• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2001.04a
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• pp.7-10
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• 2001

Adsorption of the pesticides (phosphamidon, fenitrothion, triadimefon and diniconazole) in natural zeolite (CL $I_{N}$) and several synthetic zeolites was incestigated. The pesticides were not adsorbed on zeolites (Na-Pl, SOD, ANA, JBW and CAN) synthesized from Jeju scoria. The distribution coefficient ( $K_{D}$) and the Freundlich constant ( $K_{F}$) decreased in the following sequences. FC $C_{W}$ (waste catalytic cracking catalyst)>FA $U_{F}$ (FAU Synthesized from coal fly ash)>(FAU+Na-Pl)$_{SF}$ (the mixture of FAU and Na-Pl synthesized from the ratio of Jeju scoria 6 to coal fly ash 4 by weight)>CL $I_{N}$ among the zeolites; diniconazole>fenitrothion> triadimefon>phosphamidon. As the temperature was increased, the amount of pesticide adsorbed per unit mass of zeolite increased for FC $C_{W}$, FA $U_{F}$ and (FAU+Na-Pl)$_{SF}$ but it decreased for CL $I_{N}$, for all the pesticides used in this study. It was independent of pH for phosphamidon, fenitrothion and triadimefon, but decresed as pH was increased for all the zeolites used in this study.y.udy.y.y.y.y.y.y.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.2
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• pp.296-303
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• 2001

Kachanov-Rabotnov(K-R) creep damage theory was reviewed, and applied to design a creep curve for type 316LN stainless steel. Seven coefficients used in the theory, i.e., A, B, k, m, λ, r, and q were determined, and their physical meanings were analyzed clearly. In order to quantify a damage parameter ($\omega$), cavity amount was measured in the crept specimen taken from interrupted creep test with time variation, and then the amount was reflected into K-R damage equations. Coefficient λ, which is regarded as a creep tolerance feature of a material, increased with creep strain. Mater curve with λ=2.8 was well coincided with an experimental one to the full lifetime. The relationship between damage parameter and life fraction was matched with the theory at exponent ${\gamma}$=24 value. It is concluded that K-R damage equation was reliable as the modelling equation for type 316LN stainless steel. Coefficient data obtained from type 316LN stainless steel can be utilized for life prediction of operating material.

• Nuclear Engineering and Technology
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• v.49 no.4
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• pp.700-709
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• 2017

This work looks at the effect of changes in kinetic parameters on simultaneous reactivity insertions and beam tube flooding in a typical material testing reactor-type research reactor with low enriched high density ($U_3Si_2-Al$) fuel. Using a modified PARET code, various ramp reactivity insertions (from $0.1/0.5 s to $1.3/0.5 s) plus beam tube flooding ($0.5/0.25 s) accidents under uncontrolled conditions were analyzed to find their effects on peak power, net reactivity, and temperature. Then, the effects of changes in kinetic parameters including the Doppler coefficient, prompt neutron lifetime, and delayed neutron fractions on simultaneous reactivity insertion and beam tube flooding accidents were analyzed. Results show that the power peak values are significantly sensitive to the Doppler coefficient of the system in coupled accidents. The material testing reactor-type system under such a coupled accident is not very sensitive to changes in the prompt neutron life time; the core under such a coupled transient is not very sensitive to changes in the effective delayed neutron fraction.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.24 no.9
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• pp.679-684
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• 2012

This paper aims to evaluate performances of ventilation and insulation of 6types PVS(Passive ventilation skin) by numerical simulation. The results are as follows. 1) The result of Performance of ventilation by pressure difference, it was shown that the amount of ventilation changed bigger under 1Pa and amount of ventilation increased according to increase opening area (${\alpha}A$). Although same opening area of PVS, it can predict that pressure differences cause ventilation differences. 2) In case of same opening area of PVS, however, it was changed the amount of ventilation each types of PVS that is distinguished opening area by flow coefficient. 3) Dynamic U-value that represents performance of insulation PVS was similar change upper ${\alpha}A40\;cm^2/m^2$, great change in casse of 0.1 Pa pressure difference. In case of ${\alpha}A10\;cm^2/m^2$, it was changed bigger under 0.3 Pa pressure difference, ${\alpha}A20\;cm^2/m^2$ of PVS was changed under 0.2 Pa pressure difference.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3647-3653
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• 2012

In active noise control filter, IIR filter structure which used for control filter assures the stability property. The stability characteristics of IIR filter structure is mainly determined by pole location of control filter within unit disc, so stable selection of the value of control filter coefficient is very important. In this paper, we proposed novel adaptive stabilized Filtered_U LMS algorithms with IIR filter structure which has better convergence speed and less computational burden than conventional FIR structures, for multi-channel active noise control with vehicle enclosure signal case. For better convergence speed in adaptive algorithms, fuzzy LMS algorithms where convergence coefficient computed by a fuzzy PI type controller was proposed.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1407-1416
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• 2018

The purpose of this study was to train a model for the ulna and radius bone segmentation based on Convolutional Neural Networks and to verify the segmentation model. The data consisted of 840 training data, 210 tuning data, and 200 verification data. The learning model for the ulna and radius bone bwas based on U-Net (19 convolutional and 8 maximum pooling) and trained with 8 batch sizes, 0.0001 learning rate, and 200 epochs. As a result, the average sensitivity of the training data was 0.998, the specificity was 0.972, the accuracy was 0.979, and the Dice's similarity coefficient was 0.968. In the validation data, the average sensitivity was 0.961, specificity was 0.978, accuracy was 0.972, and Dice's similarity coefficient was 0.961. The performance of deep convolutional neural network based models for the segmentation was good for ulna and radius bone.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.153-172
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• 2022

In this study, the authors intend to analyze factors contributing to research performance using Backpropagation Neural Network and Support Vector Machine. The analyzing factors contributing to lecturer research performance start from defining the features. The next stage is to collect datasets based on defining features. Then transform the raw dataset into data ready to be processed. After the data is transformed, the next stage is the selection of features. Before the selection of features, the target feature is determined, namely research performance. The selection of features consists of Chi-Square selection (U), and Pearson correlation coefficient (CM). The selection of features produces eight factors contributing to lecturer research performance are Scientific Papers (U: 154.38, CM: 0.79), Number of Citation (U: 95.86, CM: 0.70), Conference (U: 68.67, CM: 0.57), Grade (U: 10.13, CM: 0.29), Grant (U: 35.40, CM: 0.36), IPR (U: 19.81, CM: 0.27), Qualification (U: 2.57, CM: 0.26), and Grant Awardee (U: 2.66, CM: 0.26). To analyze the factors, two data mining classifiers were involved, Backpropagation Neural Networks (BPNN) and Support Vector Machine (SVM). Evaluation of the data mining classifier with an accuracy score for BPNN of 95 percent, and SVM of 92 percent. The essence of this analysis is not to find the highest accuracy score, but rather whether the factors can pass the test phase with the expected results. The findings of this study reveal the factors that have a significant impact on research performance and vice versa.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.709-715
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• 2023

In this study, the performance evaluation of image quality for noise reduction was implemented using the U-net deep learning architecture in computed tomography (CT) images. In order to generate input data, the Gaussian noise was applied to ground truth (GT) data, and datasets were consisted of 8:1:1 ratio of train, validation, and test sets among 1300 CT images. The Adagrad, Adam, and AdamW were used as optimizer function, and 10, 50 and 100 times for number of epochs were applied. In addition, learning rates of 0.01, 0.001, and 0.0001 were applied using the U-net deep learning model to compare the output image quality. To analyze the quantitative values, the peak signal to noise ratio (PSNR) and coefficient of variation (COV) were calculated. Based on the results, deep learning model was useful for noise reduction. We suggested that optimized hyper parameters for noise reduction in CT images were AdamW optimizer function, 100 times number of epochs and 0.0001 learning rates.

• Journal of Radiation Protection and Research
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• v.26 no.2
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• pp.73-78
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• 2001

In order to find the exposure conversion coefficients for 3"X3" NaI spectrum, we measured the exposure rates with the pressurized ion chamber at 29 different areas in the range of $4{\sim}23{\mu}R\;h^{-1}$, and also measured the gamma spectra with 3"X3" and 4"X4" NaI detectors, simultaneously. The exposure conversion coefficient of the total energy method was determined using the linear relation between the measured exposure rate and the gamma spectrum energy. In order to find the exposure conversion coefficients of the energy band method, we applied the exposure conversion coefficients recommended by NCRP to the 4"X4" NaI spectra, and calculated the exposure rates due to $^{40}K,\;^{238}U$, and $^{232}Th$ series respectively. Using the linearly proportional relation between the obtained $^{232}Th$ series exposure rate and peak area of 2614 keV that represents the $^{232}Th$ series, we obtained the exposure conversion coefficients for $^{232}Th$ series. We also determined the conversion coefficients for $^{238}U$ series and $^{40}K$ using a similar method.

• Korean Chemical Engineering Research
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• v.50 no.3
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• pp.499-504
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• 2012

Characteristics of overall heat transfer were investigated in a three-phase slurry bubble column with relatively low surface tension media, which has been frequently encountered in the fields of industry. The heat transfer phenomena was examined in the system which was composed of a coaxial vertical heater and a proper of bubble column. The heat transfer coefficient was estimated from the measured mean value of temperature difference between the heater surface and the column proper at the steady state condition. Effects of gas velocity ($U_G$), solid fraction in the slurry phase ($C_S$) and surface tension (${\sigma}_L$) of continuous liquid media on the overall heat transfer coefficient (h) in the bubble column were determined. The mean value of temperature difference was estimated from the data of temperature difference fluctuations with a variation of time. The amplitude and mean value of temperature difference fluctuations with respect to the elasped time appeared to decrease with decreasing the surface tension of liquid phase. The overall heat transfer coefficient between the immersed heated and the bubble column increased with an increase in the gas velocity or solid fraction in the slurry phase, but it decreased with an increase in the surface tension of continuous liquid media. The overall heat coefficient in the slurry bubble column with relatively low surface tension media was well correlated in term of operating variables and dimensionless groups within this experimental conditions.