Inverse scattering using a spectral domain moment method and non-linear optimization

2002 ◽  
Author(s):  
T.A. Maniatis ◽  
K.S. Nikita ◽  
N.K. Uzunoglu
Keyword(s):  
Inverse Scattering ◽  
Moment Method ◽  
Linear Optimization ◽  
Spectral Domain ◽  
Non Linear

Inverse Scattering Scheme Based on the Moment Method in the Spectral Domain, Part I: Theory

1992 ◽  
Vol 14 (1) ◽  
pp. 16-28 ◽  
Author(s):  
Se-Yun Kim ◽  
Hyun-Chul Choi ◽  
Jae-Min Lee ◽  
Jung-Woong Ra
Keyword(s):  
Inverse Scattering ◽  
Basis Function ◽  
Moment Method ◽  
Spectral Domain ◽  
Scattering Problems ◽  
Actual Mechanism ◽  
Spectral Components ◽  
Key Factor ◽  
The Moment ◽  
Measurement Location

Recently, electromagnetic and ultrasonic imaging of inhomogeneous objects by applying the moment-method procedures of forward scattering problems in the reverse sequence have been developed. In this paper, the inverse scattering formulation has been modified to be applicable in the spectral domain. Compared to previous schemes, the suggested formulation illustrates clearly the actual mechanism of the inverse scattering process by explicit separation of the contributions from several variables, such as the measurement location, basis function, and geometry of objects. The ill-posedness inherent in inverse scattering problems was also explained easily in this spectral scheme by the exponentially-decaying behavior of high-frequency spectral components of the scattered field. It implies that enlargement of the discretized cell size is a key factor in regularizing the ill-posedness. In particular, since the singular kernel to be integrated on each cell became regular in the modified scheme, various types of basis functions instead of pulse function were adopted without additional difficulties. This advantage is expected to play an important role in regularizing the noise effect by selecting polynomial basis function on the enlarged cells of discretization in the spectral inverse scattering scheme.

scholarly journals Design of a New Synthetic Acceptance Sampling Plan

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 653 ◽  
Author(s):  
Saeed Dobbah ◽  
Muhammad Aslam ◽  
Khushnoor Khan
Keyword(s):  
Standard Deviation ◽  
Normal Distribution ◽  
Operating Characteristic ◽  
Linear Optimization ◽  
Sampling Plan ◽  
Quality Characteristic ◽  
Acceptance Sampling ◽  
Acceptance Sampling Plan ◽  
Non Linear

In this paper, we propose a new synthetic sampling plan assuming that the quality characteristic follows the normal distribution with known and unknown standard deviation. The proposed plan is given and the operating characteristic (OC) function is derived to measure the performance of the proposed sampling plan for some fixed parameters. The parameters of the proposed sampling plan are determined using non-linear optimization solution. A real example is added to explain the use of the proposed plan by industry.

Dual regularization in non-linear inverse scattering problems

2016 ◽  
Vol 24 (7) ◽  
pp. 1215-1239 ◽  
Author(s):  
Konstantin P. Gaikovich ◽  
Petr K. Gaikovich ◽  
Yelena S. Maksimovitch ◽  
Alexander I. Smirnov ◽  
Mikhail I. Sumin
Keyword(s):  
Inverse Scattering ◽  
Scattering Problems ◽  
Inverse Scattering Problems ◽  
Dual Regularization ◽  
Non Linear

scholarly journals Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization

2017 ◽  
Vol 1 (2) ◽  
pp. 82 ◽  
Author(s):  
Tirana Noor Fatyanosa ◽  
Andreas Nugroho Sihananto ◽  
Gusti Ahmad Fanshuri Alfarisy ◽  
M Shochibul Burhan ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Non Linear ◽  
Speed Up ◽  
Linear Problems

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result

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