• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.1-17
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• 2013

In this article, several types of hybrid forecasting models are suggested. In particular, hybrid models using the generalized additive model (GAM) are newly suggested as an alternative to those using neural networks (NN). The prediction performances of various hybrid and non-hybrid models are evaluated using simulated time series data. Five different types of seasonal time series data related to an additive or multiplicative trend are generated over different levels of noise, and applied to the forecasting evaluation. For the simulated data with only seasonality, the autoregressive (AR) model and the hybrid AR-AR model performed equivalently very well. On the other hand, if the time series data employed a trend, the SARIMA model and some hybrid SARIMA models equivalently outperformed the others. In the comparison of GAMs and NNs, regarding the seasonal additive trend data, the SARIMA-GAM evenly performed well across the full range of noise variation, whereas the SARIMA-NN showed good performance only when the noise level was trivial.

• Journal of ILASS-Korea
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• v.7 no.3
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• pp.1-6
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• 2002

In this study numerical and experimental study on the spray atomization characteristics of a GDI injector is performed. To carry out numerical analysis, four hybrid models that are composed of conical sheet disintegration model, LISA model, DDB model, and RT model are used. The experimental results to evaluate the prediction accuracy of hybrid models are obtained by using phase Doppler particle analyzer and spray visualization system. It is shown that the prediction accuracy of hybrid model concerning spray developing process and spray tip penetration is good for all hybrid models, but the hybrid breakup models show different prediction of accuracy in the case of local radial SMD distribution.

• Journal of Korean Library and Information Science Society
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• v.32 no.1
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• pp.309-328
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• 2001

The purpose of this study is to survey various digital library models that form basic concept of the hybrid information services and to suggest the needs of hybrid information services in digital environments as previous stage towards a building of generic information model appropriate to hybrid information environments. This study deals with the change of information service environments and information services of traditional and digital environments, Also addressed are relationships between digital library and information services. Finally. this study suggest the needs of hybrid information services in digital environments and survey various digital library models that form basic concept of the digital library models.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.203-206
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• 1996

Prediction of firm bankruptcy have been extensively studied in accounting, as all stockholders in a firm have a vested interest in monitoring its financial performance. The objective of this paper is to develop the hybrid models for bankruptcy prediction. The proposed hybrid models are two phase. Phase one are (a) DA-assisted neural network, (b) Logit-assisted neural network, and (c) Genetic-assisted neural network. And, phase two are (a) DA-assisted Case based reasoning, and (b) Genetic-assisted Case based reasoning. In the variables selection, We are focusing on three alternative methods - linear discriminant analysis, logit analysis and genetic algorithms - that can be used empirically select predictors for hybrid model in bankruptcy prediction. Empirical results using Korean medium-sized firms data show that hybrid models are very promising neural network models and case based reasoning for bankruptcy prediction in terms of predictive accuracy and adaptability.

• Journal of Korean Society on Water Environment
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• v.31 no.5
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• pp.491-506
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• 2015

Distributed models represent watersheds using a network of numerous, uniform calculation units to provide spatially detailed and consistent evaluations across the watershed. However, these models have a disadvantage in general requiring a high computing cost. Semi-distributed models, on the other hand, delineate watersheds using a simplified network of non-uniform calculation units requiring a much lower computing cost than distributed models. Employing a simplified network of non-uniform units, however, semi-distributed models cannot but have limitations in spatially-consistent simulations of hydrogeochemical processes and are often not favoured for such a task as identifying critical source areas within a watershed. Aiming to overcome these shortcomings of both groups of models, a hybrid watershed model STREAM (Spatio-Temporal River-basin Ecohydrology Analysis Model) was developed in this study. Like a distributed model, STREAM divides a watershed into square grid cells of a same size each of which may have a different set of hydrogeochemical parameters reflecting the spatial heterogeneity. Like many semi-distributed models, STREAM groups individual cells of similar hydrogeochemical properties into representative cells for which real computations of the model are carried out. With this hybrid structure, STREAM requires a relatively small computational cost although it still keeps the critical advantage of distributed models.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1089-1098
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• 2018

This study aims at developing and applying a hybrid model to the wind power prediction (WPP). The hybrid model for a very-short-term WPP (VSTWPP) is achieved through analytical data, multiple linear regressions and least square methods (MLR&LS). The data used in our hybrid model are based on the historical records of wind power from an offshore region. In this model, the WPP is achieved in four steps: 1) transforming historical data into ratios; 2) predicting the wind power using the ratios; 3) predicting rectification ratios by the total wind power; 4) predicting the wind power using the proposed rectification method. The proposed method includes one-step and multi-step predictions. The WPP is tested by applying different models, such as the autoregressive moving average (ARMA), support vector machine (SVM), and artificial neural network (ANN). The results of all these models confirmed the validity of the proposed hybrid model in terms of error as well as its effectiveness. Furthermore, forecasting errors are compared to depict a highly variable WPP, and the correlations between the actual and predicted wind powers are shown. Simulations are carried out to definitely prove the feasibility and excellent performance of the proposed method for the VSTWPP versus that of the SVM, ANN and ARMA models.

• Journal of Mechanical Science and Technology
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• v.17 no.3
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• pp.449-456
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• 2003

In this study, numerical and experimental analysis on the spray atomization characteristics of a GDI injector is performed. For numerical approach, four hybrid models that are composed of primary and secondary breakup model are considered. Concerning the primary breakup, a conical sheet disintegration model and LISA model are used. The secondary breakup models are made based on the DDB model and RT model. The global spray behavior is also visualized by the shadowgraph technique and local Sauter mean diameter and axial mean velocity are measured by using phase Doppler particle analyzer Based on the comparison of numerical and experimental results, it is shown that good agreement is obtained in terms of spray developing process and spray tip penetration at the all hybrid models. However, the hybrid breakup models show different prediction of accuracy in the cases of local SMD and the spatial distribution of breakup.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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• pp.159-168
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• 2022

The time series of foreign trade turnover is complex and variable and contains linear and nonlinear information. This paper proposes preprocessing the dataset by the EMD algorithm and combining the linear prediction advantage of the SARIMA model with the nonlinear prediction advantage of the EMD-LSTM model to construct the SARIMA-EMD-LSTM hybrid model by the weight assignment method. The forecast performance of the single models is compared with that of the hybrid models by using MAPE and RMSE metrics. Furthermore, it is confirmed that the weight assignment approach can benefit from the hybrid models. The results show that the SARIMA model can capture the fluctuation pattern of the time series, but it cannot effectively predict the sudden drop in foreign trade turnover caused by special reasons and has the lowest accuracy in long-term forecasting. The EMD-LSTM model successfully resolves the hysteresis phenomenon and has the highest forecast accuracy of all models, with a MAPE of 7.4304%. Therefore, it can be effectively used to forecast the Sino-Russia foreign trade turnover time series post-epidemic. Hybrid models cannot take advantage of SARIMA linear and LSTM nonlinear forecasting, so weight assignment is not the best method to construct hybrid models.

• Wind and Structures
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• v.38 no.1
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• pp.1-13
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• 2024

In order to effectively simulate nonstationary stochastic turbulent wind fields, four separation hybrid (SEP-H) models are proposed in the present study. Based on the assumption that the lateral turbulence component at one single-point is uncorrelated with the longitudinal and vertical turbulence components, the fluctuating wind is separated into 2nV-1D and nV1D nonstationary stochastic vector processes. The first process can be expressed as double proper orthogonal decomposition (DPOD) or proper orthogonal decomposition and spectral representation method (POD-SRM), and the second process can be expressed as POD or SRM. On this basis, four SEP-H models of nonstationary stochastic turbulent wind fields are developed. In addition, the orthogonal random variables in the SEP-H models are presented as random orthogonal functions of elementary random variables. Meanwhile, the number theoretical method (NTM) is conveniently adopted to select representative points set of the elementary random variables. The POD-FFT (Fast Fourier transform) technique is introduced in frequency to give full play to the computational efficiency of the SEP-H models. Finally, taking a long-span bridge as the engineering background, the SEP-H models are compared with the dimension-reduction DPOD (DR-DPOD) model to verify the effectiveness and superiority of the proposed models.

• Journal of Environmental Impact Assessment
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• v.14 no.4
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• pp.157-164
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• 2005

Existing models that predict of Daily water supply include statistical models and neural network model. The neural network model was more effective than the statistical models. Only neural network model, which predict of Daily water supply, is focused on estimation of the operational control. Neural network model takes long learning time and gets into local minimum. This study proposes Neuro Genetic hybrid model which a combination of genetic algorithm and neural network. Hybrid model makes up for neural network's shortcomings. In this study, the amount of supply, the mean temperature and the population of the area supplied with water are use for neural network's learning patterns for prediction. RMSE(Root Mean Square Error) is used for a MOE(Measure Of Effectiveness). The comparison of the two models showed that the predicting capability of Hybrid model is more effective than that of neural network model. The proposed hybrid model is able to predict of Daily water, thus it can apply real time estimation of operational control of water works and water drain pipes. Proposed models include accidental cases such as a suspension of water supply. The maximum error rate between the estimation of the model and the actual measurement was 11.81% and the average error was lower than 1.76%. The model is expected to be a real-time estimation of the operational control of water works and water/drain pipes.