• Journal of Korean Medical classics
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• v.25 no.4
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• pp.123-147
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• 2012

Objective : By establishing the Five Viscera Source Point Acupuncture as the targeted acupuncture treatment for stadardization, as the first step, this study was conducted to sort the indications of each acupuncture remedies, which can be referred as one of the most important factors in acupuncture treatment, based on Yeoungchu. Method : This study selected only the contents related to indications of five viscera, by extracting the relevant sentences from Yeoungchu using the search words Liver(Liver Meridian, First Yin), Heart(Pericardium, Heart Meridian, Second Yin), Spleen(Spleen meridian, Third Yin), Lung(Lung Meridian, Third Yin), and Kidney(Kidney Meridian, Second Yin). Result & Conclusion : 1. We selected and extracted text related to liver disease from Chapter 16, heart (pericardium) disease from Chapter 16, spleen disease from Chapter 19, lung disease from Chapter 17, and finally kidney disease from Chapter 17 of Yeoungchu. 2. The basic theory of applying Five Viscera Source Point Acupuncture to five viscera diseases is first assorting the diseases according to its state (i.e. deficiency or excess), then draining the source point of the appropriate viscus in case of excess, or supplementing the source point of the appropriate viscus in case of deficiency. 3. For the correct application of Five Viscera Source Point Acupuncture, the classification of the disease, not only the judgement on its state, must be presented systematically and synthetically in combination with Four Examinations. Therefore the follow-up studies needs to be conducted.

• Journal of Convergence for Information Technology
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• v.9 no.11
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• pp.81-91
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• 2019

Recently, social network service (SNS) is actively used by public. Among them, Twitter has a lot of tweets including sentiment and it is convenient to collect data through open Aplication Programming Interface (API). In this paper, we analyze social issues and suggest the possibility of using them in marketing through sentimental information of users. In this paper, we collect twitter text about social issues and classify as positive or negative by sentiment classifier to provide qualitative analysis. We provide a quantitative analysis by analyzing the correlation between the number of like and retweet of each tweet. As a result of the qualitative analysis, we suggest solutions to attract the interest of the public or consumers. As a result of the quantitative analysis, we conclude that the positive tweet should be brief to attract the users' attention on the Twitter. As future work, we will continue to analyze various social issues.

• Knowledge Management Research
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• v.22 no.3
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• pp.91-106
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• 2021

With the recent rapid development of deep learning technology, the demand for analyzing huge text documents in the national R&D field from various perspectives is rapidly increasing. In particular, interest in the application of a BERT(Bidirectional Encoder Representations from Transformers) language model that has pre-trained a large corpus is growing. However, the terminology used frequently in highly specialized fields such as national R&D are often not sufficiently learned in basic BERT. This is pointed out as a limitation of understanding documents in specialized fields through BERT. Therefore, this study proposes a method to build an R&D KoBERT language model that transfers national R&D field knowledge to basic BERT using further pre-training. In addition, in order to evaluate the performance of the proposed model, we performed classification analysis on about 116,000 R&D reports in the health care and information and communication fields. Experimental results showed that our proposed model showed higher performance in terms of accuracy compared to the pure KoBERT model.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.351-357
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• 2021

At the time when interest in the use of big data is rising in the face of the technological paradigm shift of the 4th industrial revolution, interest in the use of patented big data is increasing, especially as the proportion of intangible assets of companies increases. In addition to quantitative information, patent data contains various information such as unstructured text such as title, abstract, claim, citation and citation relations, drawings, and technology classification. It is judged that the use of treatment is important. Therefore, in this study, in order to systematically operate the innovative patent strategy framework and to secure a competitive advantage by strengthening the fundamental technological competitiveness of the company, we propose a method of using patent big data centering on the case of Company A, and verify its validity. I would like to suggest some implications. Through this, it is intended to raise awareness of the use of patent big data, and to suggest ways to use patent big data in connection with the company's company-wide strategy, business strategy, and functional strategy.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.43-50
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• 2022

For high-quality education of the modern generation of students, forms of organizing the educational process and the latest methods of obtaining knowledge that differ from traditional ones are necessary. The importance of multimedia teaching tools is shown, which are promising and highly effective tools that allow the teacher not only to present an array of information in a larger volume than traditional sources of information, but also to include text, graphs, diagrams, sound, animation, video, etc. in a visually integrated form. Approaches to the classification of multimedia learning tools are revealed. Special features, advantages of multimedia, expediency of use and their disadvantages are highlighted. A comprehensive analysis of the capabilities of multimedia teaching tools gave grounds for identifying the didactic functions that they perform. Several areas of multimedia application are described. Multimedia technologies make it possible to implement several basic methods of pedagogical activity, which are traditionally divided into active and passive principles of student interaction with the computer, which are revealed in the article. Important conditions for the implementation of multimedia technologies in the educational process are indicated. The feasibility of using multimedia in education is illustrated by examples. Of particular importance in education are game forms of learning, in the implementation of which educational elements based on media material play an important role. The influence of the game on the development of attention by means of works of media culture, which are very diverse in form and character, is shown. The importance of the role of multimedia in student education is indicated. In the educational process of multimedia students, a number of educational functions are implemented, which are presented in the article. Recommendations for using multimedia are given.

• Information Systems Review
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• v.19 no.2
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• pp.1-19
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• 2017

Original scripts of e-learning lectures for the CEOs of corporation S were analyzed using topic analysis, which is a text mining method. Twenty-two topics were extracted based on the keywords chosen from five-year records that ranged from 2011 to 2015. Research analysis was then conducted on various issues. Promising topics were selected through evaluation and element analysis of the members of each topic. In management and economics, members demonstrated high satisfaction and interest toward topics in marketing strategy, human resource management, and communication. Philosophy, history of war, and history demonstrated high interest and satisfaction in the field of humanities, whereas mind health showed high interest and satisfaction in the field of in lifestyle. Studies were also conducted to identify topics on the proportion of content, but these studies failed to increase member satisfaction. In the field of IT, educational content responds sensitively to change of the times, but it may not increase the interest and satisfaction of members. The present study found that content production for CEOs should draw out deep implications for value innovation through technology application instead of simply ending the technical aspect of information delivery. Previous studies classified contents superficially based on the name of content program when analyzing the status of content operation. However, text mining can derive deep content and subject classification based on the contents of unstructured data script. This approach can examine current shortages and necessary fields if the service contents of the themes are displayed by year. This study was based on data obtained from influential e-learning companies in Korea. Obtaining practical results was difficult because data were not acquired from portal sites or social networking service. The content of e-learning trends of CEOs were analyzed. Data analysis was also conducted on the intellectual interests of CEOs in each field.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.1-8
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• 2020

In recent years, due to the exponential increase in internet data, many fields such as deep learning have developed, but side effects generated as commercial advertisements, such as viral marketing, have been discovered. This not only damages the essence of the internet for sharing high-quality information, but also causes problems that increase users' search times to acquire high-quality information. In this study, we define advertisement as "a text that obscures the essence of information transmission" and we propose a model for filtering information according to that definition. The proposed model consists of advertisement filtering and advertisement filtering performance improvement and is designed to continuously improve performance. We collected data for filtering advertisements and learned document classification using KorBERT. Experiments were conducted to verify the performance of this model. For data combining five topics, accuracy and precision were 89.2% and 84.3%, respectively. High performance was confirmed, even if atypical characteristics of advertisements are considered. This approach is expected to reduce wasted time and fatigue in searching for information, because our model effectively delivers high-quality information to users through a process of determining and filtering advertisement paragraphs.

• Cartoon and Animation Studies
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• s.10
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• pp.1-16
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• 2006

This thesis aims to recall the importance of language in comics by studying comic strips in Korean daily newspapers: the comic strips are analyzed for rhetorical puns in its language text as they representatively show the value and role of language in comics. Moreover, Korean comic strips, as they developed into current affairs comics, acquired a stronger media characteristic of communicating information compared to other genres of cartoons. As a result, comics strips have become a genre where language plays an important role and the words needing to be able to convey the meaning quickly and implicitly. Due to tight control of national authority, the language technique developed into an indirect expression rather than a stronger direct imaging technique. The political oppression of the comic strip paradoxically brought on the rhetorical development in the creative techniques. Based on this analysis, the writer studied the rhetorical puns of the texts Korean comic strips by implementing the classification techniques of rhetoric expressions. As a result, through quotes and analysis of actual comic strips, the writer confirmed that Korean comic strips do actually show tremendously vast rhetorical puns in its language application techniques. The writer was also able to conclude that the rhetorical puns in comics were the force entertaining and impressing the readers, and also acting as the creative principle. Concluding this study, the writer emphasizes that language, not only in comic strips, is a combination of words and images and is also an important factor in all cartoons in general. Thus the thesis proposes that the training of humanistic thoughts and linguistic sensitivity are as important as learning to draw in the creation of cartoons.

• Herbal Formula Science
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• v.19 no.1
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• pp.35-49
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• 2011

Objectives : This study aims to reveal the characteristics of formulas in "Xi$\v{a}$o' Er Y$\`{a}$o Zh$\`{e}$ng Zh$\'{\i}$ Ju$\'{\e}$. Methods : For that objectives, We analyzes formulas in "Xi$\v{a}$o' Er Y$\`{a}$o Zh$\`{e}$ng Zh$\'{\i}$ Ju$\'{\e}$. In the text, 132 formulas were described. To comprehend the formulas, we classified them as several bases. Results : After those analyses, we bring to a conclusion as follows. 1. 30 formulas are described that treated convulsive diseases (j$\={i}$ngf$\={e}$ng, 惊風). Next, g$\={a}$n(疳), parasite infection, diarrhea/dysentery, dermatosis and etc were in the order. 2. Classified by the formulation, Yu$\'{\a}$nj$\`{i}$(圓劑) was the best(70 kinds of formulas, 53%). S$\v{a}$nj$\`{i}$(散劑) was a form of 41 formulas(31%). T$\={a}$ngj$\`{i}$(湯劑) and g$\={a}$oj$\`{i}$(膏劑) were a form of 5 formulas each. 10 formulas were assumed the form of w$\`{a}$iy$\`{o}$ngj$\`{i}$(外用劑). 3. We researched in-depth analysis of Yu$\'{\a}$nj$\`{i}$. As a results, dosage, additive(輔料) and the time to take of Yu$\'{\a}$nj$\`{i}$ were decomposed. Also, the formulas that treated convulsive diseases were analyzed by the herbs classification. Conclusions : Though the formulas that treated convulsive diseases were hard to application at local clinic, overall nosology of pediatrics was reflected comparatively. "Xi$\v{a}$o' Er Y$\`{a}$o Zh$\`{e}$ng Zh$\'{\i}$ Ju$\'{\e}$ was expected to play a role for reconsideration of formulas' formulation.

• The Journal of Bigdata
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• v.4 no.2
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• pp.1-12
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• 2019

As Artificial Intelligence (AI) technology develops, it is applied to various fields such as image, voice, and text. AI has shown fine results in certain areas. Researchers have tried to predict the stock market by utilizing artificial intelligence as well. Predicting the stock market is known as one of the difficult problems since the stock market is affected by various factors such as economy and politics. In the field of AI, there are attempts to predict the ups and downs of stock price by studying stock price patterns using various machine learning techniques. This study suggest a way of predicting stock price patterns based on the Convolutional Neural Network(CNN) among machine learning techniques. CNN uses neural networks to classify images by extracting features from images through convolutional layers. Therefore, this study tries to classify candlestick images made by stock data in order to predict patterns. This study has two objectives. The first one referred as Case 1 is to predict the patterns with the images made by the same-day stock price data. The second one referred as Case 2 is to predict the next day stock price patterns with the images produced by the daily stock price data. In Case 1, data augmentation methods - random modification and Gaussian noise - are applied to generate more training data, and the generated images are put into the model to fit. Given that deep learning requires a large amount of data, this study suggests a method of data augmentation for candlestick images. Also, this study compares the accuracies of the images with Gaussian noise and different classification problems. All data in this study is collected through OpenAPI provided by DaiShin Securities. Case 1 has five different labels depending on patterns. The patterns are up with up closing, up with down closing, down with up closing, down with down closing, and staying. The images in Case 1 are created by removing the last candle(-1candle), the last two candles(-2candles), and the last three candles(-3candles) from 60 minutes, 30 minutes, 10 minutes, and 5 minutes candle charts. 60 minutes candle chart means one candle in the image has 60 minutes of information containing an open price, high price, low price, close price. Case 2 has two labels that are up and down. This study for Case 2 has generated for 60 minutes, 30 minutes, 10 minutes, and 5minutes candle charts without removing any candle. Considering the stock data, moving the candles in the images is suggested, instead of existing data augmentation techniques. How much the candles are moved is defined as the modified value. The average difference of closing prices between candles was 0.0029. Therefore, in this study, 0.003, 0.002, 0.001, 0.00025 are used for the modified value. The number of images was doubled after data augmentation. When it comes to Gaussian Noise, the mean value was 0, and the value of variance was 0.01. For both Case 1 and Case 2, the model is based on VGG-Net16 that has 16 layers. As a result, 10 minutes -1candle showed the best accuracy among 60 minutes, 30 minutes, 10 minutes, 5minutes candle charts. Thus, 10 minutes images were utilized for the rest of the experiment in Case 1. The three candles removed from the images were selected for data augmentation and application of Gaussian noise. 10 minutes -3candle resulted in 79.72% accuracy. The accuracy of the images with 0.00025 modified value and 100% changed candles was 79.92%. Applying Gaussian noise helped the accuracy to be 80.98%. According to the outcomes of Case 2, 60minutes candle charts could predict patterns of tomorrow by 82.60%. To sum up, this study is expected to contribute to further studies on the prediction of stock price patterns using images. This research provides a possible method for data augmentation of stock data.