• Korean Journal of Oriental Medicine
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• v.17 no.2
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• pp.115-120
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• 2011

Objectives : The purpose of this study is to develop an apparatus which can take a facial image by self-operated capturing technique. The user can obtain one's facial image immediately after adjusting facial tilt and focusing distance. The system has been designed for classifying Sasang typology based on facial image. Methods : The system is composed of a Webcam, one-way glass mirror and mini LCD. The Webcam takes a facial image which is displayed on the mini LCD. Then the user can see and adjust to the right position in the real time through the image mirror-reflected from the mini LCD. The optical sensor is used to estimate the proper focusing distance. To verify the performance of the system, 11 characteristic points on the facial image are used and compared with high performance DSLR camera(D700) by applying the coefficient of variance and Bland-Altman Plot. Results : The developed system and D700 show enough agreement with the small coefficient of variance to analyse constitutional types with a facial im mage. However, the result of Bland-Altman plot shows that the width parameters have distortions owing to short focusing distance. Conclusions : The system is expected to be utilized on u-healthcare services for home environment after improving the distortion in the width parameters.

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2012.05a
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• pp.308-308
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• 2012

Electrodeposition of thermoelectric materials, including binary and ternary compounds, have been attracting attentions, because its many advantages including cost-effectiveness, rapid deposition rate, and ease of control their microstructure and crystallinity by adjusting electrodeposition parameters. In this work, $Bi_xTe_y$ films were potentiostatically electrodeposited using Au/Ni(80/20 nm)/Si substrate as the working electrode in solutions consisting of 10mM $TeO_2$ and 1M $HNO_3$ where $Bi(NO_3)_3$ was varied from 2.5 to 10 mM. Prior to electrodeposition potentiostatically, linear sweep voltammograms (LSV) were acquired with a standard three-electrode cell. The $Bi_xTe_y$ films deposited using the electrolyte containing low Bi ions shows p-type conductivity, which might be attributed by the large incorporation of Te phases. Near stoichiometric $Bi_2Te_3$ thin films were obtained from electrolytes containing 5mM $Bi(NO_3)_3$. This film shows the maximum Seebeck coefficient of $-100.3{\pm}12.7{\mu}V/K$. As the increase of Bi ions in electrolytes decreases the Seebeck coefficient and resistivity. The maximum power factor of $336.2{\mu}W/m{\cdot}K^2$ was obtained from the film deposited using the solution of 7.5mM $Bi(NO_3)_3$.

• Korean Chemical Engineering Research
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• v.57 no.1
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• pp.58-64
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• 2019

This study presents a novel method for preparing monodisperse polyethylene glycol diacrylate (PEGDA) microparticles in a dispensing needle based microfluidic device. The microfluidic devices are manufactured by manually assembling various off-the-shelf products without using additional equipment. In this microfluidic device, the volumetric flow rates of the dispersed phase of PEGDA solution and the continuous phase of oil are controlled to generate monodisperse PEGDA droplets. The PEGDA droplet contains photo-initiator thus it is crosslinked to microparticle by photopolymerization at the ends of the device. The particle size is easily controlled by adjusting the volume flow rate and the size of the microfluidic device. The monodispersity of the particles is calculated by a coefficient of variation of 2.57%. To demonstrate the biological applications of PEGDA particles, cells are encapsulated and observed for proliferation and viability.

• Journal of Korea Water Resources Association
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• v.54 no.3
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• pp.157-166
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• 2021

Forecasting dam inflow based on high reliability is required for efficient dam operation. In this study, deep learning technique, which is one of the data-driven methods and has been used in many fields of research, was manipulated to predict the dam inflow. The Long Short-Term Memory deep learning with Sequence-to-Sequence model (LSTM-s2s), which provides high performance in predicting time-series data, was applied for forecasting inflow of Soyang River dam. Various statistical metrics or evaluation indicators, including correlation coefficient (CC), Nash-Sutcliffe efficiency coefficient (NSE), percent bias (PBIAS), and error in peak value (PE), were used to evaluate the predictive performance of the model. The result of this study presented that the LSTM-s2s model showed high accuracy in the prediction of dam inflow and also provided good performance for runoff event based runoff prediction. It was found that the deep learning based approach could be used for efficient dam operation for water resource management during wet and dry seasons.

• Journal of Korea Port Economic Association
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• v.29 no.3
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• pp.25-37
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• 2013

This study investigates the export behavior of port of Pyeongtaek-Dangjin and Daesan. The monthly data cover the period from January 2002 to December 2012. This paper tests whether the exchange rate and the industrial production are stationary or not, rejecting the null hypothesis of a unit root in each of the level variables and of a unit root for the residuals from the cointegration at the 5 percent significance level. The error-correction model is estimated to find that Daesan port is faster than Pyeongtaek-Dangjin in adjusting the short-run disequilibrium. This paper finds that the exchange rate coefficient of Daesan port is higher than that of Pyeongtaek-Dangjin port, while the industrial production coefficient of the former is much smaller than that of the latter. The industrial production coefficient is, however, much higher than the exchange rate coefficient in both ports. The rolling regression shows that the influence of exchange rate and industrial production tends to increase in Pyeongtaek-Dangjin port but tends to decrease in Daesan. The impulse response functions indicate that export volumes respond much greater to the positive shocks in industrial production than in exchange rate, and the exchange rate shock decays very fast, while the industrial production shock lasts very long.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.1
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• pp.147-153
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• 2019

The prestressing steel wire, which is applied a tension to reinforce the structure, is applied flexure simultaneously by the duct and the deviator. In order to evaluate the deflected tensile performance of the prestressing steel wire subjected to both tensile and flexural stresses, the numerical analysis for 600 cases with variables of wire diameters, mandrel diameters, and friction coefficient between mandrel and steel wire was performed. As the result of analysis, the larger the diameter of the steel wire was, the lower the deflected tensile performance was, and the effect decreased with the increase of the wire elongation. The effect of mandrel diameter and friction coefficient between mandrel and wire on the deflected tensile performance of the wire was very small. But the deflected tensile performance and the friction coefficient between mandrel and strand showed a relatively high correlation. Therefore, it is necessary to make enough large elongation to secure the deflected tensile performance. If there is a restriction on the elongation, it is necessary to reduce the diameter of the steel wire to an appropriate value, and to increase the friction between steel wires by adjusting the surface condition of the steel wire.

• Research in Community and Public Health Nursing
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• v.22 no.2
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• pp.162-172
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• 2011

Purpose: This study was conducted to evaluate differences in pain, pain interference, and fatigue, according to the level of physical activity in the elderly with chronic pain. Methods: Data were collected between January and March 2011 from 116 elders with chronic pain living in community settings. The data were analyzed using the SPSS/WIN 17.0 program for descriptive statistics, $x^2$ test, t-test, ANOVA and ANCOVA. The reliability of the instruments was tested with Cronbach's ${\alpha}$ coefficient, which ranged from .91 to .93. Results: The results were as follows. The mean total physical activity was 2287.4 MET-min/week, and 41.4% of the investigated subjects were classified into the low physical activity group. After adjusting for age and sex, the moderate and high physical activity groups were significantly lower in pain (F=6.33, p=.002), pain interference (F=11.57, p<.001), and fatigue (F=3.16, p=.046) than the low physical activity group. Conclusion: Results from this study suggest that the level of physical activity can influence pain, pain interference and fatigue. Therefore, incorporating more physical activities into daily routines, inactive elderly individuals may improve their pain, pain interference and fatigue.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.4
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• pp.832-839
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• 2009

In this paper, we introduce an design of fuzzy relation-based fuzzy neural networks with multi-output. Fuzzy relation-based fuzzy neural networks comprise the network structure generated by dividing the entire input space. The premise part of the fuzzy rules of the network reflects the relation of the division space for the entire input space and the consequent part of the fuzzy rules expresses three types of polynomial functions such as constant, linear, and modified quadratic. For the multi-output structure the neurons in the output layer were connected with connection weights. The learning of fuzzy neural networks is realized by adjusting connections of the neurons both in the consequent part of the fuzzy rules and in the output layer, and it follows a back-propagation algorithm. In addition, in order to optimize the network, the parameters of the network such as apexes of membership functions, learning rate and momentum coefficient are automatically optimized by using real-coded genetic algorithm. Two examples are included to evaluate the performance of the proposed network.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.02a
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• pp.367-367
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• 2013

Commercial applications of indium tin oxide (ITO) can be separated into two useful areas. As it is perceived to bear electrical properties and optical transparency at once, its chance to apply to promising fields, usually for an optical device, gets greater in the passing time. ITO is one of the transparent conducting oxides (TCO), and required to carry the relative resistance less than $10^{-3}{\Omega}$/cm and transmittances over 80 % in the visible wavelength of light. Because ITO has considerable refractive index, there exist applications for anti-reflection coatings. Anti-reflection properties require gradual change in refractive index from films to air. Such changes are obtained from film density or nano-clustered fractional void. Glancing angle deposition (GLAD) method is a well known process for adjusting nanostructure of the films. From its shadowing effects, GLAD helps to deposit well-controlled porous films effectively. In this study, we are comparing the reference sample to samples coated with controlled ITO multilayer accumulated by an e-beam evaporation system. At first, the single ITO layer samples are prepared to decide refractive index with ellipsometry. Afterwards, ITO multilayer samples are fabricated and fitted by multilayer ellipsometric model based on single layer data. The structural properties were measured by using atomic force microscopy (AFM), and by scanning X-ray diffraction (XRD) measurements. The ellipsometry was used to determine refractive indices and extinction coefficient. The optical transmittance of the film was investigated by using an ultraviolet-visible (UV-Vis) spectrophotometer.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.5
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• pp.705-711
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• 2013

In this paper, we introduce an design of multi-output fuzzy neural networks based on Interval Type-2 fuzzy set. The proposed Interval Type-2 fuzzy set-based fuzzy neural networks with multi-output (IT2FS-based FNNm) comprise the network structure generated by dividing the input space individually. The premise part of the fuzzy rules of the network reflects the individuality of the division space for the entire input space and the consequent part of the fuzzy rules expresses three types of polynomial functions with interval sets such as constant, linear, and modified quadratic inference for pattern recognition. The learning of fuzzy neural networks is realized by adjusting connections of the neurons in the consequent part of the fuzzy rules, and it follows a back-propagation algorithm. In addition, in order to optimize the network, the parameters of the network such as apexes of membership functions, uncertainty factor, learning rate and momentum coefficient were automatically optimized by using real-coded genetic algorithm. The proposed model is evaluated with the use of numerical experimentation.