• The Journal of Society for e-Business Studies
• /
• v.17 no.3
• /
• pp.73-84
• /
• 2012

With transition to Knowledge society and introduction of information industry, there are many companies which have higher stock price than the suggested value from its financial information. To explain similar cases in capital markets, many researchers focus on non-financial information such as Web Traffic data or intangible assets such as intellectual property rights rather than traditional financial analysis. Besides, the relationships between financial and non-financial information with firm value are changed according to industry lifecycle. As Industry grows, financial information of company is more important for firm valuation in Capital market. We'd like to review the changes of relationships between financial information and firm valuation in Capital market especially for "Software", "Digital Contents", and "Internet" companies listed in Kosdaq market during 2000~2011. The result of data analysis shows the financial information gets more important after 2007. Inversely, it provides analytical bases that related industry gets mature. Also we show that intangible properties are more relevant to stock price of those technical based companies than others.

• The Journal of Korean Academic Society of Nursing Education
• /
• v.9 no.2
• /
• pp.212-221
• /
• 2003

The purpose of this study was to evaluate the effects and define the educational strategies of the cyber education offered to students for Bachelor of Science degree program(RN-BSN). The participants in this study were 67 students in two courses, Growth and Development, and Nursing Process. The main components of the cyber classes were the electronic board and on-line discussion board. The study was conducted from March 4, 2001 to July 23, 2001 at Y University in Seoul, Korea. To examine the effects of the cyber education, learning motivation, and learning satisfaction were measured by questionnaires to the students before the initial class and after completing the class. Learning achievement was measured by the grades for each course. The data were analyzed using frequencies, t-test, and paired t-test. The specific results of the course evaluation were compared and analysis was done to examine differences between traditional classroom teaching and cyber teaching. The study results are as follows: 1. There were no significant differences on total scores for learning motivation between pre-cyber education and post-cyber education. However, there were significant decreases in the post test compared to the pre test for the items, ' I'd like to get as high a score as possible compared to other students (t=-2.20, p=.03)' and ' I'm sure to acquire good grades(t=-5.22, p=.00) '. 2. The average score for learning satisfaction using cyber education was high at 3.52. 3. To define learning achievement using cyber education, grades for students taking cyber classes this year were compared to student grades for a class using traditional lassroom teaching last year. The score was significantly higher for classroom students in the 'Growth and Development' course (t=-3.5, p<.001), and the score was significantly higher for the cyber education students in the 'Nursing Process' course (t=4.3, p<.000). 4. The average of post scores on computer competency was significantly higher on six items, general knowledge about computers, data management and data research, ability to communicate using computer, and internet surfing. On the basis of the above findings, this study suggests that cyber education in nursing courses is effective and readily available. However, it is recommended that consideration be given to characteristics of the course when developing cyber education programs for nursing courses.

• The Journal of Korean Obstetrics and Gynecology
• /
• v.23 no.3
• /
• pp.123-138
• /
• 2010

Purpose: The purpose of this study is to identify the real condition of urinary incontinence(UI), interstitial cystitis(IC), atrophic viginitis(AV) for elderly women and analyze the cognition of traditional korean medicine(TKM) for them. Methods: We utilized questionnaire from May to June, 2010. Questionnaires were taken from 125 women using senior welfare center and nursing home, aged over 65 up to 92. The data were analyzed by $X^2$-test using SPSS/PC ver 18.0 program. Results: The prevalences of UI, IC and AV symptoms were 50.4%, 40.8%, 56%, respectively. The average I-QoL score for UI was $82.62{\pm}21.16$, and the average ICSI score for IC was $8.16{\pm}2.50$. After adjustment for each of the variables considered in this study, alcohol was associated with UI and age, BMI(body mass index) were associated with IC. Most of respondents have no experience(94.4%) or don't know (79.2%) about TKM for UI, IC and AV. 44 women(37.3%) indicated that they weren't willing to use TKM for UI, IC and AV. Reasons for not taking TKM were because of 'no knowledge of TKM(34.1%)' and 'more accustomed to western treatment(34.1%)'. Conclusion: Although the prevalence of UI, IC, AV for elderly women was high, the actual percentage of treatment for these diseases was low, furthermore, patients were not aware of TKM and had very few experiences of TKM for these diseases. The development and increased promotion about TKM program for elderly women's urogenital diseases is needed.

• Korea Science and Art Forum
• /
• v.22
• /
• pp.169-183
• /
• 2015

As time is rapidly changing, the culture to represent an era is getting more subdivided and complex. Due to cultural diversity, the influence, cause, characteristics which could be understood in individual field centered by space in the past cannot be understood now only by the viewpoint of one field, and it has become difficult to predict and correspond to the change of the future. With the development of information and knowledge delivery system, various cultural contents to form a space are being created and lapsed, but there are a lot of parts which cannot be explained or understood by only one point of view. To inspect these situation, this study is aimed to draw the Tangibility and Intangibility Value causes that became the influence with Jongmyo Shrine, designated from UNESCO at February 1995, a traditional space with historical superiority, analyze the key factors that became the main factor to form the space, and consider the importance of the related factors. The unconstructured data technique which is applied as the method of analysis in this study can be said to be a new value judgement and viewpoint in interpreting the space. Therefore, this study is a new trial to provide a frame for multilaterally interpreting the various traditional space and culture of Korea from the past to the present.

• Journal of the Korea Society of Computer and Information
• /
• v.12 no.6
• /
• pp.167-175
• /
• 2007

Fuzzy logic is used to represent qualitative knowledge and provides interpretability to a controlling system model in bioinformatics. This paper focuses on a bioinformatics data classification which is an important bioinformatics application. This paper reviews the two traditional controlling system models The sequence-based threshold controller have problems of optimal range decision for threshold readjustment and long processing time for optimal threshold induction. And the binary-based threshold controller does not guarantee for early system stability in the GPCR data classification for optimal threshold induction. To solve these problems, we proposes a fuzzy-based threshold controller for ART1 clustering in GPCR classification. We implement the proposed method and measure processing time by changing an induction recognition success rate and a classification threshold value. And, we compares the proposed method with the sequence-based threshold controller and the binary-based threshold controller The fuzzy-based threshold controller continuously readjusts threshold values with membership function of the previous recognition success rate. The fuzzy-based threshold controller keeps system stability and improves classification system efficiency in GPCR classification.

• The Journal of Advanced Prosthodontics
• /
• v.10 no.6
• /
• pp.395-400
• /
• 2018

PURPOSE. This study tried to find the most significant factors predicting implant prognosis using machine learning methods. MATERIALS AND METHODS. The data used in this study was based on a systematic search of chart files at Seoul National University Bundang Hospital for one year. In this period, oral and maxillofacial surgeons inserted 667 implants in 198 patients after consultation with a prosthodontist. The traditional statistical methods were inappropriate in this study, which analyzed the data of a small sample size to find a factor affecting the prognosis. The machine learning methods were used in this study, since these methods have analyzing power for a small sample size and are able to find a new factor that has been unknown to have an effect on the result. A decision tree model and a support vector machine were used for the analysis. RESULTS. The results identified mesio-distal position of the inserted implant as the most significant factor determining its prognosis. Both of the machine learning methods, the decision tree model and support vector machine, yielded the similar results. CONCLUSION. Dental clinicians should be careful in locating implants in the patient's mouths, especially mesio-distally, to minimize the negative complications against implant survival.

• Natural Product Sciences
• /
• v.24 no.4
• /
• pp.235-240
• /
• 2018

Betula platyphylla var. japonica (Betulaceae), also known as Asian white birch, is an endemic medicinal tree, the bark of which has been used in Chinese traditional medicine for the treatment of various inflammatory diseases. In our continuing search for bioactive compounds from Korean natural resources, a phytochemical investigation of the bark of B. platyphylla var. japonica led to the isolation of 7-oxo-${\beta}$-sitosterol (1) and soyacerebroside I (2) from its ethanol extract as main components by liquid chromatography (LC)/mass spectrometry (MS)-based analysis. The structures of isolates were identified by comparison of $^1H$ and $^{13}C$ nuclear magnetic resonance spectroscopic data and physical data with the previously reported values and LC/MS analyses. To the best of our knowledge, this is the first study to demonstrate that the isolated compounds, 7-oxo-${\beta}$-sitosterol and soyacerebroside I, were isolated in B. platyphylla var. japonica. We examined the effects of the isolates on the regulation of adipocytes and osteoblast differentiation. These isolates (1 and 2) produced fewer lipid droplets compared to the untreated negative control in Oil Red O staining of the mouse mesenchymal stem cell line without altering the amount of alkaline phosphatase staining. The results demonstrated that both compounds showed marginal inhibitory effects on adipocyte differentiation but did not affect osteoblast differentiation.

• Women's Health Nursing
• /
• v.5 no.2
• /
• pp.247-261
• /
• 1999

The purpose of this study was to figure out related factors to the self-reported climacteric symptoms and the relationship among the health promoting behaviors, climacteric symptoms and degree of Sanhujori, the Korean traditional postpartal care. A cross-sectional survey design was employed in this study. The subjects were 108 middle-aged women who were non-hystrectomized and ranged in age from 40 to 60 years. They were selected in seoul and Kyoung-ki province, Korea, Data were collected from Oct.25 Nov. 10, 1997 by a structured questionnaire. The instruments used for this study were the revised health Promotion Lifestyle(HPLP) developed by Walker, Sechrist & Pender, and revised Climacteric Symptoms Scale developed by Chi, Sung Ai. the data were analyzed by the SPSS/$PC^+$ program using t-test, ANOVA and Scheffe test as a post hoc and Pearson Correlation Coefficient. The results of the study were as follows ; 1. The mean score of health promoting behaviors was low($2.42{\pm}0.35$). There were statistically significant differences in the score of health promoting behaviors according to the educational background, family income, marital satisfaction, whether or not taking a restorative food and degree of Sanhujori, especially the period (t=-2.07, F=2.60~7.57, p<0.05). 2. The mean score of score self-reported climacteric symptoms was 1.69%;99% of middle-aged women had symptoms. There were statically significant differences in the score of middle -aged women's self-reported climacteric symptoms according to the age, number of children, educational background, occupation, family income, marital satisfaction, whether or not receiving hormon replacement therapy (HRT) or consultation by a professional, perceived health status and self evaluation of Sanhujori(t=-2.04~3.69, F=2.87~11.63, p<0.05). 3. women's degree of Sanhujori was a positive correlation with health promoting behaviors(r=0.34, p=0.00) and negative correlation with the degree of self-reported climacteric symptoms(r=-0.19,p=0.03). 4. The influencing factors to the climacteric symptoms were self actualization, interpersonal support, and perceived health status among the health promoting behaviors with 57% of variance($R^2$=0.57). 5. The middle-aged women's type of coping pattern for the climacteric symptoms was classified as active behavioral coping, spiritual & psychological coping, and negative coping. In conclusion, to intervene the middle aged women's climacteric symptoms and develop nursing strategies for their health, health promoting behavior, especially ; self actualization, interpersonal support, and perceived health status should be considered. And, as the primary prevention strategy for women's health during the period of childbearing and also middle age, especially for the climacteric symptoms, Sanhujori should be reconsidered.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.25 no.2
• /
• pp.174-179
• /
• 2015

Failure Mode and Effects Analysis (FMEA) has been used by Dynamic Positioning (DP) system for risk and reliability analysis. However, there are limitations associated with its implementation in offshore project. 1) since the failure data measured from the SCADA system is missing or unreliable, assessments of Severity, Occurrence, Detection are based on expert's knowledge; 2) it is not easy for experts to precisely evaluate the three risk factors. The risk factors are often expressed in a linguistic way. 3) the relative importance among three risk factors are rarely even considered. To solve these problems and improve the effectiveness of the traditional FMEA, we suggest a Fuzzy-FMEA method for risk and failure mode analysis in Dynamic Positioning System of offshore. The information gathered from DP FMEA report and DP FMEA Proving Trials is expressed using fuzzy linguistic terms. The proposed method is applied to an offshore Dynamic Positioning system, and the results are compared with traditional FMEA.

• The Korean Journal of Applied Statistics
• /
• v.32 no.6
• /
• pp.795-835
• /
• 2019

Big data has been generated in various fields. Many companies have now tried to make profits by building a system capable of analyzing big data based on artificial intelligence (AI) techniques. Integrating AI technology has made analyzing and utilizing vast amounts of data increasingly valuable. In particular, demand forecasting with maximum accuracy is critical to government and business management in various fields such as finance, procurement, production and marketing. In this case, it is important to apply an appropriate model that considers the demand pattern for each field. It is possible to analyze complex patterns of real data that can also be enlarged by a traditional time series model or regression model. However, choosing the right model among the various models is difficult without prior knowledge. Many studies based on AI techniques such as machine learning and deep learning have been proven to overcome these problems. In addition, demand forecasting through the analysis of stereotyped data and unstructured data of images or texts has also shown high accuracy. This paper introduces important areas where demand forecasts are relatively active as well as introduces machine learning and deep learning techniques that consider the characteristics of each field.