• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.17 no.2
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• pp.227-234
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• 2019

A user-friendly modeling interface is developed for a process-based total system performance assessment framework (APro) specialized for a generic geological disposal system for high-level radioactive waste. The APro modeling interface is constructed using MATLAB, and the operator splitting scheme is used to combine COMSOL for simulation of multiphysics and PHREEQC for the calculation of geochemical reactions. As APro limits the modeling domain to the generic disposal system, the degree of freedom of the model is low. In contrast, the user-friendliness of the model is improved. Thermal, hydraulic, mechanical and chemical processes considered in the disposal system are modularized, and users can select one of multiple modules: "Default process" and multi "Alternative process". APro mainly consists of an input data part and calculation execution part. The input data are prepared in a single EXCEL file with a given format, and the calculation part is coded using MATLAB. The final results of the calculation are created as an independent COMSOL file for further analysis.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.85-90
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• 2021

In this paper, fall detection using individual wearable devices for older people is considered. To design a low-cost wearable device for reliable fall detection, we present a comprehensive analysis of two representative models. One is a machine learning model composed of a decision tree, random forest, and Support Vector Machine(SVM). The other is a deep learning model relying on a one-dimensional(1D) Convolutional Neural Network(CNN). By considering data segmentation, preprocessing, and feature extraction methods applied to the input data, we also evaluate the considered models' validity. Simulation results verify the efficacy of the deep learning model showing improved overall performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.425-427
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• 2022

The grade of glioma is important information related to survival and thus is important to classify the grade of glioma before treatment to evaluate tumor progression and treatment planning. Glioma grading is mostly divided into high-grade glioma (HGG) and low-grade glioma (LGG). In this study, image preprocessing techniques are applied to analyze magnetic resonance imaging (MRI) using the deep neural network model. Classification performance of the deep neural network model is evaluated. The highest-performance EfficientNet-B6 model shows results of accuracy 0.9046, sensitivity 0.9570, specificity 0.7976, AUC 0.8702, and F1-Score 0.8152 in 5-fold cross-validation.

• Tunnel and Underground Space
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• v.33 no.5
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• pp.348-372
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• 2023

In relation to the high-level radioactive waste disposal project in deep fractured rock aquifer environments, it is essential to evaluate hydrogeological characteristics for evaluating the suitability of the site and operational stability. Such subsurface hydrogeological data is obtained through in-situ tests using boreholes excavated at the target site. The accuracy and reliability of the investigation results are directly related to the selection of appropriate test methods, the performance of the investigation system, standardization of the investigation procedure. In this report, we introduce the detailed procedures for the representative test method, the constant pressure injection test (CPIT), which is used to determine the key hydrogeological parameters of the subsurface fractured rock aquifer, namely hydraulic conductivity and storativity. This report further refines the standard test method suggested by the KSRM in 2022 and includes practical field application case conducted in volcanic rock aquifers where this investigation procedure has been applied.

• Tunnel and Underground Space
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• v.34 no.3
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• pp.231-247
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• 2024

In the field of high-level radioactive waste disposal targeting deep rock environments, hydraulic characteristic information serves as the most important key factor in selecting relevant disposal sites, detailed design of disposal facilities, derivation of optimal construction plans, and safety evaluation during operation. Since various rock types are mixed and distributed in a small area in Korea, it is important to conduct preliminary work to analyze the hydrogeological characteristics of rock aquifers for various rock types and compile the resulting data into a database. In this paper, we obtained hydraulic conductivity data, which is the most representative field hydraulic characteristic of a high-depth volcanic bedrock aquifer, and also analyzed and evaluated the field data. To acquire field data, we used a high-performance hydraulic testing system developed in-house and applied standardized test methods and investigation procedures. In the process of hydraulic characteristic data analysis, hydraulic conductivity values were obtained for each depth, and the pattern of groundwater flow through permeable rock joints located in the test section was also evaluated. It is expected that the series of data acquisition methods, procedures, and analysis results proposed in this report can be used to build a database of hydraulic characteristics data for high-depth rock aquifers in Korea. In addition, it is expected that it will play a role in improving technical know-how to be applied to research on hydraulic characteristic according to various bedrock types in the future.

• Food Science of Animal Resources
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• v.31 no.1
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• pp.92-99
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• 2011

This study was conducted to investigate the effect of a deep sea water (DSW) supplement on the quality characteristics of chicken meat. One-day-old broiler chicks (Ross 308) were assigned to three groups and supplemented with water (control) or DSW diluted with deionized water at 1:40 (DSW1:40) and 1:20 (DSW1:20) ratios, respectively, for 28 d. The control was fed a basal diet containing 0.18% salt. Five birds were slaughtered from each group, and the breast meat was collected and stored at $4^{\circ}C$ for 9 d. The DSW supplementation did not affect cholesterol content in the chicken meat. The DSW 1:40 supplement decreased fat content (p<0.05), water-holding capacity (p<0.05), and sodium and potassium contents (p<0.05) but increased unsaturated fatty acid content (p<0.05) and the $L^*$ value (p<0.05) of the meat. The DSW 1:20 supplement increased the $a^*$ value (p<0.05) but decreased thiobarbituric acid reactive substance inhibition, the $L^*$ value (p<0.05), and the $b^*$ value (p<0.05) in chicken meat. However, the DSW 1:20 supplement did not affect water-holding capacity, fatty acid composition, or mineral content. DSW supplementation at a higher concentration increased red color but decreased lipid oxidation stability. However, further studies are needed to support our findings.

• 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.

• The Korea Swine Journal
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• v.29 no.1 s.329
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• pp.218-219
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• 2007

• KCID journal
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• v.16 no.2
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• pp.68-72
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• 2009

• KAPE Magazine
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• s.159
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• pp.6-7
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• 2009