• Journal of the Korea Computer Graphics Society
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• v.28 no.2
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• pp.11-19
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• 2022

Recently, various researches on medical image generation have been suggested, and it becomes crucial to accurately evaluate the quality and diversity of the generated medical images. For this purpose, the expert's visual turing test, feature distribution visualization, and quantitative evaluation through IS and FID are evaluated. However, there are few methods for quantitatively evaluating medical images in terms of fidelity and diversity. In this paper, images are generated by learning a chest CT dataset of non-small cell lung cancer patients through DCGAN and PGGAN generative models, and the performance of the two generative models are evaluated in terms of fidelity and diversity. The performance is quantitatively evaluated through IS and FID, which are one-dimensional score-based evaluation methods, and Precision and Recall, Improved Precision and Recall, which are two-dimensional score-based evaluation methods, and the characteristics and limitations of each evaluation method are also analyzed in medical imaging.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.25-31
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• 2023

The brain-machine interface(BMI) is a next-generation interface that controls the device by decoding brain waves(also called Electroencephalogram, EEG), EEG is a electrical signal of nerve cell generated when the BMI user thinks of a command. The brain-machine interface can be applied to various smart devices, but complex computational process is required to decode the brain wave signal. Therefore, it is difficult to implement a brain-machine interface in an embedded system implemented in the form of an edge device. In this study, we proposed a new type of brain-machine interface system using IoT technology that only measures EEG at the edge device and stores and analyzes EEG data in the cloud computing. This system successfully performed quantitative EEG analysis for the brain-machine interface, and the whole data transmission time also showed a capable level of real-time processing.

• Korean Journal of Medicinal Crop Science
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• v.28 no.6
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• pp.395-411
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• 2020

Background: This study investigated the anti-obesity effect of the flavonoid rich fraction (FRF) and its constituent, rutin obtained from the leaf of Morus alba L., on the lipid accumulation mechanism in 3T3-L1 adipocyte and C57BL/6 mouse models. Methods and Results: In Oil Red O staining, FRF (1,000 ㎍/㎖) treatments showed inhibition rate of 35.39% in lipid accumulation compared to that in the control. AdipoRedTM assay indicated that the triglyceride content in 3T3-L1 adipocytes treated with FRF (1,000 ㎍/㎖) was reduced to 23.22%, and free glycerol content was increased to 106.04% that of the control. FRF and its major constituent, rutin affected mRNA gene expression. Rutin contributed to the inhibition of Sterol regulatory element binding protein-1c (SREBP-1c) gene expression, and inhibited the transcription factors SREBP-1c, peroxisome proliferator-activated receptor gamma (PPAR-γ), CCAAT/enhancer binding protein α (C/EBPα), fatty acid synthase (FAS) and acetyl-CoA carboxylase (ACC). In addition, the effect of FRF administration on obesity development in C57BL/6 mice fed high-fat diet (HFD) was investigated. FRF suppressed weight gain, and reduced liver triglyceride and leptin secretion. FRF exerted potential anti-inflammatory effects by improving insulin resistance and adiponectin levels, and could thus be used to help counteract obesity. The mRNA expressions of PPAR-γ, FAS, ACC, and CPT-1 were determined in liver tissue. Quantitative real-time PCR analysis was also performed to evaluate the expression of IL-1β, IL-6, and TNF-α in epididymal adipose tissue. Compared to the control group, mice fed the HFD showed the up-regulation in PPAR-γ, FAS, IL-6, and TNF-α genes, and down-regulation in CPT1 gene expression. FRF treatement markedly reduced the expression of PPAR-γ, FAS, IL-6, and TNF-α compared to those in HFD control, whereas increased the expression level of CPT1. Conclusions: These results suggest that the FRF and its major active constituent, rutin, can be used as effective anti-obesity agents.

• Journal of Korea Entertainment Industry Association
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• v.14 no.7
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• pp.389-395
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• 2020

This discussion is a study that proposes to introduce a strategy to positively interpret the narrative meaning in media language based on a generalized cognitive environment in order to activate correct media literacy in the era of cultural convergence. have. In particular, by using advertising content that has the most reinforced creativity related to cultural interpretation, it was induced to grasp the informational manifestation of that era and to reproduce meaning interpretation with relevance. In addition, it attempted to utilize an argumentative writing strategy in the process of reproducing Korean language learners' writing, which was capable of cognitive interpretation. The intention of the public service advertisement content developer always expects a positive effect in the social and cultural aspect, and the learner dreams of reflection and a correct future through the effect. The research on activating media literacy in the era of cultural convergence, which I intended, has not yet been much discussed. We hope that the proposed discussion of this study will be actively utilized in mass media language education of the contents of textbooks for Korean language learners, and we are sorry for the part that does not contain quantitative analysis contents, and we expect the results in subsequent thesis.

• Journal of the Korea Convergence Society
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• v.12 no.6
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• pp.225-232
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• 2021

The purpose of this study is to identify the psychological characteristics of the elderly and to investigate the variables that affect the life satisfaction of the elderly. To achieve this purpose, psychological characteristics such as self-esteem, alienation, loss, and depression were selected. A survey was conducted on the elderly living in Gwangju Metropolitan City. A total of 218 copies were collected and 203 copies were analyzed, excluding poor responses. To draw the results of the research, quantitative analysis was conducted through questionnaires, and SPSSWIN 21.0 statistical program was used as an analysis tool. The analysis methods were frequency and ratio analysis, technical statistical analysis, bivariate correlation analysis, and linear regression analysis. The results of the analysis are as follows: First, the relationship between the psychological characteristics of the elderly showed significant correlations among the self-esteem, alienation, loss, depression, and life satisfaction of the elderly. Second, for the elderly, alienation and depression had a negative effect on life satisfaction, and self-esteem had a positive effect on life satisfaction. However, loss did not have a significant effect on life satisfaction. In conclusion, it suggests that it is necessary to develop and operate policies and programs to improve self-esteem and reduce alienation and depression in order to improve the quality of life of the elderly.

• Journal of Digital Convergence
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• v.15 no.7
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• pp.479-487
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• 2017

The purpose of this study was to identify the most influential soccer players by appling social network analysis. The subjects were the German national soccer team and the Korean national soccer team participated in the 2016 Brazil World Cup. The pass collected data provided by FIFA were analyzed by social network analysis using the Ucinet6 program and pass success rate. The results are as follows. First, the soccer player with a lot of passes had a high connection centrality in pass-through networks and high proximity. Second, the German national soccer team has appeared key players as Phillip Lahm and Kroos player, and a key player of the Korean national soccer team was Ki,S.Y. Third, the German national soccer team's quantitative indicator value of proximity center and pass success rate appeared higher than the Korean national soccer team's.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.281-289
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• 2023

The purpose of this study was to analyze the research trends of Korean articles on early childhood software education. For this purpose, 55 articles published in domestic KCI journals on the topic of early childhood software education from 2017 to 2022 were selected for analysis and analyzed according to the year of publication, research method, and research topic. The results showed that, first, research on early childhood software education in Korean journals was first published in 2017 and continued to be conducted every year until 2022. Second, in terms of research methods, the research type was 'quantitative research,' the data collection method was 'literature survey,' and the data analysis methods were 'descriptive statistical analysis' and 'literature analysis.' In addition, 'infants' and 'kindergarten teachers' were the most common research subjects. Third, the analysis of research topics showed that 'analyzing the relationship between variables in early childhood software education' was the most common. Based on these results, we recommended research topics on early childhood software education for the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.2
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• pp.97-104
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• 2018

Recently, many studies have examined UAVs (unmanned aerial vehicles), which can replace and supplement existing surveying sensors, systems, and images. This study focused on the use of UAV images and assessed the possibility of utilization in areas where it is difficult to obtain GCPs (ground control points), such as disasters. Therefore, 3D (dimensional) pointcloud data were generated using UAV images and the absolute/relative accuracy of the generated model data using GCPs and without GCPs was assessed. The results showed the 3D shape pointcloud generated by UAV image matching was proven if the relative accuracy was set, regardless of whether GCPs were used or not; the quantitative measurement error rate was within 1%. Even if the absolute accuracy was low, the 3D shape pointcloud that had been post processed quickly was sufficient to be utilized when it is impossible to acquire GCPs or urgent analysis is required. In particular, the results can obtain quantitative measurements and meaningful data, such as the length and area, even in cases with the ground reference point surveying and post-process.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.655-667
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• 2014

This paper has identified five major categories of the T2D patients' concerns based on an online forum where the patients voluntarily verbalized their naturally occurring emotional reactions and concerns related to T2D. We have emphasized the fact that the lexical and phrasal analysis brought to the forefront the prevailing negative reactions and desires for clear information, professional advice, and emotional support. This study used lexical and phrasal analysis based on text-mining tools to estimate the potential of using a large sample of patient conversation of a specific disease posted on the internet for clinical features and patients' emotions. As a result, the study showed that quantitative analysis based on text-mining is a viable method of generalizing the psychological concerns and features of T2D patients.

• Journal of Digital Convergence
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• v.18 no.8
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• pp.37-48
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• 2020

This study aimed at understanding trends of living lab studies and deriving implications for directions of the studies by utilizing text mining. The study included network analysis and topic modelling based on keywords and abstracts from total 166 thesis published between 2011 and November 2019. Centrality analysis showed that living lab studies had been conducted focusing on keywords like innovation, society, technology, development, user and so on. From the topic modelling, 5 topics such as "regional innovation and user support", "social policy program of government", "smart city platform building", "technology innovation model of company" and "participation in system transformation" were extracted. Since the foundation of KNoLL in 2017, the diversification of living lab study subjects has been made. Quantitative analysis using text mining provides useful results for development of living lab studies.