Detection threshold for non-parametric estimation

Atto, Abdourrahmane M.; Pastor, Dominique; Mercier, Grégoire

doi:10.1007/s11760-008-0051-x

Cited by 21 publications

(32 citation statements)

References 21 publications

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“…By looking up the process documentation and onsite verification, we knew that the idler assembled into the gearbox housing when into the assembly line would go through three stations, and the relationship between them is connected in series as shown in Fig.1. For the plant, in order to balance assembly work of the assembly line, two test-beds were arranged at the detection station, that is to configure two staff who can detect parts synchronously, and the two detectors in parallel; two transport upper lines at Transport station, that is to configure two transport staff who can transport parts synchronously, and two transport lines in parallel; and three bench configured at assembly station, that is to configure three assembly staff, and the relationship between the three benches is in parallel [4]. Parts firstly pass by testing stations.…”

Section: Assembly Process Of Gearbox Quality Problem Partsmentioning

confidence: 99%

Tracing Method of Quality Issues of Gearbox and Application of Rank Sum Test

Hong¹,

Li²

2016

Advances in Computer Science Research

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Abstract-Tracing quality issues of complex automobile gearbox is realized, defective parts and assembly procedures are analyzed and confirmed, analytical method for tracing quality issues is provided and the thinking is illustrated. Furthermore, the tracing model for assembly and quality issues of the gearbox is constructed based on inference principles of the rank sum test; the research results show that the analytical method is effective and feasible in tracing quality issues of the gearbox.

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Section: Assembly Process Of Gearbox Quality Problem Partsmentioning

confidence: 99%

Tracing Method of Quality Issues of Gearbox and Application of Rank Sum Test

Hong¹,

Li²

2016

Advances in Computer Science Research

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“…The value of α is fixed by following the same reasoning as in [29] and considering that the minimum amplitude of the signal to detect is the noise maximum value. More specifically, given m random variables X 1 , X 2 , .…”

Section: Weak-sparseness-based Time-frequency Detection For Source Rementioning

confidence: 99%

Contribution of statistical tests to sparseness-based blind source separation

Aziz-Sbaï

Bey

Pastor

2012

EURASIP J. Adv. Signal Process.

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We address the problem of blind source separation in the underdetermined mixture case. Two statistical tests are proposed to reduce the number of empirical parameters involved in standard sparseness-based underdetermined blind source separation (UBSS) methods. The first test performs multisource selection of the suitable time-frequency points for source recovery and is full automatic. The second one is dedicated to autosource selection for mixing matrix estimation and requires fixing two parameters only, regardless of the instrumented SNRs. We experimentally show that the use of these tests incurs no performance loss and even improves the performance of standard weak-sparseness UBSS approaches.

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“…Many suggestions have been made to improve the performance of these WaveShrink estimators. Some of them relate to the choice of the threshold [3,4], and others address the choice of the shrinkage (parametric Bayesian shrinkage: [5][6][7][8][9][10]; non-parametric shrinkage functions: [11][12][13][14]; among others).…”

Section: Introductionmentioning

confidence: 99%

Wavelet shrinkage: unification of basic thresholding functions and thresholds

Atto

Pastor

Mercier

2009

SIViP

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This work addresses the unification of some basic functions and thresholds used in non-parametric estimation of signals by shrinkage in the wavelet domain. The soft and hard thresholding functions are presented as degenerate smooth sigmoid-based shrinkage functions. The shrinkage achieved by this new family of sigmoid-based functions is then shown to be equivalent to a regularization of wavelet coefficients associated with a class of penalty functions. Some sigmoid-based penalty functions are calculated, and their properties are discussed. The unification also concerns the universal and the minimax thresholds used to calibrate standard soft and hard thresholding functions: these thresholds pertain to a wide class of thresholds, called the detection thresholds. These thresholds depend on two parameters describing the sparsity degree for the wavelet representation of a signal. It is also shown that the non-degenerate sigmoid shrinkage adjusted with the new detection thresholds is as performant as the best up-to-date parametric and computationally expensive method. This justifies the relevance of sigmoid shrinkage for noise reduction in large databases or large size images.

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Detection threshold for non-parametric estimation

Cited by 21 publications

References 21 publications

Tracing Method of Quality Issues of Gearbox and Application of Rank Sum Test

Tracing Method of Quality Issues of Gearbox and Application of Rank Sum Test

Contribution of statistical tests to sparseness-based blind source separation

Wavelet shrinkage: unification of basic thresholding functions and thresholds

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