Processing Simulation of Virtual Manufacture in Laser Surface Treatmenthang

Processing simulation is at the bottom of the coral technology of VM and is also difficult due to the complexity of mechanism and diversity of parameters. Previously much research has been mainly carried out on the geometrical simulation or physical simulation respectively. The aim of this paper is to study the processing simulation in laser surface treatment based on the mechanism, put forward the architecture of the whole processing simulation and give the models of the processing. As a result the data structure layers in the whole simulation is presented.

蛙虹彩病毒cop基因的克隆表达及亚细胞定位

从蛙虹彩病毒(Rana gryliovirus,RGV)基因组中克隆了含凋亡相关结构域的新基因-cop(Caspaserecruit ment domain only protein,COP)基因的全部编码区,成功构建了重组表达载体,进行了原核表达,并在鲤鱼上皮瘤细胞(Epithelioma papulosumcyprini,EPC)中进行了亚细胞定位.序列分析表明,RGVcop基因全长288 bp,编码一个长为95 aa,分子量为10.4×103的推定蛋白.二级结构预测表明其含有5个α螺旋.同源性比对分

Room temperature photoluminescence of tensile-strained Ge/Si0.13Ge0.87 quantum wells grown on silicon-based germanium virtual substrate

We report a room temperature study of the direct band gap photoluminescence of tensile-strained Ge/Si0.13Ge0.87 multiple quantum wells grown on Si-based germanium virtual substrates by ultrahigh vacuum chemical vapor deposition. Blueshifts of the luminescence peak energy from the Ge quantum wells in comparison with the Ge virtual substrate are in good agreement with the theoretical prediction when we attribute the luminescence from the quantum well to the c Gamma 1-HH1 direct band transition. The reduction in direct band gap in the tensile strained Ge epilayer and the quantum confinement effect in the Ge/Si0.13Ge0.87 quantum wells are directly demonstrated by room temperature photoluminescence.

基于CoP建模的协作过滤推荐方法

传统的协作过滤推荐方法主要基于个人兴趣特征来实现推荐。在组织内部协作场景下,为实现知识共享与重用,推荐系统不仅要考虑用户兴趣,还应考虑用户和用户组的任务。传统的协作过滤推荐方法已不能满足要求。CoP是组织内部人员管理的主要形式,它的特征是其成员任务的反映。基于已有的协作过滤推荐研究与D-S理论,提出了一种CoP特征构建算法,并以此为基础研究了面向CoP的协作过滤推荐。

co-creativepen toolkit: a pen-based 3d toolkit for children cooperatly designing virtual environment

Co-CreativePen Toolkit is a pen-based 3D toolkit for children cooperatly designing virtual environment. This toolkit is used to construct different applications involved with distributedpen-based 3D interaction. In this toolkit,sketch method is encapsulated as kinds of interaction techniques. Children can use pen to construct 3D and IBR objects, to navigate in the virtual world, to select and manipulate virtual objects, and to communicate with other children. Children can use pen to select other children in the virtual world, and use pen to write message to children selected The distributed architecture of Co-CreativePen Toolkit is based on the CORBA. A common scene graph is managed in the server with several copies of this graph are managed in every client.Every changes of the scene graph in client will cause the change in the server and other client.

construction of feature-matching perception in virtual assembly

An important characteristic of virtual assembly is interaction. Traditional di-rect manipulation in virtual assembly relies on dynamic collision detection, which is very time-consuming and even impossible in desktop virtual assembly environment. Feature-matching isa critical process in harmonious virtual assembly, and is the premise of assembly constraint sens-ing. This paper puts forward an active object-based feature-matching perception mechanism and afeature-matching interactive computing process, both of which make the direct manipulation in vir-tual assembly break away from collision detection. They also help to enhance virtual environmentunderstandability of user intention and promote interaction performance. Experimental resultsshow that this perception mechanism can ensure that users achieve real-time direct manipulationin desktop virtual environment.

Semisupervised Dimensionality Reduction and Classification Through Virtual Label Regression

Semisupervised dimensionality reduction has been attracting much attention as it not only utilizes both labeled and unlabeled data simultaneously, but also works well in the situation of out-of-sample. This paper proposes an effective approach of semisupervised dimensionality reduction through label propagation and label regression. Different from previous efforts, the new approach propagates the label information from labeled to unlabeled data with a well-designed mechanism of random walks, in which outliers are effectively detected and the obtained virtual labels of unlabeled data can be well encoded in a weighted regression model. These virtual labels are thereafter regressed with a linear model to calculate the projection matrix for dimensionality reduction. By this means, when the manifold or the clustering assumption of data is satisfied, the labels of labeled data can be correctly propagated to the unlabeled data; and thus, the proposed approach utilizes the labeled and the unlabeled data more effectively than previous work. Experimental results are carried out upon several databases, and the advantage of the new approach is well demonstrated.