Framework for gradient integration by combining radial basis functions method and least-squares method
A framework with a combination of the radial basis functions (RBFs) method and the least-squares integration method is proposed to improve the integration process from gradient to shape. The principle of the framework is described, and the performance of the proposed method is investigated by simula...
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sg-ntu-dr.10356-1016372023-03-04T17:19:40Z Framework for gradient integration by combining radial basis functions method and least-squares method Huang, Lei Asundi, Anand Krishna School of Mechanical and Aerospace Engineering DRNTU::Engineering::Mechanical engineering A framework with a combination of the radial basis functions (RBFs) method and the least-squares integration method is proposed to improve the integration process from gradient to shape. The principle of the framework is described, and the performance of the proposed method is investigated by simulation. Improvement in accuracy is verified by comparing the result with the usual RBFs-based subset-by-subset stitching method. The proposed method is accurate, automatic, easily implemented, and robust and even works with incomplete data. Published version 2013-10-17T01:45:02Z 2019-12-06T20:41:57Z 2013-10-17T01:45:02Z 2019-12-06T20:41:57Z 2013 2013 Journal Article Huang, L., & Asundi, A. K. (2013). Framework for gradient integration by combining radial basis functions method and least-squares method. Applied Optics, 52(24), 6016-6021. https://hdl.handle.net/10356/101637 http://hdl.handle.net/10220/16536 10.1364/AO.52.006016 en Applied optics © 2013 Optical Society of America. This paper was published in Applied Optics and is made available as an electronic reprint (preprint) with permission of Optical Society of America. The paper can be found at the following official DOI: [http://dx.doi.org/10.1364/AO.52.006016]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law. application/pdf |
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DRNTU::Engineering::Mechanical engineering Huang, Lei Asundi, Anand Krishna Framework for gradient integration by combining radial basis functions method and least-squares method |
| description |
A framework with a combination of the radial basis functions (RBFs) method and the least-squares integration method is proposed to improve the integration process from gradient to shape. The principle of the framework is described, and the performance of the proposed method is investigated by simulation. Improvement in accuracy is verified by comparing the result with the usual RBFs-based subset-by-subset stitching method. The proposed method is accurate, automatic, easily implemented, and robust and even works with incomplete data. |
| author2 |
School of Mechanical and Aerospace Engineering |
| author_facet |
School of Mechanical and Aerospace Engineering Huang, Lei Asundi, Anand Krishna |
| format |
Article |
| author |
Huang, Lei Asundi, Anand Krishna |
| author_sort |
Huang, Lei |
| title |
Framework for gradient integration by combining radial basis functions method and least-squares method |
| title_short |
Framework for gradient integration by combining radial basis functions method and least-squares method |
| title_full |
Framework for gradient integration by combining radial basis functions method and least-squares method |
| title_fullStr |
Framework for gradient integration by combining radial basis functions method and least-squares method |
| title_full_unstemmed |
Framework for gradient integration by combining radial basis functions method and least-squares method |
| title_sort |
framework for gradient integration by combining radial basis functions method and least-squares method |
| publishDate |
2013 |
| url |
https://hdl.handle.net/10356/101637 http://hdl.handle.net/10220/16536 |
| _version_ |
1759856327804321792 |