• Journal of Korea Entertainment Industry Association
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• v.14 no.3
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• pp.27-40
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• 2020

The marathon has long been established as a representative lifestyle for all ages. With the recent expansion of the Work and Life Balance trend across the society, marathon with a relatively low barrier to entry is gaining popularity among young people in their 20s and 30s. By analyzing the issues and related words of the marathon event, we will analyze the spottainment elements of the marathon event that is popular among young people through keywords, and suggest a development plan for the differentiated event. In order to analyze keywords and related words, blogs, cafes and news provided by Naver and Daum were selected as analysis channels, and 'JTBC Marathon' and 'Culture' were extracted as key words for data search. The data analysis period was limited to a three-month period from August 13, 2019 to November 13, 2019, when the application for participation in the 2019 JTBC Marathon was started. For data collection and analysis, frequency and matrix data were extracted through social matrix program Textom. In addition, the degree of the relationship was quantified by analyzing the connection structure and the centrality of the degree of connection between the words. Although the marathon is a personal movement, young people share a common denominator of "running" and form a new cultural group called "running crew" with other young people. Through this, it was found that a marathon competition culture was formed as a festival venue where people could train together, participate together, and escape from the image of a marathon run alone and fight with themselves.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.57-78
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• 2023

COVID-19, which started in Wuhan, China in November 2019, spread beyond China in 2020 and spread worldwide in March 2020. It is important to prevent a highly contagious virus like COVID-19 in advance and to actively treat it when confirmed, but it is more important to identify the confirmed fact quickly and prevent its spread since it is a virus that spreads quickly. However, PCR test to check for infection is costly and time consuming, and self-kit test is also easy to access, but the cost of the kit is not easy to receive every time. Therefore, if it is possible to determine whether or not a person is positive for COVID-19 based on the sound of a cough so that anyone can use it easily, anyone can easily check whether or not they are confirmed at anytime, anywhere, and it can have great economic advantages. In this study, an experiment was conducted on a method to identify whether or not COVID-19 was confirmed based on a cough sound. Cough sound features were extracted through MFCC, Mel-Spectrogram, and spectral contrast. For the quality of cough sound, noisy data was deleted through SNR, and only the cough sound was extracted from the voice file through chunk. Since the objective is COVID-19 positive and negative classification, learning was performed through XGBoost, LightGBM, and FCNN algorithms, which are often used for classification, and the results were compared. Additionally, we conducted a comparative experiment on the performance of the model using multidimensional vectors obtained by converting cough sounds into both images and vectors. The experimental results showed that the LightGBM model utilizing features obtained by converting basic information about health status and cough sounds into multidimensional vectors through MFCC, Mel-Spectogram, Spectral contrast, and Spectrogram achieved the highest accuracy of 0.74.

• Journal of agricultural medicine and community health
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• v.26 no.1
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• pp.19-35
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• 2001

This study was conducted to investigate the internal changes and the countermeasure for performance improvement by Separation of Prescribing and Dispensing Practice (SPDP) in Health Center. Data were collected from two sources: Performance report before and after SPDP of 25 Health Centers in Kyongsangbuk-do and 6 Health Centers in Daegu-City and self-administerd questionnaire survey of 221 officials at health center. The results of this study were summarized as follows: Twenty-four health centers(77.4%) of 31 health centers took convenience measures for medical treatment of citizens and convenience measures were getting map of pharmacy, improvement of health center interior, introduction of order communication system in order. After the SPDP in health centers, 19.4% of health centers increased doctors and 25.8% decreased pharmacists. 58.1% of health centers showed that number of medical treatments were decreased. 96.4%, 80.6% 80.6% 96.7% of health centers showed that number of prescriptions, total medical treatment expenses, amounts paid by the insureds and the expenses to purchase drugs, respectively, were decreased. More than fifty percent(54.2%) of health centers responded that the relative importance of health works increased compared to medical treatments after the SPDP, and number of patients decreased compared to those in before the SPDP. And there was a drastic reduction in number of prescriptions, total medical treatment expenses, amounts paid by insureds, the expenses to purchase drugs after the SPDP. Above fifty percent(57.6%) of officers at health center responded that the function of medical treatment should be reduced after the SPDP. Fields requested improvement in health centers were 'development of heath works contents'(62.4%), 'rearrangement of health center personnel'(51.6%), 'priority setting for health works'(48.4%), 'restructuring the organization'(36.2%), 'quality impro­vement for medical services'(32.1%), 'replaning the budgets'(23.1%) in order. And to better the image of health centers, health center officers replied that 'health information management'(60.7%), 'public relations for health center'(15.8%), 'kindness of health center officers'(15.3%) were necessary in order. Health center officers suggested that 'vaccination program', 'health promotion', 'maternal and children health', 'communicable disease management', 'community health planning' were relatively important works, in order, performed by health center after SPDP. In the future, medical services in health centers should be cut down with a momentum of the SPDP so that health centers might reestablish their functions and roles as public health organizations, but quality of medical services must be improved. Also health centers should pay attention to residents for improving health through 'vaccination program', 'health promotion', 'mother-children health', 'acute and chronic communicable disease management', 'community health planning', 'oral health', 'chronic degenerative disease management', etc. And there should be a differentiation of relative importance between health promotion services and medical treatment services by character of areas(metropolitan, city, county).

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.167-181
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• 2018

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.

• Journal of Korean Home Economics Education Association
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• v.22 no.3
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• pp.1-18
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• 2010

The purpose of this study was to develop a teaching-learning process plan for sustainable residing creating with neighbors and to apply it to the housing section of Technology-Home Economics according to the 2007 Revised Curriculum. Teachinglearning method solving practical problems was used for the teaching-learning process plans of 6-session lessons according to the ADDIE model. In the development stage, 17 activity materials and 15 teaching learning materials (6 reading texts, 6 moving pictures, 2 internet and 1 image materials) were developed. for the 6-session lessons, based on the stages of solving practical problems. The plans applied to the 3 classes of 8, 9, and 10th grade of the H. junior and senior high school in Myun district in Kyungbook during Sept. 1st to 14th, 2009. The results showed that students actively participated when the contents and materials were related to their own experience. The 6-session lessons about sustainable residing creating with neighbors was significantly increased the sense of community between before and after. Each of the 4 stages of the teachinglearning method solving practical problems were highly participated by the students. The satisfaction with the contents and methods of the 6-session lessons were evaluated over medium to somewhat higher levels. The practical activities to solve the community space and programs were got positive comments. Problem solving process and presentation and discussion were needed to learn more. Those results might support that the teachinglearning process plan this research developed. would be appropriate to the lessons for sustainable residing creating with neighbors.

• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.2
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• pp.62-69
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• 2009

This study aimed to analyze citizen awareness of the transforming of a former military site in the city into use as an urban park, and to determine the direction of park creation in the context of urban regeneration. The study focused on the city of Uijungbu, so-called "representative military city" of Korea, which has over eight US military army corps in CBD and which islocated on the northside of Seoul. The questionnaire survey was carried out in the year of 2006, during two months from 1 June till 30 July. The research was carried out by book review as a theoretical base and a questionnaire survey to analyze citizen awareness. The conclusions of this study were as follows. 1. Areas that have been transformed from a former military site to large urban parks for the promotion of economic, social, environmental, and aesthetical value in the context of urban regeneration include Downsview Park(Toronto, Canada), and Great Park(Irvine, CA U.S.A). 2. The citizens of Euijungbu emphasized having casual rest and recreational functions in an urban park, but were also concerned with the promotion of cultural image, activation of the city's economy, etc. 3. The citizens preferred to use the former military site for a park instead of for residential and commercial purposes to introduce cultural facilities and encourage economic activation. 4. All results of this study proposed to use the former military site for encouraging and activating the urban economy, cultural promotion, social reconciliation and aesthetic value by the transforming the site into a large, multi-use park in the context of urban regeneration.

• Korean Journal of Environmental Agriculture
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• v.25 no.1
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• pp.7-13
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• 2006

Plastichouse cultivation for crops and vegetables in the winter has been widely popularized in Korea. In the vinylhouse Ultraviolet B penetration is lower than in the field, and so some problems, as plant overgrowth and outbreak of disease, occurred frequently. The effect of artificial supplement ultraviolet B $(UV-B:280{\sim}320nm)$ radiation on the physiological responses and yield of perilla (perilla frutescens) was investigated UV-B ray was radiated on perilla with the 10th leaf stage at the distance of 90, 120 and 150 cm from the plant canopy for 30 days after planting in the vinylhouse. The production of fresh perilla leaves was high in the order of plastic house, ambient+50% of supplemental UV-B, ambient ambient+100% of supplemental UV-B. Enhanced UV-B radiation affected the intensity of thirty-three proteins in 2-dimensional electrophoretic analysis of proteins and ten proteins out of them seemed to be responsive to UV-B : a protein was, ATP synthase CF1 alpha chain, down regulated and nine proteins (Chlorophyll a/b bindng protein type I, Chlorophyll a/b binding protein type II precursor, Photosystem I P700 chlorophyll a apoprotein A2, DNA recombination and repair protein recF, Galactinol synthase, S-adenosyl-L-methionine, Heat shock protein 21, Calcium-dependent protein kinase(CDPK)-like, Catalase) were up-regulated.

• Journal of dental hygiene science
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• v.13 no.3
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• pp.223-229
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• 2013

The aim of this study was to examine the effectiveness of oral health promotion program in a group of 9~18-year-old children and adolescents living in four orphanages in Dong-gu, Daejeon. The program was based on oral disease prevention program including oral health education, fluoride application and scaling every six months. Oral health status of total 109 orphans was examined by one dentists who were trained in 2010 Korean National Oral Health Survey. Dental caries index, community periodontal index and modified patient hygiene performance index (M-PHP) were checked using dental unit chair. Child oral health impact profile (COHIP) and subjective oral health recognition survey were carried out. Compared with data of 2010 national sample, the mean of decayed, missing and filled teeth showed no difference between the subjects and test values, but the means of decayed teeth, decayed surface, toothbrushing frequency of the subjects showed to become worse with advancing years in spite of oral health promotion program. COHIP, subjective oral health status showed lower than test values, too. In M-PHP and Calculus index, the subjects showed better by periodic oral health education and scaling. We suggest that oral health promotion program for orphans include oral disease treatment program as well as preventive program to improve oral health of orphans efficiently. And, oral health promotion program has to be connected with psychological support for improving quality of life of orphans.

• Sijohaknonchong
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• v.42
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• pp.213-238
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• 2015

Jeongun(丁芸) Lee, Young-do (李永道), who is deemed a representative female poet of Korea, began her literary career in May, 1946 when she published in a publication called "Bamboo Sprout, (죽순)". Her Korean identity, which was formed through her Confucius upbringing as well as traditional value system of her family, had a strong presence in her work, and she remained a quintessential figure in Korea's female sijo poet circle for 30 years until her passing in 1976. Despite the highly acclaimed talent and her noble aspirations, it is undeniable that her works did not receive fair assessment due to her private life. Against this backdrop, it is necessary to deeply inquire the literary values and beauty of Young-do Lee's sijo. As mentioned, Young-do Lee is a solidly established figure in Korea's modern poetry. The following illustrates the spirit and the world of her poetry. First, Young-do Lee lived through turbulent times and it was her country that served as the source of her sijo work. Assessing the multitude of dramatic historical events such as Japanese colonization, 8.15 Liberation of Korea, division of the nation, 6.25 Korean war, 4.19 Revolution, 5.16 military coup, it is natural that patriotism was strongly present in her work who was one of the intellectuals at the time. Second, Young-do Lee is a poet who had experienced more pain than others in terms of the turbulence of the time. Her Korean identity, which was formed through her Confucius upbringing as well as traditional value system of her family, had a strong presence in her work. Third, Jeongun Lee, Young-do is a poet of longing. The abundance and richness of her emotions were fortified through the relationship with another poet, Chihwan Yu. Fourth, Young-do Lee is a poet opened up new horizons for the modennization. The transparency of image reflected in her work and the elaborate nature of her language are outstanding. In summary, Young-do Lee was a true artist, who has a strong presence in Korea's modern poetry society, and who was a poet of patriotism, poet who suffered the turbulence of the times, and a poet of longing.