淡江大學機構典藏:Item 987654321/35898
English  |  正體中文  |  简体中文  |  Items with full text/Total items : 62805/95882 (66%)
Visitors : 3925877      Online Users : 749
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/987654321/35898


    Title: 利用遺傳演算法串疊牛頓法重建半空間介電物體之影像
    Other Titles: Image reconstruction of half space dielectric objects by a cascaded method
    Authors: 孫積賢;Sun, Chi-hsien
    Contributors: 淡江大學電機工程學系碩士班
    丘建青;Chiu, Chien-ching
    Keywords: 逆散射;介質物體;半空間;穩態遺傳演算法;串疊法;Inverse problem;Dielectric Cylinder;Half Space;Steady-state genetic algorithm;Cascaded Method
    Date: 2008
    Issue Date: 2010-01-11 07:17:47 (UTC+8)
    Abstract: 本論文模擬研究介質掩埋物體的電磁成像重建。設有一空間分成兩個半空間,ㄧ未知的不均勻介質物體掩埋在其中一半空間,吾人可以在另一半空間中安排入射波,其入射波採用多方向連續照射之方式,以收集較完整之材質特性資訊。於理論推導方面,本研究考慮完整之非線性公式,以提高解之精確度。
    數值方法之執行過程,即使介電物體具有較複雜之材質特性分佈(不平滑),或者介電體材質特性分佈與環境之材質特性具有較高之對比度,此數值方法亦能適用。
    就大部分較簡單之例子而言,遺傳演算法即可得到相當良好之解。然而,對於較複雜之例子,即考驗著遺傳演算法之強健性。本論文以演傳演算法所得之解,當作牛頓法之初始猜測值。藉由遺傳演算法之全域搜尋特性,以求得可接受之解,期望此解對於區域性搜尋之牛頓法而言,可能為適當之初始猜測值。串疊之方法比較單一遺傳演算法,或者單一牛頓迭代法,其解之精確度勢必較高。本研究模擬之數值結果顯示,此串疊之數值方法運用於重建非均勻介電物體之材質特性分佈,得到良好之重建結果。
    In this paper, a cascaded method is employed to determine the permittivity distribution of a dielectric cylinders buried in a half-space. Assume that dielectric cylinders of unknown permittivity distribution is buried in one half-space and scatters the field incident from another half-space where the scattered filed is measured. 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. A cascaded method which composed a genetic algorithm (GA) and a Newton iteration is used to maximize the objective function. First, the inverse problem is recast as a global nonlinear optimization problem, which is solved by a steady-state genetic algorithm (SSGA). Then, the solution obtained by the SSGA is taken as an initial guess for the Newton-type iteration method. Numerical results show that the performance of this combination method is better than the individual SSGA and the individual Newton-type iteration method. Satisfactory reconstruction has been obtained by using this cascaded method.
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Thesis

    Files in This Item:

    File SizeFormat
    0KbUnknown423View/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