• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.2
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• pp.185-198
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• 2016

In this work, experimental errors from an impact hammer test were investigated to develop a statistic-based technique for updating a finite element model. Digital signal processing was analyzed by using theoretical models and experiments when errors occurred during the experimental procedure. First, the duration time and peak level of the excitation signal, the stiffness and position of elastic springs connecting the specimen as well as the support, position and mass of the accelerometer were considered as error factors during the experiment. Then the picket fence effect, leakage, and exponential window function were considered as candidate error factors during the digital signal processing. Finally, methods to reduce errors are suggested.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.200-209
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• 2019

When forecasting future values, a model estimated after minimizing training errors can yield test errors higher than the training errors. This result is the over-fitting problem caused by an increase in model complexity when the model is focused only on a given dataset. Some regularization and resampling methods have been introduced to reduce test errors by alleviating this problem but have been designed for use with only a given dataset. In this paper, we propose a new optimization approach to reduce test errors by transforming a test error minimization problem into a training error minimization problem. To carry out this transformation, we needed additional data for the given dataset, termed pseudo data. To make proper use of pseudo data, we used three types of missing data imputation techniques. As an optimization tool, we chose the least squares method and combined it with an extra pseudo data instance. Furthermore, we present the numerical results supporting our proposed approach, which resulted in less test errors than the ordinary least squares method.

• The Journal of the Acoustical Society of Korea
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• v.30 no.1
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• pp.22-32
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• 2011

The present study examined the effects of the complexity of the 3D models on the results of acoustic simulation which is the predominant tool of the acoustical design of buildings. Also, the effects of the 3D model on the auralized sounds were investigated. In order to carry out the study, four 3D models with different levels of complexity were introduced for a real auditorium which have different numbers of surfaces in the persuit of the guidance of odeon room acoustic software. The set-up of models was also based on the level of transition order of the program. And the acoustic experiments were performed measuring room acoustic parameters including SPL, RT, C80, D50. Acoustic computer simulations were performed using four different models. Then, the results of the computer modeling were compared with the measured acoustical parameters. In addition, sound sources were recorded in the field and auralized sounds were made in convolution with the impulse source made from acoustic modeling. Then, subjective tests were undertaken using auralized sounds. As the results, it was found that the result of the acoustic simulation were closer to the real room acoustic properties when 3D model was more particularly made. For the subjective test, the listening materials were acknowledged as similar with the real sound source when more complex 3D model was used. Then, it could be concluded that the complexity of the 3D model affects the results of the acoustic modeling as well as subjective tests.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.3
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• pp.148-151
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• 2013

A new direction of arrival estimation method using effects of model errors and sensitivity analysis is proposed. Since a desired signal is obtained after interference rejection through correction effects of model error, the effect of channel interference on the estimation is significantly reduced. Through simulation, we show that the proposed method offers significantly improved estimation resolution and accuracy relative to existing method.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1292-1293
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• 2015

기존에는 전동기 제어기법으로 PI제어기가 주로 사용되어 왔다. 그러나 실제 시스템의 경우 외란과 센서 잡음에 노출되기 쉽고, 모델의 불확실성에 대한 오차가 발생하기 때문에 보다 강인한 제어기법이 필요한 시점이다. $H{\infty}$제어기법은 명령 추종 성능, 시스템 모델 오차와 외란, 센서 잡음에 대해 강인성을 보장하는 고급제어기법으로서 현재 그 성능을 입증 받아 산업분야에서 다양하게 적용되고 있다. 본 논문은 매입형 영구자석 동기전동기(IPMSM)의 속도제어기에 $H{\infty}$제어기와 PI제어기를 적용하여 모의실험을 통한 성능비교를 통해 모델오차와 같은 시스템의 불확실성과 외란에 대해 $H{\infty}$제어기의 강인한 제어성능을 검증하였다.

• Journal of the Korean Geotechnical Society
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• v.17 no.2
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• pp.51-57
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• 2001

지하수위의 상승에 따른 간극수압의 증가는 사면의 불안정을 야기할 수 있다. 그러나 모델링 오차, 계측오차, 모델변수의 불확실성 등과 같은 오차로 인하여 사면에서의 지하수위 변동을 예측하는 것은 매우 어렵다. 이러한 불확실성을 극복하고 지하수위 변동을 평가하기 위한 최적의 모델변수를 구하기 위하여 역해석 기법이 사용되고 있다. 본 논문에서는 사면에서의 지하수위 변동을 예측하기 위하여 포화대에서의 지하수 흐름과 불포화대에서의 지하수 흐름을 동시에 고려할 수 있는 수치해석 모델과 변수예측기법을 적용하였다. 따라서, 본 논문에서는 포화투수계수($K_{s}$ ), 포화흡인력($\psi$$_{e}$) 및 불포화 투수계수의 함수에 사용되는 경험적인 상수(b)를 주요 매개변수로 선정하여 역해석을 실시하였다. 그리고, 역해석 기법 가운데 Maximum Likelihood(MK), Maximum-A-Posterior(MAP) 및 Extended Bayesian Method(EBM)에 대하여 비교연구를 실시하였다. 위의 세가지 방법 가운데 EBM은 가상의 변수(Hyperparameter) $\beta$를 도입함으로써 현장계측치와 사전정보를 가장 잘 조화시키는 방법으로 다른 ML, MAP 보다 탁월한 방법인 것을 알 수 있었다.

• The Journal of the Korea Contents Association
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• v.13 no.8
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• pp.11-19
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• 2013

This paper presents a simple method to estimate center of distortion and correct radial distortion from fish-eye lens. If the center of image is not locate that of lens in a straight line, the disadvantage of FOV model is low accurate because of correcting distortion without estimated centre of distortion. We propose a method accurately estimating Distortion center using FOV model and 2D pattern from wide angle lens. Our method determines the center of distortion in least error between straight lines and curves with FOV model. The results of experimental measurements on synthetic and real data are presented.

• Transactions of the Korean Society of Mechanical Engineers
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• v.10 no.1
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• pp.157-163
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• 1986

A method to improve the Finite Elements Model and to identify structure stiffness damages utilizing measured dynamic characteristics is suggested. In order to show the effectiveness of the proposed method, a sample structure is adopted and tested. It is found that; (1) the Finite Elements Model can be improved with experimentally obtained dynamic characteristics satisfactorily and (2) the test results are also very helpful to search the stiffness damaged part of the sample structure via plotting the error matrix[E], especially it is aboved that the weighted error matrix is very effective to detect even the stiffness changes are relatively small.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.11-18
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• 2021

This paper shows the applicability of deep learning algorithm in predicting target position and getting correction value of impact point in order to improve the accuracy of naval gun firing. Predicting target position, the proposed model using LSTM model and RN structure is expected to be more accurate than existing method using kalman filter. Getting correction value of impact point, the another proposed model suggests a reinforcement model that manages factors which is related in ballistic calculation as data set, and learns using the data set. The model is expected to reduce error of naval gun firing. Combining two models, a ballistic calculation model for improving accuracy of naval gun firing based on deep learning algorithm was designed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.1-16
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• 2023

Accurate traffic information prediction is considered to be one of the most important aspects of intelligent transport systems(ITS), as it can be used to guide users of transportation facilities to avoid congested routes. Various deep learning models have been developed for accurate traffic prediction. Recently, ensemble techniques have been utilized to combine the strengths and weaknesses of various models in various ways to improve prediction accuracy and stability. Therefore, in this study, we developed and evaluated a traffic information prediction model using various deep learning models, and evaluated the performance of the developed deep learning models as a stacking ensemble. The individual models showed error rates within 10% for traffic volume prediction and 3% for speed prediction. The ensemble model showed higher accuracy compared to other models when no cross-validation was performed, and when cross-validation was performed, it showed a uniform error rate in long-term forecasting.