A neural net approach in analyzing photograph in PIV
A neural net approach in analyzing photograph in PIV
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DOI：
10.1109/icsmc.1991.169906
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发表时间：
1991-10
期刊：
影响因子：
--
通讯作者：
C. L. Teo;K. Lim;G. Hong;M.H.T. Yeo
中科院分区：
文献类型：
--
作者：
C. L. Teo;K. Lim;G. Hong;M.H.T. Yeo
在粒子图像测速技术（PIV）中，流体流中粒子的图像的照片是以很短的间隔拍摄的，然后通过测量单个粒子在该时间内移动的距离来确定速度场。粒子的属性被收集并馈送到神经网络以匹配照片中的粒子，从而可以测量速度。作者认为图像具有低浓度的颗粒，使颗粒的离散图像出现，而不是散斑图案。假设单个粒子的运动是完全随机的，并且要找到每个粒子的速度。对于粒子分布相当均匀的图像，所获得的结果是好的。
In particle image velocimetry (PIV), photographs of images of the particles in a fluid flow are taken a short interval apart and the velocity field is then determined by measuring the distance that individual particles moved during that time. Attributes of the particles were collected and fed to a neural net to match the particles in the photographs so that the velocity can be measured. The authors consider images with a low concentration of particles so that the discrete images of particles appear as opposed to speckle patterns. It is assumed that the motion of the individual particles is completely random and the velocity of each individual particle is to be found. Results obtained are good for images with particles fairly well spread out.>