The results are compared to the standard Minimum Entropy Deconvolution method on both simu-lated and experimental data. The experimental data is from a gearbox with gear chip fault, and the results ... Preprint submitted to Mechanical Systems and Signal Processing March 28, 2012. A few methods particularly of note include Wavelet Transform. . He, D., Wang, X., Li, S., Lin, J., & Zhao, M. (2016). Identification of multiple faults in rotating machinery based on minimum entropy deconvolution combined with. Local entropy estimation in particle velocity images using histogram deconvolution. In E. R. Dougherty (Ed.), Image Processing: Algorithms and Systems, 21-23 January 2002, San Jose, USA, Proceedings of SPIE (pp. 429-439). In this work, the nonparametric-copula-entropy and network deconvolution method is proposed for causal discovery in complex manufacturing systems. Firstly, based on copula theory and kernel density estimation method, the nonparametric-copula-entropy is introduced to improve the accuracy of association measurement between parameters, and its. In blind image deconvolution, priors are often leveraged to constrain the solution space, so as to alleviate the under-determinacy. ... Guariglia , Harmonic sierpinski gasket and applications, Entropy 20(9) (2018) 714. ... Fast image deconvolution using hyper-laplacian priors, in Proc. Neural Information Processing System (Vancouver, Canada. 2 State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, China. Corresponding Author: ... As a non-iterative algorithm, multipoint optimal minimum entropydeconvolution adjusted (MOMEDA) shows its advantage in computation efficiency. However, MOMEDA focuses only on the extraction of impulses with. For a usual two-dimensional (2D) STEM-ADF image, we have developed the deconvolution routine in order to improve the lateral resolution based on the maximum entropy method (MEM) and the Richardson–Lucy algorithm (RLA) . Here, we assume that the sample is thinner than the depth of focus (depth resolution) and that the ADF signal can be.
The main features of the code MAXED, a computer program developed to apply the maximum entropy principle to the deconvolution (unfolding) of multisphere neutron spectrometer data, are described, and the use of the code is illustrated with an example. ... Although taking data with a multisphere system is relatively easy, deducing the neutron. The maximum entropy technique was successfully applied to the deconvolution of overlapped chromatographic peaks. An algorithm was written in which the chromatogram was represented as a vector of sample concentrations multiplied by a peak shape matrix. Simulation results demonstrated that there is a trade off between the detector noise and peak resolution in the sense that an increase of the. Entropy-regularized deconvolution (ER-DC) (36) is formulated to handle data with a weak signal-to-noise ratio, with a regularization term that exploits certain characteristics specific to images originating from crowded molecular environments such as cells. Introduction. Maximum entropy toolbox for MATLAB is a free, open-source toolbox for finding the maximum entropy distribution of training data, based on a set of constraints or observables over the data. Maximum entropy models give the mathematically minimal probabilistic models of the states or configurations of a systems, given the mean values. 2 Center for Intelligent Maintenance Systems, University of Cincinnati, Ohio, 45221, USA. ... minimum entropydeconvolution (MED) and maximum correlated kurtosis deconvolution (MCKD), but visual inspection is also better, even if the fault period is not provided in advance. Moreover, the efficiency of the proposed method is verified by. Yes. Huygens treats the image as the only known plane of a 3D stack and proceeds as usual. Before deconvolving, go to the Microscopic Parameters and set the z-sampling distance to the Nyquist rate as explained in 'sampling densities' (Huygens User Guide).For optimal results we do advise to image in 3D when possible, especially when performing quantitative analysis after. The Maximum Entropy command opens from the Image Window > Process > Filters menu. Properties of Maximum EntropyDeconvolution Using Maximum EntropyDeconvolution Maximum Entropy only works with intensity images. It does not work with RGB images. If you want to process an RGB image, you will need to extract the channels and process them separately. Two types of image deconvolution (Richardson-Lucy and FFT). New: improved support for buggy X11 window managers. Analysis features: Distance, angle, 1-D and 2-D image calibration, densitometry, area, warping, wavelets, 2D-FFT, and Fourier convolution and deconvolution of images. View FFT real or imaginary components.
WANG Hongchao CHEN Jin DONG Guangming(The State Key Laboratory of Mechanical System and Vibration,Shanghai Jiao Tong University,Shanghai 200240) ... extraction of rolling bearing.But its performance is very poor when the background noise is very strong.The minimum entropy deconvolution(MED) and sparse decomposition are combined for rolling. The Maximum Entropy Deconvolution method sharpens an image while reducing noise. Maximum entropy processing works best on images having high contrast and high signal to noise ratio. See this Example.The High Pass Filter is quicker but does not reduce the noise as does maximum entropy processing.. The Maximum Entropy command opens from the Image. This can be undesirable when doing deconvolution as it can reduce. The principle of maximum entropy states that the probability distribution, which suitably represents the current state of knowledge, is the one with the most significant entropy in the context of precisely stated prior data. Unlike the naive Bayes method, maximum entropy is used. In the present work, Minimum entropydeconvolution adjusted is used as a preprocessor to improve the strength of impulses in the measured time-domain bearing signal. The effectiveness of the proposed algorithm is tested with simulated signals for the faulty bearing vibration at different levels of added Gaussian noise. Deconvolution Module. Those feature maps at deconvolution path are upsampled by Deconv2×2 and then Conv3×3+BN. On the other hand, the corresponding same-size feature maps are having Conv3×3+BN+ReLU+Conv3×3+BN. Then they are element-wise multiplied (Eltw Product) together, and ReLU, and pass to the Prediction Module. MAXIMUM ENTROPY or BURG deconvolution uses an entropy criterion to produce the predictable and random elements of the data and is a strong spectral balance. ... When using PROMAX (as in many processing systems) two jobs. This is the first study to present simultaneously both deconvolution and inversion, two powerful tools of data analysis. Featured within this volume are various geophysical convolution models and a treatment of deconvolution for a time-varying signal. The single channel time-varying deconvolution is shown equivalent to the multichannel time. Application of minimum entropy deconvolution on enhancement of gear tooth fault detection Abstract: Gearboxes are important parts of mechanical transmission systems, so that it is very pivotal for avoiding future catastrophic failures to detect faults in gearboxes at the early stage. Various vibration analysis techniques have been proposed and.
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Entropy guided deconvolution of seismic signal: a Disclosed are a. ... a Disclosed are a process and a system for improving seismic returns and other signals representative of nontime varying series which have been degraded by interaction with unknown wavelets. Use is made of a sequence of two-term operators, each derived from the most recent ...
The strict maximum entropy criterion requires one to select that particular feasible image which has the greatest entropy. One maximizes S subject to C ~ Cairn· If the unconstrained maximum of S satisfies this constraint, then this will be the maximum entropy solution - the data are too noisy for any information to be extracted.
To clarify the causality among process parameters is a core issue of data-driven production performance analysis and product quality optimization. The difficulty lies in accurately measuring and distinguishing direct and indirect associations of complex manufacturing systems. In this work, the nonparametric-copula-entropy and network deconvolution method is proposed for causal discovery in ...
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Burg's maximum entropy method has been used with success in spectral estimation. This paper is an attempt to generalize the maximum entropy method to the deconvolution of positive signals from a finite set of linear measurements. In this paper, we also investigate the existence of a solution to the deconvolution problem using a geometric approach. By formulating the