复杂开采条件矿体三强回采作业技术应用

本文针对金翅岭金矿矿体开采条件极其复杂的情况，在采矿方法试验中，率先在岩金矿山采用了三强作业技术，既保证了安全回采，又取是了较好的技术经济指标。     

露天矿场有规则安全小平台开采方法

文中提出了在高阶段不分层开采的露天矿场采用“有规则安全小平台开采方法”。开采坡度能够得到控制，有效地控制了采石场经常发生的塌方事故，能提高产量，降低生产成本，安全性好，且技术简单，易于推广。     

模糊Petri网及知识表示

在建造专家系统中虽然有很多种知识表示方法，但都有不尽人意的地方，本文试图用一种新的方法－Ｐｅｔｒｉ网模型来表示知识。本文给出了Ｆｕｚｚｚｙ Ｐｅｔｒｉ网和广义Ｆｕｚｚｙ Ｐｅｔｒｉ网两种模型，并给出了相应的推理算法，一旦专家知识用Ｐｅｔｒｉ网表示后，根据Ｐｅｔｒｉ网固有的特性，我们就能处理专家系统中并行推理、无回溯推理，反向推理等问题。     

A Search for Hidden Relationships: Data Mining with Genetic Algorithms

This paper presents an algorithm that permits the search for dependencies among sets of data (univariate or multivariate time-series, or cross-sectional observations). The procedure is modeled after genetic theories and Darwinian concepts, such as natural selection and survival of the fittest. It permits the discovery of equations of the data-generating process in symbolic form. The genetic algorithm that is described here uses parts of equations as building blocks to breed ever better formulas. Apart from furnishing a deeper understanding of the dynamics of a process, the method also permits global predictions and forecasts. The algorithm is successfully tested with artificial and with economic time-series and also with cross-sectional data on the performance and salaries of NBA players during the 94–95 season.     

松软矿体有底柱高分段崩落法的改进方案

锦屏磷矿对其东山采区松软矿体所使用的分段崩落法做了集中快速采准施工、增加分段高度、实行水平或上向扇形深孔连续崩矿等改进,简化了采场结构,矿石损失率由原来的63％下降到改进后的20.34％,贫化率由23.92％下降到15.10％。     

基于Patel-Teja状态方程的统一粘度模型(Ⅱ)应用于油气藏流体粘度的预测

基于(I)报根据pVT和Tμp图形的相似性和Patel-Teja状态方程建立的预测纯流体气、液相粘度的统一模型,通过引入常规的状态方程参数的混合规则,将其应用于二元轻烃混合物共计1894个数据点及模拟天然气高压粘度的预测,平均相对误差分别为13.78%和16.75%;应用于油藏原油和天然气高压粘度的预测,结果优于现有的有关油气藏流体粘度模型.     

基于Apriori算法改进的关联规则提取算法

通过对Apriori算法的基本思想和性能的研究分析，认为Apriori算法存在一些不足。并且根据这些不足提出了相应的改进算法对Apriori算法进行优化，从而得到一种改进的Apriori算法，与原算法相比运算效率大大提高。     

Computational complexity of queries based on itemsets

We investigate determining the exact bounds of the frequencies of conjunctions based on frequent sets. Our scenario is an important special case of some general probabilistic logic problems that are known to be intractable. We show that despite the limitations our problems are also intractable, namely, we show that checking whether the maximal consistent frequency of a query is larger than a given threshold is NP-complete and that evaluating the Maximum Entropy estimate of a query is PP-hard. We also prove that checking consistency is NP-complete.     

Non-redundant data clustering

Data clustering is a popular approach for automatically finding classes, concepts, or groups of patterns. In practice, this discovery process should avoid redundancies with existing knowledge about class structures or groupings, and reveal novel, previously unknown aspects of the data. In order to deal with this problem, we present an extension of the information bottleneck framework, called coordinated conditional information bottleneck, which takes negative relevance information into account by maximizing a conditional mutual information score subject to constraints. Algorithmically, one can apply an alternating optimization scheme that can be used in conjunction with different types of numeric and non-numeric attributes. We discuss extensions of the technique to the tasks of semi-supervised classification and enumeration of successive non-redundant clusterings. We present experimental results for applications in text mining and computer vision.