• JSTS:Journal of Semiconductor Technology and Science
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• 제16권5호
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• pp.713-718
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• 2016

Predicting precise specifications of differential mixed-signal circuits is a difficult problem, because analytically derived correlation between process variations and conventional specifications exhibits the limited prediction accuracy due to the phase unbalance, for most self-tests. This paper proposes an efficient prediction technique to provide accurate specifications of differential mixed-signal circuits in a system-on-chip (SoC) based on a nonlinear statistical nonlinear regression technique. A spectrally pure sinusoidal signal is applied to a differential DUT, and its output is fed into another differential DUT through a weighting circuitry in the loopback configuration. The weighting circuitry, which is employed from the previous work [3], efficiently produces different weights on the harmonics of the loopback responses, i.e., the signatures. The correlation models, which map the signatures to the conventional specifications, are built based on the statistical nonlinear regression technique, in order to predict accurate nonlinearities of individual DUTs. In production testing, once the efficient signatures are measured, and plugged into the obtained correlation models, the harmonic coefficients of DUTs are readily identified. This work provides a practical test solution to overcome the serious test issue of differential mixed-signal circuits; the low accuracy of analytically derived model is much lower by the errors from the unbalance. Hardware measurement results showed less than 1.0 dB of the prediction error, validating that this approach can be used as production test.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2012년도 춘계학술대회 논문집
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• pp.45-54
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• 2012

In order to predict trailing edge noise from a flat plate more effectively and accurately, the prediction algorithm based on semi-analytic model for point pressure spectrum is proposed. The semi-analytic model consists of empirical models for point pressure spectra and theoretical model to determine the boundary layer characteristics needed for the empirical models. The proposed methods are applied to predict the trailing edge noise of the flat plate located in the mean flow of speed 38 m/s, for which the measured data are available. In present study, six empirical models for point pressure spectra are utilized for the predictions of trailing edge noise and their prediction results are compared to the measured data. Through the analysis of these comparisons, it is revealed that the present method based on non-frozen formula using Efimtsov model and Smol'yakov-Tkachenko model can provide more accurate and efficient predictions of trailing edge noise.

• 한국소음진동공학회논문집
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• 제22권6호
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• pp.524-534
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• 2012

In order to predict trailing edge noise from a flat plate more effectively and accurately, the prediction algorithm based on semi-analytic model for point pressure spectrum is proposed. The semi-analytic model consists of empirical models for point pressure spectra and theoretical model to determine the boundary layer characteristics needed for the empirical models. The proposed methods are applied to predict the trailing edge noise of the flat plate located in the mean flow of speed 38 m/s, for which the measured data are available. In present study, six empirical models for point pressure spectra are utilized for the predictions of trailing edge noise and their prediction results are compared to the measured data. Through the analysis of these comparisons, it is revealed that the present method based on non-frozen formula using Efimtsov model and Smol'yakov-Tkachenko model can provide more accurate and efficient predictions of trailing edge noise.

• Nuclear Engineering and Technology
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• 제53권1호
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• pp.244-252
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• 2021

Wind plays an important role in cases of unexpected radioactive pollutant dispersion, deciding distribution and concentration of the leaked substance. The accurate prediction of wind has been challenging in numerical weather prediction models, especially near the surface because of the complex interaction between turbulent flow and topographic effect. In this study, we investigated the characteristics of atmospheric dispersion of radioactive material (i.e. ¹³⁷Cs) according to the simulated boundary layer around the HANARO research nuclear reactor in Korea using the Weather Research and Forecasting (WRF)-Mesoscale Model Interface (MMIF)-California Puff (CALPUFF) model system. We examined the impacts of orographic drag on wind field, stability calculation methods, and planetary boundary layer parameterizations on the dispersion of radioactive material under a radioactive leaking scenario. We found that inclusion of the orographic drag effect in the WRF model improved the wind prediction most significantly over the complex terrain area, leading the model system to estimate the radioactive concentration near the reactor more conservatively. We also emphasized the importance of the stability calculation method and employing the skillful boundary layer parameterization to ensure more accurate low atmospheric conditions, in order to simulate more feasible spatial distribution of the radioactive dispersion in leaking scenarios.

• 한국소음진동공학회논문집
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• 제16권3호
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• pp.291-297
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• 2006

The BVI(blade vortex interaction) noise Prediction has been one of the most challenging acoustic analyses in helicopter aeromechanical Phenomenon. It is well known high resolution airloads data with accurate tip vortex positions are necessary for the accurate prediction of this phenomenon. The truly unsteady time-marching free-wake method, which is able to capture the tip vortices instability in hover and axial flights, is expanded with the rotor flapping motion and trim routine to predict unsteady airloads in forward and descent flights. And Farassat formulation 1-A based on the FW-H equation is applied for the noise prediction considering the blade flapping motion. Main objective of this study is to validate the newly developed prediction code. To achieve the objective, the descent flight condition of AH-1 OLS(operational loads survey) configuration is analyzed using present code. The predicted sectional thrust distribution and sectional airloads time histories show the present scheme is able to capture well the unsteady airloads caused by a parallel BVI. Finally, the predicted noise data, observed in two different positions where are 3.44 times of rotor radius far from the hub center, are quite reasonable agreements with the experimental data compared to the other analysis results.

• 한국정보과학회논문지:시스템및이론
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• 제26권9호
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• pp.1158-1165
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• 1999

조건부 분기 명령어(conditional branch instruction)의 잘못된 분기 예측(branch misprediction)은 프로세서의 성능 향상에 심각한 장애 요인이 되고 있다. 특히 시분할(time-sharing) 시스템과 같이 문맥 교환(context switch)이 발생하는 멀티프로그래밍 환경(multiprogramming environment)에서는 더욱 낮은 분기 예측 정확성(branch prediction accuracy)을 보인다. 본 논문에서는 문맥 교환이 발생하는 멀티프로그래밍 환경에서 높은 분기 예측 정확성을 보이는 중첩 분기 예측표 교환(Overlapped Predictor Table Switch, OPTS) 기법을 소개한다. 분기 예측표(predictor table)를 분할하여 각각의 프로세스(process)에 할당하는 OPTS 기법은 문맥 교환의 영향을 최소화함으로써 높은 분기 예측 정확성을 유지하는 분기 예측 방법이다.Abstract There is wide agreement that one of the most important impediments to the performance of current and future pipelined superscalar processors is the presence of conditional branches in the instruction stream. Accurate branch prediction is required to overcome this performance limitation. Many branch predictors have been proposed to help to alleviate this problem, including the two-level adaptive branch predictor, and more recently, hybrid branch predictor. In a less idealized environment, such as a time-sharing system, code of interest involves context switches. Context switches, even at fairly large intervals, can seriously degrade the performance of many of the most accurate branch prediction schemes. In this study, we measure the effect of context switch on the branch prediction accuracy in various situation and show the feasibility of our new mechanism, OPTS(Overlapped Predictor Table Switch), which save and restore branch history table at every context switch.

• 한국산학기술학회논문지
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• 제14권6호
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• pp.2653-2659
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• 2013

기존의 환경변화 예측연구에서는 동양학을 적용한 연구가 대부분이였다. 특히 명리학만 적용하여 환경변화 예측을 연구하였다. 이 두 학문을 적용하여 환경변화 예측연구를 하여온 경우는 없다. 그래서 본 연구는 명리학과 육효학을 적용하여 환경변화 예측의 정확성에 대하여 연구하고자 한다. 연구방법은 2012년 8월부터 12월까지 직장인을 대상으로 설문조사를 실시하였다. 분석한 결과를 살펴보면 두 학문을 분리해서 하는 경우 보다는 동시에 같이 했을 때 환경변화 예측이 더 정확하였다. 응답자의 일반적 특성에 따른 관계의 평균과 표준편차는 높았다. 환경변화도의 영역별은 평균이 높을수록 좋은 결과가 유사한 차이로 나타났다. 따라서 환경변화도에 대한 예측의 효율성이 증대하였음을 판단된다.

• 응용통계연구
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• 제26권6호
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• pp.1019-1032
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• 2013

최근 급격한 기대수명의 증가에 따라 미래 복지정책 등에 커다란 영향을 주는 장래 사망력의 정확한 예측은 중요한 이슈가 되고 있다. 사망력의 정확한 예측을 위하여 최적의 추정모형의 선택도 중요하지만 사망력에 대한 시계열 적용기간도 매우 중요한 이슈다. 이는 우리나라의 사망률 시계열이 짧고, 특히 1982년 이전 자료가 다소 불완전해서 이에 대한 고려가 필수적이기 때문이다. 본 논문에서는 우리나라 사망력 시계열을 기간에 따라 2개의 그룹(1976~2005년, 1983~2005년)으로 나누어서, 남녀별로 LC모형과 LC 코호트효과 확장모형에 대한 모수 추정값, 사망력지수와 코호트지수의 모형화 및 예측, 장래 기대수명의 예측 적합력을 각각 분석한 후 향후에 장래 기대수명 추계시 고려할 시사점을 제시하고자 한다.

• 한국전기전자재료학회논문지
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• 제31권7호
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• pp.450-454
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• 2018

Because of the rapidly changing environment and high uncertainties, the semiconductor industry is in need of appropriate forecasting technology. In particular, both the cost and time in the test process are increasing because the process becomes complicated and there are more factors to consider. In this paper, we propose a prediction model that predicts a final "good" or "bad" on the basis of preconditioning test data generated in the semiconductor test process. The proposed prediction model solves the classification and regression problems that are often dealt with in the semiconductor process and constructs a reliable prediction model. We also implemented a prediction model through various machine learning algorithms. We compared the performance of the prediction models constructed through each algorithm. Actual data of the semiconductor test process was used for accurate prediction model construction and effective test verification.

• Bioinformatics and Biosystems
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• 제1권1호
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• pp.82-87
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• 2006

현재 가장 많이 사용되는 단백질 구조 예측 방법은 비교 모델링 (comparative modeling) 방법이다. 비교 모델링 방법에서의 정확도를 높이기 위해서는 alignment의 정확도 역시 매우 필수적으로 필요하다. 비교 모델링 과정 중의 fold-recognition 단계에서 alignment의 정확도에 의해 template을 고르는 방법은 단지 가장 비슷한 template을 선택하는 방법에 비해 주목을 받지 못하고 있다. 최근에는 두 가지의 alignment에 사이의 shift 정보를 바탕으로 한 shift score라는 수치가 alignment의 성능을 표현하기 위해서 개발되었다. 우리는 더 정확한 구조 예측의 첫걸음이 될 수 있는 shift score를 예측하는 방법을 개발하였다. Shift score를 예측하기 위해 support vector regression (SVR)이 사용되었다. 사전에 구축된 라이브러리 안의 길이가 n 인 template과 구조를 알고 싶은 query 단백질 사이의 alignment는 n+2 차원의 input 벡터로 변환된다. Structural alignment가 가장 좋은 alignment로 가정되었고 SVR은 query 단백질과 template 단백질의 structural alignment과 profile-profile alignment 사이의 shift score를 예측하도록 training 되었다. 예측 정확도는 Pearson 상관계수로 측정되었다. Training 된 SVR은 실제의 shift score와 예측된 shift score 사이에 0.80의 Pearson 상관계수를 갖는 정도로 예측하였다.