• The Korean Journal of Air & Space Law and Policy
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• v.19 no.2
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• pp.97-136
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• 2004

The Northeast Asian air cargo market has expanded tremendously as a result of the opening up of the Chinese market. The importance of the Asia-Pacific region in the global air transport has also increased. The exchange of human and material resources, services, and information in Northeast Asia, which is expected to increase in the near future, requires that the airlines operating within this region adopt a more liberalized approach. This paper introduced alternatives which can be applied to the Northeast Asian airlines industry so as to bring about the integration of regional air transport: First, this paper found a need for individual Northeast Asian nations to alter their policies towards the airlines industry. Second, each country should further liberalize their respective domestic air transport. Third, there is a need for freer air service agreements to be signed between the nations of Northeast Asia. Fourth, the strategic alliances between the airlines operating in Northeast Asia should be further strengthened. Fifth, this liberalization process should be carried out in an incremental manner, beginning with more competitive airports and routes, or with less-in-demand routes. Sixth, there is a need for a shuttle system to be put into place between the main airports in China, Korea, and Japan. Seventh, these three nations jointly develop aviation safety and security systems that are in accordance with international standards. Eighth, the liberalization process of the aviation industry should be undertaken in conjunction with other related fields. Ninth, organizations linking together civil aviation organization in the Asia-Pacific area should be formed, as should each government linking together. By doing so, these countries will be able to establish regular venues through which to exchange opinions on the integration and liberalization of the air cargo market so as to induce the gradual liberalization of the actual market. The liberalization of the air transport in Northeast Asia will prove to be a daunting task in the short term. However, if the Chinese airlines continue to exhibit continuous growth and Japanese airlines are able to complete their move towards a low-cost structure, this process could be completed earlier than expected. Over the last twenty five years the air transport has undergone tremendous changes. The most important factor behind these changes has been the increased liberalization of the market. As a result, rates have decreased while demand has increased. This has resulted in turning the air transport industry, which was long perceived as an industry in decline, into a high-growth industry. The only method of increasing regional exchanges in the air transport is to pursue further liberalization. The country which implements this liberalization process at the earliest date may very well emerge as a leading force within the air transport industry.

• Journal of Chest Surgery
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• v.29 no.4
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• pp.373-380
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• 1996

Prevention of thromboembolism is the most important task in the development of bioconpatible small caliber artificial vascular graft. In normal vessels, vascular endothelial cells maintain homeosatsis by secreting numerous factors. The aim of this study is to develope a method which Improves biocompatibility of small caliver polyurethane graft using endothelial cell culture technique, and ev luate the efTectiveness of extracelluar matrix for endothelization which was produced by cultured fibroblast. Methods ; Multiporous polyurethane tube of 3 mm diameter, 0.3 mm thickness was manufactured for vascular graft. Three mongrel dogs were intubated and internal jugular veins removed. Extracelluar matrix produced by cultured flbrobast which was obtained from dog's internal jugular vein were coated to the polyurethane graft. Then, endothelial cells extracted from Jugular vein were cultured and fixed on the extracelluar matrix layer of vascular graft. Endothelial cell coated vascular grafts were implanted to the carotid arteries of experimental dogs as interposed autograft. Implanted grafts were removed after 3 and 6 weeks. As a control, PTFE graft was interposed on carotid artery. These experiments demonstrated that extracelluar matrix produced by fibroblast can afford a base for endothelial cell linings of polyurethane graft. Although thrombosis were developed on autografted en othelial cell coated graft, 33% opening was noticed, and showed less adhesion to adjacent tissue layer. These findings suggest that fiboblast produced extracelluar matrix which can be used for edothelial cell lining vascular graft, and by improving the cultured endothelial cell function, there will be a new modality for reducing thrombosis on small vascular graft.

• The Journal of the Acoustical Society of Korea
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• v.21 no.2
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• pp.199-210
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• 2002

In this paper, we carried out the study on speech recognition using the KM-Net topology design algorithm based on decision tree state-clustering to improve the performance of acoustic models in speech recognition. The Korean has many allophonic and grammatical rules compared to other languages, so we investigate the allophonic variations, which defined the Korean phonetics, and construct the phoneme question set for phonetic decision tree. The basic idea of the HM-Net topology design algorithm is that it has the basic structure of SSS (Successive State Splitting) algorithm and split again the states of the context-dependent acoustic models pre-constructed. That is, it have generated. the phonetic decision tree using the phoneme question sets each the state of models, and have iteratively trained the state sequence of the context-dependent acoustic models using the PDT-SSS (Phonetic Decision Tree-based SSS) algorithm. To verify the effectiveness of the above algorithm we carried out the speech recognition experiments for 452 words of center for Korean language Engineering (KLE452) and 200 sentences of air flight reservation task (YNU200). Experimental results show that the recognition accuracy has progressively improved according to the number of states variations after perform the splitting of states in the phoneme, word and continuous speech recognition experiments respectively. Through the experiments, we have got the average 71.5%, 99.2% of the phoneme, word recognition accuracy when the state number is 2,000, respectively and the average 91.6% of the continuous speech recognition accuracy when the state number is 800. Also we haute carried out the word recognition experiments using the HTK (HMM Too1kit) which is performed the state tying, compared to share the parameters of the HM-Net topology design algorithm. In word recognition experiments, the HM-Net topology design algorithm has an average of 4.0% higher recognition accuracy than the context-dependent acoustic models generated by the HTK implying the effectiveness of it.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of Ginseng Research
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• v.24 no.1
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• pp.8-17
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• 2000

Estrogen can influence on the expression of behaviors not associated directly with reproduction, including learning and memory. Recently estrogen has received considerable attention for its effects on neuroprotection and neural circuits in brain areas associated with cognition. Although estrogen replacement therapy may be helpful to postmenopausal women, it also results in a number of harmful side effects. Ginseng also has steroidal qualities and contains several ginsenoside components which have similar backbone structure to estrogen. The objectives of this experiment were 1) to examine the effects of estrogen and 2) to investigate the effects of ginsenosides as estrogenic agent on learning and memory using the Morris water maze, a traditional experimental task for spatial memory. In the experiments designed here, ovariectomized mice were implanted subcutaneously with Sila, itic capsules containing 17${\beta}$-estradiol (100∼250 $\mu\textrm{g}$/$m\ell$), panaxadiol (PD) and panaxatriol (PT) saponins (15∼100 $\mu\textrm{g}$/$m\ell$) diluted with sesame oil. In the first set of experiment, the effects of estradiol on learning and memory during the Morris water maze was examined. When estradiol was delivered via Silastic capsules following training improved spatial memory performance in ovariectomized female mice. In the second set of experiment, three different PD and PT saponin concentrations were delivered via Silastic implants to ovariectomized female mice and their effects were compared with estrogenic effects. Results of three separate experiments demonstrated that estradiol, PD and PT administrated by Silastic implants for 2 weeks prior to water maze training significantly improved spatial memory performance compared to ovariectomized (OVX) mice, as indicated by lower escape latency over trial. The positive effect of estradiol suggests that estrogen can affect performance on learning and memory. In addition, the positive effect of PD and PT saponins suggest that ginsenosides have an estrogen-like effects in mediating learning and memory related behavior action.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.77-88
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• 2017

Recently, with the advent of knowledge based society where information and knowledge make values, patents which are the representative form of intellectual property have become important, and the number of the patents follows growing trends. Thus, it needs to classify the patents depending on the technological topic of the invention appropriately in order to use a vast amount of the patent information effectively. IPC (International Patent Classification) is widely used for this situation. Researches about IPC automatic classification have been studied using data mining and machine learning algorithms to improve current IPC classification task which categorizes patent documents by hand. However, most of the previous researches have focused on applying various existing machine learning methods to the patent documents rather than considering on the characteristics of the data or the structure of patent documents. In this paper, therefore, we propose to use two structural fields, technical field and background, considered as having impacts on the patent classification, where the two field are selected by applying of the characteristics of patent documents and the role of the structural fields. We also construct multi-label classification model to reflect what a patent document could have multiple IPCs. Furthermore, we propose a method to classify patent documents at the IPC subclass level comprised of 630 categories so that we investigate the possibility of applying the IPC multi-label classification model into the real field. The effect of structural fields of patent documents are examined using 564,793 registered patents in Korea, and 87.2% precision is obtained in the case of using title, abstract, claims, technical field and background. From this sequence, we verify that the technical field and background have an important role in improving the precision of IPC multi-label classification in IPC subclass level.

• Korean Journal of Environment and Ecology
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• v.32 no.1
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• pp.105-117
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• 2018

This study conducted health assessment and multivariate vegetation analysis using the riparian vegetation index in 30 sites of the Geumgang mainstream and Mihocheon to obtain practical data on the river management of the Geumgang. The result showed that the number of plant communities was 54. The flora was 75 families, 185 genera, 243 species, 2 subspecies, 21 varieties, 2 varieties, and 268 taxa. The riparian vegetation index was 38.3 (3.3; G-D1 ~ 66.7; G-U2, G-U4, and G-M3), and the health of the rivers in this area was evaluated as normal (grade C). The health of rivers was the highest in the upper stream of Geumgang mainstream and lowest in the downstream of Geumgang mainstream. The relationship between riparian vegetation index and chlorophyll-a content was low. The riparian vegetation was divided into five groups of Digitaria ciliaris colony group, Salix gracilistyla colony group, Erigeron annuus colony group, the group dominated by Humulus japonicus, Salix koreensis, Miscanthus sacchariflorus, and Phragmites japonica colonies, and the group dominated by Conyza canadensis and Echinochloa crusgalli var. echinata colonies. They had the similar health conditions. The CCA analysis showed that the environmental factors affecting the distribution of vegetation were physical factors such as vegetation area, artificial structure area, waterway area, branch width, channel width, and bank height and the biological factors such as the number of species. As such, it is necessary to maintain the health condition through continuous monitoring where the health condition is high and to apply active measures such as ecological restoration where the health condition is low.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Journal of The Korean Association For Science Education
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• v.35 no.1
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• pp.37-52
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• 2015

In this study, assessment items to examine geocognition on plate tectonics were developed and applied to middle and high school students and college students. Conceptual constructs on plate tectonics are Earth interior structure, specific geomorphology, and geologic phenomena at each plate boundary. Construct for geocognition included temporal reasoning, spatial reasoning, retrospective reasoning, and system thinking. Pictorial data in each item were all obtained from GeoMapApp. Students' responses to the items were analyzed and measured cross-sectionally by Rasch model, which distinguishes persons' ability levels based on their scores for all items and compared them with item difficulty. By Rasch model analysis, Wright maps for middle and high school students and college students were obtained and compared with each other. Differential Item Functioning analysis was also implemented to compare students' item responses across school grades. The results showed: 1) Geocognition on plate tectonics was an assessable construct for middle and high school students in current science curriculum, 2) The most distinguished geocognition factor was spatial reasoning based on cross sectional analysis across school grades, 3) Geocognition on plate tectonics could be developed towards more sophisticated level through scaffolding of relevant instruction and earth science content knowledge, and 4) Geocognition was not a general reasoning separated from a task content but a content-specific reasoning related to the content of an assessment item. We proposed several suggestions for learning progressions for plate tectonics and national curriculum development based on the results of the study.

• Women's Health Nursing
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• v.4 no.1
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• pp.161-178
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• 1998

In spite of the great progress of the theory and skill of the Nursing Care & Medical area in relation to pregnancy, nurses in clinics face up to many challenges in maternity nursing care areas. The reason is that the mobility and mortality of mothers was sharply decreased and the unknown high-risk diseases of pregnancy woman in the past is made public. That's why it is difficult to meet the pregnancy woman in natural process from pregnancy to delivery in recently. Admission rooms are filled with high-risk pregnancy women. As a matter of fact, we have done nursing care into the surface symptoms and diseases of high-risk pregnancy women so far. We have been indifferent to a long period hospitalization, separation from family, and conflict of repeated examination. Therefore, it is widely spread to understand the emotional conflict experienced by high-risk pregnancy women and to need for nursing intervention to bring up about emotional support and the ability of perception in psychological crisis. Although the pregnancy woman judged in high-risk should carry out normal task of pregnancy, she have to be confronted with secondary risk situation. The health of self & fetus threatened by the risk situation could be decreased through care plan, but psychological stress increases. Therefore, the pregnancy brings into non-control state. It is important to ask that what the hospitalized pregnancy women in high-risk think of themselves status. Because misunderstanding or serious anxiety of themselves status put into mother and fetus in danger. And adaptation mode makes all the difference. I would like to consider how nurses could deal with this high-risk circumstances in the position of pregnancy woman on the basis of the above fact. This study uses phenomenological method to suggest the basis material for nurses to do nursing intervention in view of pregnancy woman. Because this method understands the nature of true life of pregnancy woman throughly. The phenomenological method is the sources to describe or explain affluently the process generated in confirmation areas and environment and is the application for readers to understand and recognize clinic reality and then apply this method to reasoning study place or other places. Specifically, the phenomenon study method, one of the phenomenological method, is applied. The use of that method is to describe and generalize the experience in environment exactly. The study of this study is as follows : Among 187 descriptive stamens from 8 study participants are classified into 42 theme cluster at the stage of the first analysis. Those theme is categorized into 8 sub-subjects such as anxiety of uncertainty, foreknowledge about risk circumstance, will power about overcome, unsettled feeling about hospital, relief, optimistic thought, family support, and indifferences. At the last stage of analysis, those things are categorized into 3 subjects. When high-risk pregnancy woman foretell the situation, they feel unsettlement about uncertainty and untrust feeling about hospital. But they are ease with family support and hospital support. On the other hand, they express indifferent 3-way structure response to the situation having will of overcome and exceeding optimistic thought. In those statements, the experience by pregnancy woman shows 3 respect subjects. 1. They are anxious of this situation and are in desperation and don't recognize their role to be carried out 2. They think of this situation as normal process of pregnancy and are not concerned that this can give themselves and fetus fatal damage. 3. The pregnancy women will never confront this situation. This study shows the pregnancy woman has anxiety and optimistic relief about the situation, and ignores and optimistic relief about the situation, and ignores many things. Therefore, nurses in clinic should give pregnancy woman knowledge and information about the high-risk and help them to deal with the situation spontaneously. High-risk pregnancy woman should have the care plan in respect of the right perception. And the nurse know that their support help out pregnancy woman overcome the crisis in this respect of the special nursing intervention.