• Journal of Electrical Engineering and Technology
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• 제12권2호
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• pp.937-943
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• 2017

To design an automatic steering controller with high performance for autonomous vehicle, it is necessary to have a precise model of the lateral dynamics with respect to the steering command input. This paper presents an empirical modeling of the steering system for an autonomous vehicle. The steering system here is represented by three individual transfer function models: a steering wheel actuator model from the steering command input to the steering angle of the shaft, a dynamic model between the steering angle and the yaw rate of the vehicle, and a dynamic model between the steering command and the lateral deviation of vehicle. These models are identified using frequency response data. Experiments were performed using a real vehicle. It is shown that the resulting identified models have been well fitted to the experimental data.

• International Journal of Concrete Structures and Materials
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• 제5권2호
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• pp.97-111
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• 2011

Modeling creep of concrete has been one of the most challenging problems in concrete. Over the years, research has proven the significance of creep and its ability to influence structural behavior through loss of prestress, violation of serviceability limit states or stress redistribution. Because of this, interest in modeling and simulation of creep has grown significantly. A research program was planned to investigate the significance of different factors affecting creep of concrete. This research investigation is divided into two folds: first, an in-depth study of the RILEM creep database and development of a homogenous database that can be used for blind computational analysis. Second: developing a probabilistic Bayesian screening method that enables identifying the significance of the different factors affecting creep of concrete. The probabilistic analysis revealed a group of interacting parameters that seem to significantly influence creep of concrete.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1995년도 춘계공동학술대회논문집; 전남대학교; 28-29 Apr. 1995
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• pp.111-120
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• 1995

통합생산시스템에서의 고장, 정비 및 가용 도는 매우 중요한 역할을 한다. 시스템 설계시의 RAM 파라메터의 결정은 시스템의 성능과 소요비용 및 구성(System Configuration)등에 크게 영향을 미친다. 이러한 시스템관련 요소의 숫자가 많거나 불확실할 경우는 시스템의 성능예측이 매우 복잡하게 된다. 이러한 시스템의 성능(performance) 평가를 위하여 발견적 방법인 GMDH(Group Method Data Handlinng) Type Modeling 방법을 이용하여 FMS의 성능 평가를 시도하였다. RAM 및 기계작업시간의 Data로부터 시스템성능의 척도로서 단위 사이클 기간동안의 생산률, 시스템내의 총 흐름시간, 각 작업장이 기계의 RAM 및 LCC등을 고려하였다. GMDH 알고리즘의 계산을 위한 프로그램을 개발하고, 이를 L형 Bracket제조시스템의 성능 예측에 시험 적용하였다. 본 Modeling에 의한 시스템의 성능예측 방법은 입출력 자료의 처리과정을 개선할 경우 FMS계획및 운영 단계에서 성능평가에 매우 유용하게 활용될수 있을 것으로 본다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 추계학술대회 논문집 학회본부 D
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• pp.791-793
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• 2000

The fuzzy inference system is a popular computing framework based on the concepts of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The advantage of fuzzy approach over traditional ones lies on the fact that fuzzy system does not require a detail mathematical description of the system while modeling. As modeling method. the Group Method of Data Handling(GMDH) is introduced by A.G. Ivakhnenko GMDH is an analysis technique for identifying nonlinear relationships between system's inputs and output. We study a Novel Neuro-Fuzzy Network (NNFN) in this paper. NNFN is a network resulting from the combination of a fuzzy inference system and polynomial neural network(PNN) (7) which is advanced structure of GMDH. Simulation involve a series of synthetic as well as experimental data used across various neurofuzzy systems.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2011년도 학술발표회
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• pp.473-473
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• 2011

Group method of data handling neural networks model (GMDH-NNM) is used to estimate daily reference evapotranspiration (ETo) using limited climatic variables such as max temperature ($T_{max}$), min temperature ($T_{min}$), mean wind speed ($W_{mean}$), mean relative humidity ($RH_{mean}$) and sunshine duration (SD). And, for the performances of GMDH-NNM, it consists of training and test performances, respectively. The training and test performances are carried out using daily time series data, respectively. From this research, we evaluate the impact of GMDH-NNM for the modeling of the nonlinear time series data. We should, thus, construct the credible data of the daily ETo data using GMDH-NNM, and can suggest the methodology for the irrigation and drainage networks system. Furthermore, this research represents that the strong nonlinear relationship such as ETo modeling can be generalized using GMDH-NNM.

• Nuclear Engineering and Technology
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• 제34권6호
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• pp.610-618
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• 2002

A study of the conceptual data modeling to realize the decommissioning database on the HRR-1&2 was carried out. In this study, the current state of the abroad decommissioning database was investigated to make a reference of the database. A scope of the construction of decommissioning database has been set up based on user requirements. Then, a theory of the database construction was established and a scheme on the decommissioning information was classified . The facility information, work information, radioactive waste information, and radiological information dealing with the decommissioning database were extracted through interviews with an expert group and also decided upon the system configuration of the decommissioning database. A code which is composed of 17 bit was produced considering the construction, scheme and information. The results of the conceptual data modeling and the classification scheme will be used as basic data to create a prototype design of the decommissioning database.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 1999년도 추계학술대회
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• pp.565-574
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• 1999

In reverse engineering area, it is rapidly developing reconstruction of surfaces from scanning or digitizing data, but geometric models of existing objects unavailable many industries. This paper describes new methodology of reverse engineering area, good strategies and important algorithms in reverse engineering area. Furthermore, proposing reconstruction of surface technique is presented. A method find base geometry and blending surface between them. Each based geometry is divided by triangular patch which are compared their normal vector for face grouping. Each group is categorized analytical surface such as a part of the cylinder, the sphere, the cone, and the plane that mean each based geometry surface. And then, each based geometry surface is implemented infinitive surface. Infinitive average surface's intersections are trimmed boundary representation model reconstruction. This method has several benefits such as the time efficiency and automatic functional modeling system in reverse engineering. Especially, it can be applied 3D scanner and 3D copier.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -2
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• pp.1053-1056
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• 2000

In general, neural networks based modeling involves trying multiple networks with different architectures and/or training parameters in order to achieve the best accuracy. Only the single best-trained neural network is chosen, while the rest are discarded. However, using only the single best network may never give the best solution in every situation. Many researchers, therefore, propose methods to improve the accuracy of neural networks based modeling. In this paper, the idea of the logical combinations of neural networks is proposed and discussed in detail. The logical combination is constructed by combining the corresponding outputs of the neural networks with the logical “And” node. The experimental results based on simulated data show that the modeling accuracy is significantly improved when compared to using only the single best-trained neural network.

• Structural Engineering and Mechanics
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• 제43권1호
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• pp.71-87
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• 2012

This paper extends a single equation, semi-analytical approach for three-span bridges to multi-span ones for the rapid and precise determination of natural frequencies and natural mode shapes of an orthotropic, multi-span plate. This method can be used to study the dynamic interaction between bridges and vehicles. It is based on the modal superposition method taking into account intermodal coupling to determine natural frequencies and mode shapes of a bridge deck. In this paper, a four- and a five-span orthotropic roadway bridge deck are compared in the first 10 modes with a finite element method analysis using ANSYS software. This simplified implementation matches numerical modeling within 2% in all cases. This paper verifies that applicability of a single formula approach as a simpler alternative to finite element modeling.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2010년도 학술발표회
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• pp.1596-1599
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• 2010

Group method of data handling neural networks model (GMDH-NNM) is used to estimate daily pan evaporation (PE) using limited climatic variables such as max temperature ($T_{max}$), min temperature ($T_{min}$), mean wind speed ($W_{mean}$), mean relative humidity ($RH_{mean}$) and sunshine duration (SD). And, for the performances of GMDH-NNM, it is composed of training and test performances, respectively. The training and test performances are carried out using daily time series data, respectively. From this research, we evaluate the impact of GMDH-NNM for the modeling of the nonlinear time series data. We should, thus, construct the credible data of the daily PE data using GMDH-NNM, and can suggest the methodology for the irrigation and drainage networks system. Furthermore, this research represents that the strong nonlinear relationship such as pan evaporation modeling can be generalized using GMDH-NNM.