Analysis of the microcapsule structure based on machine learning algorithm
Analysis of the microcapsule structure based on machine learning algorithm
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DOI：
10.1088/1742-6596/1763/1/012030
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发表时间：
2021-01
期刊：
影响因子：
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通讯作者：
Sukhbaatar Batchuluun;H. Matsune;K. Shiomori;O. Bayanjargal;Tserenkhand Baasankhuu
中科院分区：
文献类型：
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作者：
Sukhbaatar Batchuluun;H. Matsune;K. Shiomori;O. Bayanjargal;Tserenkhand Baasankhuu
对于微胶囊的制备过程，由于直径和壁厚发生了巨大变化，因此通过制备过程中的结构改造来估计其机理。最近通过机器学习技术研究微观结构。其他研究人员使用霍夫变换算法来制备微胶囊，但仅利用微胶囊直径的变化很难确定其机理。因此，建立该机制的另一种方法是分析微胶囊结构的形成。本研究采用Hough变换算法进行图像分割，检查简单的特征提取，并使用支持向量机和k近邻算法作为分类器，对水包油固体、S/O/W、乳液体系中溶剂蒸发法制备的微胶囊的结构进行分析。通过开发的检测方法分析了结构分布。微胶囊具有特定的结构分布，有单核、多核和其他聚集结构。制备条件改变了结构分布。通过增加有机相中添加的水溶性固体颗粒的量，单核结构占主导地位。
For the microcapsules preparation process, the mechanism is estimated by structure reformation during the preparation process since diameter and wall thickness drastically changed. Microstructures are recently studied by machine learning techniques. The Hough transformation algorithm is used by other researchers for the preparation of the microcapsules but it is difficult to determine the mechanism by using only a diameter change of the microcapsules. Therefore, one additional way to establish the mechanism is the analysis of the formation of the microcapsule structure. In this study, The Hough transformation algorithm was used for the image segmentation, the simple feature extractions were checked and the support vector machine and the k-nearest neighbors algorithm were used as classifiers in order to analyze the structure of the microcapsules which were prepared by solvent evaporation method from a solid in oil in water, S/O/W, emulsion system. The structural distribution was analyzed by the developed detection method. The microcapsules had a specific structural distribution which are monocore, multicore, and other aggregated structures. The structural distribution was changed by the preparation condition. The monocore structure was dominant by increasing in the amount of water soluble solid particles added in the organic phase.