• 설비공학논문집
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• 제15권5호
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• pp.337-345
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• 2003

In the case of heat exchangers operating under frosting condition, the growth of frost layer causes the heat exchanger to increase the thermal resistance and pressure loss of the air flow. In this paper, a transient characteristic prediction model of the heat transfer for multi inverter heat pump with frosting on its surface was presented taking into account the change of the fin efficiency due to the growth of the frost layer. In this dynamic simulation program, which was peformed for a basic air conditioning system model, such as evaporator, condenser, compressor, linear electronic expansion valve (LEV) and bypass circuit. The theoretical model was driven from the obtained heat transfer coefficient and mass transfer coefficient, independently. And we consider heat transfer performance was only affected by a decrease of the wind flow area. The calculated results were compared with some cases of experiments for frosting conditions.

• 한국시뮬레이션학회논문지
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• 제18권4호
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• pp.137-148
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• 2009

본 논문의 목적은 신뢰성 있는 선형회귀모델을 구축하기 위하여 후보독립변수 중 유효변수를 선택하는 알고리즘을 구현하는 것이다. 선형회귀모델을 구축하는데 있어서 데이터 상의 모든 후보독립변수를 포함하는 것은 모델의 통계적 유의성을 감소시킬 수 있으며, 차원의 저주(Curse of dimensionality)를 유발할 수 있고, 데이터의 개수보다 변수의 개수가 많을 경우 모델의 구축이 불가능한 문제점 등이 있다. 이와 같은 문제점을 해결하기 위하여 변수선택의 문제를 조합최적화의 문제로 보고 유전 알고리즘(Genetic Algorithm)을 활용하였다. 일반적으로 선형회귀모델의 통계적 유의성을 평가하는 대표적인 통계량으로는 종속변수에 대한 독립변수의 설명력을 나타내는 결정계수($R^2$), 회귀식의 통계적 유의성을 검정하는 F통계량, 회귀계수의 통계적 유의성을 검정하는 t통계량, 잔차의 표준오차 등이 있다. 모델의 통계적 유의성은 하나의 통계량으로 표현될 수 없으므로 다양한 기준을 고려한 다중목적식(Multi-objective function)을 가지는 유전 알고리즘을 설계하였다. 설계한 알고리즘의 성능평가를 위하여 다양한 조건을 가정한 시뮬레이션 데이터에 적용하였다. 그 결과 구축한 알고리즘이 유효변수를 판단함에 있어 기존의 대표적인 변수선택 알고리즘인 LARS(Least Angle Regression)에 비해 우수한 성능을 보임을 확인할 수 있었다. 또한, 주가 데이터를 이용한 포트폴리오 선택에 적용해 본 결과 우수한 응용문제 해결 능력이 있음을 확인할 수 있었다.

• Coupled systems mechanics
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• 제10권6호
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• pp.521-544
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• 2021

In this work, we present the development of a 3D lattice-type model at microscale based upon the Voronoi-cell representation of material microstructure. This model can capture the coupling between mechanic and electric fields with non-linear constitutive behavior for both. More precisely, for electric part we consider the ferroelectric constitutive behavior with the possibility of domain switching polarization, which can be handled in the same fashion as deformation theory of plasticity. For mechanics part, we introduce the constitutive model of plasticity with the Armstrong-Frederick kinematic hardening. This model is used to simulate a complete coupling of the chosen electric and mechanics behavior with a multiscale approach implemented within the same computational architecture.

• 한국정밀공학회지
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• 제19권5호
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• pp.51-55
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• 2002

This paper presents a systematic methodology and formulation for determining optimal strategies of multi-body dynamic systems, which is based on multi-body dynamics, design sensitivity, and optimization techniques, and is applicable to a wide variety of mechanical systems. The particular application discussed in this paper considers a vehicle model with four-wheel steeling capability, and the presented methodology determines an optimal steering angle ratio strategy for the vehicle. It is shown that such a strategy can improve the ride stability of the vehicle, during a variety of maneuvers, when compared against similar strategies obtained from linear and simplified vehicle models.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1244-1248
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• 2005

In this paper, we propose fuzzy c-means (FCM) to solve recognition errors in invariant range image, multi-pose face recognition. Scale, center and pose error problems were solved using geometric transformation. Range image face data was digitized into range image data by using the laser range finder that does not depend on the ambient light source. Then, the digitized range image face data is used as a model to generate multi-pose data. Each pose data size was reduced by linear reduction into the database. The reduced range image face data was transformed to the gradient face model for facial feature image extraction and also for matching using the fuzzy membership adjusted by fuzzy c-means. The proposed method was tested using facial range images from 40 people with normal facial expressions. The output of the detection and recognition system has to be accurate to about 93 percent. Simultaneously, the system must be robust enough to overcome typical image-acquisition problems such as noise, vertical rotated face and range resolution.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2009년도 춘계학술대회 논문집
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• pp.78-83
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• 2009

In this study, computer applied engineering (CAE) techniques are fully used to conduct structural and dynamic analyses of a whole huge wind turbine system including composite blades, tower and nacelle. For this study, computational fluid dynamics (CFD) is used to predict aerodynamic loads of the rotating wind-turbine blade model. Multi-body dynamic structural analyses are conducted based on the non-linear finite element method (FEM) by using super-element method for composite laminates blade. Three-dimensional finite element model of a wind turbine system is constructed including power train(main shaft, gear box, coupling, generator), bedplate and tower. The results for multi-body dynamic simulations on the wind turbine's critical operating conditions are presented in detail.

• Asia-Pacific Journal of Atmospheric Sciences
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• 제54권4호
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• pp.611-622
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• 2018

Observed climate data are processed under the assumption that their time series are stationary, as in multi-step temperature and precipitation prediction, which usually leads to low prediction accuracy. If a climate system model is based on a single prediction model, the prediction results contain significant uncertainty. In order to overcome this drawback, this study uses a method that integrates ensemble prediction and a stepwise regression model based on a mean-valued generation function. In addition, it utilizes empirical mode decomposition (EMD), which is a new method of handling time series. First, a non-stationary time series is decomposed into a series of intrinsic mode functions (IMFs), which are stationary and multi-scale. Then, a different prediction model is constructed for each component of the IMF using numerical ensemble prediction combined with stepwise regression analysis. Finally, the results are fit to a linear regression model, and a short-term climate prediction system is established using the Visual Studio development platform. The model is validated using temperature data from February 1957 to 2005 from 88 weather stations in Guangxi, China. The results show that compared to single-model prediction methods, the EMD and ensemble prediction model is more effective for forecasting climate change and abrupt climate shifts when using historical data for multi-step prediction.

• 한국지반공학회논문집
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• 제34권11호
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• pp.5-19
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• 2018

지반앵커의 정착장에 작용하는 정착응력 분포 특성은 매우 비선형적이며 공학적인 메카니즘이 비교적 복잡하기 때문에 다양한 지반조건 및 비선형적 주면전단거동을 구체적으로 모사하여 지반앵커를 설계하는데 어려움이 크다. 이런 한계로 인하여 현재 대부분의 관련 설계 기준서에는 편의상 정착장 전장에 걸쳐 일정한 주면전단응력분포를 가정하여 설계에 적용하고 있다. 따라서 본 연구에서는 인장형 앵커의 인발거동 특성을 분석하기 위하여 풍화토 지반조건을 대상으로 현장인발시험을 수행하였으며 이를 토대로 앵커 정착장의 주면전단거동을 정립하고, 정착장 거동특성을 비교적 간편하게 예측하기 위한 다중선형모델 및 이를 적용한 해석적 기법을 제안하였다. 현장시험결과와 해석적 결과가 상호 유사하게 나타남에 따라 본 연구에서 제시된 다중선형모델 및 이를 이용한 해석적 기법의 적용성 및 유효성을 확인할 수 있었다. 정착장 주면전단거동의 경우 최대인발하중 보다 작은 하중조건에서는 정착장 시작점에서 최대전단응력이 분포하게 되나 최대인발하중이 발생한 이후부터는 정착장 시작점에서 전단응력이 가장 작게 분포하고, 정착장 시작점으로부터 일정거리 이격된 지점에서 최대전단응력이 발생함을 확인하였다.

• 드라이브 ㆍ 컨트롤
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• 제20권4호
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• pp.133-139
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• 2023

A weighing system calculates the bucket's excavation amount of an excavator. Usually, the excavation amount is computed by the excavator's motion equations with sensing data. But these motion equations have computing errors that are induced by assumptions to the linear systems and identification of the equation's parameters. To reduce computing errors, some commercial weighing system incorporates particular motion into the excavation process. This study introduces a linear regression model on an artificial neural network that has fewer predicted errors and doesn't need a particular pose during an excavation. Time serial data were gathered from a 30tons excavator's loading test. Then these data were preprocessed to be adjusted by MPL (Multi Layer Perceptron) or CNN (Convolutional Neural Network) based linear regression models. Each model was trained by changing hyperparameter such as layer or node numbers, drop-out rate, and kernel size. Finally ID-CNN-based linear regression model was selected.

• 응용통계연구
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• 제4권2호
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• pp.179-193
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• 1991

이 논문에서는 1960년대에 S. Warner에 의해 제시되었던 다양한 PR 기법을 소개하고 그 것들에 대한 최우추정량을 검토하였다. 이 논문의 주요 주제 중 하나는 Warner 모형, 무관질문 모형, 다앙응담모형을 선형모형으로 표현하는 것이다. 또 다른 주제는 PR 모형의 추론을 연구함에 있어서 베이지안 접근 방법을 이용하여 고찰하는 것이다.