基于数值模拟的接水盘冲压成形工艺分析与设计

分析了冰箱接水盘的形状和结构特点 ,设计了零件的冲压成形工艺 ,运用板料成形有限元软件对其中的拉伸和翻边工艺进行数值模拟 ,得到成形过程中工件各部位的厚度分布和成形极限图 ,分析了在不同工艺条件下可能产生的缺陷及其原因 ,并确定其初始毛坯和中间毛坯的合理形状 ,有效地保证了成形工艺的可靠性     

椭圆弧数控代码的自动生成方法

提出了一种基于计算机辅助制造的数控加工代码自动生成的方法，着重阐述了该方法针对椭圆弧的数控代码自动生成过程。该方法通过分析加工图形的dxf文件，提取图形的坐标信息，再经过数据拟和，自动生成该图形的数控代码。该方法由VC＋＋6．0实现相应的功能，经实际运行表明，能满足加工精度要求。该方法摆脱了传统数控机床从加工图形到形成数控加工代码时间长且精度不高的缺点，极大她提高了工作效率。     

基于逻辑回归模型的木片和树皮的图像识别

为了提高木片生物能的利用率,需要除去木片中的树皮杂质。识别木片和树皮的图像对于实现二者分离具有重要作用。提出一种基于逻辑回归模型来对木片和树皮进行图像识别的算法。算法流程包括计算各种可以描述木片和树皮纹理的参数,利用SAS统计分析出最佳参数,将最佳参数计算所得的样本数据用于SAS LOGISTIC过程得到预测方程,再将预测方程用于逻辑回归模型来实现木片和树皮的图像识别。通过实验可知,最佳参数为灰度方差,该图像识别算法的正确率能达到97.4%。     

Recover Fault Services via Complex Service-to-Node Mappings in Wireless Sensor Networks

With the motivation of seamlessly extending wireless sensor networks to the external environment, service-oriented architecture comes up as a promising solution. However, as sensor nodes are failure prone, this consequently renders the whole wireless sensor network to seriously faulty. When a particular node is faulty, the service on it should be migrated into those substitute sensor nodes that are in a normal status. Currently, two kinds of approaches exist to identify the substitute sensor nodes: the most common approach is to prepare redundancy nodes, though the involved tasks such as maintaining redundancy nodes, i.e., relocating the new node, lead to an extra burden on the wireless sensor networks. More recently, other approaches without using redundancy nodes are emerging, and they merely select the substitute nodes in a sensor node’s perspective i.e., migrating the service of faulty node to it’s nearest sensor node, though usually neglecting the requirements of the application level. Even a few work consider the need of the application level, they perform at packets granularity and don’t fit well at service granularity. In this paper, we aim to remove these limitations in the wireless sensor network with the service-oriented architecture. Instead of deploying redundancy nodes, the proposed mechanism replaces the faulty sensor node with consideration of the similarity on the application level, as well as on the sensor level. On the application level, we apply the Bloom Filter for its high efficiency and low space costs. While on the sensor level, we design an objective solution via the coefficient of a variation as an evaluation for choosing the substitute on the sensor level.