Browsing by Subject "Wavelet"
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Item Advanced fault diagnosis techniques and their role in preventing cascading blackouts(Texas A&M University, 2007-04-25) Zhang, NanThis dissertation studied new transmission line fault diagnosis approaches using new technologies and proposed a scheme to apply those techniques in preventing and mitigating cascading blackouts. The new fault diagnosis approaches are based on two time-domain techniques: neural network based, and synchronized sampling based. For a neural network based fault diagnosis approach, a specially designed fuzzy Adaptive Resonance Theory (ART) neural network algorithm was used. Several ap- plication issues were solved by coordinating multiple neural networks and improving the feature extraction method. A new boundary protection scheme was designed by using a wavelet transform and fuzzy ART neural network. By extracting the fault gen- erated high frequency signal, the new scheme can solve the difficulty of the traditional method to differentiate the internal faults from the external using one end transmis- sion line data only. The fault diagnosis based on synchronized sampling utilizes the Global Positioning System of satellites to synchronize data samples from the two ends of the transmission line. The effort has been made to extend the fault location scheme to a complete fault detection, classification and location scheme. Without an extra data requirement, the new approach enhances the functions of fault diagnosis and improves the performance. Two fault diagnosis techniques using neural network and synchronized sampling are combined as an integrated real time fault analysis tool to be used as a reference of traditional protective relay. They work with an event analysis tool based on event tree analysis (ETA) in a proposed local relay monitoring tool. An interactive monitoring and control scheme for preventing and mitigating cascading blackouts is proposed. The local relay monitoring tool was coordinated with the system-wide monitoring and control tool to enable a better understanding of the system disturbances. Case studies were presented to demonstrate the proposed scheme. An improved simulation software using MATLAB and EMTP/ATP was devel- oped to study the proposed fault diagnosis techniques. Comprehensive performance studies were implemented and the test results validated the enhanced performance of the proposed approaches over the traditional fault diagnosis performed by the transmission line distance relay.Item Bayesian models for DNA microarray data analysis(Texas A&M University, 2005-08-29) Lee, Kyeong EunSelection of signi?cant genes via expression patterns is important in a microarray problem. Owing to small sample size and large number of variables (genes), the selection process can be unstable. This research proposes a hierarchical Bayesian model for gene (variable) selection. We employ latent variables in a regression setting and use a Bayesian mixture prior to perform the variable selection. Due to the binary nature of the data, the posterior distributions of the parameters are not in explicit form, and we need to use a combination of truncated sampling and Markov Chain Monte Carlo (MCMC) based computation techniques to simulate the posterior distributions. The Bayesian model is ?exible enough to identify the signi?cant genes as well as to perform future predictions. The method is applied to cancer classi?cation via cDNA microarrays. In particular, the genes BRCA1 and BRCA2 are associated with a hereditary disposition to breast cancer, and the method is used to identify the set of signi?cant genes to classify BRCA1 and others. Microarray data can also be applied to survival models. We address the issue of how to reduce the dimension in building model by selecting signi?cant genes as well as assessing the estimated survival curves. Additionally, we consider the wellknown Weibull regression and semiparametric proportional hazards (PH) models for survival analysis. With microarray data, we need to consider the case where the number of covariates p exceeds the number of samples n. Speci?cally, for a given vector of response values, which are times to event (death or censored times) and p gene expressions (covariates), we address the issue of how to reduce the dimension by selecting the responsible genes, which are controlling the survival time. This approach enables us to estimate the survival curve when n << p. In our approach, rather than ?xing the number of selected genes, we will assign a prior distribution to this number. The approach creates additional ?exibility by allowing the imposition of constraints, such as bounding the dimension via a prior, which in e?ect works as a penalty. To implement our methodology, we use a Markov Chain Monte Carlo (MCMC) method. We demonstrate the use of the methodology with (a) di?use large B??cell lymphoma (DLBCL) complementary DNA (cDNA) data and (b) Breast Carcinoma data. Lastly, we propose a mixture of Dirichlet process models using discrete wavelet transform for a curve clustering. In order to characterize these time??course gene expresssions, we consider them as trajectory functions of time and gene??speci?c parameters and obtain their wavelet coe?cients by a discrete wavelet transform. We then build cluster curves using a mixture of Dirichlet process priors.Item Development of Spatio-Temporal Wavelet Post Processing Techniques for Application to Thermal Hydraulic Experiments and Numerical Simulations(2012-07-16) Salpeter, NathanielThis work focuses on both high fidelity experimental and numerical thermal hydraulic studies and advanced frequency decomposition methods. The major contribution of this work is a proposed method for spatio-temporal decomposition of frequencies present in the flow. This method provides an instantaneous visualization of coherent frequency ?structures? in the flow. The significance of this technique from an engineering standpoint is the ease of implementation and the importance of such a tool for design engineers. To validate this method, synthetic verification data, experimental data sets, and numerical results are used. The first experimental work involves flow through the side entry orifice (SEO) of a boiling water reactor (BWR) using non-intrusive particle tracking velocimetry (PTV) techniques. The second experiment is of a simulated double ended guillotine break in the prismatic block gas cooled reactor. Numerical simulations of jet flow mixing in the lower plenum of a prismatic block high temperature gas cooled reactor is used as a final data set for verification purposes as well as demonstration of the applicability of the method for an actual computational fluid dynamics validation case.Item Feasibility of isotropic inversion in orthorhombic media : the Barrett unconventional model(2016-05) Yanke, Andrew James; Spikes, Kyle; Sen, Mrinal K; Fomel, Sergey BGeophysicists often relegate shale reservoirs as having higher symmetries (e.g., transversely isotropic (TI) or isotropic) than what reality demonstrates. Routine application of TI (or even isotropic) algorithms to orthorhombic media neglects the associated errors because we never know the true model in practice. This thesis evaluates the viability of isotropic post-stack and pre-stack seismic inversion to orthorhombic media using the SEAM Barrett Unconventional Model, the most realistic depositional model to date. The Barrett Model contains buried topography, simulated stratigraphy, and designated reservoir zones with orthorhombic anisotropy. I inverted the Barrett data volume for isotropic elastic property cubes, which I compared to the model volume in each symmetry-plane of an orthorhombic medium. If the stacked seismic data contained only the near offsets, post-stack inversion resolved acoustic impedances that closely matched the true model both within and outside of the reservoir zones at all well locations. Anisotropy most affected the far offsets, so muting them predictably enhanced the post-stack inversion. I maintained all offsets for pre-stack inversion, but a parabolic radon filter eliminated nonhyperbolic behavior (rather than nonhyperbolic moveout analysis) at far offsets. The pre-stack impedance attributes adequately described the vertical heterogeneity of the true model at a cross-validation well, but the inverted values increasingly relied on the initial model with depth. The inverted density estimates experienced notable oscillations relative to the initial model, particularly where steep contrasts in elastic properties occurred. Mismatch of the inverted elastic properties at the well locations can be attributed to noise, thin layering effects, band limitation, steep contrasts in elastic properties, AVO behavior stacked into the data, an inaccurate starting model, and the effects of anisotropy. The most significant sources of error include small-scale reflectivity and comprehensive filtering of nonhyperbolic phenomena. Away from the well locations, the isotropic inversion gave no visual indication of reservoir geobodies, but it sufficiently described the elastic property variations near reservoir mid-sections. Moreover, I showed that the inverted elastic properties differ from their orthorhombic models by no more than 35%. The greatest misfits occurred near reservoir contacts and geobody locations. The computed impedance models in each symmetry-plane have distinctive differences, but isotropic inversion dismisses these variations entirely. I conclude that isotropic inversion should not be a surrogate for orthorhombic methods in data preconditioning and quantitative reservoir characterization.Item Formation evaluation using wavelet analysis on logs of the Chinji and Nagri Formations, northern Pakistan(Texas A&M University, 2006-10-30) Tanyel, Emre DorukThe relatively new method of using wavelets in well log analysis is a powerful tool for defining multiple superimposed scales of lithic trends and contacts. Interpreting depositional processes associated with different scales of vertical variation within well log responses allows prediction of the lateral extent of sands and the distribution of internal flow barriers important for development of oil field recovery strategies. Wavelet analysis of grain-size variations in a 2.1 km thick fluvial section including the fluvial Chinji and Nagri Formations, northern Pakistan, revealed three major wavelengths. Reliability of the wavelength values was tested and confirmed by multiple sectioning of the dataset. These dominant wavelengths are interpreted to reflect vertical variations within individual channels, the stacking of channel belts within overbank successions due to river avulsion, and larger-scale channel stacking patterns within this foreland basin that may reflect allocyclic influences. Wavelet analysis allows quantification of the scales of periodic vertical variations that may not be strictly cyclic in nature. Comparison of total wavelet energies over all scales for each depth to the grain size and sand percentages yielded good correlations with sand proportion curves. Although changes in the wavelet energy profile were much more distinct with respect to grain size, lithic boundaries' locations were not detected based solely on the total of the wavelet energies. The data were also analyzed using Fourier transforms. Although Fourier transforms of the data yielded the smallest scale cyclicities, the higher-order cyclicities were not defined. This comparison demonstrates the power of wavelet analysis in defining types of repetitive, but not strictly cyclic, variations that are commonly observed in the sedimentary record. Assessments of Milankovitch cyclicities were performed for the Chinji and the Nagri Formations using statistical and analytical analysis methods. A clear match between Milankovitch frequency ratios and vertical lithic variations was not observed, and thus distinct climatic control on cyclic lithological trends was not demonstrated. Analysis using wavelets to determine wavelet coefficients helps quantify characteristic scales of vertical variations, cyclicities, zone thicknesses, and locations of abrupt lithic boundaries. Wavelet analysis provides methods that could be used to help automate well log analysis.Item Multivariate multiresolution with multiwavelets(2009-05) Brazile, Calandra Rachelle; Iyer, Ram V.; Roeger, Lih-Ing W.In this thesis, we will study function approximation with wavelets. Using a wavelet basis allows us to represent a function in L^2(R^n) at various levels of resolution. In other words, we have a multiresolution representation of a square integrable, multivariate function. We study smoothing, denoising, and compression of a function using multiresolution analysis. The choice of dierent wavelet bases and one multiwavelet basis are explored. Both regular and irregular sampling cases are studied. Our conclusion that multivariate multiresolution is better with wavelets than with multiwavelets is due to the difficulty of selecting the correct prelter for function approximations with multiwavelets.