Browse > Article
http://dx.doi.org/10.5516/NET.2007.39.6.725

KERNEL-BASED NOISE FILTERING OF NEUTRON DETECTOR SIGNALS  

Park, Moon-Ghu (Korea Electric Power Research Institute)
Shin, Ho-Cheol (Korea Electric Power Research Institute)
Lee, Eun-Ki (Korea Electric Power Research Institute)
Publication Information
Nuclear Engineering and Technology / v.39, no.6, 2007 , pp. 725-730 More about this Journal
Abstract
This paper describes recently developed techniques for effective filtering of neutron detector signal noise. In this paper, three kinds of noise filters are proposed and their performance is demonstrated for the estimation of reactivity. The tested filters are based on the unilateral kernel filter, unilateral kernel filter with adaptive bandwidth and bilateral filter to show their effectiveness in edge preservation. Filtering performance is compared with conventional low-pass and wavelet filters. The bilateral filter shows a remarkable improvement compared with unilateral kernel and wavelet filters. The effectiveness and simplicity of the unilateral kernel filter with adaptive bandwidth is also demonstrated by applying it to the reactivity measurement performed during reactor start-up physics tests.
Keywords
Kernel; Wavelet; Noise; Filter; Reactivity;
Citations & Related Records

Times Cited By Web Of Science : 0  (Related Records In Web of Science)
연도 인용수 순위
  • Reference
1 A. Neumaier, 'Wavelet Toolbox For Use with $MATLAB^{\circled R}$ User's Guide', The MathWorks, Inc. (1996)
2 C. Tomasi and R. Manduchi. 'Bilateral filtering for gray and color images,' Sixth International Conference on Computer Vision pp. 839-46. New Deli, India, 1998
3 M. Elad, 'On the Origin of the Bilateral Filter and Ways to Improve It,' IEEE Transactions on Image Processing, 11, no. 10, (2002)
4 E. A. Narandaraya, 'On estimating regression, ' Theory of Probability and its Application, 9, 1964
5 A. Moore, J. Schneider and K. Deng, 'Efficient Locally Weighted Polynomial Regression Predictions, ' Proceedings of the Fourteenth International Conference on Machine Learning, 1997
6 M.G. Park, H. C. Shin, B. M. Koh and S. You et al., 'Kernel Regression based Noise Smoothing of Reactivity Signal,' Proceedings of the KNS Spring Meeting, 2005
7 E..K. Lee, H.C. Shin, S.M. Bae and Y.K. Lee., 'New dynamic method to measure rod worths in zero power physics test at PWR startup,' Annals of Nuclear Energy, 32, Issue 13, 2005
8 Y. Shimazu, H. Unesaki and N. Suzuki, 'Development of a compact digital reactivity meter and a reactor physics data processor,' Nuclear Technology, 77, no. 3 (1987)
9 M.G. Park, S. M. Bae and C. S. Lee., 'Wavelet Filter Based De-Noising of Weak Neutron Flux Signal for Dynamic Control Rod Reactivity Measurement,' Proceedings of the Korean Nuclear Society Autumn Meeting, Yongpyong, Korea, October 2002