即時網路圖形搜尋系統

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姓名

蕭宋榮(Sung-Jung Hsiao) 查詢紙本館藏

畢業系所

資訊工程學系在職專班

論文名稱

即時網路圖形搜尋系統
(Real-Time Web-Based Searching System of Pattern Recognition)

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摘要(中)

綱路的圖形辨識對於跨平台的網際網路而言，是一種創新的方法。這個研究是使用聯想記憶的方式來做圖形辨識的工作，此系統為及時的主從架構式網路圖形辨識系統。
　　遠端的使用者能夠藉由瀏覽器的操作，來畫出工業元件的外型或字元，然後辨識系統能夠透過網際網路進行資料庫搜尋。伺服器端包含了儲存範例圖形的資料庫。在訓練時期，使用者能夠指定任何的圖形去做及時的處理。圖形是被記錄在伺服端資料庫。在回憶時期，一個創新的資料庫比對方法被提出。這個方法能有效的解決RNN出現虛假狀態的問題，資料庫比對的技術克服了RNN容量限制的問題。在這個新的方法中，WBPR系統分割在伺服端資料庫內的圖形記錄集，然後分別算出它們每一個分割段的W與θ值。
　　WBPR系統最後去處理每一分段的圖形辨識的工作。針對網路的圖形辨識技術，論文中有兩個模擬的實驗是被清楚的討論。第一個實驗是一些字元的辨識，第二個實驗是有關Yang-Fen自動化工程公司之工業元件圖形的辨識。這個工業元件的圖形辨識實驗，執行了四個月，該公司的工程師操作了這個網路圖形辨識系統。因此合作的計劃也將在此分析與討論。最後，這個論文也提出創新的圖形辨識方法與傳統文字輸入搜尋方法的比較。

摘要(英)

Web-based pattern recognition (PR) is a novel method for multi-platform in real-time Internet. This study attempts to use associative memory to apply pattern recognition technology to the real-time pattern recognition in a web-based recognition system with a Client-Server network structure. Remote user can draw the shape of the engineering components or characters using the browser, and the recognition system then searches the database via the Internet. The server-end includes databases for storage of a sample pattern. With training, the user can assign any pattern to what in real-time. The pattern is recorded in the server-end databases. With respect to retrieval tasks, a novel PR method is proposed that depends on matching databases. The method can efficiently solve the problem of spurious states from recurrent neural network (RNN) in the Web-based PR (WBPR) system. Database matching overcomes the capacity restrictions on RNN. In the new approach, the WBPR system divides the set of records in the server-end databases, and determines W and

關鍵字(中)

★ 網路搜尋
★ 即時搜尋系統
★ 圖形識別

關鍵字(英)

★ real-time
★ pattern recognition
★ web-based

論文目次

Chapter 1. Introduction 1
1.1 Motivation 1
1.2 Related Works 2
1.3 Overview of the Thesis 3
1.4 Organization of the Thesis 4
Chapter 2. Parallel Computing and System Analysis 5
2.1 Introduction of system structure and computing 5
2.2 Storage Phase 8
2.3 Retrieval Phase 12
Chapter 3. Establishing and managing the pattern database 14
Chapter 4. Storage Capacity Analysis and Improvement 18
4.1 Dynamic equation and stable analysis 18
4.2 Process of Improvement 22
Chapter 5. Implementing the Web-Based pattern
recognition system 25
5.1 Recognition of characters and industrial component pattern 25
5.2 Recognition process 29
5.3 Experiment Result 32
5.4 Cooperative example 35
5.5 Traditional Methods Compare with Innovative Methods 39
5.6 Algorithm Analyses 41
Chapter 6. Conclusions and Future Works 45
References 47

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指導教授

范國清(Kuo-Chin Fan)

審核日期

2003-6-29

推文