点模式指纹匹配算法研究与实现

指纹匹配是实现指纹识别的重要环节,针对如何提高识别率、减少算法复杂度,介绍了一种点模式的指纹匹配算法。其优点是:将匹配分为两步进行,减少了拒判时间;初匹配利用了细节点间的局部结构关系,既克服了图像的平移和旋转也得到了更精确的坐标校准参数;二次匹配阶段,整合多种判决条件作为识别的依据,提高了识别率。其实验结果表明,该算法复杂度低,识别率较高,有较强实用价值。     

基于振动分析的工程结构损伤识别技术综述

综述了基于振动分析的工程结构损伤识别方法,并对各种方法进行了评述。根据国内外文献,对基于振动分析的工程结构损伤识别技术的发展趋势进行了展望。     

兔大肠杆菌病原菌的分离鉴定与药敏试验

从重庆市几个种兔场临诊严重腹泻的病兔采集病料 ,分离出 3株菌 ,经形态学检查及生化鉴定 ,呈革兰氏阴性 ,两端钝圆的杆状 ,常规的生化结果均符合大肠杆菌特征 ,但 3株菌对棉子糖、蕈糖的分解及赖氨酸的利用上存在着差异。用青霉素G等 2 0种药物进行药敏试验 ,结果表明 :对头孢类、庆大霉素、妥布霉素、阿米卡星、壮观霉素、痢特灵等敏感 ;对麦迪霉素、强力霉素等中敏 ;而对青霉素G、四环素、链霉素等表现为耐药。其中 1号菌株对喹诺酮类、强力霉素、卡那霉素、复方新诺明等耐药 ,2号菌株对喹诺酮类、氯霉素、卡那霉素等敏感 ,而对复方新诺明、利福平等耐药 ,3号菌株对卡那霉素、氯霉素、强力霉素等敏感 ,而对利福平耐药。该试验结果表明 :在不同兔场 ,感染兔的大肠杆菌的株型不一致 ,并且对药物的敏感性也不一致。     

Threat, Group Identification, and Children's Ethnic Prejudice

This experiment tested predictions from social identity development theory (SIDT, Nesdale, 1999 ), that children's tendency to show out‐group prejudice depends on the strength of their in‐group identification and/or their perception of threat from the out‐group. Anglo‐Australian children (N= 480) aged 6, 7, or 9 years were assigned to a high‐status team and their identification with the in‐group (high vs. low) was manipulated together with threat from the out‐group (present vs. absent). The members of the out‐group were revealed to be of the same (Anglo‐Australian) or different (Pacific Islander) ethnicity to the in‐group. Results supported the SIDT predictions. In addition, consistent with socio‐cognitive theory (ST, Aboud, 1988 ), dislike for the out‐group at 6 years gave way to increasingly neutral reactions by 9 years of age. Ethnic composition of the out‐group did not impact differentially on liking but it did affect the children's desire to change groups. Strongly identified children were reluctant to leave their group regardless of the ethnicity of the out‐group, whereas children with low in‐group identification were more willing to change into a same‐ than into a different‐ethnicity out‐group. It is concluded that both social identity and social cognitive processes are implicated in the development of prejudice in middle childhood.     

误差补偿和时滞辨识预测控制算法

通过神经网络辨别出非线性系统的滞后时间,并采用误差补偿手段对神经网络进行修正,控制系统的反馈取自不带时滞的非线性神经网络的输出,采用预测控制策略改善整个系统的控制性能。仿真实验表明该方法具有较快的响应速度和较强的自适应性与鲁棒性,能有效克服延迟和干扰给控制品质带来的不利影响,取得了良好的控制性能。     

西方顾客价值研究理论综述

顾客价值研究的兴起是企业不断寻求竞争优势的合理和必然结果,国外学者对顾客价值的研究主要集中在以下几个方面。一是什么是顾客价值,也就是顾客价值的定义与内涵问题,西方学者们从自己的研究角度出发提出了各自不同的定义;二是在向顾客提供价值之前,如何识别顾客价值,这涉及到顾客价值的识别工具研究;再次是在正确认识顾客价值的基础上,企业如何为顾客提供优异的顾客价值,这部分的研究大都融入在前面两个部分的研究之中。     

The Use Of Shame and Dread in the Countertransference

Much has been written about paranoid anxieties as these occur in the patient. Less, however, has been written about primitive experiences as these occur in the therapist during the session. This paper recounts a patient's correct articulation of the therapist's self-perception, which, because of its accuracy, initially flooded the therapist with shame and dread. Nonetheless, the patient's perception was able to be used in a way that clarified, in the countertransferance, a piece of the patient's early experience in relation to her mother and strengthened the therapeutic relationship.     

Irregular Identification,Support Conditions,and Inverse Weight Estimation

In weighted moment condition models, we show a subtle link between identification and estimability that limits the practical usefulness of estimators based on these models. In particular, if it is necessary for (point) identification that the weights take arbitrarily large values, then the parameter of interest, though point identified, cannot be estimated at the regular (parametric) rate and is said to be irregularly identified. This rate depends on relative tail conditions and can be as slow in some examples as n^−1/4. This nonstandard rate of convergence can lead to numerical instability and/or large standard errors. We examine two weighted model examples: (i) the binary response model under mean restriction introduced by Lewbel (1997) and further generalized to cover endogeneity and selection, where the estimator in this class of models is weighted by the density of a special regressor, and (ii) the treatment effect model under exogenous selection (Rosenbaum and Rubin (1983)), where the resulting estimator of the average treatment effect is one that is weighted by a variant of the propensity score. Without strong relative support conditions, these models, similar to well known “identified at infinity” models, lead to estimators that converge at slower than parametric rate, since essentially, to ensure point identification, one requires some variables to take values on sets with arbitrarily small probabilities, or thin sets. For the two models above, we derive some rates of convergence and propose that one conducts inference using rate adaptive procedures that are analogous to Andrews and Schafgans (1998) for the sample selection model.     

Binary Response Models for Panel Data: Identification and Information

This paper considers a panel data model for predicting a binary outcome. The conditional probability of a positive response is obtained by evaluating a given distribution function (F) at a linear combination of the predictor variables. One of the predictor variables is unobserved. It is a random effect that varies across individuals but is constant over time. The semiparametric aspect is that the conditional distribution of the random effect, given the predictor variables, is unrestricted. This paper has two results. If the support of the observed predictor variables is bounded, then identification is possible only in the logistic case. Even if the support is unbounded, so that (from Manski (1987)) identification holds quite generally, the information bound is zero unless F is logistic. Hence consistent estimation at the standard pn rate is possible only in the logistic case.     

On the Testability of Identification in Some Nonparametric Models With Endogeneity

This paper examines three distinct hypothesis testing problems that arise in the context of identification of some nonparametric models with endogeneity. The first hypothesis testing problem we study concerns testing necessary conditions for identification in some nonparametric models with endogeneity involving mean independence restrictions. These conditions are typically referred to as completeness conditions. The second and third hypothesis testing problems we examine concern testing for identification directly in some nonparametric models with endogeneity involving quantile independence restrictions. For each of these hypothesis testing problems, we provide conditions under which any test will have power no greater than size against any alternative. In this sense, we conclude that no nontrivial tests for these hypothesis testing problems exist.