• Journal of the Korean Crystal Growth and Crystal Technology
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• v.8 no.3
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• pp.478-486
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• 1998

$CaTiO_3-x(La_{1/3}Nd_{1/3}TiO_3\;(0\le \textrm x\le0.8)$ system was prepared by reaction of $CaCO_3,\;LaO_3,\;Nd_2CO_3$ and ,TEX>$TiO_2$ mixture at 1673 K, which can be applied for microwave dielectric ceramic materials. The lattice parameters of(1-x))$CaTiO_3-x(La_{1/3}Nd_{1/3}TiO_3\;(0\le \textrm x\le0.8)$ system increased with the increase of x. Its structure was investigated by Rietveld profile-analysis of XRD in detail. Cations $ La^{3+}$ and Nd^{3+}$ were located at the $Ca^{2+}$ site in the range of $0\le \textrm x\le0.8$. crystal structure in $;(0\le \textrm x\le0.6)$ maintained space group Pnma with CaTiO_3 structure. The tiled and distorted $TiO_6$ was gradually released with the increase of x in $0\le \textrm x\le0.6$ .The structure was changed to a new space group of $Pmn2_1$ at the x value of 0.8. The relative dielectric constant $(\epsilon_r)$ of $(1-x)CaTiO_3-x(La_{1/3} Nd_{1/3})TiO_3$ ($(0\le \textrm x\le0.8)$) system was exponentially decreased by with the increased of x. The temperature coefficient of resonant frequency $(\tau_f)$ decreased with the increase of x in $0\le \textrm x\le0.6$ and then increased again at x=0.8 due to the change of crystal structure. The value of Q$\cdot f_o$ was 13800 (GHz) at x=0.2 and was very low under 2000 (GHz) in 0.4$\leq$x$\leq$0.8.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.5 no.1
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• pp.21-30
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• 1985

The tension leg platform (TLP) is a kind of compliant structures, and is also a type of moored stable platform with a buoyancy exceeding the weight because of having tensioned vertical anchor cables. In this paper, among the various kinds of tension leg structures, Deep Oil Technology (DOT) TLP was analyzed because it has large-displacement portions of the immersed surface such as vertical corner pontoons and small-diameter elongated members such as cross-bracing. It also has results of hydraulic model tests, comparable with theorectical analysis. Because of the vertical axes of symmetry in the three vertical buoyant legs and because there are no larger horizontal buoyant members between these three vertical members, it was decided to develop a numerical algorithm which would predict the dynamic response of the DOT TLP using the previously developed numerical algorithm Floating Vessel Response Simulation (FVRS) for vertically axisymmetric bodies of revolution. In addition, a linearized hydroelastic Morison equation subroutine would be developed to account for the hydrodynamic pressure forces on the small member cross bracing. Interaction between the large buoyant members or small member cross bracings is considered to be negligible and is not included in the analysis. The dynamic response of the DOT TLP in the surge mode is compared with the results of the TLP algorithm for various combinations of diffraction and Morison forces and moments. The results which include the Morison equation are better than the results for diffraction only. This is because the vertically axisymmetric buoyant members are only marginally large enough to consider diffractions effects. The prototype TLP results are expected to be more inertially dominated.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.5
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• pp.549-557
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• 2006

The characteristics of supercritical water oxidation have been studied to decompose the waste anionic exchange resins which were produced from a power plant. The waste resins from a power plant were mixture of anionic and cationic exchange resins. The waste anionic exchange resins had been separated from the waste resins using a solid-liquid fluidized bed. It was confirmed that the cationic exchange resins were not included in the separated anionic exchange resins by the elemental and thermogravimetric analysis. A slurry of anionic exchange resins which could be fed continuously to a supercritical water oxidation apparatus by a high pressure pump was prepared using a wet ball mill. Although the COD of liquid effluent had been reduced more than 99.9% at 25.0 MPa and $500^{\circ}C$ within 2 min, the total nitrogen content was reduced only 41%. The addition of nitric acid to the slurry could reduce the total nitrogen content in treated water. The central composite design as a statistical desist of experiments had been applied to optimize the conditions of decomposing anionic resin slurry by means of the COD and total nitrogen contents in treated waters as the key process output variables. The COD values of treated waters had been reduced sufficiently to $99.9{\sim}100%$ af the reaction conditions of $500{\sim}540^{\circ}C$, 25.0 MPa within 2 min. The effects of temperature and nitric acid concentration on COD were not significant. However, the effect of nitric acid concentration on the total nitrogen was found to be significant. The regression equation for the total nitrogen had been obtained with nitric acid concentration and the coefficient of determination($r^2$) was 95.8%.

• Journal of Oral Medicine and Pain
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• v.30 no.1
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• pp.1-14
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• 2005

The maintenance of good oral health in adults is often hindered by oral malodor and periodontal diseases which are known to be commonly caused by some species of Gram-negative anaerobic bacteria, with low sensitivity to common synthetic antibiotics or antibacterial chemical agents. Therefore the development of a nonharmful natural antibacterial oral rinsing remedy against the causative bacteria is thought to be very important. The purpose of this study is to obtain the basic data for development of a nonharmful natural antibacterial oral rinsing remedy using colloidal silver. The author applied colloidal silver solution with concentration of 10, 30, 50, 80 ppm to some strains in species of Prevotella intermedia, Porphyromonas gingivalis, Fusobaterium nucleatum, and evaluated the effects of colloidal silver on the growth of experimental bacterial strains in aspects of minimal inhibitory concentration (MIC), minimal bactericidal concentration (MBC) and growth pattern after incubation for 24, 48, 72 hours. The obtained results were as follows: MIC of colloidal silver solution against experimental strains was 30 ppm in P. intermedia, 10 or 30 ppm in P. gingivalis, and 30, 50, or 80 ppm in F. nucleatum. And MBC of colloidal silver solution against experimental strains was 30 ppm in P. intermedia, 30 or 50 ppm in P. gingivalis, 30 or 80 ppm in F. nucleatum. Therefore it was concluded that colloidal silver exhibited bacteriostatic or/and bacteriocidal effects against some experimental strain. And the inhibition of growth of experimental strains were markedly or considerably exhibited under 30 ppm$\sim$50 ppm of colloidal silver solution for 48 hours$\sim$72 hours in P. intermedia, 10 ppm$\sim$30 ppm for 24 hours$\sim$48 hours in P. gingivalis, 30 ppm for 24 hours in F. nucleatum. These results indicate that the colloidal silver inhibited effectively the growth of some species of Gram-negative anaerobic bacteria by exhibition of bacteriostatic or/and bacteriocidal effects, and can be used as a possible major ingredient of the nonharmful natural antibacterial oral rinsing remedy to oral malodor and periodontal diseases.

• Korean Journal of Mineralogy and Petrology
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• v.34 no.3
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• pp.157-167
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• 2021

Lead (Pb) is one of the key trace elements, exhibiting a peculiar partitioning behavior into silicate melts in contact with minerals. Partitioning behaviors of Pb between silicate mineral and melt have been known to depend on melt composition and thus, the atomic structures of corresponding silicate liquids. Despite the importance, detailed structural studies of Pb-bearing silicate melts are still lacking due to experimental difficulties. Here, we explored the effect of lead content on the atomic structures, particularly the evolution of silicate networks in Pb-bearing sodium metasilicate ([(PbO)_x(Na₂O)_1-x]·SiO₂) glasses as a model system for trace metal bearing natural silicate melts, using ²⁹Si solid-state nuclear magnetic resonance (NMR) spectroscopy. As the PbO content increases, the ²⁹Si peak widths increase, and the maximum peak positions shift from -76.2, -77.8, -80.3, -81.5, -84.6, to -87.7 ppm with increasing PbO contents of 0, 0.25, 0.5, 0.67, 0.86, and 1, respectively. The ²⁹Si MAS NMR spectra for the glasses were simulated with Gaussian functions for Qⁿ species (SiO₄ tetrahedra with n BOs) for providing quantitative resolution. The simulation results reveal the evolution of each Qⁿ species with varying PbO content. Na-endmember Na₂SiO₃ glass consists of predominant Q² species together with equal proportions of Q¹ and Q³. As Pb replaces Na, the fraction of Q² species tends to decrease, while those for Q¹ and Q³ species increase indicating an increase in disproportionation among Qⁿ species. Simulation results on the ²⁹Si NMR spectrum showed increases in structural disorder and chemical disorder as evidenced by an increase in disproportionation factor with an increase in average cation field strengths of the network modifying cations. Changes in the topological and configurational disorder of the model silicate melt by Pb imply an intrinsic origin of macroscopic properties such as element partitioning behavior.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.33 no.2
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• pp.103-111
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• 1988

This study was carried out with two cultivars under two levels of pH and four levels of nitrogen fertilization in a field and nutri-culture experiments to obtain the information about the effects of pH and nitrogen fertilization on the growth and yield of soybean. Acidic condition suppressed the growth of soybean plants, and thus yield and yield components of soybean decreased under acidic condition. But they increased with increased nitrogen fertilization. Especially, these respones were more remarkable under acidic condition and in the variety Jangbaegkong. Grain yield of soybean were highly correlated with the content of allantoin and total nitrogen of soybean plants in the variety Jangbaegkong, but this was not in the variety Danyeobkong. The content of protein and fat of soybean seeds decreased under acidic condition, and more nitrogen fertilization increased the protein content, but decreased the fat content.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.38 no.4
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• pp.350-359
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• 1993

The effect of gibberellic acid ($GA_3) and abscisic acid (ABA) on KCl-enhanced proteolysis of senescing leaves of rice(Oryza sativa L. cv. Chilsung) was studied. Emphasis was given to their effects on KCI-enhanced efflux of amino acids and proteinase activity. When treated singly, $GA_3 affected leaf proteolysis little, while ABA increased proteolysis, the rate of amino acid efflux, and ribulose -1,5 -bisphosphate carboxylase / oxygenase (Rubisco)-degrading endoproteinase activity. An additive increase in all three parameters mentioned above was observed when leaves were treated with ABA and KCl. No such an additive effect was found when $GA_3 was treated with KCl. Both $GA_3 and ABA helped to alleviate the KCI-suppressed activity of Rubisco-degrading exoproteinases. The additive increase in proteolysis of rice leaves in the presence of both ABA and KCl could thus be ascribed to a further increase in the efflux of protein hydrolyzates and Rubisco-degrading endoproteinase activity. An increase in proteolysis was accompanied by a decrease in water absorption, and the combined treatment of ABA with KCl resulted in a further reduction of water absorption.

• Journal of IKEEE
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• v.27 no.1
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• pp.116-121
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• 2023

In this paper, we develop a deep learning structure for a complex microbial incubator that applies deep learning prediction result information. The proposed complex microbial incubator consists of pre-processing of complex microbial data, conversion of complex microbial data structure, design of deep learning network, learning of the designed deep learning network, and GUI development applied to the prototype. In the complex microbial data preprocessing, one-hot encoding is performed on the amount of molasses, nutrients, plant extract, salt, etc. required for microbial culture, and the maximum-minimum normalization method for the pH concentration measured as a result of the culture and the number of microbial cells to preprocess the data. In the complex microbial data structure conversion, the preprocessed data is converted into a graph structure by connecting the water temperature and the number of microbial cells, and then expressed as an adjacency matrix and attribute information to be used as input data for a deep learning network. In deep learning network design, complex microbial data is learned by designing a graph convolutional network specialized for graph structures. The designed deep learning network uses a cosine loss function to proceed with learning in the direction of minimizing the error that occurs during learning. GUI development applied to the prototype shows the target pH concentration (3.8 or less) and the number of cells (10⁸ or more) of complex microorganisms in an order suitable for culturing according to the water temperature selected by the user. In order to evaluate the performance of the proposed microbial incubator, the results of experiments conducted by authorized testing institutes showed that the average pH was 3.7 and the number of cells of complex microorganisms was 1.7 × 10⁸. Therefore, the effectiveness of the deep learning structure for the complex microbial incubator applying the deep learning prediction result information proposed in this paper was proven.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.