符号运算在求解车辆传动系扭振中的应用

介绍符号运算方法在用拉普拉斯变换方法求解车辆传动系扭振过程中的作用．针对弹性联轴器的匹配计算，给出了具体求解实例．实践表明，与传统的纯数值计算方法相比，符号运算方法可以方便准确地处理车辆传动系统扭振分析中的非线性等复杂问题，具有推广应用价值．     

Symbolic control of visual attention: The role of working memory and attentional control settings.

This study examined how 1 symbol is selected to control the allocation of attention when several symbols appear in the visual field. In Experiments 1-3, the critical target feature was color, and it was found that uninformative central arrows that matched the color of the target were selected and produced unintentional shifts of attention (i.e., involuntary, initiated slowly, producing long-lasting facilitatory effects). Experiment 4 tested whether such selection is the result of an attentional filter or of a competition bias due to a match of incoming information against integrated object representations stored in working memory. Here, the critical feature was shape and color was irrelevant, but matching color arrows were still selected. Thus, features of objects in working memory will bias the selection of symbols in the visual field, and such selected symbols are capable of producing unintentional shifts of attention. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于Shannon熵的飞机交流发电机振动分析

飞机交流发电机是飞机关键部件之一,监测其健康状况具有重要意义。通过设计实验数据获取平台采集交流发电机在转速上升(从6000转/分到8500转/分)、平稳运行(7500转/分)及转速下降(8500转/分到6000转/分)三个阶段的振动信号,利用符号序列Shannon熵的计算方法以及符号化方法设定合适参数对振动信号进行了处理,得出了交流发电机在三个阶段的振动信号的Shannon熵;通过初步的分析表明,交流发电机在正常状态下三个阶段的Shannon熵都比较有规律的在一定范围内变化,一旦发生振动异常将会引起Shannon熵突变;经过分析讨论,Shannon熵的突变与否是可以用来反映交流发电机在机械方面是否存在故障隐患的。     

Computer Algebra Derivation of the Bias of Linear Estimators of Autoregressive Models

Abstract. A symbolic method which can be used to obtain the asymptotic bias and variance coefficients to order O(1/n) for estimators in stationary time series is discussed. Using this method, the large‐sample bias of the Burg estimator in the AR(p) for p = 1, 2, 3 is shown to be equal to that of the least squares estimators in both the known and unknown mean cases. Previous researchers have only been able to obtain simulation results for the Burg estimator's bias because this problem is too intractable without using computer algebra. The asymptotic bias coefficient to O(1/n) of Yule–Walker as well as least squares estimates is also derived in AR(3) models. Our asymptotic results show that for the AR(3), just as in the AR(2), the Yule–Walker estimates have a large bias when the parameters are near the nonstationary boundary. The least squares and Burg estimates are much better in this situation. Simulation results confirm our findings.     

A Normalization Method of Moment Invariants for 3D Objects on Different Manifolds

3D objects can be stored in computer of different describing ways, such as point set, polyline, polygonal surface and Euclidean distance map. Moment invariants of different orders may have the different magnitude. A method for normalizing moments of 3D objects is proposed, which can set the values of moments of different orders roughly in the same range and be applied to different 3D data formats universally. Then accurate computation of moments for several objects is presented and experiments show that this kind of normalization is very useful for moment invariants in 3D objects analysis and recognition.     

论中西方龙形象及其文化意义的差异

中西方由于生存环境、民族意识和风俗信仰的不同而注入＂龙＂形象的象征意义及其文化内涵存在明显差异,了解此文化差异无论是对促进相互理解还是进行国际文化交流都有着积极的作用。应该以一种兼容并济的心态对待异域文化,积极正面地对外宣传本国文化。     

谈《红字》中红字“A”的象征意义

《红字》是一部以17世纪北美清教殖民统治下的新英格兰为背景,于1642—1649年发生在波士顿的爱情悲剧,整个故事围绕着红字"A"的各种象征意义这一条脉络主线而错综复杂地展开。     

有限域上基于Grbner基的高级综合优化方法

提出了基于多项式符号代数的高级综合方法,并使用元件库中的元件构建多项式符号代数所表示的数据通路,计算出其Grbner基.利用Grbner基对多项式进行一些基本操作,例如,多变元多项式分解、最大公因式提取、库单元映射等,从而实现了有限域上的数据通路优化.最后进行了算法复杂性分析和实验,实验在SUN工作站上通过调用Maple10来完成,实验结果证实了本方法的有效性.     

Feature selection on probabilistic symbolic objects

In data analysis tasks, we are often confronted to very high dimensional data. Based on the purpose of a data analysis study, feature selection will find and select the relevant subset of features from the original features. Many feature selection algorithms have been proposed in classical data analysis, but very few in symbolic data analysis （SDA） which is an extension of the classical data analysis, since it uses rich objects instead to simple matrices. A symbolic object, compared to the data used in classical data analysis can describe not only individuals, but also most of the time a cluster of individuals. In this paper we present an unsupervised feature selection algorithm on probabilistic symbolic objects （PSOs）, with the purpose of discrimination. A PSO is a symbolic object that describes a cluster of individuals by modal variables using relative frequency distribution associated with each value. This paper presents new dissimilarity measures between PSOs, which are used as feature selection criteria, and explains how to reduce the complexity of the algorithm by using the discrimination matrix.     

模型学习与符号执行结合的安全协议代码分析技术

符号执行技术从理论上可以全面分析程序执行空间,但对安全协议这样的大型程序,路径空间爆炸和约束求解困难的局限性导致其在实践上不可行。结合安全协议程序自身特点,提出用模型学习得到的协议状态机信息指导安全协议代码符号执行思路;同时,通过将协议代码中的密码学逻辑与协议交互逻辑相分离,避免了因密码逻辑的复杂性导致路径约束无法求解的问题。在SSH协议开源项目Dropbear上的成功实践表明了所提方法的可行性;通过与 Dropbear 自带的模糊测试套件对比,验证了所提方法在代码覆盖率与错误点发现上均具有一定优势。