• 소음진동
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• 제8권3호
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• pp.441-449
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• 1998

A self-tuning control scheme, incorporated with the simplified 1st-order ARMAX(Auto-Regressive Moving Average eXogenous) model, for single rod hydraulic cylinder which has varying dynamic characteristics is presented here. An adaptive controller is developed for the system that uses feedforward and optimal feedback control for simultaneous parameter identification and tracking control. Through experimental results, the performance comparison of the self-tuning controller with a fixed gain proportional controller clearly shows its superior ability in handling load changes in quiescent states.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권9호
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• pp.567-569
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• 2005

NARX(Nonlinear AutoRegressive with eXogenous input) neural network was used for prediction of nuclear reactor behavior which was influenced by control rods in short-term period and also by the concentration of xenon and boron in long-term period in load following operations. The developed model was designed to predict reactor power, xenon worth and axial offset with different burnup states when control rods and boron were adjusted in load following operations. Data of the Korea Next Generation PWR were collected by ONED94 code. The test results presented exhibit the capability of the NARX neural network model to capture the long term and short term dynamics of the reactor core and the developed model seems to be utilized as a handy tool for the use of a plant simulation.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2017년도 전력전자학술대회
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• pp.126-127
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• 2017

An accurate State Of Charge (SOC) estimation of battery is the most important technique for Electric Vehicles (EVs) and Energy Storage Systems (ESSs). In this paper a new integrated Unscented Kalman Filter-Particle Filter (UKF-PF) is employed to estimate the SOC of a $LiFePO_4$ battery cell and a significant improvement is obtained as compared to the other methods. The parameters of the battery is modeled by the second order Auto Regressive eXogenous (ARX) model and estimated by using Recursive Least Square (RLS) method to calculate value of each element in the model. The proposed algorithm is established by combining a parameter identification technique using RLS method with ARX model and an SOC estimation technique using UKF-PF.

• Structural Monitoring and Maintenance
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• 제6권3호
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• pp.237-254
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• 2019

This paper presents an improved time series based damage detection approach with experimental verifications for detection, localization, and quantification of damage in shear-type structures under varying mass effects using output-only vibration data. The proposed method can be very effective for automated monitoring of buildings to develop proactive maintenance strategies. In this method, Auto-Regressive Moving Average models with eXogenous inputs (ARMAX) are built to represent the dynamic relationship of different sensor clusters. The damage features are extracted based on the relative difference of the ARMAX model coefficients to identify the existence, location and severity of damage of stiffness and mass separately. The results from a laboratory-scale shear type structure show that different damage scenarios are revealed successfully using the approach. At the end of this paper, the methodology limitations are also discussed, especially when simultaneous occurrence of mass and stiffness damage at multiple locations.

• 대한토목학회논문집
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• 제38권2호
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• pp.349-356
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• 2018

본 논문은 국내에서 연구가 미진한 적재설비의 지진 취약도 평가에 적용할 수 있는 FE 해석 기반의 연결부 모델을 개발하는데 목적이 있다. 이러한 목표를 달성하기 위하여, 적재설비 거동을 파악하기 위한 진동대 실험과 Modal Test, 그리고 구성 부재를 대상으로 한 다양한 부재실험(8가지 Push-over Test)을 진행하였다. 실험결과를 바탕으로 지진취약도 평가에 적용하기 위한 적재설비의 연결부 모델을 개발하기 위하여, NX-Nastran 프로그램을 활용하여 연결부의 상세 모델링을 진행하였다. 특히, 단순 걸쇠 방식으로 연결되는 기둥 부재와 보 부재의 연결을 모사하기 위하여 면대면 표면접촉 요소와 스프링 요소를 적용하였으며, 스프링 요소의 모델은 ARX (Auto Regressive eXogenous) 기반의 수학적 모델을 개발하여 적용하였다. FE 모델 기반의 simulation 결과는 부재 실험 결과와 비교하였을 때, 상호 오차율 8% 미만의 우수한 신뢰도를 보여주었다. 결과적으로 연구에서 개발한 FE해석 기반의 연결부 모델은 적재설비의 지진 취약도 평가를 위한 해석 모델에 활용될 수 있음을 확인하였다.

• Earthquakes and Structures
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• 제8권4호
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• pp.843-857
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• 2015

System identification is regarded as the most basic technique for structural health monitoring to evaluate structural integrity. Although many system identification techniques extracting mode information (e.g., mode frequency and mode shape) have been proposed so far, it is also desired to identify physical parameters (e.g., stiffness and damping). As for high-rise buildings subjected to long-period ground motions, system identification for evaluating only the shear stiffness based on a shear model does not seem to be an appropriate solution to the system identification problem due to the influence of overall bending response. In this paper, a system identification algorithm using a shear-bending model developed in the previous paper is revised to identify both shear and bending stiffnesses. In this algorithm, an ARX (Auto-Regressive eXogenous) model corresponding to the transfer function for interstory accelerations is applied for identifying physical parameters. For the experimental verification of the proposed system identification framework, vibration tests for a 3-story steel mini-structure are conducted. The test structure is specifically designed to measure horizontal accelerations including both shear and bending responses. In order to obtain reliable results, system identification theories for two different inputs are investigated; (a) base input motion by a modal shaker, (b) unknown forced input on the top floor.

• Earthquakes and Structures
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• 제8권4호
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• pp.915-934
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• 2015

Within the context of Structural Health Monitoring (SHM), it is often the case that structural systems are described by uncertainty, both with respect to their parameters and the characteristics of the input loads. For the purposes of system identification, efficient modeling procedures are of the essence for a fast and reliable computation of structural response while taking these uncertainties into account. In this work, a reduced order metamodeling framework is introduced for the challenging case of nonlinear structural systems subjected to earthquake excitation. The introduced metamodeling method is based on Nonlinear AutoRegressive models with eXogenous input (NARX), able to describe nonlinear dynamics, which are moreover characterized by random parameters utilized for the description of the uncertainty propagation. These random parameters, which include characteristics of the input excitation, are expanded onto a suitably defined finite-dimensional Polynomial Chaos (PC) basis and thus the resulting representation is fully described through a small number of deterministic coefficients of projection. The effectiveness of the proposed PC-NARX method is illustrated through its implementation on the metamodeling of a five-storey shear frame model paradigm for response in the region of plasticity, i.e., outside the commonly addressed linear elastic region. The added contribution of the introduced scheme is the ability of the proposed methodology to incorporate uncertainty into the simulation. The results demonstrate the efficiency of the proposed methodology for accurate prediction and simulation of the numerical model dynamics with a vast reduction of the required computational toll.

• 한국측량학회지
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• 제39권5호
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• pp.289-295
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• 2021

Recently, owing to the development of autonomous vehicles, research on precisely determining the position of a moving object has been actively conducted. Previous research mainly used the fusion of GNSS/IMU (Global Positioning System / Inertial Navigation System) and sensors attached to the vehicle through a Kalman filter. However, in recent years, new technologies have been used to determine the location of a moving object owing to the improvement in computing power and the advent of deep learning. Various techniques using RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory), and NARX (Nonlinear Auto-Regressive eXogenous model) exist for such learning-based positioning methods. The purpose of this study is to compare the precision of existing filter-based sensor fusion technology and the NARX-based method in case of GNSS signal blockages using simulation data. When the filter-based sensor integration technology was used, an average horizontal position error of 112.8 m occurred during 60 seconds of GNSS signal outages. The same experiment was performed 100 times using the NARX. Among them, an improvement in precision was confirmed in approximately 20% of the experimental results. The horizontal position accuracy was 22.65 m, which was confirmed to be better than that of the filter-based fusion technique.

• 한국습지학회지
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• 제18권3호
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• pp.313-323
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• 2016

수온은 하천의 물리적 생물학적 과정에 지대한 영향을 미치는 인자로서 어류를 비롯한 수생생태계에 대한 제약조건으로 작용한다. 기후변화로 인하여 실질적인 환경의 변화가 나타나고 있는 현실에서 수온 변화에 대한 예측은 필수적이라 하겠다. 본 연구의 목적은 자연 소하천을 대상으로 하천 수온을 모의 및 그 효율을 비교 분석하고, 향후 기후변화로 인한 하천 수온의 변동을 고찰하는 것이다. 이를 위하여 본 연구에서는 캐나다 동북부의 Fourchue 강을 대상으로 하여 2011년부터 2014년까지의 하천수온을 측정하고 결정론적, 확률론적, 비선형 수온모형을 적용하여 각각의 방법론에 따른 효율성을 비교 분석하여 미래 수온 모의를 위한 모형으로 결정론적 모형인 CEQUEAU 모형을 선정하였다. 또한, 선정된 모형을 기반으로 하여 CMIP5 기후모형과 RCP 2.6, 4.5, 8.5 기후변화 시나리오를 이용하여 해당 소하천 유역의 미래 수온 변동성을 예측하고 분석하였다. 연구결과, Fourchue 강의 수온은 6월 중 평균 수온은 $0.2{\sim}0.7^{\circ}C$가 상승하고, 9월은 $0.2{\sim}1.1^{\circ}C$가 감소하는 것으로 나타나 실질적인 수온환경의 변화가 발생하는 것으로 나타나서 이에 대한 주의가 요구된다. 또한, 해당 수역에 서식하고 있는 연어류의 치사상한수온을 넘는 경우도 발생하여 이에 대한 대책이 시급한 것으로 판단된다.