Home About us Contact
|
Home About us Contact | ||
|
|
||
Wavelet Bases (wavelet + base)
Selected AbstractsA Study on the Effects of Damage Models and Wavelet Bases for Damage Identification and Calibration in BeamsCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 8 2007Vikram Pakrashi A numerical study has been performed in this article addressing these issues for single and multispan beams with an open crack. The first natural modeshapes of single and multispan beams with an open crack have been simulated considering damage models of different levels of complexity and analyzed for different crack depth ratios and crack positions. Gaussian white noise has been synthetically introduced to the simulated modeshape and the effects of varying signal-to-noise ratio have been studied. A wavelet-based damage identification technique has been found to be simple, efficient, and independent of damage models and wavelet basis functions, once certain conditions regarding the modeshape and the wavelet bases are satisfied. The wavelet-based damage calibration is found to be dependent on a number of factors including damage models and the basis function used in the analysis. A curvature-based calibration is more sensitive than a modeshape-based calibration of the extent of damage. [source] Combining wavelet-based feature extractions with relevance vector machines for stock index forecastingEXPERT SYSTEMS, Issue 2 2008Shian-Chang Huang Abstract: The relevance vector machine (RVM) is a Bayesian version of the support vector machine, which with a sparse model representation has appeared to be a powerful tool for time-series forecasting. The RVM has demonstrated better performance over other methods such as neural networks or autoregressive integrated moving average based models. This study proposes a hybrid model that combines wavelet-based feature extractions with RVM models to forecast stock indices. The time series of explanatory variables are decomposed using some wavelet bases and the extracted time-scale features serve as inputs of an RVM to perform the non-parametric regression and forecasting. Compared with traditional forecasting models, our proposed method performs best. The root-mean-squared forecasting errors are significantly reduced. [source] The discriminating power of wavelets to detect non-Gaussianity in the cosmic microwave backgroundMONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, Issue 3 2001R.B. Barreiro We investigate the power of wavelets in detecting non-Gaussianity in the cosmic microwave background (CMB). We use a wavelet-based method on small simulated patches of the sky to discriminate between a pure inflationary model and inflationary models that also contain a contribution from cosmic strings. We show the importance of the choice of a good test statistic in order to optimize the discriminating power of the wavelet technique. In particular, we construct the Fisher discriminant function, which combines all the information available in the different wavelet scales. We also compare the performance of different decomposition schemes and wavelet bases. For our case, we find that the Mallat and a`trous algorithms are superior to the 2D-tensor wavelets. Using this technique, the inflationary and strings models are clearly distinguished even in the presence of a superposed Gaussian component with twice the rms amplitude of the original cosmic string map. [source] | ||||||||||