• The Journal of Engineering Geology
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• v.18 no.4
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• pp.447-457
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• 2008

Laboratory tests for single plane sliding were conducted using the model rock slope to investigate the cut slope deformability and failure mechanism due to combined effect of engineering characteristics such as angle of sliding plane, water force, joint roughness and infillings. Also the possibility of prediction of slope failure through displacement monitoring was explored. The joint roughness was prepared in forms of saw-tooth type having different roughness specifications. The infillings was maintained between upper and lower roughness plane from zero to 1.2 times of the amplitude of the surface projections. Water force was expressed as the percent filling of tension crack from dry (0%) to full (100%), and constantly increased from 0% at the rate of 0.5%/min and 1%/min upto failure. Total of 50 tests were performed at sliding angles of $30^{\circ}$ and $35^{\circ}$ based on different combinations of joint roughness, infilling thickness and water force increment conditions. For smooth sliding plane, it was found that the linear type of deformability exhibited irrespective of the infilling thickness and water force conditions. For sliding planes having roughness, stepping or exponential types of deformability were predominant under condition that the infilling thickness is lower or higher than asperity height, respectively. These arise from the fact that, once the infilling thickness exceeds asperities, strength and deformability of the sliding plane is controlled by the engineering characteristics of the infilling materials. The results obtained in this study clearly show that the water force at failure was found to increase with increasing joint roughness, and to decrease with increasing filling thickness. It seems possible to estimate failure time using the inverse velocity method for sliding plane having exponential type of deformability. However, it is necessary to estimate failure time by trial and error basis to predict failure of the slope accurately.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.1
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• pp.107-113
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• 2009

This study has been conducted for the long-term riverbed change prediction on Geum River and Miho Stream surrounding the planned Multifunctional Administrative City and the neighboring regions by the construction of a small dam. Based on the analysis of vertical riverbed changes of the cross-sectional data for the years 1988, 2002 and 2007, minimum bed elevation significantly decreased in both Geum River and Miho Stream in 2007 as compared to 1988. Compared to 2002, however, a slight elevation change was observed. To make a long-term prediction on riverbed changes by the construction of a small dam, a one dimensional HEC-RAS 4.0 model has been used. By the fixed bed model test, the water levels were calibrated. By using the cross-sectional data of 1988 and 2002, verification was conducted under a movable bed model. According to the prediction of riverbed changes for each scenario with varying height of small dam, minor impact is expected around Miho Stream while major impact is expected around Geum River by 2017, as the small dam height increases. If the small dam is 7m-high, for example, it's been simulated that 1.59m deposition would be expected around the upper stream of Miho Stream Confluence while 1.98m scour would be expected around the downstream of the small dam.

• Journal of Broadcast Engineering
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• v.18 no.5
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• pp.730-737
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• 2013

In this paper, Constrained One-Bit Transform (C1BT) based motion estimation using extension of matching error criterion is proposed. C1BT based motion estimation algorithm exploiting Number of Non-Matching Points (NNMP) instead of Sum of Absolute Differences (SAD) that used in the Full Search Algorithm (FSA) facilitates hardware implementation and significantly reduces computational complexity. However, the accuracy of motion estimation is decreased. To improve inaccurate motion estimation, this algorithm based motion estimation extending matching error criterion of C1BT is proposed in this paper. Experimental results show that proposed algorithm has better performance compared with the conventional algorithm in terms of Peak-Signal-to-Noise-Ratio (PSNR).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.167-168
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• 2017

In this paper, we discuss the importance of what to analyze and what to predict using Big Data. The issue of how and where to apply a large amount of data that is accumulated in my daily life and which I am not aware of is a very important factor. There are many kinds of tasks that specify what to predict and how to use these data. Finding the most appropriate one is the way to increase the prediction probability. In addition, the data that are analyzed and predicted should be useful in real life to make meaningful data.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.319-330
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• 2019

Forecasting the electricity consumption through analyzing the past electricity consumption a advantageous for energy planing and policy. Machine learning is widely used as a method to predict electricity consumption. Among them, ensemble learning is a method to avoid the overfitting of models and reduce variance to improve prediction accuracy. However, ensemble learning applied to daily data shows the disadvantages of predicting a center value without showing a peak due to the characteristics of ensemble learning. In this study, we overcome the shortcomings of ensemble learning by considering the temperature trend. We compare nine models and propose a model using random forest with the linear trend of temperature.

• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.851-865
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• 2019

Discovering various features from one time series is complicated. In this paper, we introduce a multi aggregation prediction algorithm (MAPA) that uses the concepts of temporal aggregation and combining forecasts to find multiple patterns from one time series and increase forecasting accuracy. Temporal aggregation produces multiple time series and each series has separate properties. We use exponential smoothing methods in the next step to extract various features of time series components in order to forecast time series components for each series. In the final step, we blend predictions of the same kind of components and forecast the target series by the summation of blended predictions. As an empirical example, we forecast traffic accident counts using MAPA and observe that MAPA performance is superior to conventional methods.

• Journal of Power System Engineering
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• v.16 no.2
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• pp.36-42
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• 2012

본 논문은 고파워 전자소자로부터 에너지를 수확하는 열전생성기의 성능에 pellet의 기학학적 구조가 미치는 영향들을 보고한다. 열경계저항을 포함하는 열전모델을 적용하여, 다양한 경계조건들과 열원의 열율들에 대해 pellet의 높이, pellet의 단면적, thermocouple의 수를 최적화 하고, 이처럼 최적화된 pellet의 기하학적 구조를 갖는 열전생성기의 성능과 일반적인 pellet으로 구성된 열전생성기의 전력생성성능과 효율이 예측되고 비교되어진다. 예측된 결과는 최적화된 pellet으로 구성된 열전생성기가 일반적인 pellet으로 구성된 열전생성기보다 2-10배까지 생성효율이 우수함을 보여준다. 최적화된 pellet으로 구성된 열전생성기와 일반적인 pellet으로 구성된 열전생성기의 열적성능도 예측되고 비교된다.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2011.11a
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• pp.369-372
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• 2011

본 연구에서는 철도차량의 실물화재에 대한 열방출율을 축소모형으로 예측하기 위해 실험적인 조건들을 연구하였다. 축소모형의 크기는 지하철 전동차의 1/10 스케일을 적용하여 길이 1.89m, 너비 0.295m, 높이 0.235m 이며 15mm의 석고보드를 사용하였다. 축소모형의 실험적 조건으로는 한쪽 면만 개방한 4개의 개구부 환기조건과 3mm, 6mm의 종이내장재를 이용하여 열방출율을 예측하여 보았다.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.587-588
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• 2011

본 논문에서는 강사장교의 극한강도 및 파괴모드를 간략하게 예측할 수 있는 간단하고 빠른 해석법을 제안하였다. 기존의 비탄성 고유치해석의 기본 개념을 바탕으로 기둥 요소에 대한 수렴 기준을 보였고, 사장교 구조 시스템의 거더 및 주탑 요소에서 보-기둥 거동을 고려하기 위한 새로운 수렴 기준을 제시하였다. 제시된 방법의 타당성 검증을 위하여 중앙경간 길이와 거더의 높이를 변화시킨 강사장교 모델에 대하여 제안된 비탄성 고유치 해석과 비선형 탄소성 해석 결과를 비교하였다. 해석 결과, 제안된 수렴 기준을 적용한 비탄성 고유치 해석은 기존에 기둥의 수렴기준을 적용했던 방법에 비하여 강사장교의 극한강도를 보다 정확히 예측할 수 있었다. 또한, 제안된 방법은 강사장교의 파괴모드 역시 근사하게 모사 가능함을 알 수 있었다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.272-273
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• 2019

VVC(Versatile Video Coding)는 YUV 입력 영상에 대하여 Luma 성분과 Chroma 성분에 대하여 각각 다른 최적의 방법으로 블록분할 후 해당 블록에 대해서 화면 내 예측 또는 화면 간 예측을 수행하고, 예측영상과 원본영상의 차이를 변환, 양자화하여 압축한다. 이 과정에서 복원영상에는 블록화 노이즈, 링잉 노이즈, 블러링 노이즈 발생한다. 본 논문에서는 인코더에서 원본영상과 복원영상의 잔차신호에 대한 MAE(Mean Absolute Error)를 추가정보로 전송하여 이 추가정보와 복원영상을 이용하여 Deep Learning 기반의 신경망 네트워크로 영상의 품질을 높이는 방법을 제안한다. 복원영상의 노이즈를 감소시키기 위하여 영상을 $32{\times}32$블록의 임의로 분할하고, DenseNet기반의 UNet 구조로 네트워크를 구성하였다.