Determining the Castability of Simple Polyhedra

A polyhedron P is castable if its boundary can be partitioned by a plane into two polyhedral terrains. Castable polyhedra can be manufactured easily using two cast parts, where each cast part can be removed from the object without breaking the cast part or the object. If we assume that the cast parts are each removed by a single translation, it is shown that for a simple polyhedron with n vertices, castability can be decided in time and linear space using a simple algorithm. A more complicated algorithm solves the problem in time and space, for any fixed ε > 0. In the case where the cast parts are to be removed in opposite directions, a simple O(n ² )-time algorithm is presented. Finally, if the object is a convex polyhedron and the cast parts are to be removed in opposite directions, a simple algorithm is presented. Received June 1, 1994; revised May 25, 1995.     

利用分形几何确定多孔介质的孔尺寸分布

提出了利用分形几何确定多孔介质孔尺寸分布的方法。考虑多孔介质中孔间的屏蔽，采用此方法得到的孔尺寸分布范围比采用平行孔模型宽，分别采用本方法和平行孔模型测定γ－Al2O3催化剂的孔尺寸分布，构造简单立方体三维网络模型模拟压汞过程，并将模拟曲线与γ－Al2O3压汞实验曲线比较，结果表明，采用本方法所得孔尺雨分布优于平行孔模型所得孔尺寸分布。     

GJK-3型高准确度长度测量仪

本文分析了使用计数的原理来测量长度、转速的仪器，其误差产生的原因，并结合G3K-3型高准确度长度测量仪，着重介绍了消除误差的方法．     

碳/环氧复合材料的冲击拉伸

该文研究了T300碳纤维单向增强的环氧复合材料,在应变率从10~(-3)/s到10~3/s范围内的冲击拉伸行为.通过对实验数据进行拟合,得出该范围内材料对应变率具有弱的敏感性,表现在破坏强度及破坏应变随应变率增加不显著变化,平均模量几乎不受应变率的影响.分析了试件的几何尺寸效应,讨论了应力波作用对破坏形态的影响以及实验中观察到的拔出现象.从应变率在10~2～10~3/s附近材料行为某些非确定性,指出在更宽范围内了解其性能的必要性.     

基于自组织神经网络的实时统计特征抽取方法

应用人工神经网络讨论统计了特征抽取的原理，提出了相应的抽取方法。该方法具有实时性，容错性，高数据压缩率和良好的特征分辨率。通过计算机仿真和桥梁索力状态的实例计算分析证实了该技术具有广泛的应用价值。     

KNOWLEDGE-BASED FEATURE DISCOVERY FOR EVALUATION FUNCTIONS

Since Samuel's work on checkers over thirty years ago, much effort has been devoted to learning evaluation functions. However, all such methods are sensitive to the feature set chosen to represent the examples. If the features do not capture aspects of the examples significant for problem solving, the learned evaluation function may be inaccurate or inconsistent. Typically, good feature sets are carefully handcrafted and a great deal of time and effort goes into refining and tuning them. This paper presents an automatic knowledge-based method for generating features for evaluation functions. The feature set is developed iteratively: features are generated, then evaluated, and this information is used to develop new features in turn. Both the contribution of a feature and its computational expense are considered in determining whether and how to develop it further.
This method has been applied to two problem-solving domains: the Othello board game and the domain of telecommunications network management. Empirical results show that the method is able to generate many known features and several novel features and to improve concept accuracy in both domains.     

On-Line Shape recognition with incremental training using binary synaptic weights algorithm

Recognition of hand drawn shapes is beneficial in drawing packages and automated sketch entry in handheld computers. In this paper, we propose a new approach to on-line geometric shape recognition with incremental training function, which utilizes a heuristic function to reduce noise and a neural network for classification and on-line training. Instead of recognizing segments of a drawing and then performing syntactical analysis to match with a predefined shape, which is weak in terms of generalization and dealing with noise, we examine the shape as a whole. The main concept of the recognition method is derived from the fact that internal angles are very important in the perceived shape of outlines. Our application's aim is to recognize elliptic, rectangular, and triangular shapes in a way similar to human cognition of these shapes. Human beings recognize such basic shapes regardless of the variations in size, noise on the shape border, rotation and in the case of triangles, regardless of the type of the triangle. The key concept is that the neural network learns the relationships between the internal angles of a shape and its classification, therefore only a few training samples which represent the class of the shape are sufficient. Fast meremental training, which is performed on-line, is accomplished by the use of the Binary Synaptic Weights algorithm, a one pass, feedforward neural network training algorithm. Incremental training offers the advantage of adjusting the recognition capability of the system to the user's drawings. the results are very successful, such that the neural network correctly classified shapes that did not have any resemblance to the shapes in the initial training set.     

基于类间分散度和类内集中度的文本特征选择。

特征选择是文本分类的关键步骤之一,所选特征子集的优劣直接影响文本分类的结果。论文首先定义了两种特征分类能力：一种是特征对类间文档的分散程度,该分散度越大越好;另一种是特征对类内文档的聚集程度,该集中度越大越好。然后把这两种特征影响度有机地结合起来设计了一个新的特征选择方法,该方法能够对所选特征进行综合考虑,从而使获得的特征集具有较好的代表性。仿真实验表明所提特征选择方法在一定程度上能够提高文本分类性能。     

基于神经网络的特征分析在烟叶分级中的应用。

通过数字图像技术可以自动提取烟叶的颜色和形状特征,而不同特征值对分部位、颜色和等级的决定程度不同,文章结合广义回归神经网络定性分析各种不同特征对部位或颜色分级的影响,分别得到对分部位、颜色和等级有决定影响的特征量,通过建立不同神经网络得到最终等级。     

基于整体特征的快速英文字母识别方法

文章提出了一种基于整体特征的小写英文字母识别方法.首先根据字母图像的赋值背景提取其整体特征,然后构建7个模板进行模板匹配.该方法不需要对图像作复杂的细化处理、轮廓提取等,减少了可能带来的误识和拒识,也不需要现有神经网络方法的长期训练,因而简单快速.同时,不同字体的字母图像其整体特性基本相同,因此识别率较高.