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| 001 | 14181 | ||
| 005 | 20180208194944.0 | ||
| 024 | 7 | _ | |2 DOI |a 10.1007/s10948-010-0860-3 |
| 024 | 7 | _ | |2 WOS |a WOS:000289855700178 |
| 037 | _ | _ | |a PreJuSER-14181 |
| 041 | _ | _ | |a eng |
| 082 | _ | _ | |a 530 |
| 084 | _ | _ | |2 WoS |a Physics, Applied |
| 084 | _ | _ | |2 WoS |a Physics, Condensed Matter |
| 100 | 1 | _ | |a Sarreshtedari, F. |b 0 |0 P:(DE-HGF)0 |
| 245 | _ | _ | |a An Efficient SQUID NDE Defect Detection Approach by Using an Adaptive Finite-Element Modeling |
| 260 | _ | _ | |a New York, NY |b Springer |c 2011 |
| 300 | _ | _ | |a |
| 336 | 7 | _ | |a Journal Article |0 PUB:(DE-HGF)16 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a article |2 DRIVER |
| 440 | _ | 0 | |a Journal of Superconductivity and Novel Magnetism |x 1557-1939 |0 17001 |y 1 |v 24 |
| 500 | _ | _ | |3 POF3_Assignment on 2016-02-29 |
| 500 | _ | _ | |a This work was supported in part by the National Elite Foundation, Tehran, Iran. |
| 520 | _ | _ | |a Incorporating the finite-element method for the modeling of the SQUID NDE response to a predefined defect pattern, an adaptive algorithm has been developed for the reconstruction of unknown defects using an optimization algorithm for updating of the forward problem. The defect reconstruction algorithm starts with an initial estimation for the defect pattern. Then the forward problem is solved and the obtained field pattern is compared with the measured signal from the SQUID NDE system. The result is used by an optimization algorithm to update the defect structure to be incorporated in the FEM forward problem for the next iteration. Since the mentioned model based inverse algorithm normally consumes a lot of computational resources, the number of iterations plays an important role in the determination of the total response convergence time. Consequently, different optimization algorithms have been applied and their performances are compared. In this work by incorporating an efficient forward model and using the stochastic and deterministic optimization algorithms for defect updating we have investigated their performance on the inversion of the SQUID NDE signal and also their ability to defect reconstruction in the noisy environment. |
| 536 | _ | _ | |a Grundlagen für zukünftige Informationstechnologien |c P42 |2 G:(DE-HGF) |0 G:(DE-Juel1)FUEK412 |x 0 |
| 588 | _ | _ | |a Dataset connected to Web of Science |
| 650 | _ | 7 | |a J |2 WoSType |
| 653 | 2 | 0 | |2 Author |a SQUID NDE |
| 653 | 2 | 0 | |2 Author |a Defect detection algorithm |
| 653 | 2 | 0 | |2 Author |a Finite-element modeling |
| 700 | 1 | _ | |a Razmkhah, S. |b 1 |0 P:(DE-HGF)0 |
| 700 | 1 | _ | |a Hosseini, N. |b 2 |0 P:(DE-HGF)0 |
| 700 | 1 | _ | |a Schubert, J. |b 3 |u FZJ |0 P:(DE-Juel1)128631 |
| 700 | 1 | _ | |a Banzet, M. |b 4 |u FZJ |0 P:(DE-Juel1)VDB5481 |
| 700 | 1 | _ | |a Fardmanesh, M |b 5 |0 P:(DE-HGF)0 |
| 773 | _ | _ | |a 10.1007/s10948-010-0860-3 |g Vol. 24 |q 24 |0 PERI:(DE-600)2246896-1 |t Journal of superconductivity and novel magnetism |v 24 |y 2011 |x 1557-1939 |
| 856 | 7 | _ | |u http://dx.doi.org/10.1007/s10948-010-0860-3 |
| 909 | C | O | |o oai:juser.fz-juelich.de:14181 |p VDB |
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| 914 | 1 | _ | |y 2011 |
| 915 | _ | _ | |0 StatID:(DE-HGF)0010 |a JCR/ISI refereed |
| 920 | 1 | _ | |d 31.12.2010 |g IBN |k IBN-1 |l Halbleiter-Nanoelektronik |0 I:(DE-Juel1)VDB799 |x 0 |
| 920 | 1 | _ | |0 I:(DE-82)080009_20140620 |k JARA-FIT |l Jülich-Aachen Research Alliance - Fundamentals of Future Information Technology |g JARA |x 1 |
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| 980 | _ | _ | |a I:(DE-Juel1)IBN-2-20090406 |
| 980 | _ | _ | |a UNRESTRICTED |
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