• Journal of the Korean Geotechnical Society
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• v.18 no.5
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• pp.109-122
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• 2002

The water content of soil near the ground subgrade varies seasonally, and dynamic deformational characteristics of soil are affected by the variation of water content. Contrary to previous studies which used various specimens of different compaction moisture contents, the influences of water content and capillary Pressure on dynamic deformational characteristics of soil were investigated using the given specimen controlling the matric suction. RC/TS(resonant column and torsional shear) testing equipment was modified so that it can control water content with changing capillary pressure(matric suction). RC/TS tests were performed on subgrade soil collected in the KHC(Korea Highway Corporation) test road. In the field, the cross-hole tests were performed and the water contents were measured at the same site to verify the feasibility and applicability of RC/TS test results. As water content decreased, the tendency of increasing shear moduli in field was well matched with laboratory test results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.11C
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• pp.940-947
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• 2010

Motion Compensated Frame Interpolation(MCFI) has been used to reduce motion jerkiness for dynamic scenes and motion blurriness for LCD-panel display as post processing for high quality display. However, MCFI that directly uses the motion information often suffers from annoying artifacts such as blockiness, ghost effects, and deformed structures. So in this paper, we propose a novel edge-based adaptively weighted vector median filter as post-processing. At first, the proposed method generates an edge direction map through a sobel mask and a weighted maximum frequent filter. And then, outlier MVs are removed by average of angle difference and replaced by a median MV of $3{\times}3$ window. Finally, weighted vector median filter adjusts the weighting values based on edge direction derived from spatial coherence between the edge direction continuity and motion vector. The results show that the performance of PSNR and SSIM are higher up to 0.5 ~ 1 dB and 0.4 ~ 0.8 %, respectively.

• The KIPS Transactions:PartD
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• v.15D no.5
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• pp.681-690
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• 2008

In proteomics research, the identification of differentially expressed proteins observed under specific conditions is one of key issues. There are several ways to detect the change of a specific protein's expression level such as statistical analysis and graphical visualization. However, it is quiet difficult to handle the spot information of an individual protein manually by these methods, because there are a considerable number of proteins in a tissue sample. In this paper, using database and data mining techniques, the application plan of OLAP data cube and Discovery-driven exploration is proposed. By using data cubes, it is possible to analyze the relationship between proteins and relevant clinical information as well as analyzing the differentially expressed proteins by disease. We propose the measure and exception indicators which are suitable to analyzing protein expression level changes are proposed. In addition, we proposed the reducing method of calculating InExp in Discovery-driven exploration. We also evaluate the utility and effectiveness of the data cube and Discovery-driven exploration in the lung cancer 2-DE gel image.

• Journal of the Korean Geographical Society
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• v.28 no.4
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• pp.356-378
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• 1993

This study examines the competitiveness of urban industrial structure and its changing characteristics. Cluster analysis of Arizona towns based on economic functions revealed the changing characteristics of urban functions over time. The relationship between the changes of urban functions and industrial competitiveness was confirmed through shift-share analysis. The level of industrial specialization has become more closely related to urban size in terms of both population and employment, but the relationship between metropolitan location and specialization level is not clear. Also, it is validated that the economies of Arizona towns have become more diversified and, consequently, have tended to converge toward the state average in industrial structure over time.

• Journal of Oral Medicine and Pain
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• v.25 no.3
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• pp.265-276
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• 2000

스트레스가 타액선 조직을 변형시키고 파괴시킬 수 있다는 것은 이미 보고된 바 있다. 이는 구속스트레스 시에 관찰되는 apoptosis에 의한 것인데, 이 과정에 관여하는 caspase-3는 세포의 DNA를 분절시킴으로서 apoptosis를 일으킨다고 알려진 세포내 단백효소이다. 이에 기존에 관찰되었던 구속 스트레스에 의한 apoptosis의 형태적 변화가 apoptosis를 유도하는 caspase-3와 어떠한 시기적 상관관계를 가지고 있는지를 구명하기 위하여 본 실험을 시행하였다. 웅성 백서 (Sprague-Dawley, 8주) 를 사용하여 실험 전 기간에 걸쳐 구속스트레스를 가한 후 30분, 1시간, 3시간, 6시간, 24시간, 3일, 5일, 7일에 희생시켰다. 그 후 실험동물의 악하선을 절취하여 동결절편을 제작한 후, caspase-3에 대한 형광항체로 면역형광법을 시행하여 관찰하였다. 1. 정상대조군에서는 caspase-3가 타액선 조직 전반에서 미약하게 관찰되었다. 2. 구속스트레스 부여 30분에서 caspase-3는 강반응을 보였고, 실험기간이 경과됨에따라 점차 6시간군에서 부터는 현저히 감소하였다. 3. Caspase-3는 구속 스트레스 30분에 도관과 선포세포 모두에서 발현되었으나, 선포세포에서는 조기에 급격히 소실되었고 도관세포에서는 전 실험 기간에 걸쳐 서서히 소실되었다. 이와 같은 연구 결과에서, 세포내 caspase-3는 조직의 형태적 변화가 나타나기 이전에 발현하는 것으로 보아 caspase-3는 형태적 변화를 예견할 수 있는 진단 지표로 사용될 수 있을 것으로 사료되며 이후 임상적으로 적용하기 위한 지속적인 연구가 필요할 것으로 생각된다.

• New Physics: Sae Mulli
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• v.68 no.12
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• pp.1364-1373
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• 2018

A recognizable form having meaning is called a sign in semiotics. The sign is transformed into a physical counter form in this work. Its internal structure is restricted on the linguistic concept structure. We borrow the concept of a mathematical function from the utility function of a rational personal in the economy. Universalizing the utility function by introducing the consistency of independency on the manner of construction, we construct the probability. We introduce a random variable for the probability and join it to a position variable. Thus, we propose a physical sign and its serial changes in the forms of stochastic equations. The equations estimate three patterns (jumping, drifting, diffusing) of possible solutions, and we find them in the one-day stock-price flow. The periods of jumping, drifting and diffusing were about 2, 3.5, and 6 minutes for the Kia stock on 11/05/2014. Also, the semiotic sign (icon, index, symbol) can be expected from the equations.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.259-259
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• 2019

시계열 자료들을 분석하고자 하는 경우 자료가 정상성(stationarity)을 만족하는 경우는 드물다. 특히 계절성을 제거한 자료들에서는 정량화하기 어려운 주기성이 많이 관찰된다. 즉, 어떤 특정지역에서 나타나는 현상이 다른 기상 현상에 영향을 미칠 것은 자명한 일이나 그 관련성이 선형(linearity)일 가능성은 극히 드물다. 따라서 그들 사이의 관련성이 선형성에 근거한 지표들로 정량화되어야 한다. 이러한 문제점을 해결하기 위해서 다양한 방법이 사용되며 그중에서 웨이블릿 분석을 통해 본 연구를 진행하였다. 웨이블릿 변환(wavelet transforms)은 특수한 함수의 집합으로 구성되어 기존 웨이블릿 신호의 분석을 위해 사용되는 방법이다. 이 변환은 푸리에 변환에서 변형된 방법으로 특정한 기저 함수(base function)를 이용하여 기존의 시계열 자료를 주파수로 바꾸는 변환이다. 웨이블릿 변환에서 기저 함수를 모 웨이블릿이라고 하며 이를 천이, 확대 및 축소 과정을 통해 주파수를 구성한다. 웨이블릿 분석은 모 웨이블릿을 분해하고 재결합하여 시계열 분석을 할 수 있다. 모 웨이블릿 함수에는 Haar, Daubechies, Coiflets, Symlets, Morlet, Mexican Hat, Meyer 등의 여러 가지 종류의 모 웨이블릿 함수가 있으며 모 웨이블릿이 달라지면 결과가 다르게 나타난다. 기존에는 Morlet 웨이블릿을 주로 이용하여 주파수분석에 사용하여 결과를 도출하였다. 그리고 시계열 자료는 크게 백색잡음(White Noise), 장기기억(Long Term Memory), 단기기억(Short Term Memory)으로 나뉜다. 각 시계열 자료의 종류에 따라 임의의 시계열 자료를 산정하여 그에 따른 웨이블릿 분석을 통해 모 웨이블릿의 특성을 도출하였다. 본 연구에서는 웨이블릿 분석을 통해 시계열 자료의 최적 모 웨이블릿을 결정하고자 남방진동지수(SOI), 북극진동지수(AOI)의 자료를 이용하여 웨이블릿 분석을 시도하였다. 웨이블릿 분석은 모 웨이블릿에 따라 달라지는 결과를 토대로 분석하였으며 이를 정상성과 지속성에 따라 분류된 시계열에 적용하여 최적 모 웨이블릿을 결정하고자 하였다. 본 연구에서는 임의의 시계열 자료에서 설정한 최적의 모 웨이블릿을 AOI와 SOI와 같은 실제 시계열 자료에 대입하여 분석을 진행하였다. 본 연구에서는 시계열 자료의 종류를 구분하고 자료의 특성에 따라 가장 적합한 모 웨이블릿을 구하고자 하였다.

• Journal of Broadcast Engineering
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• v.26 no.2
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• pp.167-174
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• 2021

Recently, deep learning technology is widely used in various computer vision applications, such as object recognition, classification, and image generation. In particular, the deep learning-based super-resolution has been gaining significant performance improvement. Fast super-resolution convolutional neural network (FSRCNN) is a well-known model as a deep learning-based super-resolution algorithm that output image is generated by a deconvolutional layer. In this paper, we propose an FPGA-based convolutional neural networks accelerator that considers parallel computing efficiency. In addition, the proposed method proposes Optimal-FSRCNN, which is modified the structure of FSRCNN. The number of multipliers is compressed by 3.47 times compared to FSRCNN. Moreover, PSNR has similar performance to FSRCNN. We developed a real-time image processing technology that implements on FPGA.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.6
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• pp.257-264
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• 2019

Recently, deep neural network based approaches have shown a good performance for various fields of natural language processing. A huge amount of training data is essential for building a deep neural network model. However, collecting a large size of training data is a costly and time-consuming job. A data augmentation is one of the solutions to this problem. The data augmentation of text data is more difficult than that of image data because texts consist of tokens with discrete values. Generative adversarial networks (GANs) are widely used for image generation. In this work, we generate sentimental texts by using one of the GANs, CS-GAN model that has a discriminator as well as a classifier. We evaluate the usefulness of generated sentimental texts according to various measurements. CS-GAN model not only can generate texts with more diversity but also can improve the performance of its classifier.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.997-1008
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• 2022

Today, AI(Artificial Intelligence) technology is being extensively researched in various fields, including the field of malware detection. To introduce AI systems into roles that protect important decisions and resources, it must be a reliable AI model. AI model that dependent on training dataset should be verified to be robust against new attacks. Rather than generating new malware detection, attackers find malware detection that succeed in attacking by mass-producing strains of previously detected malware detection. Most of the attacks, such as adversarial attacks, that lead to misclassification of AI models, are made by slightly modifying past attacks. Robust models that can be defended against these variants is needed, and the Robustness level of the model cannot be evaluated with accuracy and recall, which are widely used as AI evaluation indicators. In this paper, we experiment a framework to evaluate robustness level by generating an adversarial sample based on one of the adversarial attacks, C&W attack, and to improve robustness level through adversarial training. Through experiments based on malware dataset in this study, the limitations and possibilities of the proposed method in the field of malware detection were confirmed.