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首页> 外文期刊>Cold regions science and technology >Field measurements of suspended frazil ice. Part Ⅰ: A support vector machine learning algorithm to identify frazil ice particles
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Field measurements of suspended frazil ice. Part Ⅰ: A support vector machine learning algorithm to identify frazil ice particles

机译：悬浮的巴西冰的现场测量。第一部分：支持向量机学习算法，用于识别巴西冰粒

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摘要

Field images of in-situ frazil ice particles captured using a submersible camera system called the FrazilCam have proven difficult to analyse due to the presence of suspended sediment particles. McFarlane et al. (2017) accounted for this by subtracting an appropriately-scaled sediment size distribution from the overall size distribution, resulting in an estimate of the size distribution of frazil ice particles. However, this method over-compensated for the effect of suspended sediment particles and completely eliminated certain portions of the size distribution representing ice particles with diameters on the order of similar to 0.1 mm. In order to process FrazilCam images with greater accuracy, a machine learning algorithm has been trained to classify each individual particle as ice or sediment during image processing, resulting in more accurate size distributions of the frazil ice particles. The methodology used to train and validate the machine learning algorithm is described, and the data previously presented by McFarlane et al. (2017) are reanalysed. This resulted in a decrease in the mean diameters for each deployment reported by McFarlane et al. (2017); however, the overall trends reported remained the same.
机译：事实证明，由于存在悬浮的沉积物颗粒，使用名为FrazilCam的潜水照相机系统捕获的原位巴西泡沫颗粒的现场图像难以分析。 McFarlane等。 （2017）通过从总体粒度分布中减去适当比例的沉积物粒度分布来解决这一问题，从而得出了巴西冰粒的粒度分布的估计值。但是，这种方法对悬浮的沉积物颗粒的影响进行了过度补偿，并完全消除了尺寸分布的某些部分，这些部分代表了直径近似于0.1毫米的冰粒。为了更精确地处理FrazilCam图像，已经训练了机器学习算法，以在图像处理过程中将每个单独的粒子分类为冰或沉积物，从而使FrazilCam粒子的尺寸分布更加准确。描述了用于训练和验证机器学习算法的方法，以及McFarlane等人先前提供的数据。 （2017）被重新分析。这导致McFarlane等人报告的每次部署的平均直径减小。 （2017）;但是，报告的总体趋势保持不变。

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  • 来源
    《Cold regions science and technology》 |2019年第9期|102812.1-102812.9|共9页
  • 作者

    McFarlane Vincent; Loewen Mark; Hicks Faye;

  • 作者单位

    Univ Alberta, Dept Civil & Environm Engn, Edmonton, AB, Canada;

    Univ Alberta, Dept Civil & Environm Engn, Edmonton, AB, Canada;

    Univ Alberta, Dept Civil & Environm Engn, Edmonton, AB, Canada;

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