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Multiresolution Analysis (multiresolution + analysis)

Distribution by Scientific Domains

Chemistry	83%

Selected Abstracts

Multiresolution analysis on identification and dynamics of clusters in a circulating fluidized bed

AICHE JOURNAL, Issue 3 2009
Tung-Yu Yang
Abstract A new wavelet-threshold criterion was developed to distinguish the cluster and the void phases from the transient solids holdup/concentration fluctuation signals when measured in a 108 mm-i.d. × 5.75 m-high circulating fluidized bed with FCC particles (dp = 78 ,m, ,p = 1,880 kg/m3). An appropriate level of approximation subsignal was systematically specified as a threshold for cluster identification, based on multiresolution analysis (MRA) of wavelet transformation. By the established threshold, the dynamic properties of clusters including the appearance time fraction of clusters Fcl, average cluster duration time ,cl, cluster frequency fcl, and local average solids holdup in clusters ,sc, at different radial and axial positions were determined under the turbulent, transition and fast fluidization flow regimes. The results also describe the dynamic properties of clusters and flow patterns in the splash zone along with the dense bottom region of the circulating fluidized beds. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source]

Wavelet Packet-Autocorrelation Function Method for Traffic Flow Pattern Analysis

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 5 2004
Xiaomo Jiang
A detailed understanding of the properties of traffic flow is essential for building a reliable forecasting model. The discrete wavelet packet transform (DWPT) provides more coefficients than the conventional discrete wavelet transform (DWT), representing additional subtle details of a signal. In wavelet multiresolution analysis, an important decision is the selection of the decomposition level. In this research, the statistical autocorrelation function (ACF) is proposed for the selection of the decomposition level in wavelet multiresolution analysis of traffic flow time series. A hybrid wavelet packet-ACF method is proposed for analysis of traffic flow time series and determining its self-similar, singular, and fractal properties. A DWPT-based approach combined with a wavelet coefficients penalization scheme and soft thresholding is presented for denoising the traffic flow. The proposed methodology provides a powerful tool in removing the noise and identifying singularities in the traffic flow. The methods created in this research are of value in developing accurate traffic-forecasting models. [source]

Multiresolution analysis on identification and dynamics of clusters in a circulating fluidized bed

Principal-component analysis of multiscale data for process monitoring and fault diagnosis

AICHE JOURNAL, Issue 11 2004
Seongkyu Yoon
Abstract An approach is presented to multivariate statistical process control (MSPC) for process monitoring and fault diagnosis based on principal-component analysis (PCA) models of multiscale data. Process measurements, representing the cumulative effects of many underlying process phenomena, are decomposed by applying multiresolution analysis (MRA) by wavelet transformations. The decomposed process measurements are rearranged according to their scales, and PCA is applied to these multiscale data to capture process variable correlations occurring at different scales. Choosing an orthonormal mother wavelet allows each principal component to be a function of the process variables at only one scale level. The proposed method is discussed in the context of other multiscale approaches, and illustrated in detail using simulated data from a continuous stirred tank reactor (CSTR) system. A major contribution of the paper is to extend fault isolation methods based on contribution plots to multiscale approaches. In particular, once a fault is detected, the contributions of the variations at each scale to the fault are computed. These scale contributions can be very helpful in isolating faults that occur mainly at a single scale. For those scales having large contributions to the fault, one can further compute the variable contributions to those scales, thereby making fault diagnosis much easier. A comparison study is done through Monte Carlo simulation. The proposed method can enhance fault detection and isolation (FDI) performance when the frequency content of a fault effect is confined to a narrow-frequency band. However, when the fault frequency content is not localized, the multiscale approaches perform very comparably to the standard single-scale approaches, and offer no real advantage. © 2004 American Institute of Chemical Engineers AIChE J, 50: 2891,2903, 2004 [source]

Multiresolution of quasicrystal diffraction spectra

ACTA CRYSTALLOGRAPHICA SECTION A, Issue 6 2009
Avi Elkharrat
A method for analyzing and classifying two-dimensional pure point diffraction spectra (i.e. a set of Bragg peaks) of certain self-similar structures with scaling factor , > 1, such as quasicrystals, is presented. The two-dimensional pure point diffraction spectrum , is viewed as a point set in the complex plane in which each point is assigned a positive number, its Bragg intensity. Then, by using a nested sequence of self-similar subsets called ,-lattices, we implement a multiresolution analysis of the spectrum ,. This analysis yields a partition of , simultaneously in geometry, in scale and in intensity (the `fingerprint' of the spectrum, not of the diffracting structure itself). The method is tested through numerical explorations of pure point diffraction spectra of various mathematical structures and also with the diffraction pattern of a realistic model of a quasicrystal. [source]

Cross-directional Estimation and Predictive Control of Paper Machines Using DWT

ASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING, Issue 1-2 2001
Zhihuan Song
This paper proposes a novel approach for cross-directional (CD) estimation, modeling and control on paper machines based on discrete wavelet transforms (DWT). The CD basis weight variations are approximated at various resolutions using wavelet multiresolution analysis (WMRA). The controllable component of CD variations can be extracted from the original samples by choosing a suitable threshold resolution. An acceptable response model describing the relationship between the settings of the slice-screws to the basis weight profile is obtained. The controller synthesis, model prediction, optimization and parameter estimation are all performed in the DWT domain. The size of optimization and control problems associated with such large dimensions can be significantly reduced. [source]