构建项目课程模式下的新教学评价体系

评价体系的改革是项目课程体系改革的重要一环,在给出传统教学评价体系基础上,构建新教学评价体系,并以具体的工作任务为例说明新教学评价的实施,实践证明,新教学评价体系能激发学生学习的积极性,主动性,有利于培养学生的综合职业能力,缩短学校与社会接轨的时间。     

基于BP网络的洪灾风险评价算法

为了解决洪灾风险评价问题,文中在分析传统洪灾风险评价算法不足的基础上,结合BP网络特点,提出了基于BP网络的洪灾风险评价算法（Flood Risk Evaluation algorithm on BPNet,FRE—BPN）,同时为了解决BP算法对复杂高维样本数据建模效率差、精度低的缺陷,利用粗糙集对BP网络进行约简。仿真实验表明,粗糙集约简使得BP网络大大降低了洪灾风险评价模型建立的复杂度,同时FRE-BPN算法对洪灾数据集进行风险评价时的平均耗时大大下降。     

电子对抗训练中通信干扰效果评估方法研究

研究电子对抗训练中通信干扰的基本原理,分析通信干扰的基本方程和评估通信干扰效果的基本准则,利用这一准则对语音模拟通信干扰效果进行评估,并得出结果。     

关联规则挖掘在教学评价中的应用研究

教学评价是院校最重要的工作之一,而科学的评教方法又显得尤为重要。本文介绍一种更适合于关系型数据库关联规则挖掘的、基于SQL的频繁项关联规则挖掘算法,并将其应用于教学评价。通过对大量的样本数据进行试验,得出了应用以往传统的教学评价方法所无法得到重要的教学参考信息。     

高校教育信息化综合评价研究

高校教育信息化的评价要受基础设施建设、信息资源建设、信息技术应用、信息化人才等因素的影响,而各影响因素不能都以定量的形式体现。采用定性分析与定量分析相结合的思路,给出了高校教育信息化的评价方法,较好地解决了高校教育信息化的评价问题。     

A Web services-based framework for building componentized digital libraries

We present a new Web services-based framework for building componentized digital libraries (DLs). We particularly demonstrate how traditional RDBMS technology can be easily deployed to support several common digital library services. Configuration and customization of the framework to build specialized systems is supported by a wizard-like tool which is based on a generic metamodel for DLs. Such a tool implements a workflow process that segments the DL design tasks into well-defined steps and drives the designer along these steps. Both the framework and the configuration tool are evaluated in terms of several performance and usability criteria. Our experimental evaluation demonstrates the feasibility and superior performance of our framework, as well as the effectiveness of the wizard tool for setting up DLs.     

Web proxy cache replacement scheme based on back-propagation neural network

Web proxy caches are used to reduce the strain of contemporary web traffic on web servers and network bandwidth providers. In this research, a novel approach to web proxy cache replacement which utilizes neural networks for replacement decisions is developed and analyzed. Neural networks are trained to classify cacheable objects from real world data sets using information known to be important in web proxy caching, such as frequency and recency. Correct classification ratios between 0.85 and 0.88 are obtained both for data used for training and data not used for training. Our approach is compared with Least Recently Used (LRU), Least Frequently Used (LFU) and the optimal case which always rates an object with the number of future requests. Performance is evaluated in simulation for various neural network structures and cache conditions. The final neural networks achieve hit rates that are 86.60% of the optimal in the worst case and 100% of the optimal in the best case. Byte-hit rates are 93.36% of the optimal in the worst case and 99.92% of the optimal in the best case. We examine the input-to-output mappings of individual neural networks and analyze the resulting caching strategy with respect to specific cache conditions.     

A new algorithm for sparse interpolation of multivariate polynomials

To reconstruct a black box multivariate sparse polynomial from its floating point evaluations, the existing algorithms need to know upper bounds for both the number of terms in the polynomial and the partial degree in each of the variables. Here we present a new technique, based on Rutishauser’s qd$qd$-algorithm, in which we overcome both drawbacks.     

Analyzing the performance of a cluster-based architecture for immersive visualization systems

Cluster computing has become an essential issue for designing immersive visualization systems. This paradigm employs scalable clusters of commodity computers with much lower costs than would be possible with the high-end, shared memory computers that have been traditionally used for virtual reality purposes. This change in the design of virtual reality systems has caused some development environments oriented toward shared memory computing to require modifications to their internal architectures in order to support cluster computing. This is the case of VR Juggler, which is considered one of the most important virtual reality application development frameworks based on open source code.     

Algorithm-system scalability of heterogeneous computing

Scalability is a key factor of the design of distributed systems and parallel algorithms and machines. However, conventional scalabilities are designed for homogeneous parallel processing. There is no suitable and commonly accepted definition of scalability metric for heterogeneous systems. Isospeed scalability is a well-defined metric for homogeneous computing. This study extends the isospeed scalability metric to general heterogeneous computing systems. The proposed isospeed-efficiency model is suitable for both homogeneous and heterogeneous computing. Through theoretical analyses, we derive methodologies of scalability measurement and prediction for heterogeneous systems. Experimental results have verified the analytical results and confirmed that the proposed isospeed-efficiency scalability works well in both homogeneous and heterogeneous environments.