• 한국지능시스템학회논문지
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• 제21권4호
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• pp.449-455
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• 2011

본 논문에서는 원격제어 시스템의 시간지연 문제를 분석하고 그 문제를 신경망으로 보상한다. 스미스 예측기는 시간지연 시스템에서 정확한 모델을 필요로 한다. 스미스 예측기의 모델링 오차를 보상하기 위해 신경회로망을 사용한다. 스미스 예측기를 구성하기 위해 Radial Basis Function(RBF) 신경회로망이 사용된다. 시뮬레이션과 실험을 통해 제안하는 방법의 동작을 검증한다.

• 한국통신학회논문지
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• 제8권4호
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• pp.156-163
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• 1983

旣存의 44,736Mbps 傳送路에 適用하여 컬러TV信號를 傳送할수 있는 合成信號形DPCM시스템이 設計, 構成되었다. 使用된 豫測方法은 4개의 畵素를 利用하는 旣存의 2次元豫測方法이며 5비트에 해당하는 非線形量子器가 使用되었다. 實時間處理를 위해 주로 ECL素子가 많이 使用되었다. 實驗結果를 통하여 本DPCM시스템은 旣存의 傳送路를 使用하여 TV放送信號를 CATV品質이상으로 傳送할 수 있는 能力이 있음을 確認할 수 있었다.

• 한국통신학회논문지
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• 제30권11A호
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• pp.1021-1026
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• 2005

본 논문에서는 시변 페이딩 채별에 적합한 멀터스텝 LS 선형예측기를 갖는 새로운 예측 CDMA 폐루프 전력 제어 방식을 제안한다. 제안된 방식은 다중 전력 제어 그룹 지연을 효과적으로 보상하여 주며 단일 스텝 예측기를 갖는 기존의 예측 CLPC 방식이나 비예측 CLPC 방식에 비하여 우수한 성능 이득을 갖는다.

• Communications for Statistical Applications and Methods
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• 제24권6호
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• pp.583-604
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• 2017

The most popular regression model for the analysis of time-to-event data is the Cox proportional hazards model. While the model specifies a parametric relationship between the hazard function and the predictor variables, there is no specification regarding the form of the baseline hazard function. A critical assumption of the Cox model, however, is the proportional hazards assumption: when the predictor variables do not vary over time, the hazard ratio comparing any two observations is constant with respect to time. Therefore, to perform credible estimation and inference, one must first assess whether the proportional hazards assumption is reasonable. As with other regression techniques, it is also essential to examine whether appropriate functional forms of the predictor variables have been used, and whether there are any outlying or influential observations. This article reviews diagnostic methods for assessing goodness-of-fit for the Cox proportional hazards model. We illustrate these methods with a case-study using available R functions, and provide complete R code for a simulated example as a supplement.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권12호
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• pp.702-707
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• 2003

This study describes a new technique for human sensibility evaluation using electroencephalogram(EEG). Production of EEG is assumed to be linear. The linear predictor coefficients and the linear cepstral coefficients of EEG are used as the feature parameters of sensibility and pattern classification performances of them are compared. Using the better parameter, a human sensibility evaluation algorithm is designed. The obtained results are as follows. The linear predictor coefficients showed the better performance in pattern classification than the linear cepstral coefficients. Then, using the linear predictor coefficients as the feature parameter, a human sensibility evaluation algorithm is developed at the base of a multi-layer neural network. This algorithm showed 90% of accuracy in comfortableness evaluation in spite of fluctuations in statistics of EEG signal.

• 한국공작기계학회논문집
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• 제10권6호
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• pp.95-101
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• 2001

This work is concerned with construction of the intelligence stress predictor far compression strength evaluation using neural network-ultrasonic waves. The contact pressure in jointed plates was measured by using ultrasonic technique. Neural network is used to evaluate and predict contact pressure from the results of the calibration curves. The organized neural system was leaned with the accuracy of 99%, as a result of learning the ultrasonic echo ratio to the contact pressure measurement between SM45C and STS410 materials. And it could be evaluated and predicted with the accuracy of 90% in the evaluation of ultrasonic echo ratio difference in the same surface roughness and contact pressure, and 85% in the prediction of virtual ultrasonic echo ratio. Thus the proposed stress predictor is very useful for the evaluation and prediction of the contact pressure between SM45C and STS410 materials.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 하계종합학술대회 논문집(5)
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• pp.189-192
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• 2001

A new configuration of acoustic echo canceler with stepsize predictor and comparator is proposed in this paper. Conventional acoustic echo cancelers using ES(Exponential Step)algorithm has fast convergence speed, but very weak in interference of environment. The proposed stepsize predictor and comparator improve conventional acoustic echo canceler's defects. The Stepsize predictor generates a stepsize value using residual power of error signal. The stepsize comparator selects the stepsize value that is better performance in a acoustic echo canceler using a stepsize decision factor. The Simulation results show superiority of the proposed acoustic echo canceler in environment interference.

• Computers and Concrete
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• 제13권2호
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• pp.173-185
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• 2014

Multivariate analysis is a statistical technique that investigates relationship between multiple predictor variables and response variable and it is a very commonly used statistical approach in cement and concrete industry. During model building stage, however, many predictor variables are included in the model and possible collinearity problems between these predictors are generally ignored. In this study, use of partial least squares (PLS) analysis for evaluating the relationships among the cement and concrete properties is investigated. This regression method is known to decrease the model complexity by reducing the number of predictor variables as well as to result in accurate and reliable predictions. The experimental studies showed that the method can be used in the multivariate problems of cement and concrete industry effectively.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 A
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• pp.258-260
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• 1995

A novel phase error predictor is proposed for servo system in home-use VCRs. The multirate system in VCRs is converted into a single period sampling system with faster sampling time by using the proposed novel phase error predictor. And the disturbances can be measured much faster. From the experimental results, we can see that the performance of the control system is improved greatly. The phase lock time of the proposed servo system is ten times faster than that of the conventional system.

• 한국시뮬레이션학회논문지
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• 제8권3호
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• pp.17-28
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• 1999

Recently studies have shown that conditional branches can be accurately predicted by recording the path leading up to the branch. But path predictors are more complex and uncompatible with existing pattern branch predictors. In order to solve these problems, we propose a simple path branch predictor(SPBP) that hashes together two most recent branch instruction addresses. In addition, we propose a pattern/path hybrid branch predictor composed of the SPBP and existing pattern branch predictors. Through the trace-driven simulation of six benchmark programs, the performance improvement by the proposed pattern/path hybrid branch prediction is analysed and validated. The proposed predictor can improve the prediction accuracy from 94.21% to 95.03%.