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81.
珠海市成年人体质现状的研究 总被引:7,自引:2,他引:5
为了配合国家体委《全民健身计划纲要》的实施 ,为了对本市成年人体质现状有个全面了解 ,1 997年 1 1月 3日— 1 2月 5日在珠海市全市范围内对五类 (农业、工业、商业、科教、机关 )不同工作性质成年人进行了抽样测试。根据对归纳整理后的原始资料中有用数据进行统计处理 ,第一次对本市成年人体质现状有了较为全面的了解。即珠海市成年人体质状况好于全国、全省平均水平 ,在个别指标上具有地域性特点 ,并就如何提高本市成年人的体质状况提出一些看法和建议 ,为推动全民健身的发展作出贡献 相似文献
82.
介绍了一种简单实用的峰值采样方法,能兼顾高、低频段,获取数字化的峰值信号,其电路的实现比较简单,适用频带较宽,经实验证明该方法切实可行。 相似文献
83.
入侵检测是信息安全研究的重要组成部分,基于网络的入侵检测系统通过详细分析计算机网络中传输的网络数据包进行入侵检测。由于检测速率与数据包采集速率不匹配,以及检测所需成本的限制,在收集用于检测的网络数据包时,必须选择有效的采样策略。本文引入博弈模型框架上的原始入侵数据包采样策略,在此基础上再进行分析和扩展。本文针对单一采样策略的缺陷与不足,引入风险管理的思想来分析在决策者不同效用偏好情况下的采样策略选择问题,并且通过具体的实例,说明了基于风险差异的采样策略选择的有效性。 相似文献
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85.
Margaret Wu 《Educational Measurement》2010,29(4):15-27
In large-scale assessments, such as state-wide testing programs, national sample-based assessments, and international comparative studies, there are many steps involved in the measurement and reporting of student achievement. There are always sources of inaccuracies in each of the steps. It is of interest to identify the source and magnitude of the errors in the measurement process that may threaten the validity of the final results. Assessment designers can then improve the assessment quality by focusing on areas that pose the highest threats to the results. This paper discusses the relative magnitudes of three main sources of error with reference to the objectives of assessment programs: measurement error, sampling error, and equating error. A number of examples from large-scale assessments are used to illustrate these errors and their impact on the results. The paper concludes by making a number of recommendations that could lead to an improvement of the accuracies of large-scale assessment results. 相似文献
86.
徐华 《中国现代教育装备》2014,(9):42-44
结合信号与系统课程教学实践,从软件仿真和硬件电路验证两个方面演示了抽样定理,结合最新的学术前沿压缩感知技术,介绍了其和抽样定理之间的关系。教学实践表明,软件仿真、硬件系统演示以及前沿技术介绍等使学生能够多方面、多角度地理解和加深相关知识点,在课堂教学中取得了较好的效果。 相似文献
87.
在光伏逆变系统中,内部电磁干扰是影响其工作稳定性和可靠性的重要因素,能否有效地控制内部电磁干扰决定着系统的成败.文章以一款3 kW离网光伏逆变器设计为例,分析了系统内部主要电磁干扰的分布,并提出了具体解决方案. 相似文献
88.
Learning from imbalanced datasets is difficult. The insufficient information that is associated with the minority class impedes making a clear understanding of the inherent structure of the dataset. Most existing classification methods tend not to perform well on minority class examples when the dataset is extremely imbalanced, because they aim to optimize the overall accuracy without considering the relative distribution of each class. In this paper, we study the performance of SVMs, which have gained great success in many real applications, in the imbalanced data context. Through empirical analysis, we show that SVMs may suffer from biased decision boundaries, and that their prediction performance drops dramatically when the data is highly skewed. We propose to combine an integrated sampling technique, which incorporates both over-sampling and under-sampling, with an ensemble of SVMs to improve the prediction performance. Extensive experiments show that our method outperforms individual SVMs as well as several other state-of-the-art classifiers. 相似文献
89.
90.
Ana-Maria ?imundi? 《Biochemia medica : ?asopis Hrvatskoga dru?tva medicinskih biokemi?ara / HDMB》2013,23(1):12-15
By writing scientific articles we communicate science among colleagues and peers. By doing this, it is our responsibility to adhere to some basic principles like transparency and accuracy. Authors, journal editors and reviewers need to be concerned about the quality of the work submitted for publication and ensure that only studies which have been designed, conducted and reported in a transparent way, honestly and without any deviation from the truth get to be published. Any such trend or deviation from the truth in data collection, analysis, interpretation and publication is called bias. Bias in research can occur either intentionally or unintentionally. Bias causes false conclusions and is potentially misleading. Therefore, it is immoral and unethical to conduct biased research. Every scientist should thus be aware of all potential sources of bias and undertake all possible actions to reduce or minimize the deviation from the truth. This article describes some basic issues related to bias in research. 相似文献