• Journal of Information Processing Systems
• /
• 제7권1호
• /
• pp.1-16
• /
• 2011

With the increasing number of vehicles in use in our daily life and the rise of traffic congestion problems, many methods and models have been developed for real time optimisation of traffic lights. Nevertheless, most methods which consider real time physical queue sizes of vehicles waiting for green lights overestimate the optimal cycle length for such real traffic control. This paper deals with the development of a generic hybrid model describing both physical traffic flows and control of signalised intersections. The firing times assigned to the transitions of the control part are considered dynamic and are calculated by a simplified optimisation method. This method is based on splitting green times proportionally to the predicted queue sizes through input links for each new cycle time. The proposed model can be easily translated into a control code for implementation in a real time control system.

• International Journal of Control, Automation, and Systems
• /
• 제1권1호
• /
• pp.101-110
• /
• 2003

This paper introduces an identification method for nonlinear models in the form of rule-based Fuzzy-Neural Networks (FNN). In this study, the development of the rule-based fuzzy neural networks focuses on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The FNN modeling and identification environment realizes parameter identification through synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a HCM (Hard C-Means) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the identification algorithm of a GA hybrid scheme. The proposed GA hybrid scheme effectively combines the GA with the improved com-plex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model having sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process, and NOx emission process data from gas turbine power plants).

• Steel and Composite Structures
• /
• 제39권4호
• /
• pp.471-491
• /
• 2021

In this study, we estimate the ultimate load of rectangular concrete-filled steel tubes (CFST) by developing a novel hybrid predictive model (ANN-BCMO) which is a combination of balancing composite motion optimization (BCMO) - a very new optimization technique and artificial neural network (ANN). For this aim, an experimental database consisting of 422 datasets is used for the development and validation of the ANN-BCMO model. Variables in the database are related with the geometrical characteristics of the structural members, and the mechanical properties of the constituent materials (steel and concrete). Validation of the hybrid ANN-BCMO model is carried out by applying standard statistical criteria such as root mean square error (RMSE), coefficient of determination (R²), and mean absolute error (MAE). In addition, the selection of appropriate values for parameters of the hybrid ANN-BCMO is conducted and its robustness is evaluated and compared with the conventional ANN techniques. The results reveal that the new hybrid ANN-BCMO model is a promising tool for prediction of the ultimate load of rectangular CFST, and prove the effective role of BCMO as a powerful algorithm in optimizing and improving the capability of the ANN predictor.

• 한국분무공학회지
• /
• 제9권3호
• /
• pp.42-49
• /
• 2004

The present article investigates the influence of droplet drag models on predictions of diesel spray behaviors under ultra-high injection pressure conditions. To consider drop deformation and shock disturbance, this study introduces a new hybrid model in predicting drag coefficient from the literature findings. Numerical simulations are first conducted on transient behaviors of single droplet to compare the hybrid model with earlier conventional model. Moreover, using two different models, extensive numerical calculations are made for diesel sprays under ultra-high pressure sprays. It is found that the droplet drag models play an important role in determining the transient behaviors of sprays such as spray tip velocity and penetration lengths. Numerical results indicate that this new hybrid model yields the much better conformity with measurements especially under the ultra-high injection pressure conditions.

• 전기학회논문지P
• /
• 제63권3호
• /
• pp.189-194
• /
• 2014

Due to the increasing of power consumption, it is difficult to construct accurate prediction model for daily peak power demand. It is very important work to know power demand in next day for manager and control power system. In this research, we develop a daily peak power demand prediction method based on hybrid type composed of AR and Neuro-Fuzzy model. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.

• 정보관리학회지
• /
• 제19권4호
• /
• pp.35-51
• /
• 2002

본 논문에서는 다중 모델 기계학습 기법을 이용하여 자동 문서 분류의 성능과 신뢰도를 향상시킬 수 있는 연구와 실험 결과를 기술하였다. 기존의 다중 모델 기계 학습법들이 훈련 데이터 또는 학습 알고리즘의 편향에 의한 오류를 극복하고자 한 것인데 비해 본 논문에서 제안한 메타 학습을 이용한 하이브리드 다중 모델 방식은 이 두 가지의 오류 원인을 동시에 해소하고자 하였다. 다양한 문서 집합에 대한 실험 결과. 본 논문에서 제안한 하이브리드 다중 모델 학습법이 전반적으로 기존의 일반 다중모델 학습법들에 비해 높은 성능을 보였으며, 다중 모델의 결합 방식으로서 메타 학습이 투표 방식에 비해 효율적인 것으로 나타났다.

• 성인간호학회지
• /
• 제25권2호
• /
• pp.170-182
• /
• 2013

Purpose: This study investigated the effectiveness of simulation training with a hybrid model of student nurses' performance ability and reported self confidence. Methods: A nonequivalent control group with pre-posttest was designed. Data collection was done during the first semester in 2012 at a college of nursing in Seoul. Nursing performance ability and reported self confidence related to taking care of patients with urinary problems were evaluated. The treatment group (n=96) received simulation training of a catheterization procedure with a hybrid model involving standardized patients and a mannequin. Nursing students in the comparison group (n=84) did not receive the simulation training but would receive it prior to their next clinical practicum's. Results: The treatment group showed a significantly higher performance ability and reported self confidence than that of the comparison group. The perceived helpfulness and contentment of the simulation training in experimental group was high. Conclusion: The findings of this study demonstrated that simulation with a hybrid model was effective in teaching skills prior to the clinical experience which suggests that skill development is not dependent on the actual clinical situation. Nurse educators should consider simulation training as a tool beyond that of clinical practicum.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2023년도 학술발표회
• /
• pp.100-100
• /
• 2023

단기 강우 예측에는 주로 물리과정 기반 수치예보모델(NWPs, Numerical Prediction Models) 과 레이더 기반 확률론적 방법이 사용되어 왔으며, 최근에는 머신러닝을 이용한 레이더 기반 강우예측 모델이 단기 강우 예측에 뛰어난 성능을 보이는 것을 확인하여 관련 연구가 활발히 진행되고 있다. 하지만 머신러닝 기반 모델은 예측 선행시간 증가 시 성능이 크게 저하되며, 또한 대기의 물리적 과정을 고려하지 않는 Black-box 모델이라는 한계점이 존재한다. 본 연구에서는 이러한 한계를 극복하기 위해 머신러닝 기반 blending 기법을 통해 물리과정 기반 수치예보모델인 Weather Research and Forecasting (WRF)와 최신 머신러닝 기법 (cGAN, conditional Generative Adversarial Network) 기반 모델을 결합한 Hybrid 강우예측모델을 개발하고자 하였다. cGAN 기반 모델 개발을 위해 1시간 단위 1km 공간해상도의 레이더 반사도, WRF 모델로부터 산출된 기상 자료(온도, 풍속 등), 유역관련 정보(DEM, 토지피복 등)를 입력 자료로 사용하여 모델을 학습하였으며, 모델을 통해 물리 정보 및 머신러닝 기반 강우 예측을 생성하였다. 이렇게 생성된cGAN 기반 모델 결과와 WRF 예측 결과를 결합하는 머신러닝 기반 blending 기법을 통해Hybrid 강우예측 결과를 최종적으로 도출하였다. 본 연구에서는 Hybrid 강우예측 모델의 성능을 평가하기 위해 수도권 및 안동댐 유역에서 발생한 호우 사례를 기반으로 최대 선행시간 6시간까지 모델 예측 결과를 분석하였다. 이를 통해 물리과정 기반 모델과 머신러닝 기반 모델을 결합하는 Hybrid 기법을 적용하여 높은 정확도와 신뢰도를 가지는 고해상도 강수 예측 자료를 생성할 수 있음을 확인하였다.

• 창의정보문화연구
• /
• 제6권3호
• /
• pp.141-148
• /
• 2020

최근, 코비드 환경은 대학의 교육 시스템에 심각한 영향을 끼쳤다. 대학의 재학률과 취업률에 큰 영향을 미치는 학생의 진로지도 방법은 코비드 환경에서 변할 수밖에 없다. 기존의 진로지도 활동은 오프라인 상담 방법을 주로 사용하지만, 코비드 환경에서의 진로지도 활동은 온라인 상담 방법을 더 중요하게 고려해야 한다. 본 논문에서는 온라인과 오프라인 교육 환경에서 효과적으로 사용할 수 있는 하이브리드(온라인과 오프라인) 진로지도 모델을 제안한다. 온라인과 오프라인의 특성을 가진 하이브리드 제안 모델은 코비드 환경에서도 학생들을 효과적으로 지도 할 수 있다. 본 논문에서는 제안 모델을 실제 상담 사례에 적용하여 제안 모델의 유용성을 보였다.

• Archives of Pharmacal Research
• /
• 제13권3호
• /
• pp.221-226
• /
• 1990

A physiological pharmacokinetic hybrid model was developed in order to predict the disposition kinetics of diphenylhydantoin (DPH) in the brain from the plasma conentration data of DPH. The model was constructed under the assumptions of well-stirred, plasma flow-limited and lienar tissue diposition kinetics of DPH. DPH was administered intravenously to the rats at a dose of 10 mg/kg together with/without sodium salicylate (SA;10 mg/kg) and the DPH concentrations in the plasma and brain were determined. Plasma protein binding of DPH concentrations in the plasma and brain were determined. Plasma protein binding of DPH was also determined using equilibrium dialysis technique. Then the model was tested for its predictability of DPH concentrations in the brian from the plasma data of DPH. It was found that the predicted values of DPH concentrations in the brian were in fair agreement with the experimental values in the rats of both treatments. The 2-fold increase in the brain concentration of DPH by SA-coadinistration was predicted well from the plasma concentration and plasma free fraction ($f_p$) data of DPH using the model. Therefore, the hybrid model was concluded to be very useful for the prediction of the concentrations of DPH in the brain from the plasma concentration data. Finally, DPH concentrations in the human brian was calculated using this model from plasma DPH data in the literature, yet the scale-up of this model to the human is not convinced.