• Korea Science and Art Forum
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• v.28
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• pp.105-116
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• 2017

This study is a basic research to suggest user-centered general hospital environmental design guidelines, which aims to analyze user's behavior characteristics across generation in general hospital. For this purpose, this study constructed an analysis tool through the literature review with regard to generation and behavior characteristics in general hospital. Besides, an online survey regarding user's behavior in general hospital was conducted targeting from 20s to 60s, 300 persons for each group, total 1,500 persons for about 3 weeks since September 1, 2016. The results of this study are as follows: (1) Based on the generation, there were significant differences in relevant categories of their visiting frequency, visiting purpose, visiting hour, transportation, companion, behavior during the wait and selection of a general hospital. (2) In all generation, they responded that they have visited once or twice per year. People in 20s and 30s responded that their visit for the hospital is to receive specific treatment, while other people in 40s, 50s and 60s visit the hospital majorly for routine check-ups. Therefore, it is imperative for a health check-up center to design an environmental plan that reflects the characteristics of elders in 40s, 50s and 60s. (3) People in 40s, 50s and 60s usually visit a general hospital in the mornings of weekdays, while generations in 20s and 30s responded that they mostly visit the hospital in the mornings of weekend. (4) When they visit a general hospital, people in their 20s are usually using public transportations, while people in their 30s to 60s are using their own vehicle. (5) People in their 20s majorly visited 'lobby'. In older generations, they tend to visit 'outpatient clinic'. Therefore, it is necessary to build an outpatient clinic environment that considers the elderly. (6) Patients majorly responded that they are using their cell phone, while waiting for their clinic call. In elder generations, they responded that they are more likely watching TVs, reading books/magazines or doing nothing. Therefore, it is essential to provide cell-phone related services and environmental supports. Visually attractive media can be utilized for this purpose.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.

• Korean Journal of Animal Reproduction
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• v.21 no.2
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• pp.167-176
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• 1997

This study was carried out to evaluate the potential for preselection of transgenic embryos prior to transfer into recipient animals. In these experiments, I used a 3.2 kb transgene which contained the neomycin resistance gene (neo) and lac Z gene driven by the $\beta$ actin promoter (iacZ Ineo). Oocytes were aspirated from abattoir ovaries, matured in TCM-199 supplemented with 10% fetal bovine serum (FBS), 5 ${\mu}\textrm{g}$/ml LH, 0.5 ${\mu}\textrm{g}$/ml FSH, 100 unit/ml penicillin, and 100 ${\mu}\textrm{g}$/ml streptomycin for 22 to 24 hrs then inseminated with a sperm suspension of 1 X 10$^6$ sperm/ml containing 5 ${\mu}\textrm{g}$/ml of heparin. At 18-20 hrs after insemination, cumulus cells were removed by vortexing and pronuclei of centrifuged zygotes microinjected with the lacZ/neo construct (3 ng/$\mu$l). All cultures were carried out in CR1aa with transfected BRL monolayers containing 3 mg/ml BSA, 20 $\mu$/ml NEM amino acids and 40 $\mu$I/ml BME amino acids. To identify the appropriate concentration of G418 for selection, non-microinjected zygotes were cultured in the presence of 0, 50, 100 and 200 $\mu$g/ml of G418. After 8 days of culture in these treatments, 44/145 (30.3%), 13/150 (8. 7%), 1/151 (0.7%) and 0/134 (0.0%) devel-oped to the blastocyst stage in 0, 50, 100 and 200 $\mu$g/ml of G418, respectively. A total of 1,127 zygotes were microinjected and placed into culture (without G418) and subsequently 710 (63.0%) cleaved. At 48 hrs post-injection, embryos ($\geq$2-cell) were randomly assigned to control (medium alone) or G418 (100 ${\mu}\textrm{g}$/ml) treatments. A control culture of 740 non-microinjected embryos from the same replicates of embryos were also placed into control medium. After 8 days in culture, 54/343 (15.7%) and 22/367 (6.0 %) of the microinjected embryos developed to the blastocyst stage in control and G418 media, respectively. A total of 151/740 (27.2%) of the non-microinjected embryos placed in the control medium developed to the blastocyst stage. The blastocysts in the control treatment had a mean of 70.7 ${\pm}$ 4.7 cells of which 23.1 ${\pm}$ 2.6 (32.7%) stained for $\beta$-Gal activity. B1astocysts in the G418 treatment had a mean of 48.8${\pm}$7.5 cells of which 40.3 ${\pm}$ 4.1 (82.6%) stained for $\beta$-Gal ac tivity (P<0.05). In the control treatment 26 of 30 (87.0%) blastocysts had some cells with $\beta$-Gal activity while all of the blastocysts in the G418 treatment had some cell with $\beta$-Gal ac tivity (P<0.05). However, ICM colonies in either control or G418 treatments were not expressed any epiblast cell except of trophetoderm celIs. The $\beta$-actin promoter/lacZ gene may not be e expression or silence expression in epiblast cells These results clearly show an enrichment of blastocysts expressing the transgene in the majority of their cells after culture in the presence of G418. The exogeneous gene was not express a and silence in ICM colonies especiallly epiblast cells except of trophectederm cells. Even though the higher rate cell number expressed of exogeneous gene in the G418 treatments, a total cell number was decrease significantly (P<0.05) of which might be also drop of the establishment of ES like-cell colonies and production of transgenic animals. However, futher studies need to determine the viability of these selected embryos and the avability of production of transgenic offspring.

• Cartoon and Animation Studies
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• s.9
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• pp.127-142
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• 2005

Not only in Korea but throughout the entire world millions of people are in contact with images. Images have become a medium through which to transmit anything from simple visualizations of moving images to knowledge and information. The age of the internet has arisen thanks to scientific development, and the internet generation's acquisition of information is continuously becoming faster. The spectators, ufo must choose amongst the excessive amount of available information, are changing along with it just as quickly. The method of visual transmission has changed to match the demands of the fast-changing pace of the new generation. In order to receive an instantaneous selection amongst much information, the primary requisite is attracting one's attention, and then presenting a corresponding feeling of satisfaction. The early stages of film arose from the desire to capture one's actual situation as it realty is. Unsatisfied with the still picture, people developed the motion picture. Research has succeeded in reproducing 3-dimensional images more realistic than the actual image we perceive as a result of the difference in visual perspective of both eyes and their response to rays of light From color film to 3-dimensional pictures, people enjoy the magnificent results of this. All fields within the category of film are continuously studying the human desire to pursue their visual side, namely the pursuit of visual images with a maximum sense of reality. The images that millions of people around the world see now are flat. The screen's depth and optical illusions effectively give a sense of reality while conveying information. However, although the flat screen is able to create a sense of depth using the different visual perspective of each eye for the realization of a cubic effect, there are limitations. Entering the 21s1 century, there is a quickly-arising branch within the field of image media which seeks to overcome these limitations Although 3-dimensional images began in films, entering the latter half of the 20th century, due to development of 3-dimensional images using the mediums of the animation field, cellular phones, advertisement screens, television etc., without restriction is designated as 'image.'. With research having started around 1900 and continuing for over 100 years, we are now able to witness the popularization of 3-dimensional films happening before our very eyes. Within our own country, we can frequently see them at amusement parks and museums. In the future, through the popularization of HDTV etc., there is a good outlook for practical use of 3-dimensional images in televisions with advanced picture qualify as well as in other areas. Together with the international current, research on 3-dimensional films has been activated in Korea and is rising as a main current in the film industry. Within this context, the contents and understanding of 3-dimensional images must keep in step with the pace of technical advancements. In order to accelerate of development of film contents to keep in pace with technical developments, this dissertation presents the techniques and technical aspects of future developments, and shows the need to prepare in advance to make the field grow- and thereby avoid having a lack of experts and being conquered by other nations in the field - rather than only advancing the technical aspects and importing the contents. This dissertation aims to stimulate interest and continual research by progressive-thinking people related to the film industry. Part II looks into the definition and types of 3-dimensional motion pictures, the terminology, the fundamentals of image formation, current market fluctuations, and looks into 3-dimensional techniques which can be borrowed and introduced in 3-dimensional animations. Part III concerns 3-dimensional animated films. It analyzes 3-dimensional production techniques while using the introduction of specific animation techniques in the 2004 production Lee Sun Shin and Nelson - Naval Heroes 3-dimensional animation produced in 2004 by Clay & Puppet Stop-Motion Animation & Computer Graphic. Original Korean title: 해전영웅 이순신과 넬슨. as an example, and it also looks into how current film techniques used in animations can be applied in 3-dimensional films. Additionally, the actual stages of the various fields of 3-dimensional animations are presented. Given the current direction and advancement of 3-dimensional films making use of animations and the possible realization of this field, the author plans to weigh the development of this yet unexploited new market Not looking at the current progress of the field, but rather the direction of the hypothetical types of animation techniques, the author predicts the marketability and possibility of development of each area.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.