用神经网络分析干裂图案，96%准确识别淀粉浆体干燥时用的溶剂。
Machine Learning Based Identification of Solvents from Post-Desiccation Patterns
- 通过图像分析提取九种形态特征，构建频率直方图作为输入。
- 神经网络模型平均识别准确率达96%，含裂纹面积分布的特征组合最优。
- 方法可推广至材料科学等领域，用于基于图案的成分反推。
我们提出一种优化的断裂图案分类协议，利用人工神经网络识别淀粉-液体悬浮液在完全蒸发后干燥裂解过程中所使用的溶剂。采用图像分析技术，对单一溶剂（水、乙醇、丙酮）及不同浓度的水-乙醇混合溶剂所形成的干燥悬浮液图案进行表征。基于九个形态学特征（尺寸、形状、几何结构与取向有序性）生成频率直方图，并将其作为人工神经网络的输入数据，以确定最优特征组合提升溶剂识别精度。实验结果显示，所有溶剂分类的平均准确率达到 $96(\± 1)%$。其中包含裂纹面积分布的特征集表现最佳。该方法可为其他科学与工程领域中的模式识别提供特征优化参考。
原文摘要 · Abstract (English)
We introduce an optimized protocol of fracture pattern classification using an artificial neural network to identify the solvent involved in the desiccation cracking process of starch-liquid slurries, even after it has been completely evaporated. For this purpose, image analysis techniques were used to characterize patterns obtained from drying suspensions using single solvents (water, ethanol, acetone) and two-component solvents (water-ethanol mixtures at different concentrations). Frequency histograms were generated based on nine morphological features, taking into account their size, shape, geometry and orientational ordering. Subsequently, we used these histograms as input data into artificial neural network variants to determine the set of features that lead to the higher accuracy in solvent identification. We obtained an average accuracy of $96(\pm 1)\%$ considering all solvents in the analysis. The highest accuracy was obtained with sets of features that include the crack area distribution. The proposed protocol can help to determine the combination of features that optimize pattern recognition in other fields of science and engineering.
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