• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.555-564
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• 2021

Thalamus is known to play an important role in the regulation of nerve function. Thalamus, located in the center of the brain, is involved in sleep, arousal, and emotional regulation, and has been reported to be associated with multiple sclerosis, essential tremors, and neurodegenerative diseases such as Parkinson's disease. In addition, it has been reported that iron deposits in the thalamus can cause depressive symptoms with age. Although there are discrepancies between studies, it can be deduced that the thalamus region has a clear effect on neurological disorders due to a strong relationship between the thalamus and neurological functions such as emotional control and processing. Through tractography analysis, the connectivity between the detailed areas of each subcortical region was investigated in the form of a matrix, showing strong connectivity and weak interhemispheric connectivity. In the 59> group, the WM connectivity of thalamus was found to be weaker than those of the two groups. Comparisons between the two groups showed that the young groups (10-39 and 40-59) had higher connection intensity than the 59> group and that statistically significant differences in 3 connection pathways were found in each hemisphere. A decrease in thalamus-related connection strength in aging has shown that it can affect emotional and neurological disorders such as anxiety and depression, and network measurements can help assess cognitive impairment across clinical conditions.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.547-554
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• 2021

In order to quickly and accurately diagnose pneumonia on a chest X-ray image, different batch sizes of 4, 8, 16, and 32 were applied to the same Xception deep learning model, and modeling was performed 3 times, respectively. As a result of the performance evaluation of deep learning modeling, in the case of modeling to which batch size 32 was applied, the results of accuracy, loss function value, mean square error, and learning time per epoch showed the best results. And in the accuracy evaluation of the Test Metric, the modeling applied with batch size 8 showed the best results, and the precision evaluation showed excellent results in all batch sizes. In the recall evaluation, modeling applied with batch size 16 showed the best results, and for F1-score, modeling applied with batch size 16 showed the best results. And the AUC score evaluation was the same for all batch sizes. Based on these results, deep learning modeling with batch size 32 showed high accuracy, stable artificial neural network learning, and excellent speed. It is thought that accurate and rapid lesion detection will be possible if a batch size of 32 is applied in an automatic diagnosis study for feature extraction and classification of pneumonia in chest X-ray images using deep learning in the future.

• Tunnel and Underground Space
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• v.31 no.4
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• pp.270-288
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• 2021

We proposed a numerical method to simulate the hydro-mechanical behavior of rock fracture using a grain-based distinct element model (GBDEM) in the paper. As a part of DECOVALEX-2023 Task G, we verified the method via benchmarks with analytical solutions. DECOVALEX-2023 Task G aims to develop a numerical method to estimate the coupled thermo-hydro-mechanical processes within the crystalline rock fracture network. We represented the rock sample as a group of tetrahedral grains and calculated the interaction of the grains and their interfaces using 3DEC. The micro-parameters of the grains and interfaces were determined by a new methodology based on an equivalent continuum approach. In benchmark modeling, a single fracture embedded in the rock was examined for the effects of fracture inclination and roughness, the boundary stress condition and the applied pressure. The simulation results showed that the developed numerical model reasonably reproduced the fracture slip induced by boundary stress condition, the fracture opening induced by fluid injection, the stress distribution variation with fracture inclination, and the fracture roughness effect. In addition, the fracture displacements associated with the opening and slip showed good agreement with the analytical solutions. We expect the numerical model to be enhanced by continuing collaboration and interaction with other research teams of DECOVALEX-2023 Task G and validated in further study experiments.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.699-706
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• 2021

This study aims to identify the effects of factors on aggression in adolescents, focusing on the individual, parent, friend factors and SNS usage. In particular, this study is to provide a basis for easing aggression in adolescence by considering the emotional relationship of parents and friends. This study analyzed frequency, t-test, one-way batch distribution analysis(ANOVA), and multi-linear regression, using the data from the 7th year of the Korean Children and Youth Panel Survey. As a result, adolescents who frequently use SNS are more aggressive than adolescents who use less. Among the parental factors, the more abuse and excessive interference were found to be more aggressive, and the higher the coach, the lower the aggressiveness. Furthermore, among the friend factors, it has been shown that the higher the alienation, the more aggressive adolescents are. In order to reduce aggression among adolescents, it is necessary to prepare an integrated program considering the emotional relationship of parents and friends, who are the most influential neighbors, rather than simply restricting the use of SNS.

• The Journal of the Korea Contents Association
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• v.21 no.8
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• pp.376-387
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• 2021

This study is to analyze the research trends related to the 'glass ceiling' phenomenon using big data analysis methods and to suggest social implications. To analyze the research trends of 'glass ceiling', the historical event that broke the 'glass ceiling' was set as an important issue, and keywords were collected by dividing park's term into three. Before, throughout and after, her term. As a result of frequency analysis, research was conducted based on 'public servants' which was selected as the main keyword in the first period, while 'women's work family compatibility' was chosen as the main keyword group in the second period. In the third period, keywords for women's occupational groups were being diversified. As a result of applying CONCOR techniques to make the studied main topics grouped, we were able to confirm that the main issues were the differentiating factors, the customary gender discrimination culture, the jobs aimed for studying, the work-family balance, the glass ceiling and the organizational performance adjustment factors, the public sector, organizational performance, and the private sector. Besides work-family compatibility support system, it was suggested as a social implication that research on improving the system to resolve the glass ceiling factor and to expand the target jobs to give solutions to real-life issues were needed, and also suggested that research on the 'glass ceiling' which the general public perceives through social medias or articles in the news, was needed in the future.

• Journal of the Korea Society of Computer and Information
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• v.26 no.6
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• pp.155-166
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• 2021

The purpose of this study is to investigate whether depression and social support have a multiple mediating effect in affecting the life satisfaction of the elderly with public and private transfer income. To this end, the 7th panel data (2017) among the data of the Korean Retirement & Income Study (KReLS) was used for analysis, and the analysis was conducted using structural equation modeling (SEM). As a result of the analysis, first, it was analyzed that the higher the public transfer income of the elderly, the lower the level of depression, the higher the social support, and the higher the satisfaction of life. Second, the partial mediating effect of depression was verified in the influence of the elderly's public transfer income and private transfer income on life satisfaction. Third, the partial mediating effect of social support was verified in the influence of the elderly's public transfer income and private transfer income on life satisfaction. Fourth, it was verified that the multiple mediating effects of depression and social support were significant in the effect of the elderly's public and private transfer income on life satisfaction. Based on the results of this analysis, policy proposals were made, such as revitalizing the Community Care program to strengthen the social support network of the elderly.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.59-66
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• 2021

Humans mainly recognize surrounding objects using visual and auditory information among the five senses (sight, hearing, smell, touch, taste). Major research related to the latest object recognition mainly focuses on analysis using image sensor information. In this paper, after emitting various chirp audio signals into the observation space, collecting echoes through a 2-channel receiving sensor, converting them into spectral images, an object recognition experiment in 3D space was conducted using an image learning algorithm based on deep learning. Through this experiment, the experiment was conducted in a situation where there is noise and echo generated in a general indoor environment, not in the ideal condition of an anechoic room, and the object recognition through echo was able to estimate the position of the object with 83% accuracy. In addition, it was possible to obtain visual information through sound through learning of 3D sound by mapping the inference result to the observation space and the 3D sound spatial signal and outputting it as sound. This means that the use of various echo information along with image information is required for object recognition research, and it is thought that this technology can be used for augmented reality through 3D sound.

• Journal of the Korean Geotechnical Society
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• v.37 no.5
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• pp.47-63
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• 2021

Recently, the demand for three-dimensional (3D) underground maps from the perspective of digital twins and the demand for linkage utilization are increasing. However, the vastness of national geotechnical survey data and the uncertainty in applying geostatistical techniques pose challenges in modeling underground regional geotechnical characteristics. In this study, an optimal learning model based on multi-layer perceptron (MLP) was constructed for 3D subsurface lithological and geotechnical classification in Seoul, South Korea. First, the geotechnical layer and 3D spatial coordinates of each borehole dataset in the Seoul area were constructed as a geotechnical database according to a standardized format, and data pre-processing such as correction and normalization of missing values for machine learning was performed. An optimal fitting model was designed through hyperparameter optimization of the MLP model and model performance evaluation, such as precision and accuracy tests. Then, a 3D grid network locally assigning geotechnical layer classification was constructed by applying an MLP-based bet-fitting model for each unit lattice. The constructed 3D geotechnical layer map was evaluated by comparing the results of a geostatistical interpolation technique and the topsoil properties of the geological map.

• The Journal of the Korea Contents Association
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• v.21 no.5
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• pp.149-156
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• 2021

This study started from the question of what we think about big data as the term "big data" was used and discussed in our daily lives in the era of the 4th industrial revolution. For the analysis, the final 30 Q samples were selected based on prior research related to big data, and 23 respondents were secured for Q analysis, and the following results were derived. First, the explanatory power of each type was 34.30% for , 8.03% for , 7.21% for , and 6.24% for , showing a total of 55.69%. Second, the Q sample emphasized by respondents by each type shows various occupational distributions in , and for 'big data', it is 'digital' and future'. So they were named 「Digital Type」. In , the distribution of 'social workers' was high, and for 'big data', 'future', 'collaboration', 'welfare', 'local residents', and 'defense' were emphasized. It was named 「welfare type」. In , the job distribution of respondents appeared evenly, and it was named as 「Convergence Type」. Because it emphasized statements such as 'convergence', 'digital', 'future', and 'sports'. is composed of association officials, sports instructors, and graduate students, and was named 「Artificial Intelligence Type」, because it emphasizes 'artificial intelligence', 'new paradigm', 'network', and 'sports'. In the age of knowledge industrialization and knowledge informatization that followed industrialization and informatization, how to process and utilize the numerous data accumulated over the years is an important task. Right now, in sports, more than anything else, it is necessary to continuously seek ways to utilize and activate accumulated big data.

• Journal of Technology Innovation
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• v.29 no.1
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• pp.1-38
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• 2021

In this paper, an exploratory analysis study was conducted on establishing a strategy to utilize living labs to enhance the innovation of the energy sector. Through the previous research literature, it was possible to confirm the concept, essential components, innovation characteristics of living labs, and types of innovation issues in the energy sector as the theoretical background. Based on this, the case studies of energy living lab (8 overseas, 1 domestic) were analyzed focusing on the possibility of utilizing living lab as an approach to innovation issues in the energy sector, establishing a customized strategy for essential components of living lab and enhancing innovation. It was confirmed that the establishment of a customized strategy for the essential components of the living lab could be a driving force in enhancing innovation, and the Living Lab is effectively used as an approach method for innovation issues(demand management, supply technology, enhance R&D acceptance and promote commercialization, technology policies) in the energy sector. As a result of the case studies, the driving force of each living lab was derived from the viewpoint of contributing to innovation, and strategies for using the living labs for each energy innovation problem were established. This study is an exploratory and descriptive analytical study of the utilization strategy and value of the living lab model as an approach to innovation issues in the energy field, which can provide a living lab strategy framework that has not been tried in the past and enables living lab activation and network formation. It can also be considered to have academic, practical, and policy implications in that it can also contribute.