• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 한국소성가공학회 2003년도 춘계학술대회논문집
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• pp.176-179
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• 2003

The mean temperature prediction of strip is very important in hot strip finishing mill because of affecting on product quality and shape. Also, temperature can be used by basic information in other on-line control models with affecting control accuracy in factory. So, FE based on-line temperature model was developed for predicting strip mean temperature accurately in various process conditions and factory environments. There are many variables in affecting strip mean temperature in on-line states of factory. But some problems are occurred in considering all variables for making temperature model because of the bad efficiency of regression or fitting analysis. In this report, we have adopted dimensional analysis for solving these problems. We have many variables with dimensions affecting strip temperature but we are able to make non-dimensional variables less than dimensional variables from the combination of dimensional variables caused by PI-Theorem in fluid mechanics. The developed models are divided by two parts. The one is interstand temperature prediction model. The other is roll gap temperature model.

• Journal of Korean Ophthalmic Optics Society
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• 제12권1호
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• pp.85-90
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• 2007

We deposited thin films of ZnS(Zinc Sulphide), in which was used antireflection coating material of glasses-lens on silicon and slide-glass substrates using spin coating method, and measured spectra of ellipsometry angles ${\Delta}$ and ${\Psi}$ in the photon-energy range of 1.5~5.0 eV using a variable angle spectroscopic ellipsometer. The optical constants, refractive index and extinction coefficient, of ZnS were determined via the dispersion parameters extracted from the curve-fitting process based on Jellison-Modine dispersion function.

• Environmental Engineering Research
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• 제19권1호
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• pp.107-113
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• 2014

Small sized manganese dioxide particles are immobilized onto the surface of sand by the wet impregnation process. The surface morphology of the solid, i.e., immobilized manganese dioxide natural sand (IMNS) is performed by taking scanning electron microscope images and characterized by the X-ray diffraction data. The specific surface area of the solid is obtained, which shows a significant increase in the specific surface area obtained by the immobilization of manganese dioxide. The $pH_{PZC}$ (point of zero charge) is found to be 6.28. Further, the IMNS is assessed in the removal of As(III) and As(V) pollutants from aqueous solutions under the batch and column operations. Batch reactor experiments are conducted for various physicochemical parametric studies, viz. the effect of sorptive pH (pH 2.0-10.0), concentration (1.0-25.0 mg/L), and background electrolyte concentrations (0.0001-0.1 mol/L $NaNO_3$). Further, column experiments are conducted to obtain the efficiency of IMNS under dynamic conditions. The breakthrough data obtained by the column experiments are employed in non-linear fitting to the Thomas equation, so as to estimate the loading capacity of the column for As(III) and As(V).

• Journal of the Korea Academia-Industrial cooperation Society
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• 제16권3호
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• pp.1630-1638
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• 2015

The finite element analysis has become an essential process to investigate the structural performance in many industry fields. In addition, the computer's performance is improving rapidly, but in large design problems, there is a limit to apply the optimal design techniques. For this, it is general to introduce a metamodel based optimization technique. The method to generate an approximate model can be classified into curve fitting and interpolation, and each representative one is response surface model and kriging interpolation method. This study proposes an ensemble model made of RSM and kriging to solve a structural design problem. The suggested method is applied to the designs of two bar and automobile outer tie rod.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• 제29권6호
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• pp.8-13
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• 2015

Recently, Illumination industry of LED module has been focused to industry technology for energy conservation of nation. The LED device is excellent to power efficiency due to semiconductor light source element. And the application to the lighting circuit technology can be designed to the sensitive lighting system for human sensitivity control. In this paper, as a process for analyzing the operating temperature of standardized electronic device including LED device has analyzed about fusing character with in designed micro fuse for electronic device protection from the over current. Using the de-rating technique, which is performed to micro fuse fusing test in the range of $-30^{\circ}C{\sim}120^{\circ}C$ thermostatic chamber. To the output data in each temperature zone, it is performed to first-order linear fitting. Additionally, applying the resistance temperature coefficient and statistical data for the reliable analysis has derived to the metal element resistance of micro fuse with temperature change of the thermostatic chamber. As a research result, The changed temperature effect of thermostatic chamber was confirmed regarding fusing time change.

• Journal of the Society of Cosmetic Scientists of Korea
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• 제23권2호
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• pp.71-96
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• 1997

We have used a novel liquid surface forces apparatus to determine the variation of disjoining pressure with film thickness for dodecane-water-dodecane emulsion films. The LSFA allows measurement of film thicknesses in the range 5-100 nm and disjoining pressure from 0-1500 Pa. Disjoining pressure isotherms are given for films stabilised by the nonionic surfactnat n-dodecyl pentaoxyethylene glycol ether$(C_{12}E_5)$ and n-decyl-$\beta$-D-glucopyranoside($C_{10}- $\beta$-Glu)$ and the anionic surfactant sodium bis(2-ethylhexyl) sulphosuccinate(AOT) in the presense of added electrolyte. For $C_{12}E_5$ and AOT, the emulsion films are indefinitely stable even for the highest concentration of NaCl tested (136.7 Nm) whereas the $C_{10}-{eta}-Glu$ film shows coalescence at this salt concentration. For film thicknesses greater than approximately 20 nm with all three surfactants, the disjoining pressure isotherms are reasonably well described in terms of electrostatic and van der Waals, forces. For the nonionic surfactant emulsion films, the charge properties of the monolayers are qualitatively similar to those seen for foam films. For AOT emulsion films, the monolayer surface potentials estimated by fitting the isotherms are similar to the values of the zeta potential measured for AOT stabilised emulsion droplets. For thin emulsion films certain systems showed isotherms which suggested the presence of an additional repulsive force with a range of approximately 20 nm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 한국퍼지및지능시스템학회 2004년도 춘계학술대회 학술발표 논문집 제14권 제1호
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• pp.111-114
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• 2004

Management of human knowledge is an interesting concept that has attracted the attention of philosophers for thousands of years. Artificial intelligence and knowledge engineering has provided some degree of rigor to the study of knowledge systems and expert systems(ES) re able to use knowledge to solve the problems and answer questions. Therefore, the process of conceptualization and inference of knowledge are fundamental problem solving activities and hence, are essential activities for solving the problem of software ES construction Especially, the access to relevant, up-to-date and reliable knowledge is very important task in the daily work of physicians and nurses. In this study, we propose the conceptualization and inference mechanism for implicit knowledge management in medical diagnosis area. To this purpose, we combined the dynamic knowledge map(KM) and relational database(RDB) into a dynamic knowledge map(DKM). A graphical user-interface of DKM allows the conceptualization of the implicit knowledge of medical experts. After the conceptualization of implicit knowledge, we developed an RDB-based inference mechanism and prototype software ES to access and retrieve the implicit knowledge stored in RDB. Our proposed system allows the fast comfortable access to relevant knowledge fitting to the demands of the current task.

• Biotechnology and Bioprocess Engineering:BBE
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• 제11권3호
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• pp.223-229
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• 2006

In this study, we have conducted kinetics and modelling studies of Centella asiatica cell growth and substrate uptake, in an attempt to evaluate cell growth for a better understanding and control of the process. In our bioreactor cultivation experiment, we observed a growth rate of 0.18/day, a value only 20% higher than was seen in the shake flask cultivation trial. However, the observed maximum cell dry weight in the shake flask, 10.5g/L, was 14% higher than was achieved in the bioreactor. Ninety seven percentage confidence was achieved via the fitting of three unstructured growth models; the Monod, Logistic, and Gompertz equations, to the cell growth data. The Monod equation adequately described cell growth in both cultures. The specific growth rate, however, was not effectively predicted with the Logistic and Gompertz equations, which resulted in deviations of up to 73 and 393%, respectively. These deviations in the Logistic and Gompertz models may be attributable to the fact that these models were developed for substrate-independent growth and fungi growth, respectively.

• Journal of the Korean Data and Information Science Society
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• 제21권5호
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• pp.863-871
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• 2010

Until now, many studies related circular data are carried out, but the focuses are mainly on mildly peaked symmetric or asymmetric cases. In this paper we studied a modeling process for sharply peaked asymmetric circular data. By using wrapped Laplace, which was firstly introduced by Jammalamadaka and Kozbowski (2003), and its mixture distributions, we considered the model fitting problem of multi-modal circular data as well as unimodal one. In particular we suggested EM algorithm to find ML estimates of the mixture of wrapped Laplace distributions. Simulation results showed that the suggested EM algorithm is very accurate and useful.

• Smart Structures and Systems
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• 제22권2호
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• pp.175-183
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• 2018

Time-series data often contain one of the most valuable pieces of information in many fields including manufacturing. Because time-series data are relatively cheap to acquire, they (e.g., vibration signals) have become a crucial part of big data even in manufacturing shop floors. Recently, deep-learning models have shown state-of-art performance for analyzing big data because of their sophisticated structures and considerable computational power. Traditional models for a machinery-monitoring system have highly relied on features selected by human experts. In addition, the representational power of such models fails as the data distribution becomes complicated. On the other hand, deep-learning models automatically select highly abstracted features during the optimization process, and their representational power is better than that of traditional neural network models. However, the applicability of deep-learning models to the field of prognostics and health management (PHM) has not been well investigated yet. This study integrates the "residual fitting" mechanism inherently embedded in the wavelet transform into the convolutional neural network deep-learning structure. As a result, the architecture combines a signal smoother and classification procedures into a single model. Validation results from rotor vibration data demonstrate that our model outperforms all other off-the-shelf feature-based models.