Advanced search

Modifying the feature-selective validation method to validate noisy data sets

Author
Organization
Abstract
Objective validation and ranking of measurements and simulations may be done by methods such as feature selective validation (FSV). FSV is used to compare two EMC-measurement results. Owing to the noisy nature of these type of data, the FSV results are corrupted. The reasons are discussed and solutions are proposed to make FSV feasible in a broader area of applications. The final solution is a combination of denoising the data and changing the weight of the data to be in accordance with our visual interpretation.
Keywords
noisy data sets, EMC-measurement, data denoising, computational electromagnetics validation, computational electromagnetics, feature-selective validation method, electromagnetic compatibility, data analysis

Citation

Please use this url to cite or link to this publication:

Chicago
Knockaert, Jos, Joan Peuteman, J Catrysse, and R Belmans. 2008. “Modifying the Feature-selective Validation Method to Validate Noisy Data Sets.” Iet Science Measurement & Technology 2 (4): 244–257.
APA
Knockaert, J., Peuteman, J., Catrysse, J., & Belmans, R. (2008). Modifying the feature-selective validation method to validate noisy data sets. IET SCIENCE MEASUREMENT & TECHNOLOGY, 2(4), 244–257.
Vancouver
1.
Knockaert J, Peuteman J, Catrysse J, Belmans R. Modifying the feature-selective validation method to validate noisy data sets. IET SCIENCE MEASUREMENT & TECHNOLOGY. 2008;2(4):244–57.
MLA
Knockaert, Jos, Joan Peuteman, J Catrysse, et al. “Modifying the Feature-selective Validation Method to Validate Noisy Data Sets.” IET SCIENCE MEASUREMENT & TECHNOLOGY 2.4 (2008): 244–257. Print.
@article{2009595,
  abstract     = {Objective validation and ranking of measurements and simulations may be done by methods such as feature selective validation (FSV). FSV is used to compare two EMC-measurement results. Owing to the noisy nature of these type of data, the FSV results are corrupted. The reasons are discussed and solutions are proposed to make FSV feasible in a broader area of applications. The final solution is a combination of denoising the data and changing the weight of the data to be in accordance with our visual interpretation.},
  author       = {Knockaert, Jos and Peuteman, Joan and Catrysse, J and Belmans, R},
  issn         = {1751-8822},
  journal      = {IET SCIENCE MEASUREMENT & TECHNOLOGY},
  keywords     = {noisy data sets,EMC-measurement,data denoising,computational electromagnetics validation,computational electromagnetics,feature-selective validation method,electromagnetic compatibility,data analysis},
  language     = {eng},
  number       = {4},
  pages        = {244--257},
  title        = {Modifying the feature-selective validation method to validate noisy data sets},
  url          = {http://dx.doi.org/10.1049/iet-smt:20070063},
  volume       = {2},
  year         = {2008},
}

Altmetric
View in Altmetric
Web of Science
Times cited: