up data operations. Through the computing power of various distributed PCs to work together to decrease the whole calculation time, not only can reduce the cost, but also balance the load of the computing resource.
This paper will use parallel computing, distributed computing, and multi-agent system technology for experiments, such as Hadoop MapReduce, Spark, JADE and multithreading technology, these technologies are applied to the intelligent system for parallel computing, decentralized computing, computational scenario design and performance testing, and improve the performance of the intelligent system.
摘 要 i
ABSTRACT ii
誌 謝 iii
目 錄 iv
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1 前言 1
1.2 研究動機與目的 2
1.3 研究貢獻 2
1.4 章節編排 3
第二章 文獻探討 4
2.1 Hadoop MapReduce 4
2.1.1 Hadoop MapReduce介紹 4
2.1.2 Spark on Hadoop YARN 6
2.2 Multithreading 7
2.3 Java Agent DEvelopment Framework 9
2.4 SPARQL語句推論時間複雜度 10
2.5 Velocity Obstacle 13
2.6 Reciprocal Velocity Obstacle 15
2.7 強化式學習(Reinforcement Learning) 17
2.8 Q-Learning 19
2.8.1 狀態(State) 25
2.8.2 動作(Action) 25
2.8.3 回饋(Reward) 26
第三章 案例研究與設計 27
3.1 以智慧家庭系統為例 27
3.1.1 智慧家庭Ontology Model Template 28
3.1.2 智慧家庭Ontology Model 29
3.2 以機器人移動防碰撞機制系統為例 31
3.2.1 問題描述 31
3.2.2 系統設計 31
第四章 實作方法 34
4.1 智慧家庭系統分散式與平行運算機制實作 34
4.1.1 Hadoop MapReduce機制實作 34
4.1.2 Multithreading機制實作 37
4.1.3 JADE代理人機制實作 39
4.1.4 系統實驗結果 42
4.2 機器人移動防碰撞機制系統 43
4.2.1 機器人移動防碰撞機制流程設計 43
4.2.2 機器人移動防碰撞機制實驗結果 45
第五章 結論與未來研究方向 48
5.1 結論 48
5.2 未來研究方向 48
參考文獻 49
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