提高前馈神经网络推广能力的若干实际方法

提高前馈神经网络的推广能力是深受关注的问题，本文根据我们最近提出了一个网络有效推广的准则，从提高网络特征提取能力，分类能力和修改神经元激活函数等几方面给出了若干实际方案，我们在任意手写数字识别问题上的实验结果证实了这些方法的有效性。     

有机化合物中碳原子化学环境的分类

进行碳－１３ＮＭＲ波谱模拟的一种常用方法为线性回归，其数学模型则由已知结构所测得的化学位来建立。显然，每一数学模型仅可用于某种相似的化学环境。因此，为了进行化学环境的分类，本文提出权重层次位图法和分子连接性指数法两种方法以进行一些参数的计算，同时采用多元分析手段，如主成分分析和聚类分析，以进行多维空间数据点的显示，所得结果比较满意，从而可为碳－１３波谱模拟中回归方程的选择提供理论依据。     

Bezier曲线树

本文提出Ｂｅｚｉｅｒ样条曲线树结构用以高效地实现对曲边几何体的各种几何运算。对任意一条Ｂｅｚｉｅｒ样条曲线，用ｄｅＣａｓｔｅｌｊａｕ算法进行分割后，将分割结果用一平衡二叉树来存储。此后，对于该曲线所作的各种几何运算可以一种逼近等级的方式来进行。先以粗糙的逼近来进行计算，必要时增加精密度，从而提高各种运算的效率。     

可拓检测的方案生成

以可拓学理论为基础，论述了可拓检测的有关定义和概念，提出了可拓检测的方案生成方法及过程，同时探讨了检测方法的优化和评价方法。     

DNA序列比对分析中的统计特征方法

提出了一种新的基于统计的序列特征定义,并以此对进化树进行构建.同时对这一方法进行了分类测试和进化树构建测试,发现所得测试结果与同样是用进化树进行构建的基于两序列比对的传统算法比较,其性能和适用性都有明显改善.     

HACCP体系用于给水处理厂浊度控制的研究

介绍了危害分析和关键控制点（HACCP）体系的基本原理及其在城市给水系统中的应用进展，并以我国南方某城市的给水处理厂为例，对HACCP体系用于水厂浊度控制开展了探索性研究。应用故障树分析技术识别了水厂中可能导致浊度超标的潜在危害，应用决策树分析技术确定了水厂浊度控制的关键控制点，建立了关键控制点和质量控制点的关键限值、监控系统以及纠偏措施，并分析了对验证程序以及文件和记录保存系统的要求。     

海底管道系统失效故障树的建立及定性分析

受自然环境、管道腐蚀、第三方破坏及管理不善等因素的影响,海底管道系统在铺设及运行过程中常常会失效。文中以海底管道穿孔、凝管、断裂和设备故障等主要失效形式为顶事件建立了海底管道系统失效故障树,通过系统的定性分析得到了海底管道系统失效故障树的37个一阶最小割集,进而确定了影响海底管道系统失效的4项主要因素,并针对这些主要因素提出了相应的解决问题的措施。     

Solving the small sample size problem in face recognition using generalized discriminant analysis

The goal of face recognition is to distinguish persons via their facial images. Each person's images form a cluster, and a new image is recognized by assigning it to the correct cluster. Since the images are very high-dimensional, it is necessary to reduce their dimension. Linear discriminant analysis (LDA) has been shown to be effective at dimension reduction while preserving the cluster structure of the data. It is classically defined as an optimization problem involving covariance matrices that represent the scatter within and between clusters. The requirement that one of these matrices be nonsingular restricts its application to datasets in which the dimension of the data does not exceed the sample size. For face recognition, however, the dimension typically exceeds the number of images in the database, resulting in what is referred to as the small sample size problem. Recently, the applicability of LDA has been extended by using the generalized singular value decomposition (GSVD) to circumvent the nonsingularity requirement, thus making LDA directly applicable to face recognition data. Our experiments confirm that LDA/GSVD solves the small sample size problem very effectively as compared with other current methods.     

Education-driven research in CAD

We argue for a new research category, named education-driven research (EDR), which fills the gap between traditional field-specific research that is not concerned with educational objectives and research in education that focuses on fundamental teaching and learning principles and possibly on their customization to broad areas (such as mathematics or physics), but not to specific disciplines (such as CAD). The objective of EDR is to simplify the formulation of the underlying theoretical foundations and of specific tools and solutions in a specialized domain, so as to make them easy to understand and internalize. As such, EDR is a difficult and genuine research activity, which requires a deep understanding of the specific field and can rarely be carried out by generalists with primary expertise in broad education principles. We illustrate the concept of EDR with three examples in CAD: (1) the Split and Tweak subdivisions of a polygon and its use for generating curves, surfaces, and animations; (2) the construction of a topological partition of a plane induced by an arbitrary arrangement of edges; and (3) a romantic definition of the minimal and Hausdorff distances. These examples demonstrate the value of using analogies, of introducing evocative terminology, and of synthesizing the simplest fundamental building blocks. The intuitive understanding provided by EDR enables the students (and even the instructor) to better appreciate the limitations of a particular solution and to explore alternatives. In particular, in these examples, EDR has allowed the author to: (1) reduce the cost of evaluating a cubic B-spline curve; (2) develop a new subdivision curve that is better approximated by its control polygon than either a cubic B-spline or an interpolating 4-point subdivision curve; (3) discover how a circuit inclusion tree may be used for identifying the faces in an arrangement; and (4) rectify a common misconception about the computation of the Hausdorff error between triangle meshes. We invite the scientific community to encourage the development of EDR by publishing its results as genuine research contributions in peer-reviewed professional journals.     

Stream Classification System Based on Susceptibility to Algal Growth in Response to Nutrients

We developed a stream classification system that is based on stream’s susceptibility to algal growth using a two-step approach. The model portrays algal biomass as a result of stream’s response to nutrient concentrations and the response is governed by various stream factors. In the first step, a nutrient-chlorophyll a relationship was developed to characterize nutrient’s effects on algal biomass. Residuals of the relationship were attributed to stream’s susceptibility to algal growth in response to nutrients and referred to as “observed” susceptibility. In the second step, conditions of other contributing factors were used to explain the variation in the residuals and the developed relationship was used to generate “predicted” susceptibility. Existing data compiled from various monitoring projects of Illinois streams and rivers were used to illustrate the approach. Streams were classified into three (high, medium, and low) categories based on their observed and predicted susceptibility values, respectively. With the available data, the model showed a 40-50% success rate for classifying the streams based on three observed and predicted susceptibility categories. Model entropy also was calculated for selecting the best model. The results show the important role of both nutrients and other contributing factors in explaining the variation of algal biomass. The study also suggests ways to fine tune the model and improve its accuracy, which would make the presented model a more viable tool for stream classification for establishing nutrient criteria to prevent surface streams from eutrophication.