• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.379-383
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• 1998

본 논문에서는 플랜트를 제어하는데 있어 광범위하게 사용되는 PD제어기의 파라미터를 결정하는 문제에 대한 연구를 하였다. 파라미터 결정 방법으로는 최근 최적화 문제에 많이 적용되고 있는 새로운 방법인 유전자 알고리즘을 사용하였다. 또한 이렇게 정해진 파라미터를 사용하여 이를 도립진자(Inverted Pendulum)에 적용하였고 도립진자의 응답특성을 분석, 고찰함으로써 PD제어기의 성능을 평가.확인하고 이 문제에 적용된 유전자 알고리즘의 우수성을 입증하였다.

• Electrical & Electronic Materials
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• v.6 no.3
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• pp.261-268
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• 1993

LIGBT의 전압-전류 특성을 디자인 파라미터와 공정 파라미터를 포함한 SPICE Simulation으로 확인하였다. 중요한 파라미터는 p-body와 n$^{-}$층 그리고 p$^{+}$ 애노드로 구성된 pnp bipolar transistor의 수평전류이득이었다. 이 전류 이득은 Ebers-Moll등식으로 얻었다. LIGBT의 On 저항은 채절 저항(R$_{E}$ )과 인가된 게이트 전압에 종속되는 유효 벌크 저항(R2)으로 구성되며 On 저항의 해석과 모델링은 디바이스의 디자인 조건을 최적화하기 위해서 기하학적 구조와 도핑 프로파일에 따른 물리적 특성으로부터 전개하여 특성해석을 위한 모델링을 실시하여 제시하였다.

• Journal of the Korean Chemical Society
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• v.54 no.1
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• pp.13-22
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• 2010

The photophysical properties of 7-diethylaminocoumarin (DEAC) laser dye have been measured in different solvents. The emission spectrum of DEAC has also been measured in cationic (CTAC) and anionic (SDS) micelles. The laser parameters have been calculated in different solvents namely acetone, dioxane, ethanol and dimethylforamide(DMF). The photoreactivity of DEAC has been studied in $CCl_4$ solvent using 366 nm light. The values of photochemical yield (${\Phi}_c$) and rate constant (k) are determined. The interaction of organic acceptors such as picric acid (PA), tetracyanoethylene (TCNE) and 7,7,8,8-tetracynoquinonedimethane (TCNQ) with DEAC are also studied using fluorescence measurements in acetonitrile ($CH_3CN$). The electrochemical investigation of (DEAC) has been carried out using cyclic voltammetry and convolutive voltammetry combined with digital simulation technique at a platinum electrode in 0.1 mol $L^{-1}$ tetrabutyl ammonium perchlorate (TBAP) in $CH_3CN$ solvent. The electrochemical parameters of the investigated compound were determined using cyclic and convolutive voltammetry. The extracted electrochemical parameters were verified and confirmed via digital simulation method.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.5
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• pp.549-555
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• 2001

Ultrasonic is a powerful nondestructive technique for getting the information of flaws and material properties of in-services facilities. We prepared 4 different 1Cr-1Mo-0.25V specimens by Isothermal aging at $630^{\circ}C$. We evaluated material degradation using ultrasonic parameters, velocity, attenuation and harmonic generation. Attenuation and nonlinear parameter derived from harmonic generation efficiency increased as degradation. Especially the second harmonic of the fundamental wave in the 1,820h aging material was observed to exceed 20dB more than that in the un-aged material. But velocity remained virtually the same for all specimens. We concluded that nonlinear parameter and attenuation are sensitive to material degradation, but velocity was not. It'll be a good parameter for evaluating the material degradation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.5
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• pp.413-420
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• 2021

This study is about a machine learning model for predicting insufficient parameters through other parameters when one of the collected parameters is insufficient. A regression model was created to predict time, temperature, humidity, CO2, and light quantity data through the machine learning regression analysis function in Matlab. In addition, the three models with the lowest RMSE values for each parameter were selected and verified. For verification, the predicted values were obtained by applying the test data to the prediction model derived from each parameter, and the correlation coefficient and error average between the measured values and the obtained predicted values were obtained and then compared.

• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.2
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• pp.165-173
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• 2011

The acoustic emission(AE) technique is a well established method to carry out structural health monitoring(SHM) of large structures. However, the real-time monitoring of the crack growth in the roller coaster support structures is not easy since the vehicle operation produces very large noise as well as crack growth. In this investigation, we present the waveform parameter filtering method to classify acoustic sources in real-time. This method filtrates only the AE hits by the target acoustic source as passing hits in a specific parameter band. According to various acoustic sources, the waveform parameters were measured and analyzed to verify the present filtering method. Also, the AE system employing the waveform parameter filter was manufactured and applied to the roller coaster support structure in an actual amusement park.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.7 no.2
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• pp.41-50
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• 1993

This paper describes an effort to develop a microcomputer-based parameter identification system for three phase and two phase brushless DC motor. Back EMF equation is derived from back EMF waveform of three phase and two phase brushless DC motor. In this paper, a new identification algorithm for the brushless DC motor parameters by Pasek's technique is developed. It is found that Pasek's equation is valid for the brushless DC motor, too. The results obtained clearly shows that it is possible to implement the identification system for the determination of the brushless DC motor parameters. To minimize errors due to the ripple component in the measured armature current, digital averaging firis employed. The whole identification process of signal generation, measuring, parameter determination is fully automated. The use of the propod method in the parameter identifition system shows that the averaged current curve is in excellent agreement with the estimated current curve. Therefore, this close agreement confirms the validity of this technique.

• Journal of the Earthquake Engineering Society of Korea
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• v.16 no.1
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• pp.27-35
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• 2012

In this study, the ground motion was synthesized using the finite fault model by the stochastic green function method, and the difference in the ground motions was evaluated by using various values of the source parameters. An earthquake with a moment magnitude of 6.5 was assumed for the example fault model. The distribution of the slip in the fault plane was calculated using the statistical data of the asperity area. The source parameters considered in this study were the location of the hypocenter in the fault plane and the ratio of the rupture to the shear wave velocity, the rise time, the corner frequency of the source spectrum, and a high frequency filter. The values of the parameters related to the stochastic element source model were adjusted for different tectonic regions, and the others were selected for several possible cases. The response spectra were constructed from the synthesized ground motion time history and compared with the different parameter values. The frequency range affected by each parameter and the differences of the spectral accelerations were evaluated.

• Journal of the Korea Computer Graphics Society
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• v.26 no.5
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• pp.15-24
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• 2020

We present a supervised learning method that estimates the simulation parameters required to simulate the fabric from the static drape shape of a given fabric sample. The static drape shape was inspired by Cusick's drape, which is used in the apparel industry to classify fabrics according to their mechanical properties. The input vector of the training model consists of the feature vector extracted from the static drape and the density value of a fabric specimen. The output vector consists of six simulation parameters that have a significant influence on deriving the corresponding drape result. To generate a plausible and unbiased training data set, we first collect simulation parameters for 400 knit fabrics and generate a Gaussian Mixed Model (GMM) generation model from them. Next, a large number of simulation parameters are randomly sampled from the GMM model, and cloth simulation is performed for each sampled simulation parameter to create a virtual static drape. The generated training data is fitted with a log-linear regression model. To evaluate our method, we check the accuracy of the training results with a test data set and compare the visual similarity of the simulated drapes.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1331-1336
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• 2023

In text-based fish disease classification using machine learning, there is a problem that the input parameters of the machine learning model are too many, but due to performance problems, the input parameters cannot be arbitrarily reduced. This paper proposes a method of optimizing input parameters specialized for Paralichthys olivaceus disease classification using SHAP analysis techniques to solve this problem,. The proposed method includes data preprocessing of disease information extracted from the halibut disease questionnaire by applying the SHAP analysis technique and evaluating a machine learning model using AutoML. Through this, the performance of the input parameters of AutoML is evaluated and the optimal input parameter combination is derived. In this study, the proposed method is expected to be able to maintain the existing performance while reducing the number of input parameters required, which will contribute to enhancing the efficiency and practicality of text-based Paralichthys olivaceus disease classification.