Data fitting with a spline using a real-coded genetic algorithm

To obtain a good approximation for data fitting with a spline, frequently we have to deal with knots as variables. The problem to be solved then becomes a continuous nonlinear and multivariate optimization problem with many local optima. Therefore, it is difficult to obtain the global optimum. In this paper, we propose a method for solving this problem by using a real-coded genetic algorithm. Our method can treat not only data with a smooth underlying function, but also data with an underlying function having discontinuous points and/or cusps. We search for the best model among candidate models by using the Bayes Information Criterion (BIC). With this, we can appropriately determine the number and locations of knots automatically and simultaneously. Five examples of data fitting are given to show the performance of our method.     

Rheology in Processing of in Situ Composites of Polypropylene and Low Melting Poing Metals

The low melting point metallic tin powder or alloy of tin and lead was blended with polypropy-lene. A kind of in situ composite has been prepared. The variations of torque were studied when the composites were mixed in Haake torque rheogeniometer. By way of capillary extrusion, effects upon rheology of the in situ com-posites of the low melting point metals (LMPM) and coupling agent for their different variety and content, were investigated. From flow curves, the results indicate that in situ composites mixed with the LMPM are a kind of pseudoplastic fluid. If the LMPM were melted, the higher the content of the LMPM , the lower apparent viscosity of composites. Meanwhile, when the coupling agent is added into composites, the viscosity of composite will go up first and drop then. This shows that the LMPM have a promoter flow action on the polypropylene.     

基于约束优化的联想记忆模型学习算法

本文提出了一种对称互连神经元网络的学习策略,利用全局约束优化方法确定连接权。优化过程采用了梯度下降技术。这种学习算法可以保证训练样本成为系统的稳定吸引子,并且具有优化意义上的最大吸引域。本文讨论了网络的存储容量,训练样本的渐近稳定性和吸引域大小。计算机实验结果说明了学习算法的优越性。     

TEMIC射频卡的速率自适应读卡算法

提出种基于TEMIC射频卡Manchester编码的速率自适应读卡算法,并对该算法进行了说明和示例。     

多冗余点到点链路下OSPF扩散算法的一种改进方案

文章讨论了OSPF路由协议的扩散算法在多冗余点到点链路情况下存在的缺陷,提出了一种基于扩散接口选举的改进方案。     

Rendering of Surface and Volume Details in Volume Data*

Aiming at the detail rendering in volume data, a new volume illumination model, called Composed Scattering Model (CSM), is presented. In order to enhance different details in volume data, scattering intensity is decomposed into volume scattering intensity and surface scattering intensity with different weight functions. According to the Gauss probability distribution of gray and gradient of data, we propose an accurate method to detect the materials in a voxel, called composed segmentation. In addition, we discuss the principle of constructing these weight functions based on the operators defined in composed segmentation. CSM can generate images containing more details than most popular volume rendering models. This model has been applied to the direct volume rendering of 3D data sets obtained by CT and MRI. The resultant images show not only rich details but also clear boundary surfaces. CSM is demonstrated as an accurate volume rendering model suited for detail enhancement in volume data sets.     

SIMILARITY LAW OF FLUCTUATING PRESSURE WITHIN HYDRAULIC JUMP AREA

Through a series of model tests of five scales for 2-D free hydraulic jump, the da-ta of fluctuating pressure acting on the floor level within the hydraulic jump were obtained. Dur-ing the experiments, Froude number varied from 2.94 to 8.61, and Reynolds number rangedfrom 2×10~4 to 6×10~5. Experiment results indicate that the amplitude scale of fluctuating pres-sure is the length scale of model, i. e. P'=L, which agrees with gravity similarity law: Thefrequency scale of the fluctuating pressure is a unity i. e., f=1, which does not satisfy thegravity similarity law.     

激光微焊接质量的模糊识别技术研究

讨论了激光微焊点焊接质量的模糊识别的概念，数学模型及其应用方法，并介绍了系统的硬件组成。     

Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans

A mobile robot exploring an unknown environment has no absolute frame of reference for its position, other than features it detects through its sensors. Using distinguishable landmarks is one possible approach, but it requires solving the object recognition problem. In particular, when the robot uses two-dimensional laser range scans for localization, it is difficult to accurately detect and localize landmarks in the environment (such as corners and occlusions) from the range scans.In this paper, we develop two new iterative algorithms to register a range scan to a previous scan so as to compute relative robot positions in an unknown environment, that avoid the above problems. The first algorithm is based on matching data points with tangent directions in two scans and minimizing a distance function in order to solve the displacement between the scans. The second algorithm establishes correspondences between points in the two scans and then solves the point-to-point least-squares problem to compute the relative pose of the two scans. Our methods work in curved environments and can handle partial occlusions by rejecting outliers.