大学物理课程网络辅助教学系统的构建与应用

本文介绍了上海交通大学大学物理课程网络辅助教学系统的设计思想、整体结构和主要的功能模块，概述了运用网络辅助教学系统实施“课堂教学＋网络辅助教学”：这种教学模式的应用情况．     

不连续不可逆二维映象的动力学特性

总结两个保守映象不可逆地分段连续链接(称为类耗散系统)以及一个保守映象与一个耗散映象不可逆地分段连续链接(称为半耗散系统)情况下得到的五项共同动力学特征：不连续边界象集构成的随机网成为唯一的混沌轨道；由于某些相点具有两个逆象而导致的相空间塌缩(类耗散)；由于系统的不连续不可逆性质而出现的胖分形禁区网；在具有吸引子共存时占据不连续边界象集随机网和胖分形禁区网区域的点滴状吸引域以及由此导致的吸引子不可预言性；即使在传统强耗散存在的情况下点滴状吸引域仍由类耗散机制主宰.以一个累积-触发电路为例，说明这五项系统动 关键词： 随机网 禁区网 点滴状吸引域     

注浆覆岩离层力学机理及其离层发育分类研究

依据覆岩离层生成的力学机理,结合煤层开采程度的不同,将可注浆层位的离层划分为三类.以岩石的应变为指标,确立了覆岩离层可注浆层位岩梁的断裂步距表达式,理论上证明了划分注浆层位的合理性.根据岩石的全应力应变试验,结合现场实际观测数据,对以应变为指标的岩梁断裂步距表达式做了初步验证.结果表明:可注浆层位的分类和断裂步距表达式的确定对注浆减沉工程具有指导意义.     

关联规则增量式更新的改进算法

关联规则是数据挖掘中的重要研究内容之一，对在事务数据库不变，只对最小支持度和最小可信度进行改变的情况下，针对关联规则的维护问题，设计了一个增量式更新的改进算法AIUA。     

活动标形及其在点啮合齿轮传动误差分析中的应用

活动标形法的论述首推苏步青译，佐佐木重夫著《微分几何学》，还有的著作从外微分形式引入活动标形,本文论证取曲面的正交曲率网为参数网时，曲面论的基本公式就是活动标形的微分形式，并用其分析了点啮合齿轮传动误差．     

试题质量评价研究

教学过程的规范化管理是保证教学质量的重要手段。考试是对教和学的效果的检验，是教学过程管理规范化必须要解决的问题，一直受到教学管理部门的重视，采取了多项措施解决有关问题，其中一个重要的手段是采用试题库。在采用试题库时，必须要有高质量的试题作保证。本探讨基于KDD技术的试题质量评价方法和指标，并通过实例来说明其有效性。     

Improved customer choice predictions using ensemble methods

In this paper various ensemble learning methods from machine learning and statistics are considered and applied to the customer choice modeling problem. The application of ensemble learning usually improves the prediction quality of flexible models like decision trees and thus leads to improved predictions. We give experimental results for two real-life marketing datasets using decision trees, ensemble versions of decision trees and the logistic regression model, which is a standard approach for this problem. The ensemble models are found to improve upon individual decision trees and outperform logistic regression.     

Solitary wave solutions of nonlinear financial markets :data-modeling-concept-practicing

This paper seeks to solve the difficult nonlinear problem in financial markets on the complex system theory and the nonlinear dynamics principle, with the data-model-concept-practice issue-oriented reconstruction of the phase space by the high frequency trade data. In theory, we have achieved the differentiable manifold geometry configuration, discovered the Yang-Mills functional in financial markets, obtained a meaningful conserved quantity through corresponding space-time non-Abel localization gauge symmetry transformation, and derived the financial solitons, which shows that there is a strict symmetry between manifold fiber bundle and guage field in financial markets. In practical applications of financial markets, we have repeatedly carried out experimental tests in a fluctuant evolvement, directly simulating and validating the existence of solitons by researching the price fluctuations (society phenomena) using the same methods and criterion as in natural science and in actual trade to test the stock Guangzhou Proprietary and the futures Fuel Oil in China. The results demonstrate that the financial solitons discovered indicates that there is a kind of new substance and form of energy existing in financial trade markets, which likely indicates a new science paradigm in the economy and society domains beyond physics.      

Reformulation of the support set selection problem in the logical analysis of data

The paper is concerned with the problem of binary classification of data records, given an already classified training set of records. Among the various approaches to the problem, the methodology of the logical analysis of data (LAD) is considered. Such approach is based on discrete mathematics, with special emphasis on Boolean functions. With respect to the standard LAD procedure, enhancements based on probability considerations are presented. In particular, the problem of the selection of the optimal support set is formulated as a weighted set covering problem. Testable statistical hypothesis are used. Accuracy of the modified LAD procedure is compared to that of the standard LAD procedure on datasets of the UCI repository. Encouraging results are obtained and discussed.     

The Method of Virtual Experts in Mathematical Diagnostics

Mathematical Diagnostics (MD) deals with identification problems arising in different practical areas. Some of these problems can be described by mathematical models where it is required to identify points belonging to two or more sets of points. Most of the existing tools provide some identification rule (a classifier) by means of which a given point is assigned (attributed) to one of the given sets. Each classifier can be viewed as a virtual expert. If there exist several classifiers (experts), the problem of evaluation of experts’ conclusions arises. In the paper for the case of supervised classification the method of virtual experts (the VE-method) is described. Based on this method, a generalized VE method is proposed where each of the classifiers can be chosen from a given family of classifiers. As a result, a new optimization problem with a discontinuous functional is stated. Examples illustrating the proposed approach are provided. The work of the second author was supported by the Russian Foundation for Fundamental Studies (RFFI) under Grant No 03-01-00668.