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.     

一个基于相邻像素兼容度的模糊加权平均滤波器

在文献[1]的基础上，提出几个改进的模糊加权平均滤波器。本文的方案不仅同时考虑了相邻像素对中心像素的兼容性以及中心像素对相邻像素的兼容性，而且还考虑到了权系数对局部环境的适应性。实验结果表明，该方法相比文献[1]中的模糊加权平均滤波器以及标准的均值滤波器、中值滤波器在噪声消除与边缘保留方面具有更好的性能。     

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.     

Locally Weighted Learning

This paper surveys locally weighted learning, a form of lazy learning and memory-based learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning fit parameters, interference between old and new data, implementing locally weighted learning efficiently, and applications of locally weighted learning. A companion paper surveys how locally weighted learning can be used in robot learning and control.     

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.     

基于曲率的透视不变性分割

曲线的透视不变性分割在基于二次曲线的三维重建中非常重要。首先给出了连续曲线零曲率点和角点的提取法，在数字图像中曲经是不连续的，曲率的变化受噪声的干扰很大。在这种情况下为了检测出零曲率点和角点，对曲线采用了高斯滤波然后再提到零曲率点和角点。实验证明这种方法效果优于直接求曲率法。     

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。     

A Modified Nonparametric Prewhitened Covariance Estimator

Abstract. This paper proposes a fully modified version of the spectral matrix estimator (and the long‐run variance estimator as a special case) proposed originally by Xiao and Linton [Journal of Time Series Analysis (2002) Vol. 23, pp. 215–250], and derives its asymptotic results. A striking feature of the modified spectral matrix estimator is to achieve the convergence rate of O(T ^?8/9) in the mean squared error (MSE), which is usually achieved under the fourth‐order spectral window. However, this estimator does not sacrifice the positive definiteness of the resulting estimate for the rate improvement; it is Hermitian and positive definite in finite samples by construction. The faster convergence rate is established by a multiplicative bias correction of the crude spectral estimator under the second‐order spectral window. The approximations to some sensible definitions of the MSE of the estimator and the bandwidths that minimize the asymptotic MSEs are also derived. Monte Carlo results indicate that for a wide variety of processes the modified spectral matrix estimator reduces the bias without inflating the variance and thus improves the MSE, compared with the crude, bias‐uncorrected estimator.     

Detecting similarities in virtual machine behavior for cloud monitoring using smoothed histograms

The growing size and complexity of cloud systems determine scalability issues for resource monitoring and management. While most existing solutions consider each Virtual Machine (VM) as a black box with independent characteristics, we embrace a new perspective where VMs with similar behaviors in terms of resource usage are clustered together. We argue that this new approach has the potential to address scalability issues in cloud monitoring and management. In this paper, we propose a technique to cluster VMs starting from the usage of multiple resources, assuming no knowledge of the services executed on them. This innovative technique models VMs behavior exploiting the probability histogram of their resources usage, and performs smoothing-based noise reduction and selection of the most relevant information to consider for the clustering process. Through extensive evaluation, we show that our proposal achieves high and stable performance in terms of automatic VM clustering, and can reduce the monitoring requirements of cloud systems.