• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2008년도 정보통신설비 학술대회
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• pp.186-189
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• 2008

In order to estimate cell images, high-performance electron microscopes are used nowadays. In this paper, we propose a new simple, fast and efficient method for real-time automatic focusing in electron microscopes. The proposed algorithm is based on the prediction-error variance, and demonstrates its feasibility by using extensive experiments. This method is fast, easy to implement, accurate, and not demanding on computation time.

• Journal of Electrical Engineering and information Science
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• 제2권6호
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• pp.138-145
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• 1997

This paper proposes an adaptive algorithm for the quantization step size control of MPEG-2, using the information obtained from the previously encoded picture. Before quantizing the DCT coefficients, the properties of reconstruction error of each macro block (MB) is predicted from the previous frame. For the prediction of the error of current MB, a block with the size of MB in the previous frame are chosen by use of the motion vector. Since the original and reconstructed images of the previous frame are available in the encoder, we can calculate the reconstruction error of this block. This error is considered as the expected error of the current MB if it is quantized with the same step size and bit rate. Comparing the error of the MB with the average of overall MBs, if it is larger than the average, small step size is given for this MB, and vice versa. As a result, the error distribution of the MB is more concentrated to the average, giving low variance and improved image quality. Especially for the low bit application, the proposed algorithm gives much smaller error variance and higher PSNR compared to TM5 (test model 5).

• 유통과학연구
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• 제12권2호
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• pp.59-71
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• 2014

Purpose - In recent years, many firms have attempted various approaches to cope with the continual increase of aviation transportation. The previous research into freight charge forecasting models has focused on regression analyses using a few influence factors to calculate the future price. However, these approaches have limitations that make them difficult to apply into practice: They cannot respond promptly to small price changes and their predictive power is relatively low. Therefore, the current study proposes a freight charge-forecasting model using time series data instead a regression approach. The main purposes of this study can thus be summarized as follows. First, a proper model for freight charge using the autoregressive integrated moving average (ARIMA) model, which is mainly used for time series forecast, is presented. Second, a modified ARIMA model for freight charge prediction and the standard process of determining freight charge based on the model is presented. Third, a straightforward freight charge prediction model for practitioners to apply and utilize is presented. Research design, data, and methodology - To develop a new freight charge model, this study proposes the ARIMAC(p,q) model, which applies time difference constantly to address the correlation coefficient (autocorrelation function and partial autocorrelation function) problem as it appears in the ARIMA(p,q) model and materialize an error-adjusted ARIMAC(p,q). Cargo Account Settlement Systems (CASS) data from the International Air Transport Association (IATA) are used to predict the air freight charge. In the modeling, freight charge data for 72 months (from January 2006 to December 2011) are used for the training set, and a prediction interval of 23 months (from January 2012 to November 2013) is used for the validation set. The freight charge from November 2012 to November 2013 is predicted for three routes - Los Angeles, Miami, and Vienna - and the accuracy of the prediction interval is analyzed using mean absolute percentage error (MAPE). Results - The result of the proposed model shows better accuracy of prediction because the MAPE of the error-adjusted ARIMAC model is 10% and the MAPE of ARIMAC is 11.2% for the L.A. route. For the Miami route, the proposed model also shows slightly better accuracy in that the MAPE of the error-adjusted ARIMAC model is 3.5%, while that of ARIMAC is 3.7%. However, for the Vienna route, the accuracy of ARIMAC is better because the MAPE of ARIMAC is 14.5% and the MAPE of the error-adjusted ARIMAC model is 15.7%. Conclusions - The accuracy of the error-adjusted ARIMAC model appears better when a route's freight charge variance is large, and the accuracy of ARIMA is better when the freight charge variance is small or has a trend of ascent or descent. From the results, it can be concluded that the ARIMAC model, which uses moving averages, has less predictive power for small price changes, while the error-adjusted ARIMAC model, which uses error correction, has the advantage of being able to respond to price changes quickly.

• Communications for Statistical Applications and Methods
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• 제16권2호
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• pp.287-297
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• 2009

일반적으로 일반화 자기회귀 조건부 이분산(GARCH)모형 하에서, 우도함수에 기반한 자료의 예측구간의 추정은 오차항의 분포에 민감하게 반응하고 더욱이 조건부분산의 경우 구간추정이 현실적으로 쉽게 풀리지 않는 문제이다. 이를 해결하기 위해 붓스트랩방법(bootstrap method)이 적용될 수 있음을 최근 연구들을 통해 밝혀졌다. 본 논문에서는 GARCH모형 하에서 자료와 변동성(조건부 분산)의 예측구간 추정을 위해 최근 소개된 Pascual 등 (2006)의 논문을 토대로 붓스트랩 방법를 정리하였다 실제 사례분석을 위해 국내 주가수익률자료를 이용하였다.

• Computers and Concrete
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• 제16권5호
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• pp.741-757
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• 2015

In the last decade, several modeling approaches have been proposed and applied to estimate the high-performance concrete (HPC) slump flow. While HPC is a highly complex material, modeling its behavior is a very difficult issue. Thus, the selection and application of proper modeling methods remain therefore a crucial task. Like many other applications, HPC slump flow prediction suffers from noise which negatively affects the prediction accuracy and increases the variance. In the recent years, ensemble learning methods have introduced to optimize the prediction accuracy and reduce the prediction error. This study investigates the potential usage of bagging (Bag), which is among the most popular ensemble learning methods, in building ensemble models. Four well-known artificial intelligence models (i.e., classification and regression trees CART, support vector machines SVM, multilayer perceptron MLP and radial basis function neural networks RBF) are deployed as base learner. As a result of this study, bagging ensemble models (i.e., Bag-SVM, Bag-RT, Bag-MLP and Bag-RBF) are found superior to their base learners (i.e., SVM, CART, MLP and RBF) and bagging could noticeable optimize prediction accuracy and reduce the prediction error of proposed predictive models.

• ETRI Journal
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• 제46권2호
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• pp.205-217
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• 2024

We propose a bandwidth prediction approach based on deep learning. The approach is intended to accurately predict the bandwidth of various types of mobile networks. We first use a machine learning technique, namely, the gradient boosting algorithm, to recognize the connected mobile network. Second, we apply a handover detection algorithm based on network recognition to account for vertical handover that causes the bandwidth variance. Third, as the communication performance offered by 3G, 4G, and 5G networks varies, we suggest a bidirectional long short-term memory model with time distribution for bandwidth prediction per network. To increase the prediction accuracy, pretraining and fine-tuning are applied for each type of network. We use a dataset collected at University College Cork for network recognition, handover detection, and bandwidth prediction. The performance evaluation indicates that the handover detection algorithm achieves 88.5% accuracy, and the bandwidth prediction model achieves a high accuracy, with a root-mean-square error of only 2.12%.

• Structural Monitoring and Maintenance
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• 제10권2호
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• pp.175-190
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• 2023

This article aims to quantitatively predict the snowmelt in extreme cold regions, considering a combination of grayscale and neural models. The traditional non-equidistant GM(1,1) prediction model is optimized by adjusting the time-distance weight matrix, optimizing the background value of the differential equation and optimizing the initial value of the model, and using the BP neural network for the first. The adjusted ice forecast model has an accuracy of 0.984 and posterior variance and the average forecast error value is 1.46%. Compared with the GM(1,1) and BP network models, the accuracy of the prediction results has been significantly improved, and the quantitative prediction of the ice sheet is more accurate. The monitoring and maintenance of the structure by quantitative prediction model by gray models was clearly demonstrated in the model.

• 한국통신학회논문지
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• 제25권1B호
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• pp.120-127
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• 2000

본 논문에서는 웨이브릿 영역에서 영역별 대역간 예측과 벡터 양자화를 이용한 다중 분광 화상데이타 압축 기법을 제안하였다. 이 방법에서는 먼저 화상데이타에서 각 대역의 반사 특성을 이용하여 영역 분류를 행한 후, 공간적으로 가장 낮은 분산을 가지고 다른 밴드와 상관성이 가장 큰 기준 대역을 웨이브릿 영역에서 영역 분류 벡터 양자화를 행한다. 또한 나머지 각 밴드는 웨이브릿 영역에서 기준 대역으로부터 영역별 예측을 통하여 대역간 중복성을 제거하였다. 그리고 원 화상의 웨이브릿 계수와 예측 영상의 웨이브릿 계수의 차이를 줄이기 위해 오차 벡터 양자화를 행한다. 실제 원격 센싱된 인공위성 화상데이터에 대한 실험을 통하여 제안한 기법의 부호화 효율이 기존의 기법에 비하여 우수함을 확인하였다.

• 한국수자원학회논문집
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• 제32권4호
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• pp.437-445
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• 1999

비모수능형회귀분석법을 이용하여 수문응답을 추정하는 방안에 대하여 연구하였다. 응답을 추정하기 위하여 평균제곱예측오차에 대한 추정량인 CL 통계량을 최소화하는 방법을 적용하였으며 가중행렬은 전통적으로 이용도는 단위행렬과 특수한 형태인 행렬인 Laplacian 행렬을 각각 이용하여 비교하였다. 또한 추정응답의 오차분산을 추정하는 방안에 대한 검토도 실행하였다. 합성자료와 실제자료에 대한 분석 결과 가중행렬과 Laplacian 행렬을 오차분산은 편기 수정된 추정치를 이용하는 것이 좋은 결과를 보여 주었다. 본 연구에서 제시된 절차 및 방법은 수문응답 분리에 있어서 안정적이고 효율적으로 적용될 수 있을 것으로 판단된다.

• 물과 미래
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• 제28권5호
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• pp.219-233
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• 1995

강우의 평균과 분산이 시 공간적으로 변하는 비정상 다변량 모형을 강우모형으로 선정하였다. 그리고 강우모형의 상태 및 매개변수의 추정을 위해 비정상 대변량 모형의 잔차항에 Kalman Filter 순환추정 알고리즘을 적용하여 강우예측모형 시스템을 구성하였다. 그후 반응시간이 짧은 도시지역에 설치된 T/M 강우관측소에 입력되는 매 시간(10분간격) 강우자료를 사용하여 호우개수방법에 의한 비정상(Non-stationary) 평균과 분산의 추정 그리고 호우속도 추정을 통한 정규잔차 공분산을 추정하여 다수의 지점들 및 선행시간들의 실시간 다변량 단기 강우예측 (On-line, Real-time, Multivariate Short-term, Rainfall Prediction)을 하였다. 강우예측시스템 모형에 의한 결과와 비정상 변량 모형에 의한 강우모의 결과가 잘 일치하였다. 그리고 예측정도를 측정하는 방법인 제곱 평균 제곱근 오차(RMSE)와 모형 효율성 계수(ME)를 분석한 결과, 강우 예측시간 즉 선행시간이 갈수록 제곱 평균 제곱근 오차가 커지고 모형 효율성 계수가 1로부터 점차 작아지는 것으로 보아 강우예측 정도가 떨어지는 것을 알 수 있었다. 또한 호우개수방법으로 구한 평균이 호우구조의 많은 부분을 차지하고 있음을 알 수 있었다.