• Bulletin of the Korean Chemical Society
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• 제28권9호
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• pp.1472-1476
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• 2007

Artificial neural networks (ANNs) are successfully developed for the modeling and prediction of normalized polarity parameter (ETN) of 216 various solvents with diverse chemical structures using a quantitative-structure property relationship. ANN with architecture 5-9-1 is generated using five molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The most positive charge of a hydrogen atom (q+), total charge in molecule (qt), molecular volume of solvent (Vm), dipole moment (μ) and polarizability term (πI) are input descriptors and its output is ETN. It is found that properly selected and trained neural network with 192 solvents could fairly represent the dependence of normalized polarity parameter on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network is applied for prediction of the ETN values of 24 solvents in the prediction set, which are not used in the optimization procedure. Correlation coefficient (R) and root mean square error (RMSE) of 0.903 and 0.0887 for prediction set by MLR model should be compared with the values of 0.985 and 0.0375 by ANN model. These improvements are due to the fact that the ETN of solvents shows non-linear correlations with the molecular descriptors.

• 한국통신학회논문지
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• 제21권4호
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• pp.996-1007
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• 1996

이 논문에서는 비최소위상(nonminimum phase) 채널을 등화할 수 있는 판정궤환(decision feedback equalizer)에 대한 자력등화 기법을 제안한다. 등화기는 선형필터와 예측에러 필터(prediction error filter)의 결합으로 이루어지며 각 부분에 대해 서로 다른 알고리듬을 적용한다. 즉 선형필터는 CMA(constant modulus algorithm)를 적용하여 계수를 추정하며, 예측에러 필터는 판정궤환 예측 알고리듬(decision feedback prediction algorithm)을 적용하여 필터의 계수를 추정한다. 제안한 알고리듬은 판정궤환 등화기의 FFF(feedforward filter)부를 이루는 선형필터가 수렴할 때 항상 작은 오율을 나타내는 계수로의 수렴을 보장한다. 모의실험을 통해 제안한 자력등화알고리듬의 유효성을 몇개의 채널에 대해 예를 들었으며 기존의 자력등화 알고리듬과 성능을 비교하였다.

• KEPCO Journal on Electric Power and Energy
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• 제5권3호
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• pp.201-207
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• 2019

An anchorage system is essential for most reinforced concrete structures to connect building components. Therefore, the prediction of strength of the anchor is very important issue for safety of the structures themselves as well as structural components. The prediction models in existing design codes are, however, not applicable for large anchors because they are based on the small size anchors with diameters under 50 mm. In this paper, new prediction models for strength of a single anchor, especially the tensile strength of a single anchor, is developed from the experimental results with consideration of size effect. Size effect in the existing models such as ACI or CCD method is based on the linear fracture mechanics which is very conservative way to consider the size effect. Therefore, new models are developed based on the nonlinear fracture mechanics rather than the linear fracture mechanics for more reasonable prediction. New models are proposed by the regression analysis of the experimental results and it can predict the tensile strength of both small and large anchors.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.301-303
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• 2004

In this work, we develop on-line traffic prediction algorithm for real-time VBR traffic. There are a number of important issues: (i) The traffic prediction algorithm should exploit the stochastic characteristics of the underlying traffic and (ii) it should quickly adapt to structural changes in underlying traffic. GOP ARIMA model effectively addresses this issues and it is used as basis in our bandwidth prediction. Our prediction model deploy Kalman filter to incorporate the prediction error for the next prediction round. We examine the performance of GOP ARIMA based prediction with linear prediction with LMS and double exponential smoothing. The proposed prediction algorithm exhibits superior performam againt the rest.

• 한국대기환경학회지
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• 제23권1호
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• pp.14-28
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• 2007

This study presented the selection of ozone ($O_3$) potential factors and designed and assessed its potential prediction model using multiple-linear regression equations in Ulsan area during the springtime from April to June, $2000{\sim}2004$. $O_3$ potential factors were selected by analyzing the relationship between meterological parameters and surface $O_3$ concentrations. In addition, cluster analysis (e.g., average linkage and K-means clustering techniques) was performed to identify three major synoptic patterns (e.g., $P1{\sim}P3$) for an $O_3$ potential prediction model. P1 is characterized by a presence of a low-pressure system over northeastern Korea, the Ulsan was influenced by the northwesterly synoptic flow leading to a retarded sea breeze development. P2 is characterized by a weakening high-pressure system over Korea, and P3 is clearly associated with a migratory anticyclone. The stepwise linear regression was performed to develop models for prediction of the highest 1-h $O_3$ occurring in the Ulsan. The results of the models were rather satisfactory, and the high $O_3$ simulation accuracy for $P1{\sim}P3$ synoptic patterns was found to be 79, 85, and 95%, respectively ($2000{\sim}2004$). The $O_3$ potential prediction model for $P1{\sim}P3$ using the predicted meteorological data in 2005 showed good high $O_3$ prediction performance with 78, 75, and 70%, respectively. Therefore the regression models can be a useful tool for forecasting of local $O_3$ concentration.

• 한국항행학회논문지
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• 제23권6호
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• pp.561-569
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• 2019

태양광 발전은 일사량만 있으면 전기에너지를 얻을 수 있기 때문에, 새로운 에너지 공급원으로 용도가 급증하고 있다. 본 논문은 실제 태양광 발전 시스템의 컨버터 출력을 이용하여 장단기 출력 예측을 하였다. 예측 알고리즘은 다중선형회귀와 머신러닝의 지도학습 중 분류모델인 서포트 벡터 머신 그리고 DNN과 LSTM 등 딥러닝을 이용하였다. 또한 기상요소의 입출력 구조에 따라 3개의 모델을 이용하였다. 장기 예측은 월별, 계절별, 연도별 예측을 하였으며, 단기 예측은 7일간의 예측을 하였다. 결과로서 RMSE 측도에 의한 예측 오차로 비교해 본 결과 다중선형회귀와 SVM 보다는 딥러닝 네트워크가 예측 정확도 측면에서 더 우수하였다. 또한, DNN 보다 시계열 예측에 우수한 모델인 LSTM이 예측 정확도 측면에서 우수하였다. 입출력 구조에 따른 실험 결과는 모델 1보다 모델 2가 오차가 적었으며, 모델 2보다는 모델 3이 오차가 적었다.

• 응용통계연구
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• 제34권5호
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• pp.697-710
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• 2021

상대오차를 이용한 예측법은 상대오차(혹은 퍼센트오차)가 중요시되는 분야, 특히 계량경제학이나 소프트웨어 엔지니어링, 또는 정부기관 공식통계 부분에서 기존 예측방법 외에 선호되는 예측방법이다. 그 동안 상대오차를 이용한 예측법은 선형 혹은 비선형 회귀분석 뿐 아니라, 커널회귀를 이용한 비모수 회귀모형, 그리고 정상시계열분석에 이르기까지 그 범위가 확장되어 왔다. 그러나, 지금까지의 분석은 고정효과(fixed effect)만을 고려한 것이어서 임의효과(random effect)에 관한 상대오차 예측법에 대한 확장이 필요하였다. 본 논문의 목적은 상대오차예측법을 일반화선형혼합모형(GLMM)에 속한 감마회귀(gamma regression), 로그정규회귀(lognormal regression), 그리고 역가우스회귀(inverse gaussian regression)의 패널자료(panel data)에 적용시키는데 있다. 이를 위해 실제 자동차 보험회사의 손해액 자료를 사용하였고, 최량예측량과 최량상대오차예측량을 각각 적용-비교해 보았다.

• 전력전자학회논문지
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• 제11권5호
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• pp.426-430
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• 2006

본 논문에서는 제어 시지연을 갖는 고성능 PI 전류제어기에 대한 새로운 예측전류 적용방법을 모색한다. 먼저 선형 영구자석 동기전동기를 사용한 선형 서보 제어시스템에 존재하는 불가피한 전류예측 오차원인을 분석하고, 전류예측 오차와 제어 시지연을 고려한 전류제어 성능 개선 방법으로 수정된 동기좌표계 비간섭 PI 전류제어기를 제안한다. 그리고 시뮬레이션 및 실험 결과를 통하여 제안된 전류제어기가 서보 제어시스템에 존재하는 전류예측 오차와 제어 시지연이 있는 경우에도 개선된 전류제어응답을 보임을 검증하였다.

• Computers and Concrete
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• 제12권2호
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• pp.151-167
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• 2013

Prediction of concrete properties is an important issue for structural engineers and different methods are developed for this purpose. Most of these methods are based on experimental data and use measured data for parameter estimation. Three typical methods of output estimation are Categorized Linear Regression (CLR), Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). In this paper a statistical cleansing method based on CLR is introduced. Afterwards, MLR and ANN approaches are also employed to predict the compressive strength of structural lightweight aggregate concrete. The valid input domain is briefly discussed. Finally the results of three prediction methods are compared to determine the most efficient method. The results indicate that despite higher accuracy of ANN, there are some limitations for the method. These limitations include high sensitivity of method to its valid input domain and selection criteria for determining the most efficient network.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1992년도 춘계학술대회
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• pp.175-178
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• 1992

This paper proposes a method called linear prediction (a high performant technique in digital speech processing) for analyzing digital ECG signals. There are several significant properties indicating that ECG signals have an important feature in the residual error signal obtained after processing by Durbin's linear prediction algorithm. The ECG signal classification puts an emphasis on the residual error signal. For each ECG's QRS complex. the feature for recognition is obtained from a nonlinear transformation which transforms every residual error signal to set of three states pulse-cord train relative to the original ECG signal. The pulse-cord train has the advantage of easy implementation in digital hardware circuits to achive automated ECG diagnosis. The algorithm performs very well feature extraction in arrythmia detection. Using this method, our studies indicate that the PVC (premature ventricular contration) detection has a at least 90 percent sensityvity for arrythmia data.