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This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License(http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.This paper aims to demonstrate the design, structure analysis, and hydrostatic pressure test of the cylinder used in 2000m water depth. The cylinder was designed in accordance with ASME pressure vessel design rule. The 1.5 times safety factor required by the general rule was applied to the design of the cylinder, because ASME rule is so excessive that it is not proper to apply to the hydrostatic pressure test. The finite element analysis was conducted for the cylinder. The cylinder was produced according to the design. The hydrostatic pressure test was conducted at the hyperbaric chamber in KRISO. The results of finite element analysis(FEM) and those of the hydrostatic pressure test were almost the same, which showed that the design was exact and

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Grading breast density is highly sensitive to normalization settings of digital mammogram as the density is tightly correlated with the distribution of pixel intensity. Also, the grade varies with readers due to uncertain grading criteria. These issues are inherent in the density assessment of digital mammography. They are problematic when designing a computer-aided prediction model for breast density and become worse if the data comes from multiple sites. In this paper, we proposed two novel deep learning techniques for breast density prediction: 1) photometric transformation which adaptively normalizes the input mammograms, and 2) label distillation which adjusts the label by using its output prediction. The photometric transformer network predicts optimal parameters for photometric transformation on the fly, learned jointly with the main prediction network. The label distillation, a type of pseudo-label techniques, is intended to mitigate the grading variation. We experimentally showed that the proposed methods are beneficial in terms of breast density prediction, resulting in significant performance improvement compared to various previous approaches.