• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.1053-1059
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• 2009

Construction system modeling can enhance work performance by following the behaviors of a system. System behaviors may originate from physical aspects of a system, namely operation level variables, or from non-physical aspects of a system known as context level variables. However, construction system modelers usually focus on only one type of system variable (i.e., operation level or context level) which can lead to less accurate results. Hybrid modeling with System Dynamics (SD) and Discrete Event Simulation (DES) is one of the approaches that has been utilized to address this issue. In this research, an SD-DES hybrid model of a steel fabrication shop is developed, and the benefits of capturing context level variables together with operation level variables in the model are discussed.

• 한국콘텐츠학회논문지
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• 제12권10호
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• pp.11-21
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• 2012

하이브리드 네트워크 인프라는 Internet2, SURFNet 등의 선도 연구망 커뮤니티에게 가장 우선적인 기술로 대두되고 있다. 그러나, 첨단(high-end) 응용의 종단 간 협업 연구를 위하여 필수적인 하이브리드 연구망 간의 인터도메인 협업 인프라는 실질적인 아키텍쳐의 설계와 구현에 있어서 아직도 많은 연구를 필요로 하고 있다. 따라서 본 논문에서는 하이브리드 연구망 기반의 분산 가상형 네트워크 운영과 리소스 정보 관리를 위한 프레임워크를 제안하고, 이를 기반으로 코어 시스템을 구현하였다. 제안된 프레임워크는 멀티도메인 하이브리드 연구망 운영과 관리를 위하여 분산형 아키텍쳐로 설계되었다. 분산 가상형 네트워크 운영 프레임워크는 네트워크 도메인 내에서 자치성과 독립적인 제어를 유지하면서 인터도메인 네트워크 간의 협업을 가능케 함으로써, 연구자 및 실험자가 스스로 생성한 가상 네트워크를 운영 관리 할 수 있는 환경을 제공할 수 있다. 본 논문에서는 제안된 프레임워크를 위한 세부적인 구조와 기술을 다루며, 이러한 환경이 어떻게 고성능 첨단(high-end) 응용을 위하여 활용될 수 있는지에 대하여 고찰한다.

• 조명전기설비학회논문지
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• 제28권12호
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• pp.18-29
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• 2014

This paper proposes the integration of photovoltaic (PV) and energy storage systems for sustained power generation. In this proposed system, whenever the PV system cannot completely meet load demands, the super capacitor provides power to meet the remaining load. A power management strategy is designed for the proposed system to manage power flows between PV array systems and supercapacitors (SC). The main task of this study was to design PV systems with storage strategies including MPPT with direct control and an advanced DC-link controller and to analyze dynamic model proposed for a PV-SC hybrid power generation system. In this paper, the simulation models for the hybrid energy system are developed using Matlab/Simulink, SimPowerSystems and Matlab/Stateflow tool. This is the key innovative contribution of the research paper. The system performances are verified by carrying out simulation studies using practical load demand profile and real weather data.

• 지능정보연구
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• 제19권1호
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• pp.1-17
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• 2013

본 연구에서는 시계열 예측을 위해 선형 모형과 비선형 모형의 하이브리드 모형 및 순수 모형의 성과를 비교 평가하였다. 이를 위해 5가지 서로 다른 패턴을 가지는 데이터를 생성하여 시뮬레이션을 진행하였다. 본 연구에서 고려한 선형 모형은 AR(autoregressive model)과 SARIMA(seasonal autoregressive integrated moving average model)이고 비선형 모형은 인공신경망(artificial neural networks model)과 GAM(generalized additive model)이다. 특히, GAM은 여러 장점에도 불구하고 시계열 예측을 위한 비선형 모형으로 기존 연구들에서는 거의 쓰이지 않았던 모형이다. 시뮬레이션 결과, seasonality를 가지는 시계열에 대해서는 AR 및 AR-AR 모형이, trend를 가지는 시계열에 대해서는 SARIMA 및 SARIMA와 다른 모형의 하이브리드 모형이 다른 모형에 비해 높은 성과를 보였다. 한편, 인공신경망과 GAM을 비교하면, 트렌드와 계절성이 더해진 시계열에 대해 SARIMA와 GAM의 하이브리드 모형이 거의 모든 노이즈(noise) 수준에 대해 높은 성과를 보인 반면, 노이즈 수준이 미미한 경우에 한해 SARIMA와 인공신경망의 하이브리드 모형이 높은 성과를 보였다.

• 한국산업정보학회논문지
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• 제24권5호
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• pp.53-64
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• 2019

본 연구에서는 재사용 가능한 제품을 대상으로 순방향물류(Forward logistics)에서 부터 역방향물류(Reverse logistics)에 이르기까지 전체 물류비용과 수요와 회수에 따른 제조업자에서의 재고관리, 재사용을 위한 과정에서 발생하는 청소공정비용 및 폐기비용을 고려한 재사용 네트워크 모델(Reusable network model)을 제안한다. 제안 모델의 유효성을 검증하기 위하여 최적화 기법 중 하나인 유전자 알고리즘(Genetic algorithm: GA)을 이용한다. 파라미터가 해(Solution)에 미치는 영향을 알아보기 위해서 세 가지 파라미터 조건에서 우선 순위형 GA(Priority-based GA: priGA)와, 각 세대(Generation)마다 파라미터가 조정되는 개량형 하이브리드 GA(Modified hybrid genetic algorithm: mhGA)를 사이즈가 다른 4가지 예제에 적용하여 시뮬레이션을 실시한다.

• Advances in Computational Design
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• 제5권4호
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• pp.445-465
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• 2020

This paper presents a study conducted with the aim of developing a model of tendering based on a technique of artificial intelligence by managing and controlling the factors of success or failure of construction projects through the evaluation of the process of invitation to tender. Aiming to solve this problem, analysis of the current environment based on SWOT (Strengths, Weaknesses, Opportunities, and Threats) is first carried out. Analysis was evaluated through a case study of the construction projects in Algeria, to bring about the internal and external factors which affect the process of invitation to tender related to the construction projects. This paper aims to develop a mean to identify threats-opportunities and strength-weaknesses related to the environment of various national construction projects, leading to the decision on whether to continue the project or not. Following a SWOT analysis, novel artificial intelligence models in forecasting the project status are proposed. The basic principal consists in interconnecting the different factors to model this phenomenon. An artificial neural network model is first proposed, followed by a model based on fuzzy logic. A third model resulting from the combination of the two previous ones is developed as a hybrid model. A simulation study is carried out to assess performance of the three models showing that the hybrid model is better suited in forecasting the construction project status than RNN (recurrent neural network) and FL (fuzzy logic) models.

• 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.

• Journal of information and communication convergence engineering
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• 제3권4호
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• pp.209-212
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• 2005

This study proposes hybrid case-based reasoning and genetic algorithms model for customer classification. In this study, vertical and horizontal dimensions of the research data are reduced through integrated feature and instance selection process using genetic algorithms. We applied the proposed model to customer classification model which utilizes customers' demographic characteristics as inputs to predict their buying behavior for the specific product. Experimental results show that the proposed model may improve the classification accuracy and outperform various optimization models of typical CBR system.

• Journal of Information Technology Applications and Management
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• 제23권1호
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• pp.45-59
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• 2016

An ensemble classifier is a method that combines output of multiple classifiers. It has been widely accepted that ensemble classifiers can improve the prediction accuracy. Recently, ensemble techniques have been successfully applied to the bankruptcy prediction. Bagging and random subspace are the most popular ensemble techniques. Bagging and random subspace have proved to be very effective in improving the generalization ability respectively. However, there are few studies which have focused on the integration of bagging and random subspace. In this study, we proposed a new hybrid ensemble model to integrate bagging and random subspace method using genetic algorithm for improving the performance of the model. The proposed model is applied to the bankruptcy prediction for Korean companies and compared with other models in this study. The experimental results showed that the proposed model performs better than the other models such as the single classifier, the original ensemble model and the simple hybrid model.

• 한국자동차공학회논문집
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• 제17권1호
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• pp.130-136
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• 2009

A lumped parameter model of Li-ion battery in hybrid electric vehicle(HEV) is constructed and system parameters are identified by using recursive least square estimation for different C-rates, SOCs and temperatures. The system characteristics of pole and zero in frequency domain are analyzed with the parameters obtained from different conditions. The parameterized model of Li-ion battery indicates highly dependant of temperatures. The system pole and internal resistance changes 6.6 and 18 times at $-20^{\circ}C$, comparing with those at $25^{\circ}C$, respectively. These results will be utilized on constructing model-based state observer or an on-line identification and an adaptation of the model parameters in battery management systems for hybrid electric vehicle applications.