苏炳添、谢震业百米跑技术变化特征的比较分析

目的:探索短跑选手苏炳添、谢震业成绩提高过程中百米跑技术的变化特征,为其他短跑运动员训练提供参考。方法:通过信息技术和数理统计等方法,对其成绩提高过程技术数据进行测量分析,并与国外优秀选手进行比较。结果:①苏炳添、谢震业在成绩进步过程中,均对自身技术进行了一些尝试和改变,在自身较高步频、中等步长情况下,取得了较为理想的成绩;②从技术风格类型看,成绩提高过程中,苏炳添经历了“步长型-均衡型-步频型-均衡型”的转变;谢震业经历了“步频型-均衡型-步频型”的转变,在取得当前最佳成绩时,苏炳添采用了均衡型技术,谢震业采用了步频型技术;③与世界选手各分段步频均值相比,谢震业、苏炳添属于较高步频的跑法,在全程步频分配上,两者均在M型跑法下取得较好的成绩,符合高步频跑法的特点。结论:与世界优秀选手的分段步频相比,谢、苏两者均属于步频较高的选手,多波峰的M型跑法是较为适合其自身的跑法。     

对不同蹲踞式起跑姿势起跑后前两步的运动学研究

本文运用测力板,对单个对象进行不同起跑姿势的起跑测试,主要根据压力板上的着地痕迹,确定步长,计算速度等参数,对不同起跑姿势起跑后前两步进行运动学方面的分析研究。本研究得出以下结论：在本实验条件下,给实验对象设计的五种蹲踞式起跑姿势中,第二种姿势最适合该运动员起跑,但不是运动员平时的习惯起跑姿势。     

中、外优秀女子三级跳远运动员助跑步长与速度研究

通过文献资料调研、现场拍摄法、视频图像解析法等对国内、外优秀女子三级跳远运动员的助跑步长和速度进行了纵向的整体性研究及横向的层次性研究。研究发现,助跑速度是运动成绩提高的源动力;国外优秀运动员水平越高,助跑最后10m的速度与比赛成绩的相关性就越大;最后两步步长随着竞技水平的提高有增加的趋势,并具有明显的个体差异性;在横向上,国外优秀运动员助跑最后两步的速度,随着竞技水平的不断提高而增加,呈加速攻板状态,国内优秀运动员在上板瞬间整体呈减速状态;国外优秀运动员的比赛成绩与助跑最后两步步长呈负相关关系,与最后两步助跑速度呈正相关关系,国内优秀运动员助跑最后两步步长与速度匹配的实效性不如国外优秀运动员。     

古入声字在辉县方言中的调类演变

文章从调类这个角度对辉县方言入声发展演变的状况进行考察,指出辉县方言的入声调不分阴阳,调值为一中短促调,并且正由促声向舒声转化,入声舒化后入声调归平声的较多。并探讨了造成变化的原因,展示了辉县方言入声发展演变的规律、特点。     

S_5的子群

小阶数对称群在有限群论研究中具有重要的作用,但随着n的增大,结构也越复杂。文章利用传递与正规性计算出了S5的所有子群,并给出了严格的证明。     

公法线长度测量的应用

测量公法线长度是齿轮精度检验常用的方法之一,在齿轮设计、齿轮精度检验及齿轮测绘中应用广泛.测量公法线长度的工具简单,测量技术易掌握且能保证测量精度,基本可以满足普通齿轮的精度测量与测绘计算.     

天然肠衣搭配组装算法

天然肠衣原料长短不一,按要求搭配组装成品,运算量很大。采用迭代算法并基于平均值变动规律的研究,建立起用于肠衣成品搭配组装的4个算法模型,其特点是：先行剔除剩余原料,随机应变安排次序,均匀调节长短搭配,即时检验约束条件,可同时保障可行性与最优性,得到了最好的结果,且优于整数规划模型。     

Simulation of droplet topography on super-hydrophobic surface

We established a novel droplet model (with-gravity model) to show the gravity effect of the droplet in the contact angle experiment.By using with-gravity model, we obtained a three-dimensional topography of the droplet including the height of the droplet, the shape of the baseline and the circumference of the baseline.Comparing the with-gravity model with the ideal spherical model, our model considered the measurement error caused by gravity effect in the contact angle experiment which is a key point to influence the three-dimension topography of the droplet.From the calculation of our model, we found that there were two important points to enhance the measurement error: the size of the droplet and the contact angle.With the droplet and the contact angle became larger, measurement error was obviously increased.     

基于贝叶斯估计的Context量化器设计方法

介绍了一种基于贝叶斯估计的Context量化器设计方法.通过将自适应码长增量与贝叶斯估计中的先验概率估计进行结合,使得该量化器在不需要先验知识的情况下,既考虑到Context量化本身的特点,又使得编码后信源的自适应码长最短,最终保证了Context量化的自适应性.实验结果表明,该Context量化器设计方法能获得最优量化结果,达到设计目标.     

An analysis on document length retrieval trends in language modeling smoothing

Document length is widely recognized as an important factor for adjusting retrieval systems. Many models tend to favor the retrieval of either short or long documents and, thus, a length-based correction needs to be applied for avoiding any length bias. In Language Modeling for Information Retrieval, smoothing methods are applied to move probability mass from document terms to unseen words, which is often dependant upon document length. In this article, we perform an in-depth study of this behavior, characterized by the document length retrieval trends, of three popular smoothing methods across a number of factors, and its impact on the length of documents retrieved and retrieval performance. First, we theoretically analyze the Jelinek–Mercer, Dirichlet prior and two-stage smoothing strategies and, then, conduct an empirical analysis. In our analysis we show how Dirichlet prior smoothing caters for document length more appropriately than Jelinek–Mercer smoothing which leads to its superior retrieval performance. In a follow up analysis, we posit that length-based priors can be used to offset any bias in the length retrieval trends stemming from the retrieval formula derived by the smoothing technique. We show that the performance of Jelinek–Mercer smoothing can be significantly improved by using such a prior, which provides a natural and simple alternative to decouple the query and document modeling roles of smoothing. With the analysis of retrieval behavior conducted in this article, it is possible to understand why the Dirichlet Prior smoothing performs better than the Jelinek–Mercer, and why the performance of the Jelinek–Mercer method is improved by including a length-based prior.