Wenhan Zeng

Fast algorithm of the robust Gaussian regression filter for areal surface analysis

In this paper, the general model of the Gaussian regression filter for areal surface analysis is explored. The intrinsic relationships between the linear Gaussian filter and the robust filter are addressed. A general mathematical solution for this model is presented. Based on this technique, a fast algorithm is created. Both simulated and practical engineering data (stochastic and structured) have been used in the testing of the fast algorithm. Results show that with the same accuracy, the processing time of the second-order nonlinear regression filters for a dataset of 1024*1024 points has been reduced to several seconds from the several hours of traditional algorithms.

Roundness filtration by using a robust regression filter

In roundness measurement, filtration is a basic step before the evaluation of the roundness deviation parameters. Currently the widely used phase-correct 2RC and Gaussian filters are not robust against measurement outliers. This paper introduces a robust roundness filtration technology which is based on the robust Gaussian regression filter. Mathematical solution and its algorithms calculated in time and frequency domains are both presented. Both simulated and practical roundness profile data have been used to test the speed and accuracy of the algorithm. Results show that with the proposed robust algorithm, the outliers influence on the roundness deviation parameters can be reduced significantly, while still keeping the similar transmission characteristics with the standard Gaussian filter very well. Also, the difference between the results of the two presented algorithms is very tiny and negligible.

Selecting a semantic similarity measure for concepts in two different CAD model data ontologies

Semantic similarity measure technology based approach is one of the most popular approaches aiming at implementing semantic mapping between two different CAD model data ontologies. The most important problem in this approach is how to measure the semantic similarities of concepts between two different ontologies. A number of measure methods focusing on this problem have been presented in recent years. Each method can work well between its specific ontologies. But it is unclear how accurate the measured semantic similarities in these methods are. Moreover, there is yet no evidence that any of the methods presented how to select a measure with high similarity calculation accuracy. To compensate for such deficiencies, this paper proposes a method for selecting a semantic similarity measure with high similarity calculation accuracy for concepts in two different CAD model data ontologies. In this method, the similarity calculation accuracy of each candidate measure is quantified using Pearson correlation coefficient or residual sum of squares. The measure with high similarity calculation accuracy is selected through a comparison of the Pearson correlation coefficients or the residual sums of squares of all candidate measures. The paper also reports an implementation of the proposed method, provides an example to show how the method works, and evaluates the method by theoretical and experimental comparisons. The evaluation result suggests that the measure selected by the proposed method has good human correlation and high similarity calculation accuracy.

Wavelet Analysis for the Extraction of Morphological Features for Orthopaedic Bearing Surfaces

In this chapter three generations of biorthogonal wavelet transforms, for the extraction of morphological structures from micro/nano scalar surfaces in the field of bioengineering, are introduced. The aim of the chapter is to create a “tool box” of wavelet techniques capable of complex analysis and interpretation of surface topography data; leading to the accurate extraction of roughness, waviness, form and form error, as well as functionally critical morphological features from micro/nano scalar surfaces of orthopaedic joints for in-vitro and clinically retrieved applications.

The detection of transient behaviour in environmental vibration for the Watt balance

The detection of transient behaviour in environmental vibration data is an important issue when considering the precision of the Watt balance, an electromechanical apparatus for the new definition of the kilogram in the International System of Units (SI). In this paper, the authors have developed a two-stage method for the analysis of large numbers of datasets containing measured vibration data. In the first stage, all the datasets are explored and statistical methods used to identify those datasets that potentially contain transient events. In the second stage, the transient events are separated from the background noise. This stage is implemented using a biorthogonal wavelet technique with Bayesian denoising.

Complex ridgelets for the extraction of morphological features on engineering surfaces

In this paper, a complex ridgelets transform, which provides approximate shift invariance analysis of line singularities, is proposed to extract morphological features with linear or directional/objective property, by taking a Dual Tree Complex Wavelet Transform (DT-CWT) on the projections of the Finite Radon Transform (FRAT). The Numerical experiments show the remarkable potential of this methodology to analyse engineering and bioengineering surfaces with liner scratches in comparison to wavelet-based methods developed in our pervious work.

An fast and efficient algorithm for spline filters

Filtration is very important for surface texture characterization. Spline filters that include linear and robust filters are recommended by ISO/TS 16610-22 and 32. In this paper, a general algorithm for both the linear and the robust spline filters is presented. The main advantage of the proposed algorithm is its ability to produce different types of filters only by choosing different M-estimators as shown in this note. This proposed algorithm relies on the Cholesky decomposition technique with an improved memory management procedure. Experimental and simulated results show that the proposed algorithm is fast, efficient and stable.

Investigation on the Noise Separation in Watt Balance Experiments

The detection of stationary and non-stationary noise in environmental vibration data is an important issue when considering the precision of the Watt balance, an electromechanical apparatus for the new definition of the kilogram in the international system of Units (SI). In this paper, the authors propose a frequency histogram method to find the structure of the stationary noise from large amount of datasets. For the non-stationary noise, the authors propose a wavelet based denoising methods to distinguish the transient events from the background “noise”, to find their duration and content and to identify their location in time.