共查询到17条相似文献,搜索用时 109 毫秒
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秤漏的结构及其稳流原理 总被引:4,自引:1,他引:3
秤漏是一种特殊类型的漏刻,在隋唐及北宋前期曾是司天机构的主要计时仪器.渴乌是秤漏中最重要的部件.李兰秤漏和大型秤漏的稳流原理基本一样,都是使渴乌和浮子相连,从而在保持水头极为稳定的情况下泄水.根据对中、日、韩三国相关史料的分析,证明马上漏刻很可能就是一种小型秤漏. 相似文献
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秤漏是一种特殊类型的漏刻,在隋唐及北宋前期曾是司天机构的主要计时仪器。根据古文献记述,李兰秤漏很可能是使用弹簧联结权器和秤钩,以达到平均流速稳定的目的。 相似文献
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《自然辩证法通讯》2020,(3)
中国古代的漏刻进行连续计时,一天分为白天、黑夜两段,这种时间计量需要三方面的配合:漏刻的均匀水流,百刻制,以及昼漏、夜漏交替进行所需的改箭。漏刻之外,古人还进一步尝试研制出多种计时方法,如兵书记载的数步计时法、数珠计时法,通过人力,开展步行、拨珠活动,并进行计数。文章指出,两种计时法都预设了一昼夜百刻;当步行、拨珠操作长时段运行,即可以视作速度均匀,这其实是模拟漏刻计时过程中均匀水流导致水位变化的量化;两种计时法分白天、黑夜不同时段进行,需要参照不同日期昼夜长短数据,这与漏刻计时过程中的改箭活动有相通之处。两种计时法的出现,反映出漏刻精度逐步提高的同时,时间计量进入的发展新阶段。 相似文献
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中国古代种大葫芦法的成就及指导思想 总被引:3,自引:1,他引:2
葫芦是世界上最古老的作物。中国是它的主要起源地之一。它既可食,又可用为水上交通工具、生活器皿、皮具,还可作饲料、制烛、入药、制笙竽、玩具,用途之广是鲜见的。特别是在制陶发明和普及之前,它的盛水贮粮功能更显重要。广大民众需要硕大葫芦为器物用。于是古人在2000多年前首创特殊的种大葫芦法。《庄子》和《Fan胜之书》最早提及和载有此法。历代农者在实践中不断改进,终于形成一套完整的种植技术。至清末至少有2 相似文献
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Jun-Young Oh 《Foundations of Science》2016,21(4):567-578
The purpose of this study is to gain a better understanding of the role of abstraction and idealization in Galileo’s scientific inquiries into the law of free falling motion, and their importance in the history of science. Because there is no consensus on the use of the terms “abstraction” and “idealization” in the literature, it is necessary to distinguish between them at the outset. This paper will argue (1) for the importance of abstraction and idealization in physics and the theories and laws of physics constructed with abduction from observations and (2) that these theoretical laws of physics should be tested with deduction and induction thorough quasi-idealized entities rather than empirical results in the everyday world. Galileo’s work is linked to thought experiments in natural science. Galileo, using thought experiments based on idealization, persuaded others that what had been proven true for a ball on an inclined plane would be equally true for a ball falling through a vacuum. 相似文献
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Ron Wehrens Lutgarde M.C. Buydens Chris Fraley Adrian E. Raftery 《Journal of Classification》2004,21(2):231-253
The rapid increase in the size of data sets makes clustering all the more important
to capture and summarize the information, at the same time making clustering more
difficult to accomplish. If model-based clustering is applied directly to a large data set, it
can be too slow for practical application. A simple and common approach is to first cluster
a random sample of moderate size, and then use the clustering model found in this way
to classify the remainder of the objects. We show that, in its simplest form, this method
may lead to unstable results. Our experiments suggest that a stable method with better performance can be obtained with two straightforward modifications to the simple sampling
method: several tentative models are identified from the sample instead of just one, and
several EM steps are used rather than just one E step to classify the full data set. We find
that there are significant gains from increasing the size of the sample up to about 2,000,
but not from further increases. These conclusions are based on the application of several
alternative strategies to the segmentation of three different multispectral images, and to
several simulated data sets. 相似文献