• Journal of the Earthquake Engineering Society of Korea
• /
• v.14 no.6
• /
• pp.33-43
• /
• 2010

This paper deals with the hybrid structural test technique which has been introduced and studied currently in Korea. In this study, a Mini-MOST system which was developed as a part of NEES research was modified and improved to reduce the total simulation time to half of the original system. Using the proposed system together with the 2 dimensional small steel frame specimen, the validity and efficiency of the hybrid test technique is investigated. Even though the hybrid test has been developed as an alternative to the shaking table test and has been studied and applied for a long time in several countries, no attempt has been made to compare it directly with the shaking table test. Therefore, in this study, the hybrid test results are compared with those of the shaking table test as well as with a numerical simulation for the verification of hybrid test. From the comparison and analysis of the test results, it is concluded that the hybrid test can simulate the actual seismic behavior of structural systems very accurately and it can be a good alternative to the shaking table test.

• Asia pacific journal of information systems
• /
• v.5 no.1
• /
• pp.36-51
• /
• 1995

본 논문에서는 비구조적인 의사결정문제를 효과적으로 해결하기 위하여 감독학습 인공신경망 모형과 비감독학습 인공신경망 모형을 결합한 하이브리드 인공신경망 모형인 HYNEN(HYbrid NEural Network) 모형을 제안한다. HYNEN모형은 주어진 자료를 클러스터화 하는 CNN(Clustering Neural Network)과 최종적인 출력을 제공하는 ONN(Output Neural Network)의 2단계로 구성되어 있다. 먼저 CNN에서는 주어진 자료로부터 적정한 퍼지규칙을 찾기 위하여 클러스터를 구성한다. 그리고 이러한 클러스터를 지식베이스로하여 ONN에서 최종적인 의사결정을 한다. CNN에서는 SOFM(Self Organizing Feature Map)과 LVQ(Learning Vector Quantization)를 클러스터를 만든 후 역전파학습 인공신경망 모형으로 이를 학습한다. ONN에서는 역전파학습 인공신경망 모형을 이용하여 각 클러스터의 내용을 학습한다. 제안된 HYNEN 모형을 우리나라 기업의 도산자료에 적용하여 그 결과를 다변량 판별분석법(MDA:Multivariate Discriminant Analysis)과 ACLS(Analog Concept Learning System) 퍼지 ARTMAP 그리고 기존의 역전파학습 인공신경망에 의한 실험결과와 비교하였다.

• Journal of Korea Technology Innovation Society
• /
• v.14 no.spc
• /
• pp.1232-1255
• /
• 2011

This paper proposes a model for demand forecasting that will require less effort in the process of utilizing the new product diffusion model while also allowing for more objective and timely application. Drawing upon the theoretical foundation provided by the hype cycle model and the consumer adoption model, this proposed model makes it possible to estimate the maximum market potential based solely on bibliometrics and the scale of the early market, thereby presenting a method for supplying the major parameters required for the Bass model. Upon analyzing the forecasting ability of this model by applying it to the case of the hybrid car market, the model was confirmed to be capable of successfully forecasting results similar in scale to the market potential deduced through various other objective sources of information, thus underscoring the potentials of utilizing this model. Moreover, even the hype cycle or the life cycle can be estimated through direct linkage with bibliometrics and the Bass model. In cases where the hype cycles of other models have been observed, the forecasting ability of this model was demonstrated through simple case studies. Since this proposed model yields a maximum market potential that can also be applied directly to other growth curve models, the model presented in the following paper provides new directions in the endeavor to forecast technology diffusion and identify promising technologies through bibliometrics.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.20 no.2
• /
• pp.59-73
• /
• 2009

The library could get infinite possibilities of the space, if people had recognized that the library was not only the space for the reading and collecting of the books but also a special space for accomplishment of the investigation, learning, communication, and culture. Also, it can be desirable way for implementing the hybrid library. It means that combine with the traditional library which is the most important thing is a real space and the digital library, in contrast, the which is that the most important thing is the cyberspace. The purpose of this study is to develop a practical model for hybrid library based on the concept of information commons which is cooperation of the space, information technology and information services. This study intends to figure out the core elements involved in operative aspect of hybrid library through comparative analysis of domestic and foreign cases applied Information commons concepts to the library.

• Journal of the Earthquake Engineering Society of Korea
• /
• v.9 no.2 s.42
• /
• pp.7-15
• /
• 2005

Numerical analysis model is proposed to predict the dynamic behavior of a single-degree-of-freedom structure that is equipped with hybrid base isolation system. Hybrid base isolation system is composed of friction pendulum systems (FPS) and a magnetorheological (MR) damper. A neuro-fuzzy model is used to represent dynamic behavior of the MR damper. Fuzzy model of the MR damper is trained by ANFIS (Adaptive Neuro-Fuzzy Inference System) using various displacement, velocity, and voltage combinations that are obtained from a series of performance tests. Modelling of the FPS is carried out with a nonlinear analytical equation that is derived in this study and neuro-fuzzy training. Fuzzy logic controller is employed to control the command voltage that is sent to MR damper. The dynamic responses of experimental structure subjected to various earthquake excitations are compared with numerically simulated results using neuro-fuzzy modeling method. Numerical simulation using neuro-fuzzy models of the MR damper and FPS predict response of the hybrid base isolation system very well.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2007.12a
• /
• pp.259-260
• /
• 2007

The forecast of a container traffic has been very important for port plan and development Generally, statistic methods, such as regression analysis, ARIMA, have been much used for traffic forecasting. Recent research activities in forecasting with artificial neural networks(ANNs) suggest tint ANNs am be a promising alternative to the traditional linear methods. In this paper, a hybrid methodology that combines both ARIMA and ANN models is proposed to take advantage of the unique strength of ARIMA and ANN models in linear and nonlinear modeling. The results with port traffic data indicate tint effectiveness can differ according to the ch1racteristics of ports.

• The Korean Journal of Applied Statistics
• /
• v.32 no.6
• /
• pp.899-908
• /
• 2019

Massive in-depth neural networks with numerous parameters are powerful machine learning methods, but they have overfitting problems due to the excessive flexibility of the models. Dropout is one methods to overcome the problem of oversized neural networks. It is also an effective method that randomly drops input and hidden nodes from the neural network during training. Every sample is fed to a thinned network from an exponential number of different networks. In this study, instead of feeding one sample for each thinned network, two or more samples are used in fitting for one thinned network known as a Hybrid Dropout. Simulation results using real data show that the new method improves the stability of estimates and reduces the minimum error for the verification data.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2015.05a
• /
• pp.43-43
• /
• 2015

천수방정식을 사용하는 초기 수치모형은 프로드수($F_4$)가 변화하는 흐름 즉, 상류방향과 하류방향으로 전파하는 홍수파를 동시에 해석하기 위해 중앙 차분기법이 필요한 상류(sub-critical flow)와 흐름방향에 따른 상류이송(upwinding)기법이 필요한 사류(super-critical flow)가 나타나는 흐름해석에서 어려움이 있었다. 하지만, 근사 Riemann 해법의 등장으로 흐름방향에 관계없이 특성선을 따라 정확한 상향가중기법의 적용이 가능하게 되어, 천수방정식을 지배방정식으로 하는 수치모형이 더욱 실용적으로 적용될 수 있도록 하였다. 따라서, 현재 근사 Riemann 해법은 Godunov 형 유한체적 기법, 불연속 Galerkin 혹은 Petrov-Galerkin 유한요소기법 그리고 Boussinesq 기법에도 적용되고 있으며, 특히 Godunov 형 유한체적기법과 결합한 근사 Riemann 해법은 댐 붕괴, 하천 범람 그리고 도시 및 해안지역 침수에 이르기까지 여러 가지 문제에 폭넓게 적용되고 있다. 지금까지 홍수 모델링에 적용된 Godunov형 유한체적모형은 정형 사각격자나 비정형 삼각격자 중에서 한가지의 격자 종류만을 적용한 연구가 주로 수행되었으며, 유한요소모형과 같이 이 두 가지 격자를 동시에 적용한 연구는 거의 이루어지지 않고 있다. 일반적으로, 삼각격자는 사각격자와 는 달리 연구유역의 경계나 지형이 복잡한 경우에도 큰 노력없이 격자의 생성이 가능하나, 격자와 노드의 수가 사각격자보다 많아 계산시간이 많이 소요되는 단점이 있다. 반면, 사각격자는 하천과 같이 선형으로 변하는 지형에 대해서는 표현하기가 용이하며 계산시간의 효율성도 뛰어나다. 본 연구에서는 하천, 도시 그리고 해안지역에서의 효율적이고 정확한 홍수 모델링을 위해 삼각 및 사각격자 그리고 이 두 격자를 동시에 고려한 하이브리드 격자의 적용이 가능한 Godunov형 2차원 유한체적 모형을 개발하였다. 그리고 개발모형을 정확해가 있는 댐 붕괴 문제, 실측치가 존재하는 실험하도 및 실제하도에 삼각, 사각 그리고 혼합격자를 생성하여 모의를 수행하고, 각 적용 격자에 따른 정확성과 효율성 및 장점과 단점을 연구하였다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2017.05a
• /
• pp.27-27
• /
• 2017

신뢰성 있는 수개월 선행시간의 댐 유입량 예측은 가뭄 상황으로 진입하는 시점에서 효율적인 댐 운영을 위해 필수적이다. 최근 기후변화로 인한 강수량의 경년 및 계절 내 변동성이 증가됨에 따라서 기존의 과거 통계치를 이용한 댐 운영 의사결정은 많은 도전을 받고 있다. 최근 엘리뇨-남방진동(ENSO) 등의 전구기후지수와 지역수문기후와의 원격상관성을 활용하여 수개월 이후에 대한 수문조건을 통계적으로 예측하기 위한 연구가 시도되고 있다. 또한 매월 제공되는 역학적 예측모형으로부터 생산된 월단위 예측정보를 유량예측을 위한 유역모형에 활용하기 위하여 편이보정 및 상세화 기법이 개발되어 활용되고 있다. 본 연구에서는 댐 유입량 예측을 위해 SWAT 모형을 선정하였고 최장 6개월 선행 강수량 및 기온의 예측을 위해서 하이브리드 계절예측 시스템을 활용하였다. 이 시스템은 전지구역학적 예측모형의 자료를 편이보정을 거쳐 직접적으로 사용하는 단순 편이보정(Simple Bias Correction, SBC) 방법에 회귀모형을 이용하여 통계적인 방법으로 예측자료를 생산하는 전구기후지수 기반의 Climate Index Regression (CIR), 실시간 재분석자료 기반의 Observation-based Moving Window Regression (MWR-Obs), 역학적 예측모형의 예측자료 기반의 Moving Window Regression (MWR) 방법을 통합하여 사용하고 있다. 충주댐을 대상으로 우선 관측자료를 이용하여 SWAT 모형을 검 보정한 후, 관측기간에 대하여 하이브리드 시스템에 의한 예측 기상자료를 적용하여 모의된 댐 유입량과 관측 유입량과의 비교를 통해 예측성을 평가하였다. 본 연구는 다양한 기후정보를 활용하여 댐 유입량 예측에 있어서 예측성을 높이고자 시도되었으며, 도출된 결과는 향후 충주댐 운영에 유용한 정보를 제공할 수 있는 것으로 판단된다.

• Journal of Korea Water Resources Association
• /
• v.54 no.spc1
• /
• pp.1037-1051
• /
• 2021

Biochemical oxygen demand (BOD) concentration, one of important water quality indicators, is treated as the measuring item for the ecological chapter in lakes and rivers. This investigation employed novel two-stage hybrid paradigm (i.e., wavelet-based gated recurrent unit, wavelet-based generalized regression neural networks, and wavelet-based random forests) to predict BOD concentration in the Dosan and Hwangji stations, South Korea. These models were assessed with the corresponding independent models (i.e., gated recurrent unit, generalized regression neural networks, and random forests). Diverse water quality and quantity indicators were implemented for developing independent and two-stage hybrid models based on several input combinations (i.e., Divisions 1-5). The addressed models were evaluated using three statistical indices including the root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and correlation coefficient (CC). It can be found from results that the two-stage hybrid models cannot always enhance the predictive precision of independent models confidently. Results showed that the DWT-RF5 (RMSE = 0.108 mg/L) model provided more accurate prediction of BOD concentration compared to other optimal models in Dosan station, and the DWT-GRNN4 (RMSE = 0.132 mg/L) model was the best for predicting BOD concentration in Hwangji station, South Korea.