• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.10 no.1
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• pp.55-62
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• 2012

As an alternative of the high-level radioactive waste disposal in the subsurface repository, a deep borehole disposal is reviewed by several nuclear advanced countries. In this study, the state of the art on the borehole disposal researches was reviewed, and the possibility of borehole disposal in Korean peninsula was discussed. In the deep borehole disposal concept radioactive waste is disposed at the section of 3 - 5km depth in a deep borehole, and it has known that it has advantages in performance and cost due to the layered structure of deep groundwater and small surface disposal facility. The results show that it is necessary to acquisite data on deep geologic conditions of Korean peninsula, and to research the engineering barrier system, numerical modeling tools and disposal techniques for deep borehole disposal.

• Clinical Nutrition Research
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• v.12 no.2
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• pp.138-153
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• 2023

The prevalence of metabolic syndrome (MetS) and its cost are increasing due to lifestyle changes and aging. This study aimed to develop a deep neural network model for prediction and classification of MetS according to nutrient intake and other MetS-related factors. This study included 17,848 individuals aged 40-69 years from the Korea National Health and Nutrition Examination Survey (2013-2018). We set MetS (3-5 risk factors present) as the dependent variable and 52 MetS-related factors and nutrient intake variables as independent variables in a regression analysis. The analysis compared and analyzed model accuracy, precision and recall by conventional logistic regression, machine learning-based logistic regression and deep learning. The accuracy of train data was 81.2089, and the accuracy of test data was 81.1485 in a MetS classification and prediction model developed in this study. These accuracies were higher than those obtained by conventional logistic regression or machine learning-based logistic regression. Precision, recall, and F1-score also showed the high accuracy in the deep learning model. Blood alanine aminotransferase (β = 12.2035) level showed the highest regression coefficient followed by blood aspartate aminotransferase (β = 11.771) level, waist circumference (β = 10.8555), body mass index (β = 10.3842), and blood glycated hemoglobin (β = 10.1802) level. Fats (cholesterol [β = -2.0545] and saturated fatty acid [β = -2.0483]) showed high regression coefficients among nutrient intakes. The deep learning model for classification and prediction on MetS showed a higher accuracy than conventional logistic regression or machine learning-based logistic regression.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.1019-1029
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• 2020

In this paper, we propose a novel knowledge distillation algorithm to create an compressed deep ensemble network coupled with the combined use of local and global features of face images. In order to transfer the capability of high-level recognition performances of the ensemble deep networks to a single deep network, the probability for class prediction, which is the softmax output of the ensemble network, is used as soft target for training a single deep network. By applying the knowledge distillation algorithm, the local feature informations obtained by training the deep ensemble network using facial subregions of the face image as input are transmitted to a single deep network to create a so-called compressed ensemble DCNN. The experimental results demonstrate that our proposed compressed ensemble deep network can maintain the recognition performance of the complex ensemble deep networks and is superior to the recognition performance of a single deep network. In addition, our proposed method can significantly reduce the storage(memory) space and execution time, compared to the conventional ensemble deep networks developed for face recognition.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.435-450
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• 2022

In step-by-step site selection for geological disposal of high-level radioactive waste, parameters necessary for site selection will be acquired through deep drilling surveys from the basic survey stage. Unlike site investigations of rock mass structures such as tunnels and underground oil storage facilities, those related to the geological disposal of high-level radioactive waste are not only conducted in relatively deep depths, but also require a high level of quality control. In this report, based on the 750 m depth drilling experience conducted to acquire the parameters necessary for deep geological disposal, the methodology for deep drilling and the geology, geophysics, geochemistry, hydrogeology and rock mechanics obtained before, during, and after deep drilling are discussed. The procedures for multidisciplinary geoscientific investigations were briefly described. Regarding in-situ stress, one of the key evaluation parameter in the field of rock engineering, foreign and domestic cases related to the geological disposal of high-level radioactive waste were presented, and variations with depth were presented, and matters to be considered or agonized in acquiring evaluation parameters were mentioned.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.373-376
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• 2021

Deep neural networks have achieved almost human-level results in various tasks and have become popular in the broad artificial intelligence domains. Uncertainty estimation is an on-demand task caused by the black-box point estimation behavior of deep learning. The deep ensemble provides increased accuracy and estimated uncertainty; however, linearly increasing the size makes the deep ensemble unfeasible for memory-intensive tasks. To address this problem, we used model pruning and quantization with a deep ensemble and analyzed the effect in the context of uncertainty metrics. We empirically showed that the ensemble members' disagreement increases with pruning, making models sparser by zeroing irrelevant parameters. Increased disagreement implies increased uncertainty, which helps in making more robust predictions. Accordingly, an energy-efficient compressed deep ensemble is appropriate for memory-intensive and uncertainty-aware tasks.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2709-2716
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• 2020

Compositions of large nuclear cores (e.g. boiling water reactors) are highly heterogeneous in terms of fuel composition, control rod insertions and flow regimes. For this reason, they usually lack high order of symmetry (e.g. 1/4, 1/8) making it difficult to estimate their neutronic parameters for large spaces of possible loading patterns. A detailed hyperparameter optimization technique (a combination of manual and Gaussian process search) is used to train and optimize deep neural networks for the prediction of three neutronic parameters for the Ringhals-1 BWR unit: power peaking factors (PPF), control rod bank level, and cycle length. Simulation data is generated based on half-symmetry using PARCS core simulator by shuffling a total of 196 assemblies. The results demonstrate a promising performance by the deep networks as acceptable mean absolute error values are found for the global maximum PPF (~0.2) and for the radially and axially averaged PPF (~0.05). The mean difference between targets and predictions for the control rod level is about 5% insertion depth. Lastly, cycle length labels are predicted with 82% accuracy. The results also demonstrate that 10,000 samples are adequate to capture about 80% of the high-dimensional space, with minor improvements found for larger number of samples. The promising findings of this work prove the ability of deep neural networks to resolve high dimensionality issues of large cores in the nuclear area.

• Structural Engineering and Mechanics
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• v.91 no.5
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• pp.443-457
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• 2024

At present, the traditional concrete surface inspection methods based on artificial vision have the problems of high cost and insecurity, while the computer vision methods rely on artificial selection features in the case of sensitive environmental changes and difficult promotion. In order to solve these problems, this paper introduces deep learning technology in the field of computer vision to achieve automatic feature extraction of structural damage, with excellent detection speed and strong generalization ability. The main contents of this study are as follows: (1) A method based on DeepLabV3+ convolutional neural network model is proposed for surface detection of post-earthquake structural damage, including surface damage such as concrete cracks, spaling and exposed steel bars. The key semantic information is extracted by different backbone networks, and the data sets containing various surface damage are trained, tested and evaluated. The intersection ratios of 54.4%, 44.2%, and 89.9% in the test set demonstrate the network's capability to accurately identify different types of structural surface damages in pixel-level segmentation, highlighting its effectiveness in varied testing scenarios. (2) A semantic segmentation model based on DeepLabV3+ convolutional neural network is proposed for the detection and evaluation of post-earthquake structural components. Using a dataset that includes building structural components and their damage degrees for training, testing, and evaluation, semantic segmentation detection accuracies were recorded at 98.5% and 56.9%. To provide a comprehensive assessment that considers both false positives and false negatives, the Mean Intersection over Union (Mean IoU) was employed as the primary evaluation metric. This choice ensures that the network's performance in detecting and evaluating pixel-level damage in post-earthquake structural components is evaluated uniformly across all experiments. By incorporating deep learning technology, this study not only offers an innovative solution for accurately identifying post-earthquake damage in civil engineering structures but also contributes significantly to empirical research in automated detection and evaluation within the field of structural health monitoring.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.1
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• pp.15-26
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• 2023

This study aimed to evaluate the performance of water level classification from CCTV images in agricultural facilities such as reservoirs. Recently, the CCTV system, widely used for facility monitor or disaster detection, can automatically detect and identify people and objects from the images by developing new technologies such as a deep learning system. Accordingly, we applied the ResNet-50 deep learning system based on Convolutional Neural Network and analyzed the water level of the agricultural reservoir from CCTV images obtained from TOMS (Total Operation Management System) of the Korea Rural Community Corporation. As a result, the accuracy of water level detection was improved by excluding night and rainfall CCTV images and applying measures. For example, the error rate significantly decreased from 24.39 % to 1.43 % in the Bakseok reservoir. We believe that the utilization of CCTVs should be further improved when calculating the amount of water supply and establishing a supply plan according to the integrated water management policy.

• East Asian Economic Review
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• v.16 no.4
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• pp.363-394
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• 2012

WTO Government Procurement Agreement (GPA) was designed to liberalize and expand trade in government procurement. Revised GPA was implemented in 1996 and the latest revision was completed (but not yet implemented) in 2012, but as a plurilateral agreement. Since the end of the UR, there has been attempts by various WTO members to liberalize trade in the government procurement market - through an expansion of Parties who are signatories to GPA, and through a negotiated agreement on transparency in government procurement. The attempt to expand the Parties who are signatories to the GPA - attempt to increase the width of the coverage of the agreement - has been somewhat successful, but I argue that the goal should be to further liberate the government procurement markets of the current Party members - to reduce thresholds and other barriers which limit market access even to other GPA members, in other words, to increase the depth of coverage. Taking cue from Korea's FTA, I propose a two-level liberalization of the government procurement market under the GPA: A "light" level which would be the same as the current level of liberalization; and a "deep" level with lower thresholds and less exemptions. I argue that, as seen in Korea, with FTAs, many GPA Parties already have multiple levels of liberalization (i.e, spaghetti-bowl effect of FTAs), but by limiting the levels of liberalization to two, we can seek the best of deep liberalization but reduce the spaghetti-bowl effect.

• Journal of Korean Biological Nursing Science
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• v.23 no.2
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• pp.151-158
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• 2021

Purpose: The purpose of this study was to investigate sleep quality in older adults in nursing home with objective data collection. Methods: Participants included 74 older adults in nursing homes in Korea aged 65 years or above. Data were collected using a wearable device (Fitbit), salivary melatonin level and Sleep Disorder Inventory (SDI). The Pearson correlation coefficient was calculated to examine whether there was any correlation between sleep-related variables such as Total Sleep Time (TST), Rapid Eye Movement (REM) sleep, shallow sleep, deep sleep, salivary melatonin level and SDI. Results: There were distortion of sleep structure, as TST comprised short REM sleep (15.93±5.47%), long shallow sleep (74.18±8.08%) and short deep sleep (9.89±5.03%). Also, salivary melatonin levels were low (15.06±7.77 pg/mL). Moreover, we found than melatonin was significantly associated with TST (r = .251, p= .044), REM sleep (r= .294, p= .020) and deep sleep (r= .391, p= .002). But there was no correlation between SDI and other sleeprelated variables. Conclusion: These findings highlight that insufficient sleep structure is associated with the salivary melatonin level among older adults in nursing home. We suggest developing programs to promote sleep quality of older adults in nursing homes.