• 대한전자공학회논문지SP
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• 제42권1호
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• pp.1-7
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• 2005

웨이블릿 영역에서 영역별 양방향 예측, KLT (Karhunen-Loeve transform)/sup ［13］/, 및 3차원 SPIHT (set partition in hierarchical trees)/sup ［1］/를 이용한 인공위성 화상데이터의 부호화 방법을 제안하였다. 가시광선 영역과 적외선 영역에서 선택된 기준대역 (feature band)에 대하여 SPIHT를 행하여 부호화함으로써 대역내 (intraband) 중복성을 제거한다. 기준대역을 예측대역(prediction band)들에 대해서는 웨이블릿 변환 (wavelet transform)을 행한 후, 빛의 반사 및 역의 방사에 따라 대역별 특성이 다름을 이용하여 영역분류를 하고 영역별 양방향 예측 (classified bidirectional prediction)을 행함으로써 대역간 (interband) 중복성을 제거한다. 원 인공위성 화상데이터와 부호화 된 인공위성 화상데이터 사이의 오차값으로 구성된 오차대역 (residual band)들에 대하여 KLT를 행함으로써 대역간 중복성이 제거되고 계수값들은 고유치의 크기에 따라서 분광적으로 정렬됨으로써 3차원 SPIHT의 부호화 효율을 향상시킨다. 인공위성 화상데이터에 대한 모의실험을 통하여 제안한 방법의 부호화 효율이 기존의 방법에 비하여 우수함을 확인하였다.

• 한국멀티미디어학회논문지
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• 제10권1호
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• pp.139-146
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• 2007

Wireless Sensor network를 이용하여 객체를 추적하는 방법에 대해 많은 연구가 진행되어 왔다. 본 연구는 객체 추적에 사용되는 방법에 따라 에너지의 양과 추적의 정확도 사이에 존재하는 상관관계를 관찰하고, 움직임 예측 방법에서 에너지 소비량을 최소화할 수 있음을 확인하였다. 추적에 사용되는 에너지는 센서노드가 객체를 감지하기 위해 소모하는 것이며, 추적의 정확도는 객체의 실제위치와 감지에 의해 계산된 위치의 차이이다. 몇 가지 추적방법과 파라미터의 조절에 따라 추적의 정확도와 소비되는 에너지의 양에 차이가 있고, 움직임 예측 알고리즘을 사용할 때 가장 좋은 에너지 효율을 얻을 수 있었다. 또한 가속도를 고려한 움직임 예측 알고리즘의 개선을 통해 더 나은 정확도와 에너지 효율을 기록하였다. 시뮬레이션 결과 움직임 예측 알고리즘에서 목표의 미래위치에 따라 노드를 활성화시키는 범위는 예측 알고리즘이 정확할 경우 센서 노드의 감지범위 정도로 제한하는 것이 유리함을 알 수 있었다.

• 대기
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• 제27권2호
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• pp.235-249
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• 2017

This paper describes methodology verifying near-surface predictability of numerical weather prediction models against the surface synoptic weather station network (SYNOP) observation. As verification variables, temperature, wind, humidity-related variables, total cloud cover, and surface pressure are included in this tool. Quality controlled SYNOP observation through the pre-processing for data assimilation is used. To consider the difference of topographic height between observation and model grid points, vertical inter/extrapolation is applied for temperature, humidity, and surface pressure verification. This verification algorithm is applied for verifying medium-range forecasts by a global forecasting model developed by Korea Institute of Atmospheric Prediction Systems to measure the near-surface predictability of the model and to evaluate the capability of the developed verification tool. It is found that the verification of near-surface prediction against SYNOP observation shows consistency with verification of upper atmosphere against global radiosonde observation, suggesting reliability of those data and demonstrating importance of verification against in-situ measurement as well. Although verifying modeled total cloud cover with observation might have limitation due to the different definition between the model and observation, it is also capable to diagnose the relative bias of model predictability such as a regional reliability and diurnal evolution of the bias.

• 한국유체기계학회 논문집
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• 제2권4호
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• pp.48-56
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• 1999

For the study on loss coefficients of turbine cascade with variation of incidence angle, the wind-tunnel tests were performed under the ranges in velocity of 10 m/s, 15 m/s, 20 m/s and incidence angles from $-20^{\circ}\;to\;20^{\circ}$ by intervals of $5^{\circ}$. Comparing our results with Soderberg's prediction, differences in loss coefficient were $2.5\%\;and\;2.8\%$ each for 10 m/s and 15 m/s. A large disagreement of $30.3\%$ was showed at 20 m/s freestream velocity. The comparisons of these test results with Ainley's prediction showed an $8\%$ difference in the case of 20 m/s freestream velocity. Test results were approximately comparable with Ainley's loss prediction's in incidence angles. Generally, averaged total pressure loss seemed to be decreased as Reynolds number increased. The total pressure loss coefficients were increased parabolically, as incidence angles were increased negatively and positively from $0^{\circ}$, in all speed ranges. At the far low freestream velocities, minimum loss accurred between $-5^{\circ}\;and\;+5^{\circ}$. But this minimum range narrowed the location of this range by shifting to the direction of the angle as freestream velocity was increased.

• 대한기계학회논문집
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• 제19권2호
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• pp.475-485
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• 1995

In this study, statistical analysis of fatigue data which had obtained from respective 24 fatigue crack, was examined for SiC whisker reinforced aluminium 6061 composite alloy (SiC$_{w}$/A16061) and aluminium 6061 alloy. SiC volume fraction in composite alloy is 25%. The analysis results stress intensity factor range and 0.1 mm fatigue crack initiation life for SiC$_{w}$/A16061 composite & A16061 matrix are the log-normal distribution. And regression analysis by linear model, exponential model and multiplicative model were performed to find out the relationship between fatigue crack growth rate(da/dN) and stress intensity for find out the relationship between fatigue crack growth rate(da/dN) and stress intensity factor range(.DELTA.K) in the SiC$_{w}$/A16061 composite and examine the applicability of Paris' equation to SiC$_{w}$A16061 composite. Also computer simulation was performed for fatigue life prediction of SiC$_{w}$/A16061 composite using the statistical results of this study.udy.

• Journal of Electrical Engineering and Technology
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• 제13권3호
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• pp.1326-1336
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• 2018

This paper proposes a numerical approach to predict the critical flashover voltages of air gaps under lightning impulses. For an air gap, the impulse voltage waveform features and electric field features are defined to characterize its energy storage status before the initiation of breakdown. These features are taken as the input parameters of the predictive model established by support vector machine (SVM). Given an applied voltage range, the golden section search method is used to compute the prediction results efficiently. This method was applied to predict the critical flashover voltages of rod-rod, rod-plane and sphere-plane gaps over a wide range of gap lengths and impulse voltage waveshapes. The predicted results coincide well with the experimental data, with the same trends and acceptable errors. The mean absolute percentage errors of 6 groups of test samples are within 4.6%, which demonstrates the validity and accuracy of the predictive model. This method provides an effectual way to obtain the critical flashover voltage and might be helpful to estimate the safe clearances of air gaps for insulation design.

• 한국산림과학회지
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• 제103권2호
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• pp.233-239
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• 2014

본 연구는 지역산림지역을 중심으로 수량화이론을 이용하여 산사태 발생면적에 영향을 미치는 인자를 도출하여 각 인자의 기여도 분석을 통해 산사태 발생 위험성에 대한 예측기준을 마련하고, 그 기준을 평가하였다. 산사태 발생지 붕괴면적에 영향을 미치는 인자는 모암(화성암), 횡단사면(복합), 침엽수림(임상), 사면경사($21{\sim}30^{\circ}$ 이상)이었다. 각 인자의 Range를 추정한 결과, 횡단사면 (0.2922)이 가장 높게 나타났고, 다음으로는 모암(0.2691), 임상(0.2631), 사면경사(0.2312)순으로 나타났다. 산사태 발생 위험도 판정표를 기준으로 4개 인자의 category별 점수를 계산한 추정치 범위는 0 점에서 1.0556 점 사이에 분포하고 있으며, 중앙값은 0.5278 점이었다. I 등급의 점수는 0.7818 이상, II 등급은 0.5279~7917, III 등급은 0.2694~0.5278, IV 등급은 0.2693 이하로 나타났다. 1 등급 및 2 등급에서 산사태 발생 비율이 72%로서 비교적 높은 적중률을 보였다. 따라서 본 판정표는 산사태 위험도 판정에 활용 가능한 것으로 판단된다.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제15권3호
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• pp.191-199
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• 2011

In this paper, we adopt a position specific scoring matrix as an abstraction of amino acid type to derive two new statistical potentials for protein structure prediction, and investigated its effect on the quality of the potentials compared to that derived using residue specific amino acid identity. For stringent test of the potential quality, we carried out folding simulations of 91 residue A chain of protein 2gpi, and found unexpectedly that the abstract amino acid type improved the quality of the one-body type statistical potential, but not for the two-body type statistical potential which describes long range interactions. This observation could be effectively used when one develops more accurate potentials for structure prediction, which are usually involved in merging various one-body and many-body potentials.

• 경영과학
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• 제14권2호
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• pp.91-111
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• 1997

Neural Network(NN) is known to be suitable for forecasting corporate bankruptcy because of discriminant capability. Bandkruptcy prediction on NN by now has mostly been studied based on financial indices at specific point of time. However, the financial profile of corporates fluctuates within a certain range with the elapse of time. Besides, we need a lot of data of different bankrupt types in order to apply NN for better bankruptcy prediction. Therefore, We have decided to focus on textile and clothing industries for bankruptcy prediction with limited data. One part of the collected data was used for training and calibration, and the other was used for verification. The model makes a learning with extended data from financial indices at specific point of time. The trained model has been tested and we could get a high hitting ratio relatively.

• Computers and Concrete
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• 제12권2호
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• pp.109-129
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• 2013

This work presents an approach to model concrete shrinkage. The goal is to permit the concrete industry's experts to develop independent prediction models based on a reduced number of experimental data. The proposed approach combines fuzzy logic and genetic algorithm to optimize the fuzzy decision-making, thereby reducing data collection time. Such an approach was implemented for an experimental data set related to self-compacting concrete. The obtained prediction model was compared against published experimental data (not used in model development) and well-known shrinkage prediction models. The predicted results were verified by statistical analysis, which confirmed the reliability of the developed model. Although the range of application of the developed model is limited, the genetic-fuzzy approach introduced in this work proved suitable for adjusting the prediction model once additional training data are provided. This can be highly inviting for the concrete industry's experts, since they would be able to fine-tune their models depending on the boundary conditions of their production processes.