Cement is the most used construction material. The performance of cement hydrate depends on the constituent phases, viz. alite, belite, aluminate, and ferrites present in the cement clinker, both qualitatively and quantitatively. Traditionally, clinker phases are analyzed from optical images relying on a domain expert and simple image processing techniques. However, the non-uniformity of the images, variations in the geometry and size of the phases, and variabilities in the experimental approaches and imaging methods make it challenging to obtain the phases. Here, we present a machine learning (ML) approach to detect clinker microstructure phases automatically. To this extent, we create the first annotated dataset of cement clinker by segmenting alite and belite particles. Further, we use supervised ML methods to train models for identifying alite and belite regions. Specifically, we finetune the image detection and segmentation model Detectron-2 on the cement microstructure to develop a model for detecting the cement phases, namely, Cementron. We demonstrate that Cementron, trained only on literature data, works remarkably well on new images obtained from our experiments, demonstrating its generalizability. We make Cementron available for public use.
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集成电路(IC)的测试是一个非常昂贵的过程,但在确定IC的缺陷水平方面也是最重要的过程。 IC中的制造缺陷是使用符合故障模型对其进行建模的。拟合型号的模型涵盖了制造过程中发生的大多数物理故障。由于半导体技术的发展,功能尺寸降低,缺陷的尺寸也越来越小。这些难以检测的缺陷的测试是使用确定性测试生成(DTG)算法生成的。我们的工作旨在降低面向路径的决策成本:podem(DTG算法)而不损害测试质量。我们训练了一个元预测器,以选择给定电路和目标网的最佳模型。该合奏选择具有95%精度的最佳概率预测模型。从其CPU时间角度来看,这导致了回溯决策的数量减少,Podem的表现更好。我们表明,我们的ML引导的PoDEM算法具有元预测器的表现,其质量超过34%,而其他最先进的ML引导算法则至少高于ISCAS85基准电路的15%。
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