基于C/S体系结构的报到表决管理信息系统

以C／S体系结构为基础,设计并实现了电子报到表决管理信息系统。对其中包括门口报到子系统、电子表决子系统、会务管理子系统、视频显示子系统等各子系统的功能进行分析,提出各功能模块的设计方案。实践证明该电子报到表决管理信息系统运行安全可靠。     

机械加工过程中轴承故障诊断方法研究

为了提高机械加工过程中滚动轴承故障诊断准确度,提出了基于新的解析能量算子的轴承故障诊断方法.在分析Teager能量算子缺陷基础上,提出了新的能量算子,命名为解析能量算子;解析能量算子无需满足Teager能量算子的使用条件,且能够更好地跟踪故障信号的冲击瞬态特征;使用EMD算法分解原始振动信号,给出了多指标融合的IMF分...     

基于公共利益的城中村规划票决制探讨

根据《物权法》和《城乡规划法》的规定,城中村规划只有符合公共利益,才能成为征收土地、房屋及不动产的依据。在目前公共利益内容不确定、城市政府主导城中村规划的情况下,应引入票决制,解决城中村规划中的公共利益判断难题,为城中村改造的合法性提供法理上的保障;同时,应对票决制进行功能分析和制度设计,构建相应的救济程序,减少票决制度在操作过程中对少数人权益的损害。     

基于投票人影响因子的投票预测模型

投票预测是计算政治学的应用之一,目前绝大多数预测模型都忽视投票过程中投票人之间的相互影响.针对此问题,文中提出基于投票人影响因子的投票预测模型.首先,提出投票人影响因子,用于刻画某位投票人在投票过程中对于其他投票人投票选择的影响,同时结合预训练模型提取的投票人特征,形成影响因子图,再输入图卷积神经网络中,学习投票人的相互影响,在一定程度上模拟真实的投票博弈过程.然后,考虑到法案文本中上下文信息的关联性,利用BiLSTM(Bi-directional Long Short-Term Memory)获得法案特征向量.鉴于法案文本的规范性导致的行文近似、用词重复,使用引入TF-IDF(Term-Frequency-Inverse Document Frequency)因子的TextRank,得到法案的关键词.在国外议会网站数据集上的实验表明文中模型性能较优,消融实验也验证每个模块对文中模型的性能均有一定程度的提升.     

基于文本挖掘的高速铁路动车组故障多级分类研究

针对高速铁路信号设备故障发生后记录的文本数据,提出基于文本挖掘方式的高速铁路信号设备故障多级分类模型研究。提出TF-IDF词汇权重与词汇字典结合的特征表示方法实现信号设备故障文本数据的特征提取。多级分类模型中,基于Stacking集成学习思想设计单层分类模型,将循环神经网络BiGRU和BiLSTM作为初级学习器,设计权重组合计算方法作为次级学习器,将多级分类任务分解为各层单分类任务,并采用K折交叉验证训练Stacking模型。采用高速铁路自开通至十年的信号转辙机故障数据,通过对故障原因文本数据的分析,实现故障部位和故障原因的二级分类,经过K=5次训练,BiGRU较BiLSTM各评价指标都较高,经实验BiGRU分配权重为0.7,BiLSTM权重为0.3,组合加权对两个网络的输出计算,准确率提高为0.8814,召回率提高为0.8642。实验表明多级分类模型能够有效提升信号设备故障多级分类任务的分类评价指标,并能够保证分类结果隶属关系的正确性。     

Pattern recognition approach to identify natural clusters of acoustic emission signals

A new approach is introduced to identify natural clusters of acoustic emission signals. The presented technique is based on an exhaustive screening taking into account all combinations of signal features extracted from the recorded acoustic emission signals. For each possible combination of signal features an investigation of the classification performance of the k-means algorithm is evaluated ranging from two to ten classes. The numerical degree of cluster separation of each partition is calculated utilizing the Davies-Bouldin and Tou indices, Rousseeuw’s silhouette validation method and Hubert’s Gamma statistics. The individual rating of each cluster validation technique is cumulated based on a voting scheme and is evaluated for the number of clusters with best performance. This is defined as the best partitioning for the given signal feature combination. As a second step the numerical ranking of all these partitions is evaluated for the globally optimal partition in a second voting scheme using the cluster validation methods results. This methodology can be used as an automated evaluation of the number of natural clusters and their partitions without previous knowledge about the cluster structure of acoustic emission signals. The suitability of the current approach was evaluated using artificial datasets with defined degree of separation. In addition the application of the approach to clustering of acoustic emission signals is demonstrated for signals obtained from failure during loading of carbon fiber reinforced plastic specimens.     

Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets

We present attribute bagging (AB), a technique for improving the accuracy and stability of classifier ensembles induced using random subsets of features. AB is a wrapper method that can be used with any learning algorithm. It establishes an appropriate attribute subset size and then randomly selects subsets of features, creating projections of the training set on which the ensemble classifiers are built. The induced classifiers are then used for voting. This article compares the performance of our AB method with bagging and other algorithms on a hand-pose recognition dataset. It is shown that AB gives consistently better results than bagging, both in accuracy and stability. The performance of ensemble voting in bagging and the AB method as a function of the attribute subset size and the number of voters for both weighted and unweighted voting is tested and discussed. We also demonstrate that ranking the attribute subsets by their classification accuracy and voting using only the best subsets further improves the resulting performance of the ensemble.     

一个基于ASP的流行语投票系统

利用ASP可以直接在HTML文件中嵌入用JavaScript或VBScript脚本语言编写的脚本命令和ADO组件,对任何与ODBC兼容的数据库或OLE DB数据源的高性能连接的优点,结合数据库结构化查询语言(SQL),构建了一个动态交互的投票系统,完成流行语的网上投票,该系统可以修改泛化应用于各种类型的网上投票或调查系统。     

The impact of information and communication technologies on the costs of democracy

Recent developments in information and communication technologies (ICTs) can change the way strategic organizational decisions are negotiated and voted upon. We propose an analytical framework that investigates the effect of such technologies on the costs of the decision-making process in organizations by incorporating a technology variable in an already well-known model of public decision-making. The framework is applicable to any large group decision setting where collective decisions are made by voting, such as shareholders’ meetings, local and federal government meetings. We apply the framework to derive policies that will help to decrease the costs related to the democratic process. The first policy requires that governments should invest more in technologies that support multi-lateral communication and negotiation among citizens, and the second requires that citizens should use better voting mechanisms than simple majority voting to make decisions.