对地下水与地表水联合调度若干问题的探讨

本文对地下水与地表水联合调度过程中, 联合调度系统类型划分、管理模型中目标函数的选择以及复杂系统的优化等问题进行了探讨。提出了新的联合调度系统分类方案,指出对于复杂的地下水－地表水联合调度系统, 其优化方案的确定, 不仅需要利用多种优化方法求解, 而且还必须对求得的优化解进行模拟验证。最后给出了宁波剡江流域地下水与地表水联合调度的实例     

基于关系矩阵和模糊集合的斜坡稳定性综合评价

以关系矩阵方法为基础确定表征各影响因素在斜坡稳定系统中地位的因素活动性指数 ,并以模糊集合理论中的隶属度为量化手段确定反映具体斜坡影响因素特征的单因素分级指数 ,最后综合二指数求得体现斜坡稳定程度的稳定性系数。此方法在兰州市斜坡稳定性评价中的应用表明 :基于关系矩阵和模糊集合的斜坡稳定性综合评价是斜坡稳定性评价的有效方法 ,在总结地区性评价经验的基础上 ,可望作为一种普遍性的适宜于规划阶段的低成本斜坡稳定性评价方法加以推广     

A new texture and shape based technique for improving meningioma classification

Over the past decade, computer‐aided diagnosis is rapidly growing due to the availability of patient data, sophisticated image acquisition tools and advancement in image processing and machine learning algorithms. Meningiomas are the tumors of brain and spinal cord. They account for 20% of all the brain tumors. Meningioma subtype classification involves the classification of benign meningioma into four major subtypes: meningothelial, fibroblastic, transitional, and psammomatous. Under the microscope, the histology images of these four subtypes show a variety of textural and structural characteristics. High intraclass and low interclass variabilities in meningioma subtypes make it an extremely complex classification problem. A number of techniques have been proposed for meningioma subtype classification with varying performances on different subtypes. Most of these techniques employed wavelet packet transforms for textural features extraction and analysis of meningioma histology images. In this article, a hybrid classification technique based on texture and shape characteristics is proposed for the classification of meningioma subtypes. Meningothelial and fibroblastic subtypes are classified on the basis of nuclei shapes while grey‐level co‐occurrence matrix textural features are used to train a multilayer perceptron for the classification of transitional and psammomatous subtypes. On the whole, average classification accuracy of 92.50% is achieved through the proposed hybrid classifier; which to the best of our knowledge is the highest. Microsc. Res. Tech. 77:862–873, 2014. © 2014 Wiley Periodicals, Inc.     

美国高校董事类型组合及其意义

董事类型组合是影响董事会治理效率的重要因素.对美国高校董事类型及其组合意义进行分析,希望为我国设置了董事会的高校提供一些有益的借鉴.     

天然染料的应用及研究进展

文章介绍了天然染料的分类、染色方法、应用及天然染料研究的进展。     

基于半监督稀疏鉴别嵌入的高光谱遥感影像分类

为了有效利用已标记与未标记样本提高高光谱遥感影像分类精度,提出一种新的半监督流形学习方法——半监督稀疏鉴别嵌入算法(SSDE)。该算法结合了近邻流形结构及稀疏性的优点,不仅保留样本间的稀疏重构关系,而且通过引入少量有标记的训练样本以及大量无标记训练样本来获得高维数据的内在属性以及低维流形结构,实现鉴别特征提取,提高分类精度。在Washington DC Mall和Indian Pine数据集上的分类识别实验表明,该算法能够较为有效地发现高维空间中数据的内蕴结构,分类性能比其他算法有明显的提升。在随机选取8个有类别标记和60个无类别标记的数据作为训练样本的情况下,本文提出的SSDE算法在上述两个数据集上的分类精度分别达到了77.36%和97.85%。     

基于监督保局子空间虚假近邻准则的原始特征选择

提出一种基于监督保局投影(SLPP)与虚假最近邻(FNN)准则的原始特征选择方法。该方法首先将非线性原始数据映射到监督保局子空间,消除样本数据输入变量之间的相关性;然后,利用虚假近邻点方法计算剔除每个原始特征前后输入样本在监督保局子空间里的相似性测度,获得每个原始特征对类别变量不同程度的解释力;最后,从全特征开始逐步剔除解释能力弱的特征进而获得多组特征子集,并建立最近邻分类器,识别率最高且含特征数最少的特征子集即为最优特征子集。采用合成数据对该方法进行了仿真验证,结果表明,该方法可获得与数据集本质分类特征吻合的最佳特征子集。将该方法应用于选择真实的低阻油气层特征,获得的最佳特征子集比全特征集合的特征数量减少了50%以上,分类识别率高出8%。结果显示该方法具有优秀的原始特征选择能力,是一种有效的非线性特征选择方法。     

核电站安全分级在核岛环形起重机上的应用

核电站安全分级作为物项分级的基础,决定着核电站各系统、设备和构筑物设计制造规范分级、抗震分级以及质保分级的划分。文中以核岛环形起重机为例,对比介绍了CPR1000与EPR三代核电站核岛环形起重机在安全分级、设计制造规范分级、抗震分级以及质保分级等方面的划分与应用,对后续核电站各类起重设备的设计、采购与制造具有一定的指导与借鉴意义,也为相关起重设备的国产化提供了参考基础。     

Acquisition of optimal attribute subset through genetic algorithm using GNP‐based class association rule mining

Attribute selection is a technique to prune less relevant information and discover high‐quality knowledge. It is especially useful for the classification of a large database, because the preprocessing of data increases the possibility that predictor attributes given to the mining algorithm become more relevant to the class attribute. In this paper, a method to acquire the optimal attribute subset for the genetic network programming (GNP) based class association rule mining has been proposed, and this attribute selection process using genetic algorithm (GA) leads to a higher accuracy for classification. Class association rule mining through GNP is conducted with a small subset of data rather than the original large number of attributes; thus simple but important rules are obtained for classification while the local optimal problem is avoided. Simulation results with educational data show that the classification accuracy is largely improved from 52.73 to 74.54%, when classification is made using the optimal attribute subset. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

鄂尔多斯盆地旬邑地区长6油层组储层特征及分类评价

鄂尔多斯盆地旬邑地区上三叠统延长组长6油层组是典型的低孔低渗储层,岩性主要是长石岩屑砂岩和岩屑长石砂岩,孔隙类型主要是残余粒间孔及长石溶蚀孔隙,孔喉组合主要是细孔细喉型与细孔微细喉型,储层非均质性强。储层物性主要受岩石成分、沉积相带、孔隙结构及成岩作用共同影响。长6油层组储层分为Ⅰ类、Ⅱ类、Ⅲ类、Ⅳ类,综合评价其储层主要属于Ⅰ、Ⅱ类储层。