• Korean Journal of Biological Psychiatry
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• v.22 no.2
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• pp.78-86
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• 2015

Objectives Although ginseng has been reported to protect neuronal cells and improve various cognitive functions, relationship between ginseng supplementation and response inhibition, one of the important cognitive domains has not been explored. In addition, effects of ginseng on in vivo human brain have not been investigated using the diffusion tensor imaging (DTI). The purpose of the current study is to investigate changes in intrusion errors and white matter microstructure after Korean Red Ginseng supplementation using standardized neuropsychological tests and DTI. Methods Fifty-one healthy participants were randomly allocated to the Korean Red Ginseng (n = 26) or placebo (n = 25) groups for 8 weeks. The California Verbal Learning Test was used to assess the number of intrusion errors. Intelligence quotient (IQ) was measured with the Korean Wechsler Adult Intelligence Scale. Depressive and anxiety symptoms were evaluated using Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale, and Hopkins Symptom Checklist-25. The fractional anisotropy (FA) was measured from the brain DTI data. Results After the 8-week intervention, Korean Red Ginseng supplementation significantly reduced intrusion errors after adjusting age, sex, IQ, and baseline score of the intrusion errors (p for interaction = 0.005). Change in FA values in the left anterior corona radiata was greater in the Korean Red Ginseng group compared to the placebo group (t = 4.29, p = 0.04). Conclusions Korean Red Ginseng supplementation may be efficacious for improving response inhibition and white matter microstructure integrity in the prefrontal cortex.

• Journal of the Mineralogical Society of Korea
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• v.24 no.4
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• pp.289-300
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• 2011

Atomistic origins of carbon solubility into silicates are essential to understand the effect of carbon on the properties of silicates and evolution of the Earth system through igneous and volcanic processes. Here, we investigate the atomic structure and NMR properties of dissolved carbon in enstatite using Raman spectroscopy and quantum chemical calculations. Raman spectrum for enstatite synthesized with 2.4. wt% of amorphous carbon at 1.5 GPa and $1,400^{\circ}C$ shows vibrational modes of enstatite, but does not show any vibrational modes of $CO_2$ or ${CO_3}^{2-}$. The result indicates low solubility of carbon into enstatite at a given pressure and temperature conditions. Because $^{13}C$ NMR chemical shift is sensitive to local atomic structure around carbon and we calculated $^{13}C$ NMR chemical shielding tensors for C substituted enstatite cluster as well as molecular $CO_2$ using quantum chemical calculations to give insights into $^{13}C$ NMR chemical shifts of carbon in enstatite. The result shows that $^{13}C$ NMR chemical shift of $CO_2$ is 125 ppm, consistent with previous studies. Calculated $^{13}C$ NMR chemical shift of C is ~254 ppm. The current calculation will alllow us to assign potential $^{13}C$ NMR spectra for the enstatite dissolved with carbon and thus may be useful in exploring the atomic environment of carbon.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.11 no.1
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• pp.45-53
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• 1991

The underground structure, which has infinite or semi-infinite boundary conditions, is subjected by body forces and in-situ stresses. It also has stress concentration, which causes material nonlinear behavior, in the vicinity of the excavated surface. In this paper, some methods which can be used to transform domain integrals into boundary integrals are reviewed in order to analyze the effect of the body forces and the in-situ stresses. First, the domain integral of the body force is transformed into boundary integral by using the Galerkin tensor and divergence theorem. Second, it is transformed by writing the domain integral in cylindrical coordinates and using direct integration. The domain integral of the in-situ stress is transformed into boundary integral applying the direct integral method in cylindrical coordinates. The methodology is verified by comparing the results from the boundary element analysis with those of the finite element analysis. Coupling the above boundary elements with finite elements, the nonlinear behavior that occurs locally in the vicinity of the excavation is analyzed and the results are verified. Thus, it is concluded that the domain integrals of body forces and in-situ stresses could be performed effectively by transforming them into the boundary integrals, and the nonlinear behavior can be reasonably analyzed by coupled nonlinear finite element and boundary element method. The result of this research is expected to he used for the analysis of the underground structures in the effective manner.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.6
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• pp.48-60
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• 2011

Nowadays, the 3D community is actively researching on 3D imaging and free-viewpoint video (FVV). The free-viewpoint rendering in multi-view video, virtually move through the scenes in order to create different viewpoints, has become a popular topic in 3D research that can lead to various applications. However, there are restrictions of cost-effectiveness and occupying large bandwidth in video transmission. An alternative to solve this problem is to generate virtual views using a single texture image and a corresponding depth image. A critical issue on generating virtual views is that the regions occluded by the foreground (FG) objects in the original views may become visible in the synthesized views. Filling this disocclusions (holes) in a visually plausible manner determines the quality of synthesis results. In this paper, a new approach for handling disocclusions using depth based inpainting algorithm in synthesized views is presented. Patch based non-parametric texture synthesis which shows excellent performance has two critical elements: determining where to fill first and determining what patch to be copied. In this work, a noise-robust filling priority using the structure tensor of Hessian matrix is proposed. Moreover, a patch matching algorithm excluding foreground region using depth map and considering epipolar line is proposed. Superiority of the proposed method over the existing methods is proved by comparing the experimental results.

• Korean Journal of Materials Research
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• v.10 no.5
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• pp.375-381
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• 2000

Aluminium foil for electrolytic capacitors was anodized at the voltage of 100V and 140V for 10 minutes in ammonium adipate solution to form aluminum oxide layer on aluminum substrate as an dielectric film. The thickness, the stoichiometry and the crystal structure of the layer were investigated by using RBS and TEM . In addition EIS technique was employed to study the effects of addition of sulfuric acid on the increment of the foil surface area. It was found that the thickness values of the layers anodized at 100V and 140V were about 130 nm and 190 nm respectively and the stoichiometry of the elements of aluminum and oxygen was 2:3. The anodic oxide layer was shown to be amorphous. but the structure irradiated with electron beam resulted in the transformation into crystalline structure of $${\gamma}$-Al_2$$O_3$ . From a comparison of the impedance results and the capacitance variation to investigate the ef- fects of sulfuric acid addition to the etching bath of hydrochloric acid, the EIS techinque could be useful to analyze the capacitance variation.

• The Korean Journal of Rheology
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• v.10 no.4
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• pp.217-226
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• 1998

The anisotropy caused by the fiber orientation, which is inevitably generated by the flow during injection molding of short fiber reinforced polymers, greatly influences dimensional accuracy, mechanical properties, and other quality of the final product. Since the filling stage of the injection molding process plays a vital role in determining fiber orientation, an accurate analysis of flow field for the filling stage is needed. Unbalanced filling occurs when a complex or a multi-cavity mold is used leading to development of regions where the fiber suspension is under compression. It is impossible to make an accurate calculation of the flow field during filling with the analysis assuming incompressible fluid. A mold with four cavities with different filling times was produced to compare the numerical analysis results with the experimental data. There was a good agreement between the experimental and theoretical results when the compressibility of the polymer melt was considered for the numerical simulation. The fiber orientation states for compressible and incompressible fluids were also compared qualitatively as well as quantitatively in this study.

• The Korean Journal of Rheology
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• v.10 no.4
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• pp.202-216
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• 1998

Improved version of previous 'Orthotropic' closure approximation, termed 'ORW' has been numerically developed using new homogeneous flow data. Previous 'Orthotropic' closure approximation, i.e., ORF or ORL showed non-physical oscillation for interaction coefficient $C_1$<0.001 at simple shear flow. It also shows non-physcial oscillation and under-prediction compared with 'Distribution Function Calculation' at non-homogeneous flow of center-gated disk. These phenomena are mainly due to the flow data of 'Distribution Function Calculation' which were used for least-square optimization. ORW obtained by fitting flow data of low interaction coefficient does not show non-physical oscillation and results in reasonably good behaviors at non-homogeneous flows as well as homogeneous flows. Fitting function forms have not been found to improve overall behaviors. It has been found that considering all the eigenvalues of orientation tensor (including the third eigenvalues) might end up with a better closure approximation than just considering the first and second eigenvalues. It is, however, very important and yet difficult to select appropriate function forms of eigenvalues. Numerical simulation including coupling and in-plane velocity gradient effects were performed for injection mold filing process with a film-gated strip and a center-gated disk using ORW and various other closure approximations for comparisons. Although ORW is in excellent agreement with 'Distribution Function Calculation', the predicted results seem to have consistent error in comparison with experimental data. The diffusivity term with constant interaction coefficient might have to be further investigated in order to accurately describe orientation states.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.97-105
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• 2020

Recently, Artificial Neural Network receives attention as a data prediction method. Among these, a Long Shot-term Memory (LSTM) model specialized for time-series data prediction was utilized as a prediction method of hydrological time series data. In this study, the LSTM model was constructed utilizing deep running open source library TensorFlow which provided by Google, to predict inflows of multipurpose dams. We predicted the inflow of the Yongdam Multipurpose Dam which is located in the upper stream of the Geumgang. The hourly flow data of Yongdam Dam from 2006 to 2018 provided by WAMIS was used as the analysis data. Predictive analysis was performed under various of variable condition in order to compare and analyze the prediction accuracy according to four learning parameters of the LSTM model. Root mean square error (RMSE), Mean absolute error (MAE) and Volume error (VE) were calculated and evaluated its accuracy through comparing the predicted and observed inflows. We found that all the models had lower accuracy at high inflow rate and hourly precipitation data (2006~2018) of Yongdam Dam utilized as additional input variables to solve this problem. When the data of rainfall and inflow were utilized together, it was found that the accuracy of the prediction for the high flow rate is improved.

• Journal of Korea Water Resources Association
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• v.53 no.4
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• pp.273-283
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• 2020

Quantitative forecasting of groundwater levels for the assessment of groundwater variation and vulnerability is very important. To achieve this purpose, various time series analysis and machine learning techniques have been used. In this study, we developed a prediction model based on LSTM (Long short term memory), one of the artificial neural network (ANN) algorithms, for predicting the daily groundwater level of 11 groundwater wells in Hankyung-myeon, Jeju Island. In general, the groundwater level in Jeju Island is highly autocorrelated with tides and reflected the effects of precipitation. In order to construct an input and output variables based on the characteristics of addressing data, the precipitation data of the corresponding period was added to the groundwater level data. The LSTM neural network was trained using the initial 365-day data showing the four seasons and the remaining data were used for verification to evaluate the fitness of the predictive model. The model was developed using Keras, a Python-based deep learning framework, and the NVIDIA CUDA architecture was implemented to enhance the learning speed. As a result of learning and verifying the groundwater level variation using the LSTM neural network, the coefficient of determination (R²) was 0.98 on average, indicating that the predictive model developed was very accurate.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.2
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• pp.39-57
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• 2018

The purpose of this study was to construct an artificial intelligence model that learns and estimates the relationship between vehicle queue length and link travel time in urban areas. The vehicle queue length estimation model is modeled by three models. First of all, classify whether vehicle queue is a link overflow and estimate the vehicle queue length in the link overflow and non-overflow situations. Deep learning model is implemented as Tensorflow. All models are based DNN structure, and network structure which shows minimum error after learning and testing is selected by diversifying hidden layer and node number. The accuracy of the vehicle queue link overflow classification model was 98%, and the error of the vehicle queue estimation model in case of non-overflow and overflow situation was less than 15% and less than 5%, respectively. The average error per link was about 12%. Compared with the detecting data-based method, the error was reduced by about 39%.