• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.128.3-128
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• 2001

In this paper we proposed that the speed of induction motor is controlled by a PI controller, which could control unknown motor using Neural Network for auto-tuning of the PI parameter. The parameters of the PI controller were adjusted to reduce the speed error of the controlled motor. The input parameters of the Neural Network controller are the speed, q-axis current, and speed reference of the induction motor respectively. The usefulness of proposed controller will be confirmed by simulation which we compare with conventional PI controller.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.856-861
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• 1989

The control of a class of large scale systems formed by an arbitrary linear interconnections of linear time-invariant subsystems with unknown parameters is investigated. An approach is developed for improving the robustness of such a large scale system. In doing so, the new parameter adaptation algorithm(PAA) is used and a sufficient condition of stability is discussed by using the sector theory.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.160-165
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• 1989

This paper deals with the robust controller design of robotic manipulator to track a desired trajectory in spite of the presence of unmodelled dynamics in cause of nonlinearity and parameter uncertainty. The approach followed in this paper is based on model reference adaptive control technique and convergence on hyperstability theory but it does away with assumption that process is characterized by a linear model remaining time invariant during adaptation process. A computer simulation has been performed to demonstrate the performance of the designed control system in task coordinates for stanford manipulator with payload and disturbances.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.872-877
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• 1990

Robustness of a model refernece direct adaptive pole placement control for not necessarily minimum phase systems is studied subject to unmodeled dynamics and bounded disturbances. The adaptive control scheme involves two estimators for the system and the controller parameter estimation, respectively. The robustness is obtaind under some weak assumptions and by using both a normalized least-squares algorithm with dead zone and an appropriate nonlinear feedback.

• 한국데이터정보과학회:학술대회논문집
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• 2004.04a
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• pp.15-23
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• 2004

When X and Y have independent two parameter exponential distributions, we develop a Bayesian testing procedures for the equality of two location parameters. Under the noninformative prior, we propose a Bayesian test procedures for the equality of two location parameters using fractional Bayes factor and intrinsic Bayes factor. Simulation study and some real data examples are provided.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.79-82
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• 2002

In this paper, we propose an adaptive repetitive control algorithm for the system the task of which is repetitive. The feedforward controller in the repetitive control system is modified by using the system parameter identifier in order to improve the convergence characteristics. The proposed algorithm is applied to the tracking control of a linear BLDC motor to which a periodic reference input is applied. It is illustrated by simulation results that the proposed adaptive repetitive control method yields better control performance than existing repetitive control even when modeling errors exist.

• Journal of the Korean Mathematical Society
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• v.58 no.1
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• pp.1-28
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• 2021

We study a continuous data assimilation algorithm for the three-dimensional simplified Bardina model utilizing measurements of only two components of the velocity field. Under suitable conditions on the relaxation (nudging) parameter and the spatial mesh resolution, we obtain an asymptotic in time estimate of the difference between the approximating solution and the unknown reference solution corresponding to the measurements, in an appropriate norm, which shows exponential convergence up to zero.

• Korean Journal of Clinical Laboratory Science
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• v.50 no.1
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• pp.1-10
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• 2018

During early pregnancy, before the development of a functioning thyroid gland, thyroid stimulating hormone (TSH) is a very sensitive marker of thyroid dysfunction during pregnancy. Normal values have been modified during gestation with a downward shift. The fetus is influenced by the TSH supplied by the mother. TSH and free thyroxine (FT4) concentrations vary during pregnancy and conventional units can vary between laboratories. A downward shift of the TSH reference range occurs during pregnancy, with a decrease in both the lower and upper limits of maternal TSH, relative to the typical non-pregnant TSH reference range. Each laboratory produces its own reference TSH and FT4 concentrations because there are many different assays that yield different results in pregnancy. Therefore, automated immunoassays used for serum FT4 analysis are still used widely, but the important considerations discussed above must be noted. The use of population-based, trimester-specific reference ranges remains the best way to handle this issue The slight downward shift in the upper reference range of TSH occurring in the latter first trimester (7~12 weeks) of pregnancy, typically not observed prior to 7 weeks. Their use indicates high or low levels in a quantitative manner independent of the reference ranges. These data highlight the importance of calculating population-based pregnancy-specific thyroid parameter reference intervals. A precision medicine initiative in this area will require the collection and analysis of a large number of genetic, biological, psychosocial, and environmental variables in large cohorts of individuals. Large prospective randomized controlled trials will be needed to resolve these controversies.

• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.603-612
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• 2022

The study aims were to evaluate a machine-learning, algorithm-based, forest biomass-estimation model to estimate subnational forest biomass and to comparatively analyze REDD+ forest reference emission levels. Time-series Landsat satellite imagery and ESA Biomass Climate Change Initiative information were used to build a machine-learning-based biomass estimation model. The k-nearest neighbors algorithm (kNN), which is a non-parametric learning model, and the tree-based random forest (RF) model were applied to the machine-learning algorithm, and the estimated biomasses were compared with the forest reference emission levels (FREL) data, which was provided by the Paraguayan government. The root mean square error (RMSE), which was the optimum parameter of the kNN model, was 35.9, and the RMSE of the RF model was lower at 34.41, showing that the RF model was superior. As a result of separately using the FREL, kNN, and RF methods to set the reference emission levels, the gradient was set to approximately -33,000 tons, -253,000 tons, and -92,000 tons, respectively. These results showed that the machine learning-based estimation model was more suitable than the existing methods for setting reference emission levels.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.9 no.1
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• pp.54-62
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• 1995

This paper is a study on a speed control of an induction motor used the MRA-DSM(Mode1 Reference Adaptive-Discrete Sliding Mode) controller. In this paper, when controls motor speed, DSM algorithm is proposed for having Robustness against disturbance and parameter variation. and it is also proposed MRA-DSM including the additional load model reference algorithm, which can be compensated the discontinuous control imputs at sliding mode and followed the model Preference independent of parameter variation of control subjects. The control system is composed of the parallel processing control system using the microprocessor for maximizing the performance of control systems and the real time processing. Also it simplifies the hardware composed of controlling the system by software and improves the reliability of the system. And while MRA-DSM control, faster response characteristics of 27.2 % is obtained than DSM control.