地应力对岩心渗透率伤害实验及机理分析

生产实际情况表明，有许多油气田对应力较为敏感。随着生产的进行，储集层压力下降，有效应力不断变化，引起油藏产能逐渐降低。为研究这一规律和变化规律，阐述了应用岩民测试有效应力对渗透率影响的实验原理，在对实验数据进行统计分析的基础上，拟合出有效应力与渗透经的函数关系（二次多项式）。应用毛细管渗流概念以有弹性厚壁筒厚理，研究了随着毛细管管壁内外压差变化时，毛细管内半径变化有其对毛细管渗流能力的影响，所得结论与实验结果一致。在实际生产中，应控制油藏压力快速大幅度下降，避免有效应力变化引起油藏渗流能力下降和减产。     

David W. Johnson: Award for Distinguished Contributions of Applications of Psychology to Education and Training.

Provides the biography of David W. Johnson and announces that he has won the Award for Distinguished Contributions of Applications of Psychology to Education and Training for programmatic research, comprehensive research reviews, and revision of theory over four decades of persistence that have provided the knowledge foundation for cooperative learning, constructive controversy, conflict resolution, and peer mediation training. A selected bibliography is also provided. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction

This paper introduces and evaluates a new class of knowledge model, the recursive Bayesian multinet (RBMN), which encodes the joint probability distribution of a given database. RBMNs extend Bayesian networks (BNs) as well as partitional clustering systems. Briefly, a RBMN is a decision tree with component BNs at the leaves. A RBMN is learnt using a greedy, heuristic approach akin to that used by many supervised decision tree learners, but where BNs are learnt at leaves using constructive induction. A key idea is to treat expected data as real data. This allows us to complete the database and to take advantage of a closed form for the marginal likelihood of the expected complete data that factorizes into separate marginal likelihoods for each family (a node and its parents). Our approach is evaluated on synthetic and real-world databases.     

构造性神经网络在测井岩性识别中的应用

测井岩性识别是石油勘探中十分重要的基础工作,准确的岩性识别结果可以为勘探和开发提供可靠的依据。人工神经网络方法可对复杂的高维数据进行非线性映射,在模式识别领域得到越来越广泛的应用。针对传统的BP神经网络算法存在收敛速度慢、隐层数以及隐层节点数难以确定等缺点,研究了一种构造性神经网络学习算法---级联算法（Cascade Correlation Algorithm,简称CC算法）及其在石油工程中的应用。采用该算法对甘肃镇原-泾川地区进行岩性识别研究,通过与BP神经网络的识别结果进行比较,体现出构造性神经网络的优越性。     

Constructive Sets in Computable Sets

The original interpretation of the constructive set theory CZF in Martin-Loef‘s type theory uses the‘extensional identity types’.It is generally believed that these‘types’do not belong to type theory.In this paper it will be shown that the interpretation goes through without identity types.This paper will also show that the interpretation can be given in an intensional type theory.This reflects the computational nature of the interpretation.This computational aspect is reinforced by an ω-Set moel of CZF.     

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.     

影响建陶产品生命周期因素的分析与探讨

本文从建陶产品生命周期缩短的现象入手，应用产品生命周期理论和相关的数学模型，对建陶产品生命周期的各个影响因素进行了分析和探讨。     

景观设计教学中空间逻辑分析与艺术情感表达的培养

随着社会的发展,人居环境日新月异,景观设计专业顺理成章的成为各大院校的热门专业。但课程设置体系中存在着诸多问题。未能做到因材施教。本文针对艺术院校景观专业的现存问题,提出课程教学应着手于小尺度空间。以工作模型作为主要研究载体,渐进的培养掌生综合分析问题的能力,提高学生的空间感知,运用构成艺术语言转译空间精神,营造个性的艺术空间。     

虚拟数控车床加工技术的研究

论文提出了数控车床仿真系统的总体方案，深入研究了虚拟制造的关键技术及具体实现方法。在OpenGL与VC＋＋的平台上，基于NC代码直接驱动仿真加工过程的虚拟现实技术，利用3ds Max对静态物体进行几何建模，利用重画技术和粒子系统方法对动态物体进行行为建模和物理建模，然后将几何建模、行为建模和物理建模结合起来模拟整个数控加工过程。     

基于构造性神经网络的时间序列混合预测模型 *

针对传统时间序列预测模型不适应非线性预测而适应非线性预测的 BP算法存在收敛速度慢 ,且容易陷入局部极小等问题 ,提出一种基于构造性神经网络的时间序列混合预测模型。采用构造性神经网络模型 (覆盖算法 )得出的类别值对统计时间序列模型的预测值进行修正 ,建立一种同时考虑时间序列自身周期变化和外生变量因子对时间序列未来变化趋势影响的混合预测模型 ,涵盖了实际问题的线性和非线性两方面 ,提高了预测精度。将该模型应用到粮食产量的预测中 ,取得了较好的预测效果。