液态硅铁直接颗粒化的原理和方法

从理论上说明在一定条件下,液态硅铁合金自由出口处形成液滴的体积仅与液体的表面张力和出流孔直径有关。且当该体积液滴的重力和上部液体的内聚力相等时,液滴即可分离自由下落。据此理论进行了试验,结果表明,液态硅铁合金可直接进入冷却液中形成球状颗粒。且当出流孔径d=8mm,浇注温度为1420℃,落差H=150mm,油层/水层为700mm/1000mm时,76%的颗粒形状几乎都近于圆球形,颗粒体积与理论计算值最大偏差不超过10%。同时,这种液态硅铁合金直接颗粒化的方法与机械破碎法相比具有简易、快捷、节能、高效的优点。     

数据挖掘技术在学生评估中的应用

近几年随着科技和现代教育的发展,数据库和网络技术得到飞速的发展和广泛的应用,智能教学系统也就大量地出现,这样就有大量的数据伴随着出现,而怎样处理这些数据使人们从中获得有用的信息变成一个热门的话题。数据挖掘技术就是将大量数据以一定的方式进行处理,得到对人们有用的信息,来帮助人们学习和预测。将数据挖掘技术引入到教学过程中学生的个人学习素质评估当中,创新性地通过决策树算法和聚类算法相结合的方法对学生数据分析,挖掘潜在的信息资源,找出影响个人综合素质的关键因素及各因素之间的关系,总结优秀学生的个人综合素质的培养方法,弥补不足,提高学生的个人综合素质。     

The Buck Stops Where?

SUMMARY

University students-whether adult, distance learner, or traditional age-need access to university services and quick accurate answers to their questions beyond traditional “business” hours. Students' busy schedules and changing life patterns dictate that university services meet their needs. At DePaul University in Chicago, student focus groups repeatedly pointed to the need for one central place to get an answer or solve a problem. DePaul Central was created as an information and referral service to satisfy that student need, at the same capitalizing on the value of the librarian skill set.     

基于驱动的进程创建监控的实现

介绍了驱动开发技术背景及应用,讲述SSDTHOOK方法,实现监控NtCreateProcessEx系统服务。RING3应用程序和驱动程序的交互,RING3应用程序和驱动程序的同步实现基于驱动的进程创建监控。     

基于C#的网络教学网站的设计与研究

远程教育和校园的信息化建设成为网络应用一个新的热点。网络考试作为远程教育和校园信息化建设的一部分,有着其独特的需求和作用。该文就网络考试系统的总体结构与功能以及实现技术进行了逐一的论述。     

A comment on “Using locally estimated geodesic distance to optimize neighborhood graph for isometric data embedding”

A geodesic distance-based approach to build the neighborhood graph for isometric embedding is proposed to deal with the highly twisted and folded manifold by Wen et al. [Using locally estimated geodesic distance to optimize neighborhood graph for isometric data embedding, Pattern Recognition 41 (2008) 2226-2236]. This comment is to identify the error in their example and the ineffectiveness of their algorithm.     

Commentary on Krishnaswamy et al. - Quantifying and mapping biodiversity and ecosystem services: Utility of a multi-season NDVI based Mahalanobis distance surrogate

Remote sensing is a powerful tool for characterizing, estimating or modelling species diversity. Differences in environmental properties of different habitats should lead to differences of spectral responses, which can be detected by satellite imagery. Hence, spectral distance may be related to species diversity. Based on previous studies, Krishnaswamy et al. [Krishnaswamy, J., Bawa, K. S., Ganeshaiah, K. N., & Kiran, M. C. (2009). Quantifying and mapping biodiversity and ecosystem services: Utility of a multi-season NDVI based Mahalanobis distance surrogate. Remote Sensing of Environment.] used spectral distance to estimate species diversity. Since a noisy scatterplot of species versus spectral diversity is expected, the commonly used Ordinary Least Square regression may fail to detect trends which occur across other quantiles than the mean.Krishnaswamy et al. [Krishnaswamy, J., Bawa, K. S., Ganeshaiah, K. N., & Kiran, M. C. (2009). Quantifying and mapping biodiversity and ecosystem services: Utility of a multi-season NDVI based Mahalanobis distance surrogate. Remote Sensing of Environment.] proposed a quantile-quantile plot method as an alternative to conventional regression based approaches which are inappropriate for dependent pair-wise dissimilarity or similarity data. By this commentary I demonstrate the utility of a quantile regression technique to complement the Krishnaswamy et al. [Krishnaswamy, J., Bawa, K. S., Ganeshaiah, K. N., & Kiran, M. C. (2009). Quantifying and mapping biodiversity and ecosystem services: Utility of a multi-season NDVI based Mahalanobis distance surrogate. Remote Sensing of Environment.] graphical approach in terms of a predictive model.     

Learning a color distance metric for region-based image segmentation

In this paper we describe an experiment where we studied empirically the application of a learned distance metric to be used as discrimination function for an established color image segmentation algorithm. For this purpose we chose the Mumford–Shah energy functional and the Mahalanobis distance metric. The objective was to test our approach in an objective and quantifiable way on this specific algorithm employing this particular distance model, without making generalization claims. The empirical validation of the results was performed in two experiments: one applying the resulting segmentation method on a subset of the Berkeley Image Database, an exemplar image set possessing ground-truths and validating the results against the ground-truths using two well-known inter-cluster validation methods, namely, the Rand and BGM indexes, and another experiment using images of the same context divided into training and testing set, where the distance metric is learned from the training set and then applied to segment all the images. The obtained results suggest that the use of the specified learned distance metric provides better and more robust segmentations, even if no other modification of the segmentation algorithm is performed.     

Suitability evaluation of urban construction land based on geo-environmental factors of Hangzhou, China

Suitability evaluation of urban construction land based on geo-environmental factors is the process of determining the fitness of a given tract of land for construction. This process involves a consideration of the geomorphology, geology, engineering geology, geological hazards, and other geological factors and is the basis of urban construction land planning and management. With the support of Geographic Information Systems (GIS), grid analysis, and geo-spatial analysis techniques, four factor groups comprising nine separate subfactors of geo-environmental attributes were selected to be used in the evaluation of the suitability level for construction land in Hangzhou. This was based on K-means clustering and back-propagation (BP) neural network methods due to their advantages in fast computing, unique adaptive capacity, and self-organization. Simultaneously, the evaluation results based on K-means clustering and BP neural network were compared and analyzed, and the accuracy evaluation was set. The results showed that the geo-environmental suitability evaluation results of construction land based on K-means clustering and BP neural network were similar in terms of the distribution and scale of construction land suitability level. At the same time, the results of the two evaluation methods were consistent with the variability in suitability level, engineering geology, and hydrogeology of Hangzhou. The results also showed that the real advantage of the methods proposed in this paper lies in their capacity to streamline the mapping process and to ensure that the results are consistent throughout. The suitability level of the urban construction land based on the geo-environment in Hangzhou was divided into four construction sites: land for building super high-rise and high-rise buildings, land for building multistorey buildings, land for low-rise buildings, and nonbuilding land. The results of the suitability evaluation for each category will provide a scientific basis for decision-making in urban development in Hangzhou.     

EPON中ONU的自动加入技术及实现

在介绍了EPON系统的结构、工作原理的基础上,分析了多点控制协议,利用该协议完成自动发现和注册。然后探讨了EPON系统中ONU自动加入和注册存在的安全隐患,结合EPON自身特点,提出了一种基于注册过程的认证机制。仿真结果表明,ONU能够快速准确地在OLT上实现注册。