• Food Engineering Progress
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• v.21 no.1
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• pp.79-87
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• 2017

This study was carried out to extract ginsenosides in by-products from honeyed red ginseng. Response surface methodology (RSM) was used to optimize the extraction conditions. Based on D-optimal design, independent variables were ethanol (extraction solvent) concentration (30-90%, v/v), extraction temperature ($25-70^{\circ}C$), and extraction time (5-11 h). Extraction yield (Y1) and total ginsenosides (Y2) in the extract were analyzed as dependent variables. Results found that extraction yield increased with increasing extraction temperature and time, whereas it was decreased with increasing ethanol concentration. Similar trends were found for the content of ginsenosides in the extracts, except for ethanol concentration, which was increased with increasing ethanol concentration. Regression equations derived from RSM were suggested to coincide well with the results from the experiments. The optimal extraction conditions for extraction yield and total ginsenosides were an extraction temperature of $56.94^{\circ}C$, ethanol concentration of 57.90%, and extraction time of 11 h. Under these conditions, extraction yield and total ginsenoside contents were predicted to be 84.52% and 9.54 mg/g, respectively.

• Korean Journal of Optics and Photonics
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• v.30 no.4
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• pp.142-145
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• 2019

Normally, a corner cube retroreflector (CCRR) sheet is used as a retroreflective mirror array (RRMA) in a volumetric display. Each CCRR unit reflects light in the retroreflective direction, which is parallel to the incident light, and it makes a blurred image, as it shifts the position of light within its dimensions. Adopting a "curved planar wall" and "parabolic focusing" (x-axis), a hybrid-t(transverse direction)-RRMA is proposed, to improve the image quality and brightness. The improvement of image contrast is achieved by tuning a "linear v-shaped groove" structure to a "parabolic v-shaped groove". Also, the system has been simplified and the brightness enhanced 4 times by removing the half mirror.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.5
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• pp.64-74
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• 2019

Although a non-rigid registration has high demands in clinical practice, it has a high computational complexity and it is very difficult for ensuring the accuracy and robustness of registration. This study proposes a method of applying a non-rigid registration to 3D magnetic resonance images of brain in an unsupervised learning environment by using a deep-learning network. A feature vector between two images is produced through the network by receiving both images from two different patients as inputs and it transforms the target image to match the source image by creating a displacement vector field. The network is designed based on a U-Net shape so that feature vectors that consider all global and local differences between two images can be constructed when performing the registration. As a regularization term is added to a loss function, a transformation result similar to that of a real brain movement can be obtained after the application of trilinear interpolation. This method enables a non-rigid registration with a single-pass deformation by only receiving two arbitrary images as inputs through an unsupervised learning. Therefore, it can perform faster than other non-learning-based registration methods that require iterative optimization processes. Our experiment was performed with 3D magnetic resonance images of 50 human brains, and the measurement result of the dice similarity coefficient confirmed an approximately 16% similarity improvement by using our method after the registration. It also showed a similar performance compared with the non-learning-based method, with about 10,000 times speed increase. The proposed method can be used for non-rigid registration of various kinds of medical image data.

• Geophysics and Geophysical Exploration
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• v.24 no.2
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• pp.53-66
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• 2021

Velocity model building is an essential procedure in seismic data processing. Conventional techniques, such as traveltime tomography or velocity analysis take longer computational time to predict a single velocity model and the quality of the inversion results is highly dependent on human expertise. Full-waveform inversions also depend on an accurate initial model. Recently, deep neural network techniques are gaining widespread acceptance due to an increase in their integration to solving complex and nonlinear problems. This study investigated cases of seismic velocity model building using deep neural network techniques by classifying items according to the neural networks used in each study. We also included cases of generating training synthetic velocity models. Deep neural networks automatically optimize model parameters by training neural networks from large amounts of data. Thus, less human interaction is involved in the quality of the inversion results compared to that of conventional techniques and the computational cost of predicting a single velocity model after training is negligible. Additionally, unlike full-waveform inversions, the initial velocity model is not required. Several studies have demonstrated that deep neural network techniques achieve outstanding performance not only in computational cost but also in inversion results. Based on the research results, we analyzed and discussed the characteristics of deep neural network techniques for building velocity models.

• Clean Technology
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• v.28 no.3
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• pp.249-257
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• 2022

The adsorption equilibria of nitrogen on a region of nanoporous carbonaceous adsorbent with local molecular orientation (LMO) were calculated by grand canonical Monte Carlo simulation at 77.16 K. Regions of LMO of identical size were arranged on a regular lattice with uniform spacing. Microporosity was predominately introduced to the model by removing successive out-of-plane domains from the regions of LMO and tilting pores were generated by tilting the basic structure units. This pore structure is a more realistic model than slit-shaped pores for studying adsorption in nanoporous carbon adsorbents. Their porosities, surface areas, and pore size distributions according to constrained nonlinear optimization were also reported. The adsorption in slit shaped pores was also reported for reference. In the slit shaped pores, a clear hysteresis loop was observed in pores of greater than 5 times the nitrogen molecule size, and in capillary condensation and reverse condensation, evaporation occurred immediately at one pressure. In the LMO pore model, three series of local condensations at the basal slip plane, armchair slip plane and interconnected channel were observed during adsorption at pore sizes greater than about 6 times the nitrogen molecular size. In the hysteresis loop, on the other hand, evaporation occurred at one or two pressures during desorption.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.6
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• pp.367-374
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• 2022

This papter presents the use of the automatic differential method based on the backpropagation method to obtain the design sensitivity and its application to topology optimization considering the stress constraints. Solving topology optimization problems with stress constraints is difficult owing to singularities, the local nature of stress constraints, and nonlinearity with respect to design variables. To solve the singularity problem, the stress relaxation technique is used, and p-norm for stress constraints is applied instead of local stresses for global stress measures. To overcome the nonlinearity of the design variables in stress constraint problems, it is important to analytically obtain the exact design sensitivity. In conventional topology optimization, design sensitivity is obtained efficiently and accurately using the adjoint variable method; however, obtaining the design sensitivity analytically and additionally solving the adjoint equation is difficult. To address this problem, the design sensitivity is obtained using a backpropagation technique that is used to determine optimal weights and biases in the artificial neural network, and it is applied to the topology optimization with the stress constraints. The backpropagation technique is used in automatic differentiation and can simplify the calculation of the design sensitivity for the objectives or constraint functions without complicated analytical derivations. In addition, the backpropagation process is more computationally efficient than solving adjoint equations in sensitivity calculations.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.103-112
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• 2009

In a nuclear power plant (NPP), periodic sensor calibrations are required to assure sensors are operating correctly. However, only a few faulty sensors are found to be calibrated. For the safe operation of an NPP and the reduction of unnecessary calibration, on-line calibration monitoring is needed. In this paper, principal component-based Auto-Associative support vector regression (PCSVR) was proposed for the sensor signal validation of the NPP. It utilizes the attractive merits of principal component analysis (PCA) for extracting predominant feature vectors and AASVR because it easily represents complicated processes that are difficult to model with analytical and mechanistic models. With the use of real plant startup data from the Kori Nuclear Power Plant Unit 3, SVR hyperparameters were optimized by the response surface methodology (RSM). Moreover the statistical techniques are integrated with PCSVR for the failure detection. The residuals between the estimated signals and the measured signals are tested by the Shewhart Control Chart, Exponentially Weighted Moving Average (EWMA), Cumulative Sum (CUSUM) and generalized likelihood ratio test (GLRT) to detect whether the sensors are failed or not. This study shows the GLRT can be a candidate for the detection of sensor drift.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.157-164
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• 2023

This paper suggests O(n²) polynomial time heuristic algorithm for corporate tax structure optimization problem that has been classified as NP-complete problem. The proposed algorithm constructs tax tree levels that the target holding company is located at root node of Level 1, and the tax code categories(T_e) 1,4,3,2 are located in each level 2,3,4,5 sequentially. To find the maximum tax-relief path from source(S) to target(T), firstly we connect the minimum witholding tax rate _minr_w(u, v) arc of node u point of view for transfer the profit from u to v node. As a result we construct the spanning tree from all of the source nodes to a target node, and find the initial feasible solution. Nextly, we find the alternate path with minimum foreign tax rate _minr_fi(u, v) of v point of view. Finally we choose the minimum tax-relief path from of this two paths. The proposed heuristic algorithm performs better optimal results than linear programming and Tabu search method that is a kind of metaheuristic method.

• Journal of Technology Innovation
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• v.30 no.1
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• pp.57-104
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• 2022

The government has supported the innovation of private firms by intervening the market for various purposes, such as preventing market failure, alleviating information asymmetry, and allocating resources efficiently. Although the government's R&D budget increased rapidly in the 2000s, it is not clear whether the government intervention has made desirable impact on the market. To address this, the current study attempts to explore this issue by doing a systematic literature review on foreign and domestic papers in an integrated way. In total, 168 studies are analyzed using contents analysis approach and various lens, such as policy additionality, policy tools, firm size, unit of analysis, data and method, are adopted for analysis. Overlapping policy target, time lag between government intervention and policy effects, non-linearity of financial supports, interference between different polices, and out-dated R&D tax incentive system are reported as factors hampering the effect of the government intervention. Many policy prescriptions, such as program evaluation indices reflecting behavioral additionality, an introduction of policy mix and evidence-based policy using machine learning, are suggested to improve these hurdles.

• International Area Studies Review
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• v.15 no.1
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• pp.173-193
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• 2011

Most of the research regarding economic effects of policy loans has thus far been focused on whether policy loans can improve the financial status or the management performance of small and medium enterprises (SMEs). Unlike previous researches, this study implemented an empirical analysis focused on the contribution of policy loans to easing the liquidity restriction of investment. To analyze whether investment liquidity restriction can be alleviated or not, this study attempted an empirical analysis utilizing the nonlinear Euler equation induced through optimization of investment and GMM (generalized method of moments) as its analysis methodology. With the SMEs that received policy financing from the Small and medium Business Corporation (SBC) in 2004, this study analyzed three years of panel data before(2001~2003) and after(2004~2006) receipt of policy loans. According to the empirical results, it appears that policy loans had effects on resolving liquidity restriction of investment, implying that policy financing eases the liquidity restriction of SME investment and would contribute to the growth and development of SMEs. Further, I checked robustness of empirical results using Tobin's q model. The empirical results also support that policy loans help to resolve liquidity constraint. With these results, it is understood that the critical view to date, which has emphasized the ineffectiveness of policy financing due to it having no or insignificant economic effects, may be wrong.