• The Journal of Korean Academy of Prosthodontics
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• v.57 no.2
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• pp.95-101
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• 2019

Purpose: The aim of this study is to evaluate the effects of various primers on the microtensile bond strength (${\mu}TBS$) of resin cements to cobalt-chromium (Co-Cr) dental casting alloy. Materials and methods: Four adhesive primers (Universal primer, Metal primer II, Alloy primer, and Metal/Zirconia primer) and two resin cements (Panavia F2.0, G-CEM LinkAce) were tested. One hundred fifty Co-Cr beams were prepared from Co-Cr ingots via casting ($6mm\;ength{\times}1mm\;width{\times}1mm\;thick$). The metal beams were randomly divided into ten groups according to the adhesive primers and resin cements used; the no-primer groups served as the control (n = 15). After sandblasting with aluminum oxide ($125{\mu}m$ grain), the metal and resin cements were bonded together using a silicone mold. Prior to testing, all metal-resin beams were examined under stereomicroscope, and subjected to the ${\mu}TBS$ test. The mean value of each group was analyzed via one-way ANOVA with Tukey's test as post hoc (${\alpha}=.05$) using SPSS software. Results: The mean ${\mu}TBS$ of all groups was ranged from 20 to 28 MPa. There is no statistically significant difference between groups (P > .05). Mixed failure, which is the combination of adhesive and cohesive failures, is the most prevalent failure mode in both the Panavia F2.0 and G-Cem LinkAce groups. Conclusion: The ${\mu}TBS$ of all tested groups are relatively high; however, the primers used in this study result in no favorable effect in the ${\mu}TBS$ of Panavia F2.0 and G-Cem LinkAce resin cement to Co-Cr alloy.

• Journal of Korea Water Resources Association
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• v.56 no.6
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• pp.403-417
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• 2023

In this research, a methodology was developed for constructing an appropriate rainfall image database for estimating rainfall intensity based on CCTV video. The database was constructed in the Large-Scale Climate Environment Chamber of the Korea Conformity Laboratories, which can control variables with high irregularity and variability in real environments. 1,728 scenarios were designed under five different experimental conditions. 36 scenarios and a total of 97,200 frames were selected. Rain streaks were extracted using the k-nearest neighbor algorithm by calculating the difference between each image and the background. To prevent overfitting, data with pixel values greater than set threshold, compared to the average pixel value for each image, were selected. The area with maximum pixel variability was determined by shifting with every 10 pixels and set as a representative area (180×180) for the original image. After re-transforming to 120×120 size as an input data for convolutional neural networks model, image augmentation was progressed under unified shooting conditions. 92% of the data showed within the 10% absolute range of PBIAS. It is clear that the final results in this study have the potential to enhance the accuracy and efficacy of existing real-world CCTV systems with transfer learning.