• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.41.2-41.2
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• 2021

In this study, we generate He I 1083 nm images from Solar Dynamic Observatory (SDO)/Atmospheric Imaging Assembly (AIA) images using a novel deep learning method (pix2pixHD) based on conditional Generative Adversarial Networks (cGAN). He I 1083 nm images from National Solar Observatory (NSO)/Synoptic Optical Long-term Investigations of the Sun (SOLIS) are used as target data. We make three models: single input SDO/AIA 19.3 nm image for Model I, single input 30.4 nm image for Model II, and double input (19.3 and 30.4 nm) images for Model III. We use data from 2010 October to 2015 July except for June and December for training and the remaining one for test. Major results of our study are as follows. First, the models successfully generate He I 1083 nm images with high correlations. Second, the model with two input images shows better results than those with one input image in terms of metrics such as correlation coefficient (CC) and root mean squared error (RMSE). CC and RMSE between real and AI-generated ones for the model III with 4 by 4 binnings are 0.84 and 11.80, respectively. Third, AI-generated images show well observational features such as active regions, filaments, and coronal holes. This work is meaningful in that our model can produce He I 1083 nm images with higher cadence without data gaps, which would be useful for studying the time evolution of chromosphere and coronal holes.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2020.11a
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• pp.161-162
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• 2020

Major advanced ports around the world are preparing for environmental regulations such as increased efficiency of ports and low emission of pollutants in ports by utilizing fourth industrial technologies and ICT technologies such as AI, big data, self-driving cars and connected cars. It is also investing in developing fully unmanned terminals to solve the problem of workforce reduction caused by avoidance of 3D industries. However, the introduction of advanced technology is being delayed in domestic ports, which has led to a drop in port efficiency. In addition, port safety accidents have also occurred frequently, seriously affecting port marketing. Thus, the characteristics and types of each container terminal in Korea were analyzed and the factors for introducing autonomous cooperative driving were classified into five section factors and 15 division factors. Hierarchically classified factors will be surveyed on workers working in shipping lines, port construction, container terminals and related ministries.

• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.571-580
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• 2022

Marine deposited waste is a major cause of problems such as a lot of damage and an increase in the estimated amount of garbage due to abandoned fishing grounds caused by ghost fishing. In this paper, we implement a real-time marine deposited waste detection artificial intelligence system to understand the actual conditions of waste fishing gear usage, distribution, loss, and recovery, and study methods for performance improvement. The system was implemented using the yolov5 model, which is an excellent performance model for real-time object detection, and the 'data screening process' and 'class segmentation' method of learning data were applied as performance improvement methods. In conclusion, the object detection results of datasets that do screen unnecessary data or do not subdivide similar items according to characteristics and uses are better than the object recognition results of unscreened datasets and datasets in which classes are subdivided.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.1-7
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• 2023

The advent of deep learning technologies has led to the development of various medical applications, making healthcare services more convenient and effective. Among these applications, heart rate estimation is considered a vital method for assessing an individual's health. Traditional methods, such as photoplethysmography through smart watches, have been widely used but are invasive and require additional hardware. Recent advancements allow for contactless heart rate estimation through facial image analysis, providing a more hygienic and convenient approach. In this paper, we propose a lightweight methodology capable of accurately estimating heart rate in mobile environments, using a specialized 2-channel network structure based on 2D convolution. Our method considers both subtle facial movements and color changes resulting from blood flow and muscle contractions. The approach comprises two major components: an Encoder for analyzing image features and a regression layer for evaluating Blood Volume Pulse. By incorporating both features simultaneously our methodology delivers more accurate results even in computing environments with limited resources. The proposed approach is expected to offer a more efficient way to monitor heart rate without invasive technology, particularly well-suited for mobile devices.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.208-216
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• 2023

The purpose of our study is to design datasets for Artificial Intelligence learning for cold sea fish farming. Salmon is considered one of the most popular fish species among men and women of all ages, but most supplies depend on imports. Recently, salmon farming, which is rapidly emerging as a specialized industry in Gangwon-do, has attracted attention. Therefore, in order to successfully develop salmon farming, the need to systematically build data related to salmon and salmon farming and use it to develop aquaculture techniques is raised. Meanwhile, the catch of pollack continues to decrease. Efforts should be made to improve the major factors affecting pollack survival based on data, as well as increasing the discharge volume for resource recovery. To this end, it is necessary to systematically collect and analyze data related to pollack catch and ecology to prepare a sustainable resource management strategy. Image data was obtained using CCTV and underwater cameras to establish an intelligent aquaculture strategy for salmon and pollock, which are considered representative fish species in Gangwon-do. Using these data, we built learning data suitable for AI analysis and prediction. Such data construction can be used to develop models for predicting the growth of salmon and pollack, and to develop algorithms for AI services that can predict water temperature, one of the key variables that determine the survival rate of pollack. This in turn will enable intelligent aquaculture and resource management taking into account the ecological characteristics of fish species. These studies look forward to achievements on an important level for sustainable fisheries and fisheries resource management.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.701-708
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• 2024

The advent of transformer architectures in deep learning has been a major breakthrough in natural language processing research. Object name recognition is a branch of natural language processing and is an important research area for tasks such as information retrieval. It is also important in the biomedical field, but the lack of Korean biomedical corpora for training has limited the development of Korean clinical research using AI. In this study, we built a new biomedical corpus for Korean biomedical entity name recognition and selected language models pre-trained on a large Korean corpus for transfer learning. We compared the name recognition performance of the selected language models by F1-score and the recognition rate by tag, and analyzed the errors. In terms of recognition performance, KlueRoBERTa showed relatively good performance. The error analysis of the tagging process shows that the recognition performance of Disease is excellent, but Body and Treatment are relatively low. This is due to over-segmentation and under-segmentation that fails to properly categorize entity names based on context, and it will be necessary to build a more precise morphological analyzer and a rich lexicon to compensate for the incorrect tagging.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.6
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• pp.855-860
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• 2018

The IT technologies applying to IoT(Internet of Things), Big Data, and AI(Artificial Intelligence) are needed in the era of 4th industrial revolution. So, the IT convergence courses of computer science major which will be required in the companies in order to prepare the crises of 4th industry revolution are necessary. And, one approach to cope with this problem is the training of IT convergence man power based on NCS(National Competency Standard) education. In this paper, we propose and analyze the NCS education courses for computer science major in order to teach the students who are needed in the Korean domestic companies preparing the 4th industrial revolution. The skills and applications of Chatbot, Blockchain, and CPS(Cyber Physical System) for the post mobile and post Internet technologies are included in the proposed courses.

• Korean Journal of Community Nutrition
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• v.21 no.6
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• pp.558-573
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• 2016

Objectives: The purpose of this study was to evaluate the main sources of dietary sodium and potassium intake in Koreans by gender, age and regions. Methods: We used the data from 2010-2012 KNHANES. A total of 20,387 subjects aged 8 years and older were included. Intakes were compared by gender, age (8-18, 19-49 and >50 years) and geographical regions in Korea. Dishes were classified into 28 dish groups based on cooking methods. Statistical analysis was performed by using the SAS 9.3 and SUDAAN 11.0.1 software. Results: The mean sodium intake of Koreans was $4866.5{\pm}35.9mg/day$, which was 2.4 times higher than the adequate intake (AI) of sodium for Koreans. We found that daily sodium intakes were significantly different by age, gender and regions. Men and aged over 50 years had significantly higher sodium intake than women and other age groups. The mean potassium intake in Koreans was $3002.2{\pm}19.4mg/day$ and daily potassium intakes were significantly different by age, gender and regions. Women and age 50 years and over had significantly higher potassium intakes than men and other age groups. The average Na/K ratio was $2.89{\pm}0.01$ and was highest in men and in the age group of 19-49 years. The major sources of dietary sodium were soup and stew, followed by Kimchi, noodles and dumpling, pickled vegetables and seasonings, which represented 63.1 % of total sodium intakes. Soup and stew or Kimchi were the primary sources of dietary sodium intake. The major sources of dietary potassium were cooked rice, followed by soup and stew, Kimchi, fruits and beverages. Conclusions: Sodium and potassium intakes and the major sources of those were significantly different by gender, age groups and regions. Therefore, different approaches based on gender, age and regions are needed to decrease sodium intake and increase potassium intake.

• Journal of Ginseng Research
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• v.40 no.4
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• pp.366-374
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• 2016

Background: In this study, we screened and identified an endophyte JG09 having strong biocatalytic activity for ginsenosides from Platycodon grandiflorum, converted ginseng total saponins and ginsenoside monomers, determined the source of minor ginsenosides and the transformation pathways, and calculated the maximum production of minor ginsenosides for the conversion of ginsenoside Rb1 to assess the transformation activity of endophyte JG09. Methods: The transformation of ginseng total saponins and ginsenoside monomers Rb1, Rb2, Rc, Rd, Rg1 into minor ginsenosides F2, C-K and Rh1 using endophyte JG09 isolated by an organizational separation method and Esculin-R2A agar assay, as well as the identification of transformed products via TLC and HPLC, were evaluated. Endophyte JG09 was identified through DNA sequencing and phylogenetic analysis. Results: A total of 32 ${\beta}$-glucosidase-producing endophytes were screened out among the isolated 69 endophytes from P. grandiflorum. An endophyte bacteria JG09 identified as Luteibacter sp. effectively converted protopanaxadiol-type ginsenosides Rb1, Rb2, Rc, Rd into minor ginsenosides F2 and C-K, and converted protopanaxatriol-type ginsenoside Rg1 into minor ginsenoside Rh1. The transformation pathways of major ginsenosides by endophyte JG09 were as follows: $Rb1{\rightarrow}Rd{\rightarrow}F2{\rightarrow}C-K$; $Rb2{\rightarrow}C-O{\rightarrow}C-Y{\rightarrow}C-K$; $Rc{\rightarrow}C-Mc1{\rightarrow}C-Mc{\rightarrow}C-K$; $Rg1{\rightarrow}Rh1$. The maximum production rate of ginsenosides F2 and C-K reached 94.53% and 66.34%, respectively. Conclusion: This is the first report about conversion of major ginsenosides into minor ginsenosides by fermentation with P. grandiflorum endophytes. The results of the study indicate endophyte JG09 would be a potential microbial source for obtaining minor ginsenosides.

• Nutrition Research and Practice
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• v.15 no.sup1
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• pp.110-121
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• 2021

BACKGROUND/OBJECTIVES: Coronavirus disease 2019 (COVID-19) cases were first reported in December 2019, in China, and an increasing number of cases have since been detected all over the world. The purpose of this study was to collect significant news media reports on food services during the COVID-19 crisis and identify public communication and significant concerns regarding COVID-19 for suggesting future directions for the food industry and services. SUBJECTS/METHODS: News articles pertaining to food services were extracted from the home pages of major news media websites such as BBC, CNN, and Fox News between March 2020 and February 2021. The retrieved data was sorted and analyzed using Python software. RESULTS: The results of text analytics were presented in the format of the topic label and category for individual topics. The food and health category presented the effects of the COVID-19 pandemic on food and health, such as an increase in delivery services. The policy category was indicative of a change in government policy. The lifestyle change category addressed topics such as an increase in social media usage. CONCLUSIONS: This study is the first to analyze major news media (i.e., BBC, CNN, and Fox News) data related to food services in the context of the COVID-19 pandemic. Text analytics research on the food services domain revealed different categories such as food and health, policy, and lifestyle change. Therefore, this study contributes to the body of knowledge on food services research, through the use of text analytics to elicit findings from media sources.