• Communications for Statistical Applications and Methods
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• 제24권1호
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• pp.67-80
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• 2017

Over the last few decades, ensemble forecasts based on global climate models have become an important part of climate forecast due to the ability to reduce uncertainty in prediction. Moreover in ensemble forecast, assessing the prediction uncertainty is as important as estimating the optimal weights, and this is achieved through a probabilistic forecast which is based on the predictive distribution of future climate. The Bayesian model averaging has received much attention as a tool of probabilistic forecasting due to its simplicity and superior prediction. In this paper, we propose a new Bayesian model averaging method for probabilistic ensemble forecasting. The proposed method combines a deterministic ensemble forecast based on a multivariate regression approach with Bayesian model averaging. We demonstrate that the proposed method is better in prediction than the standard Bayesian model averaging approach by analyzing monthly average precipitations and temperatures for ten cities in Korea.

• 전자공학회논문지
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• 제50권12호
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• pp.261-269
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• 2013

본 연구에서는 자극에 대한 유발전위 발현시점의 변화와 유발전위에 혼입된 무작위 잡음을 시간지연현상과 자음혼입 가법모형으로 모델링 하였다. 동기시점 불일치에 따른 평균화 처리과정의 유발전위 신호의 왜곡을 개선하기 위하여 시간지연추정을 잡음제거 위너필터에 결합한 복합적 시간지연보상-잡음개선 위너필터-앙상블평균 처리기법 (DWEA: Delay compensated Wiener filtered ensemble averaging)을 적용하였다. 제시한 방법의 성능은 임의의 시간지연과 크기의 변화를 변화시킨 백색잡음 데이터를 합성한 대리모의실험을 통하여 검증하였다. 모의실험데이터에 대하여 DWEA 방법이 위너필터링앙상블평균 방법과 기존의 앙상블평균방법보다 우수 하였다. DWEA 방법은 10% MSE 오차한계에 대하여 잡음이득 7까지 동작 가능하였다. 실험결과를 통하여 DWEA 방법은 잡음의 혼입과 동기 불일치 현상을 보이는 유발전위의 신호개선의 가능성을 제시하였다.

• 전기학회논문지
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• 제58권8호
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• pp.1638-1643
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• 2009

Uroflowmetry is a convenient clinical test to screen the benign prostatic hyperplasia(BPH) common in the aged men. A load cell is located beneath the urine container to measure the weight of urine. However, it is sensitive to the impact applied on the bottom of the container by the urine stream, which could be a noise source lowering the reliability of the system. With this aim, our study proposed a noise reduction technique by computing ensemble average of the weighted signals that were acquired from three-load cells forming a regular triangle beneath the urine container. Simulated urination experiment was performed with three different collection methods, all of which demonstrated significant noise reduction by ensemble averaging. Furthermore, the best results can be obtained without any special urine collection devices. Thus, our novel method can be usefully applied to uroflowmetry for enhancing measurement in terms of accuracy and reliability.

• Journal of the Korean Wood Science and Technology
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• 제47권4호
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• pp.385-392
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• 2019

In our previous study, we investigated the use of ensemble models based on LeNet and MiniVGGNet to classify the images of transverse and longitudinal surfaces of five Korean softwoods (cedar, cypress, Korean pine, Korean red pine, and larch). It had accomplished an average F1 score of more than 98%; the classification performance of the longitudinal surface image was still less than that of the transverse surface image. In this study, ensemble methods of two different convolutional neural network models (LeNet3 for smartphone camera images and NIRNet for NIR spectra) were applied to lumber species classification. Experimentally, the best classification performance was obtained by the averaging ensemble method of LeNet3 and NIRNet. The average F1 scores of the individual LeNet3 model and the individual NIRNet model were 91.98% and 85.94%, respectively. By the averaging ensemble method of LeNet3 and NIRNet, an average F1 score was increased to 95.31%.

• Advances in Computational Design
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• 제8권1호
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• pp.37-59
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• 2023

Squat reinforced concrete (SRC) shear walls are a critical part of the structure for both office/residential buildings and nuclear structures due to their significant role in withstanding seismic loads. Despite this, empirical formulae in current design standards and published studies demonstrate a considerable disparity in predicting SRC wall shear strength. The goal of this research is to develop and evaluate hybrid and ensemble artificial neural network (ANN) models. State-of-the-art population-based algorithms are used in this research for hybrid intelligence algorithms. Six models are developed, including Honey Badger Algorithm (HBA) with ANN (HBA-ANN), Hunger Games Search with ANN (HGS-ANN), fitness-distance balance coyote optimization algorithm (FDB-COA) with ANN (FDB-COA-ANN), Averaging Ensemble (AE) neural network, Snapshot Ensemble (SE) neural network, and Stacked Generalization (SG) ensemble neural network. A total of 434 test results of SRC walls is utilized to train and assess the models. The results reveal that the SG model not only minimizes prediction variance but also produces predictions (with R²= 0.99) that are superior to other models.

• Archives of Pharmacal Research
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• 제10권1호
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• pp.1-8
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• 1987

One of the problems of derivatie spectrophotometry, the decrease of signal-to-noise ratio by derivative operations, was solved by three concepts of digital filtering, ensemble averaging, least squares polynomial smoothing and Fourier smoothing. The suthors made several compouter programs written in APPLE SOFT BASIC language for the actual applications of the concepts of these digital filters on UV spectrophotometer system. As a result, ensemble averaging could not be used as a routine operation for the spectrophotometer used. The maximum S/N ratio enhancement factors achieved by least squares polynomial smoothing were 6.17 and 7.47 for the spectra of Gaussian and Lorentzian distribution models, and by Fourier smoothing 16.42 and 11.78 for the spectra of two models, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.1975-1988
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• 2019

Short-term traffic flow prediction plays an important role in intelligent transportation systems (ITS) in areas such as transportation management, traffic control and guidance. For short-term traffic flow regression predictions, the main challenge stems from the non-stationary property of traffic flow data. In this paper, we design an ensemble cascading prediction framework based on extremely randomized trees (extra-trees) using a boosting technique called EET to predict the short-term traffic flow under non-stationary environments. Extra-trees is a tree-based ensemble method. It essentially consists of strongly randomizing both the attribute and cut-point choices while splitting a tree node. This mechanism reduces the variance of the model and is, therefore, more suitable for traffic flow regression prediction in non-stationary environments. Moreover, the extra-trees algorithm uses boosting ensemble technique averaging to improve the predictive accuracy and control overfitting. To the best of our knowledge, this is the first time that extra-trees have been used as fundamental building blocks in boosting committee machines. The proposed approach involves predicting 5 min in advance using real-time traffic flow data in the context of inherently considering temporal and spatial correlations. Experiments demonstrate that the proposed method achieves higher accuracy and lower variance and computational complexity when compared to the existing methods.

• 호남수학학술지
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• 제36권1호
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• pp.11-27
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• 2014

In this paper, we propose closures for multi-phase flow models, which satisfy boundary conditions and conservation constraints. The models governing the evolution of the fluid mixing are derived by applying an ensemble averaging procedure to the microphysical equations characterized by distinct phases. We consider compressible multi species multi-phase flow with surface tension and transport.

• 오토저널
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• 제14권6호
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• pp.39-47
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• 1992

The engine combustion is one of the most important process affecting performance and emissions. One effective way to improve the engine combustion is to control motion of the charge inside a cylinder by means of optimum induction system design, because the flame speed is mainly determined by the turbulence in a gasoline engine. This paper describes the measurement and characterization of mean velocity and turbulence intensity inside the cylinder of a 4-valve gasoline engine using laser Doppler velocimeter(LDV) under motoring(non-firing) conditions. Since the measured LDV data in each cycle show small cycle variation during compression stroke in the tested engine, the mean velocity and turbulence intensity are calculated by ensemble averaging method neglecting cycle variation effects. In the ensemble averaging method, the effects of the calculation window, in which velocities are assumed as the same crank angle, on mean velocity and turbulence intensity are fully investigated. In addition, the effects of measuring point on the flow characteristics are studied. With large calculation window, the mean velocity is shown to be less sensitive with respect to crank angle and turbulence intensity decrease in its absolute amplitude. When the piston approch to the top dead center of compression, the turbulence intensity is found to be homogeneous in the cylinder.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1990년도 춘계학술대회
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• pp.7-8
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• 1990

In this study, a new ensemble average technique was developed to measure cardiac output during treadmill exercise. Each dZ/dt peak (C point) was used as a starting point for ensemble averaging, instead of conventionally used R wave of ECG in order to prevent the peak dZ/dt waveform from blurring. In ease of using R wave as a reference, time interval from R wave to the peak of dZ/dt varies for each heart beat. Stroke volume, heart rate, and cardiac output of five male were successfully measured with Balke protocol using the new ensemble average technique.