English  |  正體中文  |  简体中文  |  Items with full text/Total items : 62797/95867 (66%)
Visitors : 3728400      Online Users : 760
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/35914


    Title: 利用TE入射波重建掩埋完全導體之影像
    Other Titles: Image reconstruction of a buried perfectlyconducting cylinder illuminated by transverse electric waves
    Authors: 陳岳呈;Chen, Yueh-cheng
    Contributors: 淡江大學電機工程學系碩士班
    丘建青;Chiu, Chien-ching
    Keywords: 完全導體;遺傳演算法;半空間;TE極化電磁波;Electromagnetic imaging;Transverse electric (TE);Half-space structure;Perfect Conductor;Steady-state genetic algorithm
    Date: 2005
    Issue Date: 2010-01-11 07:19:16 (UTC+8)
    Abstract: 本論文之目的在於研究利用遺傳演算法對完全導體的影像重建問題。針對不同平面波入射的情況,就完全導體的逆散射問題進行探討。
    首先探討完全導體掩埋在半空間介質中的逆散射,將一個未知形狀、未知材質的二維完全導體掩埋在半空間介質中,吾人在第一層以TE極化電磁波照射掩埋於第二層的物體,並於第一層量得其散射場。吾人將利用接收到的散射場及適當的邊界條件,導出一組非線性積分方程式,再利用動差法將此積分方程式化為矩陣形式,把成像問題化成一個求最佳化問題。再引用遺傳演算法將逆散射問題轉成求解最佳化的問題。
    遺傳演算法是一種模擬自然界生物進化的搜尋法則,利用簡單的位元複製、交配及突變,可完成搜尋的程序。不論初始的猜測值為何,遺傳演算法總會收斂到整體的極值(global extreme),而非只收斂於局部極值,藉此可重建物體的形狀函數及導電率。所以在數值模擬中,即使初始的猜測值與實際值相去甚遠,我們仍可求得精準的數值解,成功的重建出物體形狀函數與導電率。而以微分為基礎求取極值的方法(calculus-based method),常會陷入區域極值(local extreme)的陷阱裡。最後將電磁成像的結果與原先假設者做比較,藉以驗證並改進電磁成像理論。
    The paper presents a computational approach to the image reconstruction of a perfectly conductor cylinder illuminated by transverse electric (TE) waves. A perfectly conducting cylinder of unknown shape buried in one half-space and scatters the incident wave from another half-space where the scattered field is recorded. Based on the boundary condition and the measured scattered field, a set of nonlinear integral equations is derived and the imaging problem is reformulated into an optimization problem. The steady state genetic algorithm is then employed to find out the global extreme solution of the cost function. Numerical results demonstrated that, even when the initial guess is far away from the exact one, good reconstruction can be obtained. In such a case, the gradient-based methods often get trapped in a local extreme. In addition, the effect of different noise on the reconstruction is investigated.
    Appears in Collections:[電機工程學系暨研究所] 學位論文

    Files in This Item:

    File SizeFormat
    0KbUnknown225View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback