Local toolpath smoothing for five-axis machine tools

When five axis CNC machine tools follow series linear toolpath segments, the drives experience velocity, acceleration and jerk discontinuities at the block transition points. The discontinuities result in fluctuations on machine tool motions which lead to poor surface quality. This paper proposes to insert quintic and septic micro-splines for the tool tip and tool-orientation, respectively, at the adjacent linear toolpath segments. Optimal control points are calculated for position and orientation splines to achieve C³ continuity at the junctions while respecting user-defined tolerance limits. The geometrically smoothed corners are traveled at a smoothly varying feed with cubic acceleration trajectory profile. The proposed method is experimentally demonstrated to show improvements in motion smoothness and tracking accuracy in five-axis machining of free-form surfaces found in dies, molds and aerospace parts.     

Assessing continuous bivariate effects among different groups through nonparametric regression models: An application to breast cancer detection

In many applications, the joint effect of two continuous covariates on the target binary response may vary across groups defined by levels of a given factor. A testing procedure that would enable this type of surface-by-factor interactions to be detected has been designed. To accomplish this goal, a logistic generalized additive model (GAM) with bivariate continuous interactions varying across groups defined by levels of a factor is considered. A local scoring algorithm based on local linear kernel smoothers was implemented to estimate the proposed logistic GAM. Bootstrap resampling techniques were used for the purpose of testing for factor-by-surface interactions. Given the high computational cost involved, binning techniques were used to speed up computation in the estimation and testing processes. The adequacy of the bootstrap-based test was assessed by means of a simulation study. If a factor-by-surface interaction is detected in the model, it is then established that the use of the odds-ratio curves is very useful in obtaining a direct interpretation of the fitted model. The benefits of using this methodology when analyzing real data are illustrated by applying the technique to the outputs produced by a computerized system dedicated to the early detection of breast cancer.     

A 3D contact smoothing method using Gregory patches

In this work, a method is developed for smoothing three‐dimensional contact surfaces. The method can be applied to both regular and irregular meshes. The algorithm employs Gregory patches to interpolate finite element nodes and provide tangent plane continuity between adjacent patches. The resulting surface interpolation is used to calculate gaps and contact forces, in a variationally consistent way, such that contact forces due to normal and frictional contact vary smoothly as slave nodes transition from one patch to the next. This eliminates the ‘chatter’ which typically occurs in a standard contact algorithm when a slave node is situated near a master facet edge. The elimination of this chatter provides a significant improvement in convergence behaviour, which is illustrated by a number of numerical examples. Furthermore, smoothed surfaces also provide a more accurate representation of the actual surface, such that resulting stresses and forces can be more accurately computed with coarse meshes in many problems. This fact is also demonstrated by the numerical examples. Published in 2002 by John Wiley & Sons, Ltd.     

Hybrid LES — Review and assessment

In the late eighties and up to the beginning of nineties computation of turbulent flows is mostly dominated by RANS (Reynolds Averaged Navier-Stokes Simulation) type modelling. During the last few years URANS (Unsteady RANS) and LES (Large Eddy Simulation) type of approaches have been attempted with some success. Yet, there have been many difficulties when LES is applied to practical engineering problems and to high Reynolds number flows as energy dissipating eddies become really small and mesh resolution required for a reasonably resolved LES approaches that of DNS (Direct Numerical Simulation). An alternative solution suggested was to combine RANS and LES, which in general referred to as Hybrid LES. There have been many proposals for combining RANS and LES in different ways. In this article, some of the issues involved in performing hybrid LES reported in the recent literature is briefly reviewed.     

基于双阈值的视频平滑控制方法

在现有的TCP／IP网络中进行多媒体通信，服务质量是人们关注的重要内容。作为服务质量中的一项指标，视频平滑已成为研究和应用开发的一个热点问题。分析当前控制视频平滑的方式，提出一种基于双阈值视频平滑的控制方法。这种方法能够有效地改善视频平滑性能。     

New software developments for quality mesh generation and optimization from biomedical imaging data

In this paper we present a new software toolkit for generating and optimizing surface and volumetric meshes from three-dimensional (3D) biomedical imaging data, targeted at image-based finite element analysis of some biomedical activities in a single material domain. Our toolkit includes a series of geometric processing algorithms including surface re-meshing and quality-guaranteed tetrahedral mesh generation and optimization. All methods described have been encapsulated into a user-friendly graphical interface for easy manipulation and informative visualization of biomedical images and mesh models. Numerous examples are presented to demonstrate the effectiveness and efficiency of the described methods and toolkit.     

基于Loop细分算法的人体模型网格平滑

网格平滑是实现三维模型离散造型的主要方法。为了实现数字人体几何模型的光顺效果,本文提出一种基于Loop细分算法的三维人体模型的网格平滑方法。细分曲面是用低分辨率的控制网格和定义在控制网格上的一种细分规则来表示曲面的,它能有效改善三维人体几何模型的表面不光滑以及分辨率低的缺点。实验证明,该方法对基于参数化建模方法的、多曲率网格人体模型取得了很好的效果,实现简单高效,特征保持效果也很好。     

基于Legendre正交多项式非参数混合模型的图像分割

针对有限混合模型中参数估计方法对先验假设存在过分依赖的问题,提出了一种非参数的Legendre正交多项式图像混合模型分割方法。首先,设计了一种基于Legendre正交多项式的图像非参数混合模型,并用最小均方差(MISE)估计每一个模型的平滑参数;其次,利用EM算法求解正交多项式系数和模型的混合比。此方法不需要对模型作任何假设,可以有效克服模型失配问题。通过图像的分割实验表明,该方法比其他非参数混合模型分割效果更好。     

A joint introduction to Gaussian Processes and Relevance Vector Machines with connections to Kalman filtering and other kernel smoothers

The expressive power of Bayesian kernel-based methods has led them to become an important tool across many different facets of artificial intelligence, and useful to a plethora of modern application domains, providing both power and interpretability via uncertainty analysis. This article introduces and discusses two methods which straddle the areas of probabilistic Bayesian schemes and kernel methods for regression: Gaussian Processes and Relevance Vector Machines. Our focus is on developing a common framework with which to view these methods, via intermediate methods a probabilistic version of the well-known kernel ridge regression, and drawing connections among them, via dual formulations, and discussion of their application in the context of major tasks: regression, smoothing, interpolation, and filtering. Overall, we provide understanding of the mathematical concepts behind these models, and we summarize and discuss in depth different interpretations and highlight the relationship to other methods, such as linear kernel smoothers, Kalman filtering and Fourier approximations. Throughout, we provide numerous figures to promote understanding, and we make numerous recommendations to practitioners. Benefits and drawbacks of the different techniques are highlighted. To our knowledge, this is the most in-depth study of its kind to date focused on these two methods, and will be relevant to theoretical understanding and practitioners throughout the domains of data-science, signal processing, machine learning, and artificial intelligence in general.     

Robust registration based compensation method based on sparse recovery

The loss of the degree of system freedom and mismatch of prior information based on the registration based compensation (RBC) method will result in a decline in computational performance. In order to solve above problems, a robust method named SRBC is proposed where the sparse recovery method is applied instead of the sub-aperture smoothing operation. First, the SRBC utilizes the sparse recovery to get the super resolution clutter space-time spectrum. Then the transition covariance matrix is calculated. Finally, the clutter covariance matrix is reconstructed by the Capon spectrum. Compared with the traditional RBC method, the SRBC proposed does not depend on sub-aperture smoothing operation and prior information and maintains the performance of clutter range-dependence. In addition, it is also steadier when the sensor error exists. Experimental simulation demonstrates the correctness and feasibility of this method.