• Journal of applied mathematics & informatics
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• v.16 no.1_2
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• pp.143-150
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• 2004

In this paper we study the estimation problem of individual weights of objects using an A-optimal chemical balance weighing design. We assume that in this model errors are correlated and they have the same variances. The lower bound of tr$(X'G^{-1}X)^{-1}$ is obtained and a necessary and sufficient condition for this lower bound to be attained is given. There is given new construction method of A-optimal chemical balance weighing design.

• Proceedings of the KSME Conference
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• 2000.11a
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• pp.789-794
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• 2000

This study proposes an error analysis for a cubic parallel device. There are many sources of errors in the device. An error analysis is presented based on an error model formed from the relation between the universal joint error of the cubic parallel manipulator and the end effector accuracy. The analysis shows that the method can be used in evaluating the accuracy of a parallel device.

• Journal of the Korean Society of Safety
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• v.9 no.4
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• pp.42-48
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• 1994

A rotor flux error-based approach for correcting the rotor time constant estimation used in the slip frequency calculator of indirect field oriented controller is presented in this paper. The controller was derived from the d-q induction machine model. Slip frequency gain is dependent on the machine parameter errors. And parameter errors result in rotor flux error. Thus, estimated rotor flux is compared to commanded rotor flux. The error between them is used for the estimation of rotor time constant. Simulation results which demonstrate the performance of this approach are presented.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 1998.10a
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• pp.93-99
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• 1998

Mathematical model can be obtained by physical law or engineering theory. However it is always incomplete expression of the real system. In active controls to suppress vibration due to earthquake or wind load, modeling errors can often cause the problems of instability and performance degradation. In this paper, robust optimal controller design method using H$\infty$ control theory is developed for the systems which have uncertain natural frequency and design constraints. Numerical results show that the proposed H$\infty$ controller can avoid the performance degradation due to several errors and has better performance than conventional LQR method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.241-243
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• 2020

The high computational complexity of deep learning algorithms has led to the development of specialized hardware architectures. However, soft errors (bit flip) may occur in these hardware systems due to voltage variation and high energy particles. Many error correction methods have been proposed to counter this problem. In this work, we analyze an error correction mechanism based on repetition codes and an activation function. We test this method by injecting errors into weight filters and define an ideal error rate range in which the proposed method complements the accuracy of the model in the presence of error.

• 한국데이터정보과학회:학술대회논문집
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• 2006.04a
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• pp.171-179
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• 2006

For the problem of variable selection in linear models, we consider the errors are correlated with V covariance matrix. Hocking's theorems on the effects of the overfitting and the underfitting in linear model are extended to the less than full rank and correlated error model, and to the ANCOVA model.

• Proceedings of the Korean Statistical Society Conference
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• 2005.11a
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• pp.187-192
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• 2005

For the problem of variable selection in linear models, we consider the errors are correlated with V covariance matrix. Hocking's theorems on the effects of the overfitting and the undefitting in linear model are extended to the less than full rank and correlated error model, and to the ANCOVA model

• Journal of the Korea Society of Computer and Information
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• v.18 no.1
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• pp.123-136
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• 2013

The Feature model can not be guaranteed the syntactic accuracy of its model and be difficult the validation using automatic tool for its syntax, because this model is expressed by a graphical and informal structure in itself. Therefore, there is a need to formalize and check for the feature model, to precisely define syntax for construct of the model. This paper presents a Z formal specification and a model checking mechanism of the feature model to guarantee the correctness of the model. It first defines the translation rules between feature model and Z, and then converts the syntax of the feature model into the Z schema specification by applying these rules. Finally, the Z schema specification is checked syntax, type, and domain errors using the Z/Eves validation tool to assure the correctness of its specification, With the use of the proposed method, we may express more precisely the construct of the feature model. Moreover the domain analyst are able to usefully verify the errors of the generated feature model.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.25 no.4
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• pp.295-303
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• 1999

Presented in this paper are the experimental results that measure rapid prototyping (RP) errors in 3D medical models. We identified various factors that can cause dimensional errors when producing RP models, specifically in maxillofacial areas. For the experiment, we used a human dry skull. A number of linear measurements based on landmarks were first obtained on the skull. This was followed by CT scanning, 3D model reconstruction, and RP model fabrication. The landmarks were measured again on both the reconstructed models and the physical RP models, and these were compared with those on dry skull. We focused on major sources of errors, such as CT scanning, conversion from CT data to STL models, and RP model fabrication. The results show that the overall error from skull to RP is $0.64{\times}0.36mm(0.71{\times}0.66%)$ in absolute value. This indicates that the RP technology can be acceptable in the real clinical applications. A clinical case that has applied RP models successfully for treatment planning and surgical rehearsal is presented. Although the use of RP models is rare in the medical area yet, we believe RP is promising in that it has a great potential in developing new tools which can aid diagnosis, treatment planning, surgical rehearsal, education, and so on.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.84-93
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• 2018

In this paper, we propose an Elman recurrent neural network to predict and analyze a time series of power energy consumption. To this end, we consider the volatility of the time series and apply the sample variance and the detrended fluctuation analyses to the volatilities. We demonstrate that there exists a correlation in the time series of the volatilities, which suggests that the power consumption time series contain a non-negligible amount of the non-linear correlation. Based on this finding, we adopt the Elman recurrent neural network as the model for the prediction of the power consumption. As the simplest form of the recurrent network, the Elman network is designed to learn sequential or time-varying pattern and could predict learned series of values. The Elman network has a layer of "context units" in addition to a standard feedforward network. By adjusting two parameters in the model and performing the cross validation, we demonstrated that the proposed model predicts the power consumption with the relative errors and the average errors in the range of 2%~5% and 3kWh~8kWh, respectively. To further confirm the experimental results, we performed two types of the cross validations designed for the time series data. We also support the validity of the model by analyzing the multi-step forecasting. We found that the prediction errors tend to be saturated although they increase as the prediction time step increases. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric and the gas energies.