• 한국음향학회지
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• 제37권4호
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• pp.163-173
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• 2018

본 논문에서는 비행 중 비행체 표면에 작용하는 음향하중 예측을 수행하였다. 비행 중 음향하중은 비행체 표면의 압력 변동에 의해 발생한다. 기존의 비행 중 음향하중 예측방법은 반경험적 방법으로 이론과 실험 결과를 기반으로 도출한 경험식을 활용한다. 하지만 경험식의 입력 값으로 사용되는 비행체 주변 유동특성 및 경계층 파라미터를 매번 실험을 통해 얻는 것에는 한계가 있다. 따라서 본 논문에서는 전산유체해석(Computational Fluid Dynamics, CFD) 결과를 반경험적 방법과 혼합하는 하이브리드 방법을 이용하여 비행 중 비행체에 작용하는 음향하중을 예측하였다. Cone-cylinder-flare 형상 비행체에 대해 아음속, 천음속, 초음속, 최대동압도달(Maximum dynamic pressure, Max-q) 시점의 비행 환경에 대한 음향하중 예측을 수행하였다. 하이브리드 방법 적용 시 전산유체해석결과를 기반으로 한 경계층 끝단 영역 판단 방법에 대해 비교하였고 여러 연구자에 의해 제시된 경험식에 따른 음향하중 예측결과를 비교하였다.

• 전력전자학회논문지
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• 제27권1호
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• pp.48-55
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• 2022

This paper proposes a battery remaining useful life (RUL) prediction method using a deep learning-based EMD-CNN-LSTM hybrid method. The proposed method pre-processes capacity data by applying empirical mode decomposition (EMD) and predicts the remaining useful life using CNN-LSTM. CNN-LSTM is a hybrid method that combines convolution neural network (CNN), which analyzes spatial features, and long short term memory (LSTM), which is a deep learning technique that processes time series data analysis. The performance of the proposed remaining useful life prediction method is verified using the battery aging experiment data provided by the NASA Ames Prognostics Center of Excellence and shows higher accuracy than does the conventional method.

• IEIE Transactions on Smart Processing and Computing
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• 제3권4호
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• pp.234-239
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• 2014

This paper presents an empirical study on selecting a small amount of useful unlabeled data to improve the classification accuracy of semi-supervised learning algorithms. In particular, a hybrid method of unifying the simply recycled selection method and the incrementally-reinforced selection method was considered and evaluated empirically. The experimental results, which were obtained from well-known benchmark data sets using semi-supervised support vector machines, demonstrated that the hybrid method works better than the traditional ones in terms of the classification accuracy.

• Journal of Electrical Engineering and Technology
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• 제12권6호
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• pp.2166-2176
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• 2017

Electricity price prediction plays a crucial part in making the schedule and managing the risk to the competitive electricity market participants. However, it is a difficult and challenging task owing to the characteristics of the nonlinearity, non-stationarity and uncertainty of the price series. This study proposes a hybrid improved strategy which incorporates data preprocessor components and a forecasting engine component to enhance the forecasting accuracy of the electricity price. In the developed forecasting procedure, the Seasonal Adjustment (SA) method and the Ensemble Empirical Mode Decomposition (EEMD) technique are synthesized as the data preprocessing component; the Coupled Simulated Annealing (CSA) optimization method and the Least Square Support Vector Regression (LSSVR) algorithm construct the prediction engine. The proposed hybrid approach is verified with electricity price data sampled from the power market of New South Wales in Australia. The simulation outcome manifests that the proposed hybrid approach obtains the observable improvement in the forecasting accuracy compared with other approaches, which suggests that the proposed combinational approach occupies preferable predication ability and enough precision.

• 응용통계연구
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• 제29권3호
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• pp.525-537
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• 2016

본 연구에서는 시계열 자료의 비정상성과 비선형성과 같은 복잡성을 효과적으로 포용할 수 있는 경험적모드분해법(empirical mode decomposition; EMD)을 토대로 시계열 자료의 분석 및 예측을 위한 혼합(hybrid) 모형을 연구한다. EMD에 의하여 생성되는 내재모드함수(intrinsic mode function; IMF)는 해석 및 예측의 편리성을 개선하기 위하여 누적에너지의 개념을 사용하여 그룹화하였으며, 그룹화된 IMF 및 residue의 성분들은 그 성질에 따라서 ARIMA 모형 및 지수평활법과 결합된 혼합 모형으로 예측된다. 제안된 방법은 일별 코스피 지수의 예측을 위해서 적용하였다. 다양한 형태의 혼합 모형을 사용하여 코스피 지수를 예측하였으며 전통적인 예측 방법과 비교하였다. 분석 결과, 그룹화된 성분들은 코스피 지수의 움직임을 단기적, 중기적, 장기적으로 해석하는데 편리함을 주었으며, 그룹화된 IMF 및 residue를 각각 ARIMA 모형과 지수평활법으로 조합한 혼합 모형이 우수한 예측력을 보여주었다.

• 대한기계학회논문집A
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• 제31권12호
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• pp.1200-1207
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• 2007

An experimental test is presented for photoelastic stress analysis around a crack tip in tensile loaded plate. The hybrid method coupling photoelastsic fringe inputs calculated by finite element method and complex variable formulations involving conformal mappings and analytical continuity is used to calculate full-field stress around the crack tip in uniaxially loaded, finite width tensile plate. In order to accurately compare calculated fringes with experimental ones, both actual and regenerated photoelastic fringe patterns are two times multiplied and sharpened by digital image processing. Regenerated fringes by hybrid method are quite comparable to actual fringes. The experimental results indicate that Mode I stress intensity factor analyzed by the hybrid method are accurate within three percent compared with ones obtained by empirical equation and finite element analysis.

• Structural Engineering and Mechanics
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• 제86권5호
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• pp.621-633
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• 2023

Stability analysis and support system estimation of the Beheshtabad water transmission tunnel is investigated in this research. A combination approach based on the rock mass rating (RMR) and rock mass quality index (Q) is used for this purpose. In the first step, 40 datasets related to the petrological, structural, hydrological, physical, and mechanical properties of tunnel host rocks are measured in the field and laboratory. Then, RMR, Q, and height of influenced zone above the tunnel roof are computed and sorted into five general groups to analyze the tunnel stability and determine its support system. Accordingly, tunnel stand-up time, rock load, and required support system are estimated for five sorted rock groups. In addition, various empirical relations between RMR and Q i.e., linear, exponential, logarithmic, and power functions are developed using the analysis of variance (ANOVA). Based on the significance level (sig.), determination coefficient (R²) and Fisher-test (F) indices, power and logarithmic equations are proposed as the optimum relations between RMR and Q. To validate the proposed relations, their results are compared with the results of previous similar equations by using the variance account for (VAF), root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE) indices. Comparison results showed that the accuracy of proposed RMR-Q relations is better than the previous similar relations and their outputs are more consistent with actual data. Therefore, they can be practically utilized in designing the tunneling projects with an acceptable level of accuracy and reliability.

• 한국정밀공학회지
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• 제25권10호
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• pp.84-91
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• 2008

Due to the complexity of the engineering problems, it is difficult to obtain directly the stress field around the crack tip by theoretical derivation. In the paper, the hybrid method is employed to calculate full-field stress around the crack tip in uni-axially leaded finite width tensile plate, using the displacement data of given points calculated by finite element method as input data. The method uses complex variable formulations involving conformal mappings and analytical continuity. In order to accurately compare calculated fringes with experimental ones, both actual and reconstructed photoelastic fringe patterns are two times multiplied and sharpened by digital image processing. Reconstructed fringes by hybrid method are quite comparable to actual fringes. The experimental results indicate that Mode I stress intensity factor analyzed by the hybrid method are accurate within a few percent compared with ones obtained by empirical equation and finite element analysis.

• 응용통계연구
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• 제22권6호
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• pp.1143-1152
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• 2009

A canonical correlation analysis(CCA)-based method is proposed for prediction of future climate change which combines information from ensembles of atmosphere-ocean general circulation models(AOGCMs) and observed climate values. This paper focuses on predictions of future climate on a regional scale which are of potential economic values. The proposed method is obtained by coupling the classical CCA with empirical orthogonal functions(EOF) for dimension reduction. Furthermore, we generate a distribution of climate responses, so that extreme events as well as a general feature such as long tails and unimodality can be revealed through the distribution. Results from real data examples demonstrate the promising empirical properties of the proposed approaches.

• 대한산업공학회지
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• 제37권1호
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• pp.74-82
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• 2011

The Bass model is a cornerstone in diffusion theory which is used for forecasting demand of durables or new services. Three well-known estimation methods for parameters of the Bass model are Ordinary Least Square (OLS), Maximum Likelihood Estimator (MLE), Nonlinear Least Square (NLS). In this paper, a hybrid method incorporating OLS and NLS is presented and it's performance is analyzed and compared with OLS and NLS by using simulation data and empirical data. The results show that NLS has the best performance in terms of accuracy and our hybrid method has the best performance in terms of stability. Specifically, hybrid method has better performance with less data. This result means much in practical aspect because the avaliable data is little when a diffusion model is used for forecasting demand of a new product.