使用PCH对象构建高压线路:ATPDraw与Bioinformatics算法简介

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"本文档是关于使用ATPDraw软件中架空线路对象的介绍,主要讲解如何利用PCH对象创建和仿真高压传输线或电缆。PCH对象基于ATP的数据模块选项,预设了单相和多相线路模型的支持文件,用户可以直接使用,或者根据需要创建自定义支持文件。此外,文档还介绍了如何生成线路常数数据文件,特别是使用ATP_LCC程序生成JMATRI线路模型所需的输入文件,并以一个具体的500kV架空线路例子进行了说明。手册还涵盖了ATPDraw的基本使用,包括安装、转换电路文件以及获取帮助等。" ATPDraw是一款专用于电磁暂态分析的图形界面工具,它允许用户通过直观的界面创建和仿真复杂的电气系统。在"使用架空线路对象"这一章节中,主要聚焦于PCH对象的运用,这是一个专门设计用来构建高压传输线和电缆模型的程序模块。PCH对象简化了用户的工作流程,因为它内置了多种常见线路模型的支持文件,如JMATRI线路和KCLee/Clarke模型,用户可以直接使用,无需手动创建支持文件。如果预设的模型不能满足特定需求,用户可以参照之前的方法自行创建。 创建线路常数数据文件是使用PCH对象的关键步骤之一。在这个过程中,用户需要熟悉ATP中的LINE CONSTANTS和JMATRI SETUP指令。ATP_LCC程序是一个辅助工具,用户通过输入架空线路的交叉部分数据和材料信息,这个程序会自动生成LINE CONSTANTS和JMATRI SETUP所需格式的输入文件,最终生成.PCH格式的打孔文件,这使得ATP能够高效地仿真架空线路。 ATPDraw不仅提供了PCH对象的功能,还有丰富的元件库支持,可以运行在多种Windows操作系统上。安装手册详细阐述了在不同版本的Windows系统下安装和配置ATPDraw的步骤,以及如何转换现有的.CIR文件以适应ATPDraw。用户可以通过多种方式获取帮助,包括在线资源、电子邮件和FTP服务器等。 ATPDraw结合了PCH对象的便利性,使得用户能够方便地建立和仿真高压电力系统的模型,尤其在处理架空线路和电缆这类复杂问题时,其强大的功能和友好的用户界面提供了很大的帮助。通过学习和掌握这一工具,工程师们可以在电力系统设计和分析中提高效率和准确性。

精简下面表达:Existing protein function prediction methods integrate PPI networks and multivariate bioinformatics data to improve the performance of function prediction. By combining multivariate information, the interactions between proteins become diverse. Different interactions’ functions in functional prediction are various. Combining multiple interactions simply between two proteins can effectively reduce the effect of false negatives and increase the number of predicted functions, but it can also increase the number of false positive functions, which contribute to nonobvious enhancement for the overall functional prediction performance. In this article, we have presented a framework for protein function prediction algorithms based on PPI network and semantic similarity with the addition of protein hierarchical functions to them. The framework relies on diverse clustering algorithms and the calculation of protein semantic similarity for protein function prediction. Classification and similarity calculations for protein pairs clustered by the functional feature are more accurate and reliable, allowing for the prediction of protein function at different functional levels from different proteomes, and giving biological applications greater flexibility.The method proposed in this paper performs well on protein data from wine yeast cells, but how well it matches other data remains to be verified. Yet until now, most unknown proteins have only been able to predict protein function by calculating similarities to their homologues. The predictions result of those unknown proteins without homologues are unstable because they are relatively isolated in the protein interaction network. It is difficult to find one protein with high similarity. In the framework proposed in this article, the number of features selected after clustering and the number of protein features selected for each functional layer has a significant impact on the accuracy of subsequent functional predictions. Therefore, when making feature selection, it is necessary to select as many functional features as possible that are important for the whole interaction network. When an incorrect feature was selected, the prediction results will be somewhat different from the actual function. Thus as a whole, the method proposed in this article has improved the accuracy of protein function prediction based on the PPI network method to a certain extent and reduces the probability of false positive prediction results.

2023-02-27 上传