• Economic and Environmental Geology
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• v.48 no.6
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• pp.451-465
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• 2015

The free-air anomalies are computed using a data set from various types of gravity measurements in the Korean Peninsula area. The gravity values extracted from the Earth Gravitational Model 2008 are used in the surrounding region. The upward continuation technique suggested by Dragomir is used in the computation of the external free-air anomalies at various altitudes. The integration radius 10 times the altitude is used in order to keep the accuracy of results and computational resources. The direct geodesic formula developed by Bowring is employed in integration. At the 1-km altitude, the free-air anomalies vary from -41.315 to 189.327 mgal with the standard deviation of 22.612 mgal. At the 3-km altitude, they vary from -36.478 to 156.209 mgal with the standard deviation of 20.641 mgal. At the 1,000-km altitude, they vary from 3.170 to 5.864 mgal with the standard deviation of 0.670 mgal. The predicted free-air anomalies at 3-km altitude are compared to the published free-air anomalies reduced from the airborne gravity measurements at the same altitude. The rms difference is 3.88 mgal. Considering the reported 2.21-mgal airborne gravity cross-over accuracy, this rms difference is not serious. Possible causes in the difference appear to be external free-air anomaly simulation errors in this work and/or the gravity reduction errors of the other. The external gravity field is predicted by adding the external free-air anomaly to the normal gravity computed using the closed form formula for the gravity above and below the surface of the ellipsoid. The predicted external gravity field in this work is expected to reasonably present the real external gravity field. This work seems to be the first structured research on the external free-air anomaly in the Korean Peninsula area, and the external gravity field can be used to improve the accuracy of the inertial navigation system.

• Journal of Korean Academy of Nursing
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• v.20 no.1
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• pp.88-117
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• 1990

The purposes of this study are to delineate a profile of the state of a stroke patient's adaptation at 3 months after hospitalization and to explore the relationship between the level of adaptation and the variables which influence the adaptation of hemiplegic patients. To these ends, theoretical framework was derived basically from the stress adaptation model. The basic assumption underlying the level of adaptation is influenced by the presenting focal, contextual and residual stimuli. This group of stimuli is further operationalized and represented by a perception of stress. which is the perceived effect of the disability and by the mediating variables such as sociodemographic factors as an external conditioning variables and perceived social support and hardiness personality characteristics as an internal intervening variables. The dependent varibales in this study is the level of physical, psychological and social adaptation and is hypothesized to be a function of the interaction between 3 sets of variables namely, the perceived disability effect, external conditioning variables and internal intevening varibles. A total of fourty three subjects from 3 general hospitals in Seoul were observed and interviewed with the aid of 7 structured instruments. The data were collected twice on each subject : first at the pre-discharge period arid at 3 months post-discharge from hospital for the second time. The study was carried out for the period from February to August, 1988. The instruments used for the study include 4 existing scales and 3 scales developed by the researcher for this study. They are : 1) The ADL dependency scale and the scale of the clinical physical functions for the assessment of physical adaptation. 2) the SDS(self report of depression) to measure the level of psychological adaptation. 3) The scale for the amount of social activities for the measurement of the level of social adaptation. 4) The scale for the perceived effect of disability for the measurement of the focal stimuli. 5) The health related hardiness scale and the perceived interpersonal support self evaluation list(ISEL) for the measurement of the hardiness personality character and the perceived social support. The data obtained were analyzed using percentage, oneway ANOVA, Pearson coefficients correlation and stepwise multiple regression. The findings provide valuable information about the present level of physical adaptation at 3 months after discharge. The patient revealed a decreased ADL dependency and lowered limitation of physical function as compared with pre - discharge state. Psycholcgically, the average degree of depression at follow up was within normal range of depression. Socially, the amount of social activities was very low. The one way ANOVA and the correlational analysis revealed the relationship between the 3 sets of variables and the adaptation level as follows : 1) The perceived disability effect was related to the degree of the depression and the amount of social activities but was not related to the physical adaptation. 2) Among the sociodemographic variables, sex and education were related to the difference of ADL dependency and the change of physical function. These factors indicate that women more than men and educated more than the less educated were found more independent. The education was also related to the degree of depression suggesting that the higher the educational level, the more well adapted the patients were both physically and psychologically. Age, marital status and job state were not found to be related to the patient's adaptation level. 3) Among the internal intervening variables, the health related hardiness characteristic was related to the differences of ADL dependency, physical functions and the social activities, indicating that the higher the hardiness character the higher the level of physical and social adaptation. 4) The perceived social support, another internal intervening variable, was related to the degree of depression and the social activities. This data suggest that the higher the perception of social support, the better adapted the patients were psychogically and socially. In summarizing the results of the correlational analysis, the level of physical adaptation was influenced by sex, the years of education and the hardiness character. The level of psychological adaptation was influenced by the years of education, the perceived disability effect and the perceived social support. And the level of social adaptation was influenced by the perceived disability effect, the hardiness character and the perceived social support. The stepwise multiple regression analysis shows findings as follows : 1) The most important factor to explain the difference of ADL dependency was sex, indicating females were more independent than males. 2) The most important factor to explain the difference of physical function and the degree of depression was the patient's education level. 3) The strongest explaining factor for the amount of social activities was perceived self esteem(one of the subconcepts of perceived social support). Thus the most important factors influencing the level of adaptation were found to be sex, education, the hardiness character and self esteem. From the above findings, the significance of this study can be delineated as follows : 1) Corroboration of the assumed relationship between the various variables and the adaptation level as suggested in the conceptual model. 2) Support for the feasibility of the cognitive approach for nursing intervention such as hardness character training, counselling and teaching for self-care in the chronic patients.

• 한국사회정책
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• v.24 no.1
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• pp.33-66
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• 2017

Based on the premise that the systematic career process of workers in the general labor market was one of core elements of successful achievements and their establishment both at the individual and organizational level, this study set out to conduct empirical analysis of factors influencing the subjective career success of disabled workers in competitive employment at the multi-dimensional levels of individuals and organizations(corporations) and thus provide practical implications for the career management directionality of their successful vocational life with data based on practical and statistical accuracy. For those purposes, the investigator administered a structured questionnaire to 126 disabled workers at 48 companies in Seoul, Gyeonggi, Chungcheong, and Gangwon and collected data about the individual and organizational characteristics. Then the influential factors were analyzed with the multilevel analysis technique by taking into consideration the organizational effects. The analysis results show that organizational characteristics explained 32.1% of total variance of subjective career success, which confirms practical implications for the importance of organizational variables and the legitimacy of applying the multilevel model. The significant influential factors include the degree of disability, desire for growth, self-initiating career attitude and value-oriented career attitude at the individual level and the provision of disability-related convenience, career support, personnel support, and interpersonal support at the organizational level. The latter turned out to have significant moderating effects on the influences of subjective career success on the characteristic variables at the individual level. Those findings call for plans to increase subjective career success through the activation of individual factors based on organizational effects. The study thus proposed and discussed integrated individual-corporate practice strategies including setting up a convenience support system by reflecting the disability characteristics, applying a worker support program, establishing a frontier career development support system, and providing assistance for a human network.

• Journal of agricultural medicine and community health
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• v.26 no.1
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• pp.37-56
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• 2001

This study was conducted to analyze the degree of changes in knowledge and attitude toward smoking and to examine the factors affecting knowledge and attitude for smoking after providing a smoking prevention program based on social influence model for a year to middle school students. Study population consists of 665 subjects of middle school students(aged 14 years) in Gumi city in Kyeongsangbukdo Province. Among them three-hundred sixty-seven students(intervention group) were educated to a smoking prevention program for 1 year from April 1999 to April 2000. School-based four-class program to prevent smoking was developed. The program provides instruction about short and long-term negative physiologic and social consequences of smoking and also discussed the health hazards of smoking, social pressure to smoke, peer norms regarding tobacco use, and refusal skill. A 45-item self-administered structured questionnaire was designed to evaluate the change of knowledge, attitude, smoking rate and the amount of smoking. The instrument was comprised of 11 knowledge items, thirteen attitude item and demographic items. Each scales were created by summing responses to each items within each scales and high scores on the knowledge, attitude, and smoking behavioral intention scales indicated positive responses. Based on the changes before and after the implementation of smoking prevention program between intervention and control group, the change of scores on knowledge were significantly different between the control group and the intervention group(p<0.05) and the change of scores on the attitude toward smoking was significantly different between intervention and control group. The change of smoking rate were not showing a significant difference between two groups but the amount of smoking were significantly reduced in intervention group than control group. In multiple regression analysis on changes of knowledge about smoking, the variables of smoking prevention program education, previous knowledge on smoking and students' school performance were selected the significant variables. In multiple regression to analysis of the factors influencing changes in attitude toward smoking, the variables of smoking prevention program education, previous knowledge on smoking were shown to be significant. The smoking prevention program was effective on change of knowledge and attitude of middle school students. In considering that the policy should be needed to extent of implementation of school-based health education curricula based on social influence model and it would contribute to reduce smoking of students.

• The Journal of Small Business Innovation
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• v.19 no.2
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• pp.53-68
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• 2016

Motivation and activities for technological learning, entrepreneurship, innovation, and creativity are driving forces of economic development in Asian countries. In the early stages of technological development, technological learning and entrepreneurship are efficient ways in which to catch up with advanced countries because firms can accumulate skills and knowledge quickly at relatively low risk. In the later stages of technological development, however, innovation and creativity become more important. This study aims to identify a) the factors (learning capabilities) that influence technological learning performance and b) barriers to enhancing innovation capabilities for the creative economy and organizations. The major part of this study is related to learning capabilities in the post-catch-up era. Based on a literature review and observations from Korean experiences, this study proposes a technological learning model composed of various influencing factors on technological learning. Three hypotheses are derived, and data are collected from Korean machine tool manufacturers. Intense interviews with CEOs and R&D directors are conducted using structured questionnaires. Statistical analysis, such as correlation and ANOVA are then carried out. Furthermore, this study addresses how to enhance innovation capabilities to move forward. Innovation enablers and barriers are identified by case studies and policy analysis. The results of the empirical study identify several levels of firms' learning capabilities and activities such as a) stock of technology, b) potential of technical labor, c) explicit technological efforts, d) readiness to learn, e) top management support, f) a formal technological learning system, g) high learning motivation, h) appropriate technology choice, and i) specific goal setting. These learning capabilities determine firms' learning performance, especially in the early stages of development. Furthermore, it is found that the critical factors for successful technological learning vary along the stages of technology development. Throughout the statistical and policy analyses, this study confirms that technological learning can be understood as an intrinsic principle of the technology development process. Firms perform proactive and creative learning in the late stages, while reactive and imitative learning prevails in the early stages. In addition, this study identifies the driving forces or facilitating factors enhancing innovation performance in the post catch-up era. The results of the preliminary case studies and policy analysis show some facilitating factors such as a) the strategic intent of the CEO and corporate culture, b) leadership and change agents, c) design principles and routines, d) ecosystem and collaboration with partners, and e) intensive R&D investment.

• Journal of the Korea Institute of Building Construction
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• v.9 no.5
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• pp.79-86
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• 2009

In a steel structure, spray-applied fire resistive materials are crucial in preventing structural strength from being weakened in the event of a fire. The quality control of such materials, however, is difficult for manual workers, who can frequently be in short supply. These skilled workers are also very likely to be exposed to environmental hazards. Problems with construction work such as this, which are specifically the difficulty of achieving quality control and the dangerous nature of the work itself, can be solved to some degree by the introduction of automated equipment. It is, however, very difficult to automate the work process, from operation to the selection of a location for the equipment, as the environment of a construction site has not yet been structured to accommodate automation. This is a fundamental study on the possibility of the automation of spray-applied fire resistive coating work. In this study, the linkability of the cutting-edge RTLS to an automation system is reviewed, and a scenario for the automation of spray-applied fire resistive coating work and system composition is presented. The system suggested in this study is still in a conceptual stage, and as such, there are many restrictions still to be resolved. Despite this fact, automation is expected to have good effectiveness in terms of preventing fire from spreading by maintaining a certain level of strength at a high temperature when a fire occurs, as it maintains the thickness of the fire-resistive coating at a specified level, and secures the integrity of the coating with the steel structure, thereby enhancing the fire-resistive performance. It also expected that if future research is conducted in this area in relation to a cutting-edge monitoring TRS, such as the ubiquitous sensor network (USN) and/or building information model (BIM), it will contribute to raising the level of construction automation in Korea, reducing costs through the systematic and efficient management of construction resources, shortening construction periods, and implementing more precise construction

• Geophysics and Geophysical Exploration
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• v.9 no.1
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• pp.67-72
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• 2006

Injection of $CO_2$ into underground saline formations, due to their large storage capacity, is probably the most promising approach for the reduction of $CO_2$ emissions into the atmosphere. $CO_2$ storage must be carefully planned and monitored to ensure that the $CO_2$ is safely retained in the formation for periods of at least thousands of years. Seismic methods, particularly for offshore reservoirs, are the primary tool for monitoring the injection process and distribution of $CO_2$ in the reservoir over time provided that reservoir properties are favourable. Seismic methods are equally essential for the characterisation of a potential trap, determining the reservoir properties, and estimating its capacity. Hence, an assessment of the change in seismic response to $CO_2$ storage needs to be carried out at a very early stage. This must be revisited at later stages, to assess potential changes in seismic response arising from changes in fluid properties or mineral composition that may arise from chemical interactions between the host rock and the $CO_2$. Thus, carefully structured modelling of the seismic response changes caused by injection of $CO_2$ into a reservoir over time helps in the design of a long-term monitoring program. For that purpose we have developed a Graphical User Interface (GUI) driven rock physics simulator, designed to model both short and long-term 4D seismic responses to injected $CO_2$. The application incorporates $CO_2$ phase changes, local pressure and temperature changes. chemical reactions and mineral precipitation. By incorporating anisotropic Gassmann equations into the simulator, the seismic response of faults and fractures reactivated by $CO_2$ can also be predicted. We show field examples (potential $CO_2$ sequestration sites offshore and onshore) where we have tested our rock physics simulator. 4D seismic responses are modelled to help design the monitoring program.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1D
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• pp.119-126
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• 2011

High-speed railway raises global interests with the growing concerns on the green development and the green energy. However, since most of the infrastructure investment of Korea was focused on the highway projects for last forty years, the investment on the railway has been limited around 40~50% of that of the highway projects. In addition, due to the world economy crisis and unsatisfactory support of existing policy for the private investment project, the private investment is given a small deal of weight on the social overhead capital investment. Meanwhile, despite the world high-speed railway market is growing rapidly and the Korean contractors have won the international construction contracts over 70 billion USD, past records of railway projects are very rare. Therefore, it is required to develop strategies for encouraging private investment in the domestic market to achieve efficient high-speed railway development and for advancing into foreign high-speed railway market. This study carried out data collection and market analysis for both domestic and foreign market respectively. Through a structured questionnaire survey and expert interviews, contractors' perceptions on the high-speed railway market and needs for the government support are collected. Summary of strategies drawn from this study are as follows: 1) carrying out BTL high-speed railway projects and revising related policies; 2) upwarding incentive level for the private pre-investment projects considering the contractors' credit rating; 3) carrying out Honam-Jeju submarine railway project; 4) establishing a efficient consortium model for foreign market; 5) improving the capacity of the Korea Railway Association that support Korean contractors' foreign advancement; and 6) expand the budget for Global Infra-fund.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.2
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• pp.47-65
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• 2020

With the era of the Fourth Industrial Revolution in sight, innovative business models utilizing new technologies are emerging, and startups are enjoying an abundance of opportunities based on the agility to respond to disruptive innovations and the opening to new technologies. However, what is most important in creating a sustainable start-up ecosystem is not the start-up itself, but the process of research-start-investment-investment-the leap to listing and big business-in order to build a virtuous circle of startups that leads to re-investment. To this end, the environment created in the hub area where start-ups were conducted is important, and these material and non-material environmental factors are described as being inclusive by the word "entrepreneurial ecosystem." This study aims to provide implications for Korea's entrepreneurial ecosystem through the study of the interaction of the elements that make up the start-up ecosystem and the relationship of ecosystem participants in Singapore. Singapore has been consistently mentioned as the top two Asian countries in assessing the start-up environment and business environment. In this process, six elements of the entrepreneurial ecosystem presented by Isenberg(2010)-policies, finance, culture, support, human resources, and market-are the best frameworks for analyzing entrepreneurial ecosystems in terms of well encompassing prior studies related to entrepreneurial ecosystem elements, and a model of regional transformation is formed focusing on some elements to suit Singapore, the target area of study. By considering that Singapore's political nature would inevitably have a huge impact on finance, Smart Nation policy was having an impact on university education related to entrepreneurship, and that the entrepreneurial networks and global connectivity formed within Singapore's start-up infrastructure had a significant impact on Singapore's start-up's performance, researches needed to look more at the factors of policy, culture and market. In addition, qualitative research of participants in the entrepreneurial ecosystem was essential to understand the internal interaction of the elements of the start-up ecosystem, so the semi-structured survey was conducted by visiting the site. As such, this study examined the status of the local entrepreneurial ecosystem based on qualitative research focused on policies, culture and market elements of Singapore's start-up ecosystem, and intended to provide implications for regulations related to start-ups, the role of universities and start-up infrastructure through comparison with Korea. This could contribute not only to the future research of the start-up ecosystem, but also to the creation of a start-up infrastructure, boosting the start-up ecosystem, and the establishment of the orientation of the start-up education in universities.