一些特殊方程的计算机程序解法

在人们生产、生活、以及科学研究中,常常会出现一些不容易求解的非线性方程或非线性方程组等特殊方程。文章运用数学知识和计算机MATLAB语言,介绍了这些方程的求解,从而快速得到这些方程的解。     

基于全信息新陈代谢的GM（1，1）电力预测

传统的灰色预测模型所需的样本容量较少,仅4个数据就可以建立灰色预测模型。虽然传统的灰预测建模较为简单,但是忽略了对预测较为确利的新信息,容易产生预测模型老化的现象,预测精度不高。全信息新陈代谢的GM（1,1）灰色预测模型更为合理、科学,全信息建模避免了局部信息建模的局限性,每预测一个结果去除原始数列的最老数据的新陈代谢处理保证了预测数列的实效性,并用Matlab实现改进GM（1,1）模型的编程计算,应用于双流县电力需求量的预测,预测精度好。     

基于递推技术的算法设计方法的应用研究

算法是计算机科学的核心,算法设计对于开发正确、高效的程序至关重要。基于递推技术的算法设计方法通过形式化推导保证算法的可靠性,同时能较好地提高算法程序的效率。本文通过两个实例,详细介绍基于递推技术的算法设计方法形式化推导算法程序的过程。     

编程思维训练通用模型研究与实现

程序设计类课程是培养学生逻辑编程能力的基本课程之一,但目前普遍反映出学生对此类课程的学习兴趣不浓,对程序设计与实现的逻辑思维混乱不清,学习效果较差。如何脱离编程语言本身,运用模拟现实生活的思维训练模型来形象地展示编程的原理与逻辑流程,以提高学生对程序设计的逻辑思维能力和学习效果是本文研究的重点。     

Hadoop在VOD系统中的应用研究

首先对当前VOD系统进行简单介绍,指出当前VOD系统存储架构的不足,接着介绍Hadoop,分析HDFS的存储机制和特点,指出将HDFS用于存储视频文件的优势。通过将Hadoop和当前VOD系统融合,提出基于Hadoop的VOD原型系统设计方案。最后总结将Hadoop用于VOD系统的优势,给出进一步研究计划。     

Response correction techniques for surrogate‐based design optimization of microwave structures

Simulation‐based optimization has become an important design tool in microwave engineering. However, using electromagnetic (EM) solvers in the design process is a challenging task, primarily due to a high‐computational cost of an accurate EM simulation. In this article, we present a review of EM‐based design optimization techniques exploiting response‐corrected physically based low‐fidelity models. The surrogate models created through such a correction can be used to yield a reasonable approximation of the optimal design of the computationally expensive structure under consideration (high‐fidelity model). Several approaches using this idea are reviewed including output space mapping, manifold mapping, adaptive response correction, and shape‐preserving response prediction. A common feature of these methods is that they are easy to implement and computationally efficient. Application examples are provided. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2012.     

Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization

Certain tasks, such as formal program development and theorem proving, fundamentally rely upon the manipulation of higher-order objects such as functions and predicates. Computing tools intended to assist in performing these tasks are at present inadequate in both the amount of knowledge they contain (i.e., the level of support they provide) and in their ability to learn (i.e., their capacity to enhance that support over time). The application of a relevant machine learning technique—explanation-based generalization (EBG)—has thus far been limited to first-order problem representations. We extend EBG to generalize higher-order values, thereby enabling its application to higher-order problem encodings.Logic programming provides a uniform framework in which all aspects of explanation-based generalization and learning may be defined and carried out. First-order Horn logics (e.g., Prolog) are not, however, well suited to higher-order applications. Instead, we employ Prolog, a higher-order logic programming language, as our basic framework for realizing higher-order EBG. In order to capture the distinction between domain theory and training instance upon which EBG relies, we extend Prolog with the necessity operator of modal logic. We develop a meta-interpreter realizing EBG for the extended language, Prolog, and provide examples of higher-order EBG.     

基于2,4-二羟基苯甲酸铜(Ⅱ)为载体的硫氰酸根电极的电位响应

制备了基于一种简单的金属铜配合物2,4-二羟基苯甲酸铜(Ⅱ)(Cu(Ⅱ)DHBA)为载体的PVC膜硫氰酸根离子(SCN-)选择性电极。该电极在1.0×10-1～1.0×10-6mol/LSCN-浓度范围内呈现斜率为-59.5mV/dec的近Nernst电位响应,检测下限为9.1×10-7mol/L。利用紫外可见光谱及交流阻抗技术初步探讨了电极对SCN-呈现的选择性电位响应机理。该电极作为直接电位分析法的指示电极,成功运用于实验室废水中硫氰酸盐含量的测定。