Entropy/cross entropy-based group decision making under intuitionistic fuzzy environment

We study the group decision making problem under intuitionistic fuzzy environment. Based on entropy and cross entropy, we give two methods to determine the optimal weights of attributes, and develop two pairs of entropy and cross entropy measures for intuitionistic fuzzy values. Then, we discuss the properties of these measures and the relations between them and the existing ones. Furthermore, we introduce three new aggregation operators, which treat the membership and non-membership information fairly, to aggregate intuitionistic fuzzy information. Finally, several practical examples are presented to illustrate the developed methods.     

An information-theoretic approach to the written transmission of old English

Information theory offers a means for analyzing some constraints on the reading and copying process in Old English. Entropy for strings of various lengths offers a baseline measure of the uncertainty involved in transmission of Old English texts, while avoiding the pitfalls of applying models of modern reading to early medieval practice. Analysis of lengthy prose and verse texts in Old English revealed uniformly high values for entropy at all string lengths. High entropies may be the result of the language's irregular orthography, poetic koiné, and several dialects and imply that the language may have been easy to write but difficult to read. The low redundancy of the language which its high entropy values indicate suggests that the reader of Old English played an enhanced role in decoding a text and may provide an explanation for the high variability in the transmission of Old English verse.Katherine O'Brien O 'Keeffe is Professor of English at Texas A&M University and a co-director of its Interdisciplinary Group for Historical Literary Study.William Rundell is Professor of Mathematics at Texas A&M University.     

一种新的动态权重更新相关反馈方法*

提出了正负相关图像所占比例与特征值的标准方差相结合来调整对应权值的方法.为了避免相关反馈的权值调整陷入局部最优状态,引入一个干扰因子,使权值跳出局部最优区域.结果表明了该方法的优越性.     

聚酯的摩尔质量与机械性能研究——Ⅰ.动态力学性能

应用粘弹谱仪考察了不同摩尔质量的聚酯(PET、PBT、PBT/PET)的动态力学性能,结果表明摩尔质量增大将提高其模量,但亦导致结晶速度变慢,使测得的模量反而比摩尔质量较低的试样小.     

充油S-SBR加工行为的研究

在中试装置上合成相对分子质量分布较宽的充油S-SBR，考察不同环烷油和芳烃油填充量的充油S-SBR的流变性能和混炼行为。结果表明，增大油的填充量能提高充油S-SBR的流动性能，充芳烃油S-SBR相对于充环烷油的S-SBR对温度更敏感，宽相对分子质量分布的充环烷油S-SBR的混炼性能优于S-SBR1204及E-SBRl778，竟相对分子质量分布的充芳烃油S-SBR的混炼性能优于S-SBR1204，Solprene380和E-SBR1712。     

Personalized identification of abdominal wall hernia meshes on computed tomography

An abdominal wall hernia is a protrusion of the intestine through an opening or area of weakness in the abdominal wall. Correct pre-operative identification of abdominal wall hernia meshes could help surgeons adjust the surgical plan to meet the expected difficulty and morbidity of operating through or removing the previous mesh. First, we present herein for the first time the application of image analysis for automated identification of hernia meshes. Second, we discuss the novel development of a new entropy-based image texture feature using geostatistics and indicator kriging. Third, we seek to enhance the hernia mesh identification by combining the new texture feature with the gray-level co-occurrence matrix feature of the image. The two features can characterize complementary information of anatomic details of the abdominal hernia wall and its mesh on computed tomography. Experimental results have demonstrated the effectiveness of the proposed study. The new computational tool has potential for personalized mesh identification which can assist surgeons in the diagnosis and repair of complex abdominal wall hernias.     

基于Monte Carlo随机模拟的综合交通网络布局评价与决策方法

针对综合交通网络评价指标权重及属性值具有主观性和随机性的特点,提出了基于模拟运算的布局规划方案排序选优的群体随机决策方法.仿真生成满足集结的多个专家对指标重要性偏好排序统计分布的权重,同时考虑交通需求的不确定性对指标值的影响,结合客观熵权计算方案的综合评价值,由多次模拟得到的排序优势度确定方案的优劣差异.根据设计的仿真流程通过算例说明了方法应用的有效性,评价中考虑了主客观因素及随机性特征,可以为网络布局提供科学决策依据.     

基于Profibus-DP总线技术的烟支重量控制系统设计

对烟支重量控制系统的控制原理做了较为详尽的描述,介绍了系统硬件组成和软件设计。系统可实时在线对高速烟支重量进行计算,及时准确剔除不合格烟支。通过Profibus-DP总线,烟支重量控制系统实现了与人机界面系统实时、可靠、高速通信,操作人员通过人机界面可以方便的调整控制参数,对设备进行维护。     

相空间重构在语音情感识别中的研究

为了更为全面地表征语音情感状态,弥补线性情感特征参数在刻画不同情感类型上的不足,将相空间重构理论引入语音情感识别中来,通过分析不同情感状态下的混沌特征,提取Kolmogorov熵和关联维作为新的情感特征参数,并结合传统语音特征使用支持向量机（SVM）进行语音情感识别。实验结果表明,通过引入混沌参数,与传统物理特征进行识别的方案相比,准确率有了一定的提高,为语音情感的识别提供了一个新的研究途径。     

基于改进卡方统计的微博特征提取方法

通过对微博文本特征信息的分析与研究,提出一种基于改进卡方统计的微博特征提取方法。扩充微博信息分类特征,在传统的卡方统计量的基础上,引入了频度等因素,改进特征选择方法;在传统的特征项权值计算的基础上,提出了新的改进卡方统计量的方法,改进权重计算效果。对上述方法利用经典KNN和SVM算法进行了测试,实验结果表明该方法提高了微博信息分类的准确率。