Motivating drivers to correctly adjust head restraints: assessing effectiveness of three different interventions

Three types of driver educational strategies were tested to determine the most effective approach for motivating drivers to adjust their head restraints to the correct vertical position: (1) a human interactive personal contact with a member of an ICBC-trained head restraint adjustment team, (2) a passive video presentation of the consequences of correct and incorrect head restraint adjustment, and (3) an interactive three-dimensional kinetic model showing the consequences of correct and incorrect head restraint adjustment. An experimental pretest–posttest control group design was used. A different educational treatment was used in each of three lanes of a vehicle emissions testing facility, with a fourth lane with no intervention serving as a control group. Observational and self-reported data were obtained from a total of 1 974 vehicles entering and exiting the facility. The human intervention led to significantly more drivers actually adjusting their head restraints immediately after the intervention than the passive video or interactive kinetic model approaches, which were both no different from the control group. The human intervention was recommended as the most effective and was implemented successfully on a limited basis during 3 months of 1995 and again during 3 months of 1996.     

一类高斯数值求积公式的极限性质

讨论了当积分区间的长度趋向于零时Gauss-Legendre求积公式的余项的中介点车的变化的渐近性质，并提出了对应于该公式的校正公式，它有较高的代数精度。     

针对OFDM系统最大似然同步算法的音频干扰研究

正交频分复用(OFDM)系统常常利用循环前缀来进行定时同步和频偏估计。研究了最大似然同步算法,分析了同步算法的原理,提出了利用单音和多音信号来干扰系统同步,并对干扰造成的定时误差和频偏估计误差对正确解码的影响进行了分析。仿真结果表明以较小的干扰信号功率能够对系统同步取得明显的效果。     

Dependency trees in sub-linear time and bounded memory

We focus on the problem of efficient learning of dependency trees. Once grown, they can be used as a special case of a Bayesian network, for PDF approximation, and for many other uses. Given the data, a well-known algorithm can fit an optimal tree in time that is quadratic in the number of attributes and linear in the number of records. We show how to modify it to exploit partial knowledge about edge weights. Experimental results show running time that is near-constant in the number of records, without significant loss in accuracy of the generated trees. Work done at Carnegie-Mellon university. This research was sponsored by the National Science Foundation (NSF) under grant no. ACI-0121671 and no. DMS-9873442.     

乳化沥青混合料初始强度评价方法的研究

通过修正马歇尔稳定度试验和无侧限抗压强度试验两种试验方法来研究乳化沥青混合料初始强度的评价方法。在马歇尔试验中．对于水泥一乳化沥青混合料采用再修正马歇尔试验方法，并采用正交方法设计试验。试验结果和理论分析表明：采用无侧限抗压强度试验评价乳化沥青混合料初始强度更合理．并提出评价的参考标准；同时表明水泥和矿粉是影响乳化沥青混合料强度的重要因素。     

便携式空气浮力智能修正系统的理论研究

介绍了便携式浮力智能修正系统的结构，性能及特点，从理论上研究了实现空气浮力智能修正系统的基本原理，对系统在给定条件下的性能进行了数值模拟，并对设备的通用性能进行了研究，给出了较好的结果。     

Noise-Tolerant Occam Algorithms and Their Applications to Learning Decision Trees

In the distribution-independent model of concept learning of Valiant, Angluin and Laird have introduced a formal model of noise process, called classification noise process, to study how to compensate for randomly introduced errors, or noise, in classifying the example data. In this article, we investigate the problem of designing efficient learning algorithms in the presence of classification noise. First, we develop a technique of building efficient robust learning algorithms, called noise-tolerant Occam algorithms, and show that using them, one can construct a polynomial-time algorithm for learning a class of Boolean functions in the presence of classification noise. Next, as an instance of such problems of learning in the presence of classification noise, we focus on the learning problem of Boolean functions represented by decision trees. We present a noise-tolerant Occam algorithm for k-DL (the class of decision lists with conjunctive clauses of size at most k at each decision introduced by Rivest) and hence conclude that k-DL is polynomially learnable in the presence of classification noise. Further, we extend the noise-tolerant Occam algorithm for k-DL to one for r-DT (the class of decision trees of rank at most r introduced by Ehrenfeucht and Haussler) and conclude that r-DT is polynomially learnable in the presence of classification noise.     

Some approaches to optimal cluster labeling with applications to remote sensing

This paper presents some approaches for the problem of labeling clusters using information from a given set of labeled and unlabeled patterns. Assigning class labels to the clusters is formulated as finding the best label assignment over all possible label assignments with respect to a criterion. Labeling clusters is also viewed as obtaining probabilities of class labels to the clusters with the maximization of the likelihood function and probability of correct labeling as criteria. Closed form solutions are obtained for the probabilities of class labels to the clusters by maximizing a lower bound on the likelihood criterion. Fixed point iteration equations are developed for obtaining probabilities of class labels to the clusters. The problem of obtaining class labels to the clusters is further formulated as that of minimizing the variance of the proportion estimates of the classes that use both the given labeled and unlabeled patterns. Imperfections in the labels of the given labeled set are incorporated into the criteria. Furthermore, the results of application of these techniques in the processing of remotely sensed multispectral scanner imagery data are presented.     

The effect of correct response location on the difficulty level of multiple-choice questions.

The influence of position response sets on a multiple-choice achievement test was investigated. Data on 434 students were obtained from 4 alternate test forms of 100 items each. The arrangement of correct choices and distractors was randomized throughout the test by a scheme which allowed each position an equal number of correct choices. The correct choice for each item appeared in a different position on each of the forms. Results indicate that objective multiple-choice tests are relatively free of position preferences. With this type of test it appears that position response sets are negligible and certainly not a significant source of invalidity. It is suggested that the position of the most plausible distractor more logically accounts for any significant response bias than does a position preference. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

胶片分析系统形成色偏原因分析及校正方法

彩色胶片分析系统主要根据捕获图象的颜色来判断胶片的曝光量和色偏程度，因此要保持系统输入到输出彩色的一致性。本文简要地介绍了胶片分析系统，讨论了引起输入到输出色偏的原因、系统的最优设置及彩色校正方法。