• (The)Study of the Eastern Classic
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• no.55
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• pp.265-291
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• 2014

Cheongeumrok is the journal of Gwonwoo(卷宇) Hong Chan-yu(1915-2005) during the period of January 9, 1969~January 14, 1982. He was personally involved in the foundation of a corporation called Yudohoi and also all of its operation, which makes him the most knowledgeable person about its history. His Cheongeumrok thus seems worthy enough as a proper material to arrange its history. Cheongeumrok consists of total 19 books, amounting to approximately 3,300 pieces of squared manuscript paper containing 200 letters per piece. He wrote it in Chinese and sometimes followed the Hangul-style word order while writing in Chinese. Many parts of the manuscript were written in a cursive hand with many Chinese poems embedded throughout it. The manuscript offers major information related to the corporation Yudohoi extracted from his journal. 1. There was a meeting of promoters to commemorate the foundation of the corporation in November, 1968, and it was in January, 1969 that it was established after getting a permit from the Ministry of Culture and Communication in January, 1969(Permit No. of Ministry of Culture and Communication: Da(다)-2-3(Jongmu(宗務)1732.5)). 2. Its office was moved from the original location of the 3rd floor of Wonnam Building, 133-1 Wonnam-dong, Jongro-gu, Seoul(currently Daekhak Pharmacy in front of Seoul National University Hospital) to Room 388 of Gwangjang Company, 4 Yeji-dong, Jongro-gu(office of Heungsan Social Gathering) and to second floor of KyungBo building, 21 Kyansu-dong, and to 3rd floor of Geongguk Building in Gyeongwoon-dong. 3. Its operational costs were covered by the supports of Seong Sang-yeong, the eldest son of Seong Jong-ho, the chairman of the board, later Kim Won-tae and Gwon Tae-hun, next chairmen of the board, and Hong Chan-yun, a director, since 1979. 4. His Confucian activities include participating in Seonggyungwan Seokjeonje (成均館 釋奠), joining in the erection of the Parijangseo(巴里長書) Monument and the publication of its commemorative poetry book, compiling the biographies(not completed) of Confucian patriotic martyrs for independence, and participating in the establishment of family rituals and regulations as a practice member. 5. His Yudohoi had a dispute with Seonggyungwan and lost a suit at the High Court in July, 1975 and Supreme Court in February, 1976. 6. There were discussions about its unification with Seonggyungwan Yudohoi, but there was hardly any progress. 7. Yudohoi started to provide full-scale courses on Confucian and Chinese classics under the leadership of Director Hong Chan-yu in 1979, and they have continued on today. Its courses for scholarship students including those for common citizens boast a history of 29 years and 220 graduates.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.23-31
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• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.47-64
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• 2021

The global spread of COVID-19 around the world has not only affected many parts of our daily life but also has a huge impact on many areas, including the economy and society. As the number of confirmed cases and deaths increases, medical staff and the public are said to be experiencing psychological problems such as anxiety, depression, and stress. The collective tragedy that accompanies the epidemic raises fear and anxiety, which is known to cause enormous disruptions to the behavior and psychological well-being of many. Long-term negative emotions can reduce people's immunity and destroy their physical balance, so it is essential to understand the psychological state of COVID-19. This study suggests a method of monitoring medial news reflecting current days which requires striving not only for physical but also for psychological quarantine in the prolonged COVID-19 situation. Moreover, it is presented how an easier method of analyzing social media networks applies to those cases. The aim of this study is to assist health policymakers in fast and complex decision-making processes. News plays a major role in setting the policy agenda. Among various major media, news headlines are considered important in the field of communication science as a summary of the core content that the media wants to convey to the audiences who read it. News data used in this study was easily collected using "Bigkinds" that is created by integrating big data technology. With the collected news data, keywords were classified through text mining, and the relationship between words was visualized through semantic network analysis between keywords. Using the KrKwic program, a Korean semantic network analysis tool, text mining was performed and the frequency of words was calculated to easily identify keywords. The frequency of words appearing in keywords of articles related to COVID-19 emotions was checked and visualized in word cloud 'China', 'anxiety', 'situation', 'mind', 'social', and 'health' appeared high in relation to the emotions of COVID-19. In addition, UCINET, a specialized social network analysis program, was used to analyze connection centrality and cluster analysis, and a method of visualizing a graph using Net Draw was performed. As a result of analyzing the connection centrality between each data, it was found that the most central keywords in the keyword-centric network were 'psychology', 'COVID-19', 'blue', and 'anxiety'. The network of frequency of co-occurrence among the keywords appearing in the headlines of the news was visualized as a graph. The thickness of the line on the graph is proportional to the frequency of co-occurrence, and if the frequency of two words appearing at the same time is high, it is indicated by a thick line. It can be seen that the 'COVID-blue' pair is displayed in the boldest, and the 'COVID-emotion' and 'COVID-anxiety' pairs are displayed with a relatively thick line. 'Blue' related to COVID-19 is a word that means depression, and it was confirmed that COVID-19 and depression are keywords that should be of interest now. The research methodology used in this study has the convenience of being able to quickly measure social phenomena and changes while reducing costs. In this study, by analyzing news headlines, we were able to identify people's feelings and perceptions on issues related to COVID-19 depression, and identify the main agendas to be analyzed by deriving important keywords. By presenting and visualizing the subject and important keywords related to the COVID-19 emotion at a time, medical policy managers will be able to be provided a variety of perspectives when identifying and researching the regarding phenomenon. It is expected that it can help to use it as basic data for support, treatment and service development for psychological quarantine issues related to COVID-19.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.177-190
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• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.30 no.4
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• pp.342-352
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• 2020

Objectives: The characteristics of research workers are different from those working in the manufacturing industry. Furthermore, the reagents used change according to the research due to the characteristics of the laboratory, and the amounts used vary. In addition, since the working time changes almost every day, it is difficult to adjust the time according to exposure standards. There are also difficulties in setting standards as in the manufacturing industry since laboratory environments and the types of experiments performed are all different. For these reasons, the measurement of the working environment of research workers is not realistically carried out within the legal framework, there is a concern that the accuracy of measurement results may be degraded, and there are difficulties in securing data. The exposure evaluation based on an eight-hour time-weighted average used for measuring the working environment to be studied in this study may not be appropriate, but it was judged and consequently applied as the most suitable method among the recognized test methods. Methods: The investigation of the use of chemical substances in the research laboratory, which is the subject of this study, was conducted in the order of carrying out work environment measurement, sample analysis, and result analysis. In the case of the use of chemical substances, after organizing the substances to be measured in the working environment, the research workers were asked to write down the status, frequency, and period of use. Work environment measurement and sample analysis were conducted by a recognized test method, and the results were compared with the exposure standards (TWA: time weighted average value) for chemical substances and physical factors. Results: For the substances subject to work environment measurement, the department of chemical engineering was the most exposed, followed by the department of chemistry. This can lead to exposure to a variety of chemicals in departmental laboratories that primarily deal with chemicals, including acetone, hydrogen peroxide, nitric acid, sodium hydroxide, and normal hexane. Hydrogen chloride was measured higher than the average level of domestic work environment measurements. This can suggest that researchers in research activities should also be managed within the work environment measurement system. As a result of a comparison between the professional science and technology service industry and the education service industry, which are the most similar business types to university research laboratories among the domestic work environment measurements provided by the Korea Safety and Health Agency, acetone, dichloromethane, hydrogen peroxide, sodium hydroxide, nitric acid, normal hexane, and hydrogen chloride are items that appear higher than the average level. This can also be expressed as a basis for supporting management within the work environment measurement system. Conclusions: In the case of research activity workers' work environment measurement and management, specific details can be presented as follows. When changing projects and research, work environment measurement is carried out, and work environment measurement targets and methods are determined by the measurement and analysis method determined by the Ministry of Employment and Labor. The measurement results and exposure standards apply exposure standards for chemical substances and physical factors by the Ministry of Employment and Labor. Implementation costs include safety management expenses and submission of improvement plans when exposure standards are exceeded. The results of this study were presented only for the measurement of the working environment among the minimum health management measures for research workers, but it is necessary to prepare a system to improve the level of safety and health.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.66 no.4
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• pp.279-288
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• 2021

Low-density transplanting is a cultivation technology that reduces labor and production costs. In this study, the growth and yield of several varieties with different tillering characteristics were analyzed in order to establish an appropriate planting density for low-density transplanting. Varieties with Low-Tillering (LT), Medium-Tillering (MT), and High-Tillering (HT) were planted at a density of 37-80 hills/3.3 m². As the planting density decreased, the number of tillers per hill increased, but the number of tillers per square meter of hill decreased, especially for the LT variety. Decreasing density extended the tillering stage, which was longest in the LT variety. As the planting density decreased, SPAD(Soil plant analysis development, chlorophyll meter) values just before heading increased while canopy light interception decreased. Such changes were much greater in the LT variety than in the MT and HT varieties. The heading date tended to be delayed by 0-2 days as the planting density decreased, and there was no difference in the length of the period from first heading to full heading. As the number of spikelets per panicle increased, the number of spikelets per square meter did not differ according to the planting density. Decreasing planting density did not affect the grain weight; nevertheless, the yield ultimately decreased because of the decreasing ripening rate. The optimal planting density for stable low-density transplanting cultivation was determined to be over 50 hills/3.3 m2. In addition, these results suggest that LT varieties should be avoided, since these showed large decreases in growth and yield with decreasing planting density.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.265-274
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• 2021

Outlier detection research in ocean data has traditionally been performed using statistical and distance-based machine learning algorithms. Recently, AI-based methods have received a lot of attention and so-called supervised learning methods that require classification information for data are mainly used. This supervised learning method requires a lot of time and costs because classification information (label) must be manually designated for all data required for learning. In this study, an autoencoder based on unsupervised learning was applied as an outlier detection to overcome this problem. For the experiment, two experiments were designed: one is univariate learning, in which only SST data was used among the observation data of Deokjeok Island and the other is multivariate learning, in which SST, air temperature, wind direction, wind speed, air pressure, and humidity were used. Period of data is 25 years from 1996 to 2020, and a pre-processing considering the characteristics of ocean data was applied to the data. An outlier detection of actual SST data was tried with a learned univariate and multivariate autoencoder. We tried to detect outliers in real SST data using trained univariate and multivariate autoencoders. To compare model performance, various outlier detection methods were applied to synthetic data with artificially inserted errors. As a result of quantitatively evaluating the performance of these methods, the multivariate/univariate accuracy was about 96%/91%, respectively, indicating that the multivariate autoencoder had better outlier detection performance. Outlier detection using an unsupervised learning-based autoencoder is expected to be used in various ways in that it can reduce subjective classification errors and cost and time required for data labeling.

• Korean Journal of Clinical Laboratory Science
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• v.54 no.2
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• pp.110-118
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• 2022

Candidemia is a major cause of nosocomial infections resulting in increased morbidity and mortality. It remains a serious risk in inpatients and increases medical treatment costs. From 2009 to 2018, Candida strains (3,533) isolated from blood culture tests at the S Hospital were analyzed according to the period, year, sex, age, ward, etc. During the entire period, 54,739 of 717,996 blood culture tests showed a positive rate (7.6%) and the Candida isolation rate was 3,533 (6.4%) out of 1,036 patients. Among the Candida isolates, C. albicans was most common (33.8%), followed by C. tropicalis (28.6%), C. glabrata (19.8%), C. parapsilosis (7.8%), and C. krusei (4.0%). In early (2009~2013)/late (2014~2018) isolation, C. tropicalis decreased by 3.8% and C. glabrata increased by 3.4%. After 50 years of age, the higher the separation frequency. C. parapsilosis (31.3%) in 1~10s, C. tropicalis (30.3%) and C. glabrata (27.6%) in 41~50s, and C. tropicalis (28.6%) in 80s are relatively frequent. has been separated C. krusei was isolated in a relatively high proportion from females (60.9%). Therefore, a systematic and continuous nosocomial infection control system should be established for appropriate treatment as per antifungal treatment guidelines. The system should continuously monitor the distribution of Candida species and provide rapid identification results.

• Korean Journal of Environment and Ecology
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• v.36 no.3
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• pp.338-349
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• 2022

This study aims to investigate whether modular community planting, which entailed planting a variety of species of seedlings at high density, was more effective in restoring natural forests than the existing mature tree planting. We also investigated whether the planting density of the modular community planting facilitates growth or improves the tree layer coverage. We conducted outdoor experiments in which the samples were divided into a mature tree planting plot (control plot), where mature trees were planted at wide intervals, and a modular community planting (MCP) plot (treatment plot), where multiple seedlings were planted in high density. The MCP plot was further divided into the plot in which 3 seedlings were planted per m² and the plot of 1 seedling per m². We measured the specimens' survival rate, growth rate (tree height, crown width, and root collar diameter), and cover rate for 26 months from May 2019 and the predicted future tree height growth using the measured tree height. The survival rate and relative growth rate of the MCP were higher than those of the mature tree planting plot. The vertical coverage rate of the tree crown in the MCP exhibited complete coverage of the ground before 23 months, while the coverage rate of the mature tree planting decreased due to transplantation stress. The seedlings in the MCP, which were planted at high density, grew well and were predicted to grow higher than the mature trees in the large tree planting plot within 5 to 6.5 years after planting. It was due to multiple species, seedlings, high-density planting, and planting foundation improvements, such as soil enhancement and mulching. In other words, the seedlings planted in the MCP had a higher survival rate as their environmental adaptation after planting was better, and their early growth was also larger than the trees in the mature planting plot. The high-density mixed planting of various native species not only mitigated the inter-complementary environmental pressures but also facilitated growth by inducing competition between species. Moreover, the planting foundation improvement effectively increased the seedlings' viability and growth rate. A reduction in follow-up management costs is expected as the tree layer coverage sharply increases due to the higher planting density. In the MCP (3 seedlings per m² and 1 seedling per m²), the tree height growth was promoted with the higher planting density, and the crown width and root collar diameter tended to be larger with the lower planting density, but these differences were not statistically significant.

• The Journal of Korean Society for Radiation Therapy
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• v.33
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• pp.15-24
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• 2021

This study is about the introduction and usefulness evaluation of the manufacturing method of the bolus-helmet. Helmet-production for the treatment of scalp tumor patients has been tried and will continue in many creative and various ways. However, Most of the research data did not significantly reduce the psychological burden and physical and physical discomfort that the patient had to bear due to the time and economic cost required for the production of the helmet, the convenience of production, and the complexity of the process. In addition, recently, studies using more advanced technologies and equipment such as 3D-printer technology, which are being studied as a way to increase the treatment effect, are being introduced, but the time, economic cost, and psychological and physical burden are still the sole responsibility of the patient. Isn't it getting worse? The reality is that the thoughts of concern cannot be erased. Therefore, by maintaining the physical properties of the bolus and manufacturing a helmet without incurring additional costs, the physical and physical discomfort aggravated to the patient was reduced and the procedure and time for helmet manufacturing were minimized. In this way, it was possible to reduce the time, economic cost, and physical discomfort required for the production of the helmet, and it was also possible to minimize the psychological burden of the patient, although it is invisible. Additionally, in evaluating the usefulness of helmets, we are able to continuously seek and develop ways to reduce the air-gap interval, and as a result, we will be able to introduce a method to keep it within 2.0mm along with the manufacturing method through this study. I feel very welcome. Finally, I hope that anyone working in the Department of Radiation Oncology will be able to easily manufacture the helmet required for radiation therapy using a bolus through the guide-line on helmet manufacturing provided by this institute. I hope and hope that if you have any questions or inquiries that arise during the production process, please feel free to contact us through the researcher's e-mail or mobile phone at any time.