坚硬厚煤层综放工作面设备配套技术研究

阐述了坚硬厚煤层综放开采关键设备的选型、设备间的工作关系等。     

Centred contact density functions for the statistical analysis of random sets A stereological study on benign and malignant glandular tissue using image analysis

Real structures investigated in the material and biological sciences, such as minerals or tissues, can often be reduced to two phases. In a stochastic approach, the components of such binary structures may be considered as the union of grains — random sets implanted with their centres at random points — and their complementary space, which is called the pore space. The simplest stochastic germ-grain model is the Boolean model of random sets, which we use here instrumentally as a null model (reference model) for comparison with our biological material. After a brief review of basic properties of the Boolean model and related statistical methods, we introduce centred contact density functions as a new approach. Empirical contact density functions are estimated from the empirical contact distribution functions with an image analyser by dilation of the grain phase. Theoretical contact density functions are then predicted from a set of image parameters, under the assumption that the Boolean model holds. A centred contact density function is the difference between the estimated and the predicted contact density function. Apart from a random error term, centred contact density functions amount to zero irrespective of the area fraction of the grain phase, when the germ-grain model is Boolean. As a section of a spatial Boolean model is a planar Boolean model, the method is also applicable in stereological studies where digitized images are obtained from sections of a three-dimensional structure. Centred contact density functions were determined for mastopathic tissue as compared to mammary cancer, and for tumour-free prostatic tissue as compared to prostatic cancer. For each category of specimens, twenty cases with 10 images each were analysed. Benign and malignant glandular tissue of the aforementioned types deviates significantly from the Boolean model. Centred contact density functions show that malignant transformation is connected with profound geometric remodelling of the pore space.     

西安紫薇城市花园

西安紫薇城市花园是建设部批准的国家康居示范工程，其小区总体规划、建筑单体设计、成套技术等各方面均达到优秀标准，成为国内首家进行建设部最终的综合考核、验收项目。     

一种基于凸集映射的高分辨率图像恢复算法

介绍了一种基于凸集映射(POCS)算法的高分辨率图像恢复方法，通过仿真对比了其与双线性插值方法恢复高分辨率图像的差异，仿真结果表明，该方法有助于改善高分辨率图像的恢复质量。     

一种基于粗糙集理论的双向选择属性约简算法

文章首先对粗糙集的基本理论进行了简要介绍,然后重点讨论粗糙集理论中的属性约简问题,在总结分析现有属性约简算法的基础上,提出了一种双向选择属性约简算法--BSARA,并用一个例子与其它算法进行了对比分析,结果表明该算法是可行和有效的.     

Applying rough sets to market timing decisions

A lot of research has been done to predict economic development. The problem studied here is about the stock prediction for use of investors. More specifically, the stock market's movements will be analyzed and predicted. We wish to retrieve knowledge that could guide investors on when to buy and sell.Through a detailed case study on trading S&P 500 index, rough sets is shown to be an applicable and effective tool to achieve this goal. Some problems concerning time series transformation, indicator selection, trading system building in real implementation are also discussed.     

一个改进的基于区间值模糊集的双向模糊推理算法

本文引入有序权聚类算子(OWA)到区间值模糊集合的相似度度量中,提出了一种改进的双向模糊推理 算法。为采用此算法,文中给出了区间模糊集的加权匹配方向函数,最后通过一个实例说明算法如何灵活地 体现决策者的决策倾向。     

全自位辊系矫正机辊系载荷分析

对某厂 1 7辊钢板矫正机改造前、后辊系载荷及轴承使用寿命进行了分析与比较。理论分析和使用效果均表明 :全自位辊系矫正机可有效提高辊系轴承使用寿命和钢板矫正质量。     

Random and fuzzy sets in coarse data analysis

The theoretical aspects of statistical inference with imprecise data, with focus on random sets, are considered. On the setting of coarse data analysis imprecision and randomness in observed data are exhibited, and the relationship between probability and other types of uncertainty, such as belief functions and possibility measures, is analyzed. Coarsening schemes are viewed as models for perception-based information gathering processes in which random fuzzy sets appear naturally. As an implication, fuzzy statistics is statistics with fuzzy data. That is, fuzzy sets are a new type of data and as such, complementary to statistical analysis in the sense that they enlarge the domain of applications of statistical science.