• 한국생물공학회:학술대회논문집
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• 한국생물공학회 2000년도 춘계학술발표대회
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• pp.133-136
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• 2000

In recent years, hybrid neural network approaches which combine neural networks and mechanistic models have been gaining considerable interests. These approaches are potentially very efficient to obtain more accurate predictions of process dynamics by combining mechanistic and neural models in such a way that the neural network model properly captures unknown and nonlinear parts of the mechanistic model. In this work, such an approach was applied in the modeling of a full-scale coke wastewater treatment process. First, a simplified mechanistic model was developed based on the Activated Sludge Model No.1 and the specific process knowledge, Then neural network was incorporated with the mechanistic model to compensate the errors between the mechanistic model and the process data. Simulation and actual process data showed that the hybrid modeling approach could predict accurate process dynamics of industrial wastewater treatment plant. The promising results indicated that the hybrid modeling approach could be a useful tool for accurate and cost-effective modeling of biochemical processes.

• 한국자동차공학회논문집
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• 제10권6호
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• pp.27-33
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• 2002

This paper presents the comparison of prediction accuracy of hybrid models. To obtain the experimental results fur comparing with the numerical results, the macroscopic and microscopic structures of the hollow-cone spray such as spray development process, spray penetration and the distribution of mean droplet size are investigated by using a shadowgraph technique and phase Doppler particle analyzer. Also, the numerical researches using various hybrid models are performed. LISA model and WAVE model are used for the primary breakup, and TAB, DDB, and RT model are used for the secondary breakup.

• 반도체디스플레이기술학회지
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• 제10권3호
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• pp.75-81
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• 2011

This paper presents a novel method for image classification based on a hybrid genetic algorithm (GA) and support vector machine (SVM) approach which can significantly improve the classification performance for content-based image retrieval (CBIR). Though SVM has been widely applied to CBIR, it has some problems such as the kernel parameters setting and feature subset selection of SVM which impact the classification accuracy in the learning process. This study aims at simultaneously optimizing the parameters of SVM and feature subset without degrading the classification accuracy of SVM using GA for CBIR. Using the hybrid GA and SVM model, we can classify more images in the database effectively. Experiments were carried out on a large-size database of images and experiment results show that the classification accuracy of conventional SVM may be improved significantly by using the proposed model. We also found that the proposed model outperformed all the other models such as neural network and typical SVM models.

• 한국지능시스템학회논문지
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• 제21권6호
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• pp.686-691
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• 2011

This study is concerned with the identification of fuzzy models. To address the optimization of fuzzy model, we proposed an improved space search evolutionary algorithm (ISSA) which is realized with the combination of space search algorithm and Gaussian mutation. The proposed ISSA is exploited here as the optimization vehicle for the design of fuzzy models. Considering the design of fuzzy models, we developed a hybrid identification method using information granulation and the ISSA. Information granules are treated as collections of objects (e.g. data) brought together by the criteria of proximity, similarity, or functionality. The overall hybrid identification comes in the form of two optimization mechanisms: structure identification and parameter identification. The structure identification is supported by the ISSA and C-Means while the parameter estimation is realized via the ISSA and weighted least square error method. A suite of comparative studies show that the proposed model leads to better performance in comparison with some existing models.

• Structural Engineering and Mechanics
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• 제30권3호
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• pp.331-350
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• 2008

A framework for multi-platform analytical and multi-component hybrid (testing-analysis) simulations is described in this paper and illustrated with several application examples. The framework allows the integration of various analytical platforms and geographically distributed experimental facilities into a comprehensive pseudo-dynamic hybrid simulation. The object-oriented architecture of the framework enables easy inclusion of new analysis platforms or experimental models, and the addition of a multitude of auxiliary components, such as data acquisition and camera control. Four application examples are given, namely; (i) multi-platform analysis of a bridge with soil and structural models, (ii) multiplatform, multi-resolution analysis of a high-rise building, (iii) three-site small scale frame hybrid simulation, and (iv) three-site large scale bridge hybrid simulation. These simulations serve as illustrative examples of collaborative research among geographically distributed researchers employing different analysis platforms and testing equipment. The versatility of the framework, ease of including additional modules and the wide application potential demonstrated in the paper provide a rich research environment for structural and geotechnical engineering.

• 한국전산유체공학회지
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• 제16권4호
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• pp.7-13
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• 2011

A comparative analysis of thermal models in the lattice Boltzmann method(LBM) for the simulation of laminar natural convection in a square cavity is presented. A HYBRID method, in which the thermal equation is solved by the Navier-Stokes equation method while the mass and momentum conservation are resolved by the lattice Boltzmann method, is introduced and its merits are explained. All the governing equations are discretized on a cell-centered, non-uniform grid using the finite-volume method. The convection terms are treated by a second-order central-difference scheme with a deferred correction method to ensure stability of the solutions. The HYBRID method and the double-population method are applied to the simulation of natural convection in a square cavity and the predicted results are compared with the benchmark solutions given in the literatures. The predicted results are also compared with those by the conventional Navier-Stokes equation method. In general, the present HYBRID method is as accurate as the Navier-Stokes equation method and the double-population method. The HYBRID method shows better convergence and stability than the double-population method. These observations indicate that this HYBRID method is an efficient and economic method for the simulation of incompressible fluid flow and heat transfer problem with the LBM.

• Journal of Advanced Marine Engineering and Technology
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• 제40권8호
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• pp.721-725
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• 2016

This paper reviews the analysis of a given scenario according to the Hybrid Model, and why accident causation models are necessary in casualty investigations. The given scenario has been analyzed according to the Hybrid Model using its main five components, fallible decisions, line management, psychological precursors to unsafe acts, unsafe acts, and inadequate defenses. In addition, the differences between the SHEL and the Hybrid Model, and the importance of a safety barrier during an accident investigation, are shown in this paper. One unit of SHEL can be linked with another unit of SHEL. However, it cannot be used for the analysis of an accident. Therefore, we must use an accident causation model, which can be a Hybrid Model. This can explain the "How" and "Why" of accident, so it is a suitable model for analyzing them. During an accident investigation, the reason we focus on a safety barrier is to create another safety barrier or to change an existing safety barrier if that barrier fails. Hence, the paper shows how a sea accident can be investigated, and we propose a preventive way of avoiding the accident through combining the methods of different models for the future.

• The Journal of Asian Finance, Economics and Business
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• 제7권10호
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• pp.9-21
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• 2020

The aim of this paper is to predict the Borsa Istanbul (BIST) 30 index movements to determine the most accurate buy and sell decisions using the methods of Artificial Neural Networks (ANN) and Genetic Algorithm (GA). We combined these two methods to obtain a hybrid intelligence method, which we apply. In the financial markets, over 100 technical indicators can be used. However, several of them are preferred by analysts. In this study, we employed nine of these technical indicators. They are moving average convergence divergence (MACD), relative strength index (RSI), commodity channel index (CCI), momentum, directional movement index (DMI), stochastic oscillator, on-balance volume (OBV), average directional movement index (ADX), and simple moving averages (3-day moving average, 5-day moving average, 10-day moving average, 14-day moving average, 20-day moving average, 22-day moving average, 50-day moving average, 100-day moving average, 200-day moving average). In this regard, we combined these two techniques and obtained a hybrid intelligence method. By applying this hybrid model to each of these indicators, we forecast the movements of the Borsa Istanbul (BIST) 30 index. The experimental result indicates that our best proposed hybrid model has a successful forecast rate of 75%, which is higher than the single ANN or GA forecasting models.

• Ocean Systems Engineering
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• 제3권1호
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• pp.35-53
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• 2013

In this study, a hybrid floating structure with cylinder was introduced to reduce the hydrodynamic motions of the pontoon type. The hybrid floating structure is composed of cylinders and semi-opened side sections to penetrate the wave impact energy. In order to exactly investigate the hydrodynamic motions and structural behavior of the hybrid floating structure under the wave loadings, integrated analysis of hydrodynamic and structural behavior were carried out on the hybrid floating structure. Firstly, the hydrodynamic analyses were performed on the hybrid and pontoon models. Then, the wave-induced hydrodynamic pressures resulting from hydrodynamic analysis were directly mapped to the structural analysis model. And, finally, the structural analyses were carried out on the hybrid and pontoon models. As a result of this study, it was learned that the hybrid model of this study was showed to have more favorable hydrodynamic motions than the pontoon model. The surge motion was indicated even smaller motion at all over wave periods from 4.0 to 10.0 sec, and the heave and pitch motions indicated smaller motions beyond its wave period of 6.5 sec. However, the hybrid model was shown more unfavorable structural behavior than the pontoon model. High concentrated stress occurred at the bottom slab of the bow and stern part where the cylinder wall was connected to the bottom slab. Also, the hybrid model behaved with the elastic body motion due to weak stiffness of floating body and caused a large stress variation at the pure slab section between the cylinder walls. Hence, in order to overcome these problems, some alternatives which could be easily obtained from the simple modification of structural details were proposed.

• 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.