• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.15 no.2
• /
• pp.19-32
• /
• 2020

The purpose of this study was to investigate the influence factors of the university level centering on the entrepreneurial performance of the university students and full-time faculties in the situation of increasing interest in entrepreneurial university. In order to achieve the purpose of the study, a panel data was established from 2015 to 2018 on the basis of the university notification data. The panel data included universities with data on the number of university students and full-time faculty founders for at least two years. Through this, four year data from 154 universities were used for analysis. As an analysis method, frequency analysis and descriptive statistics were conducted to understand the characteristics of the university. Since then, panel negative binomial regression analysis has been conducted in consideration of the longitudinal features and distribution of the data. Also, based on the Hausman test results, the results were interpreted based on random effect model. The results of this study are as follows. First, as a result of the analysis of the entrepreneurial performance and the change trend of the domestic university from 2015 to 2018, the entrepreneurial performance of the university has been steadily increasing in the last four years, and the increase in the number of university student entrepreneurs was relatively higher than the full-time faculties. Second, economic and educational approaches need to be combined to promote university students' start-ups. The university factors that promote the start-up of university students were found to be scholarships, start-up grants, startup lectures, and startup clubs. Third, the openness and regional characteristics of the univeristy can promote the establishment of university students. Fourth, the establishment of a research environment and support for start-ups for full-time faculty members can enhance their start-up performance. The university factors that promote the start-up of full-time faculty were research funds and staffes who support start-up. The conclusions drawn from these findings are as follows. First, overall efforts are needed to develop into an entrepreneurial university. Second, in order to change into an entrepreneurial university, direct support for entrepreneurship is needed. Third, as an entrepreneurial university, it is necessary to find a way to bridge the gap by university according to region and size. Fourth, it is necessary to reinforce the support for linking the research results of universities to start-ups. Fifth, it is necessary to improve the atmosphere for full-time faculty members to be entrepreneur.

• Korean Journal of Psychosomatic Medicine
• /
• v.27 no.2
• /
• pp.101-110
• /
• 2019

Objectives : It is not clear which clinical variables are most closely associated with delirium in the Intensive Care Unit (ICU). By comparing clinical data of ICU delirium and non-delirium patients, we sought to identify variables that most effectively differentiate delirium from non-delirium. Methods : Medical records of 6,386 ICU patients were reviewed. Random Subset Feature Selection and Principal Component Analysis were utilized to select a set of clinical variables with the highest discriminatory capacity. Statistical analyses were employed to determine the separation capacity of two models-one using just the selected few clinical variables and the other using all clinical variables associated with delirium. Results : There was a significant difference between delirium and non-delirium individuals across 32 clinical variables. Richmond Agitation Sedation Scale (RASS), urinary catheterization, vascular catheterization, Hamilton Anxiety Rating Scale (HAM-A), Blood urea nitrogen, and Acute Physiology and Chronic Health Examination II most effectively differentiated delirium from non-delirium. Multivariable logistic regression analysis showed that, with the exception of vascular catheterization, these clinical variables were independent risk factors associated with delirium. Separation capacity of the logistic regression model using just 6 clinical variables was measured with Receiver Operating Characteristic curve, with Area Under the Curve (AUC) of 0.818. Same analyses were performed using all 32 clinical variables;the AUC was 0.881, denoting a very high separation capacity. Conclusions : The six aforementioned variables most effectively separate delirium from non-delirium. This highlights the importance of close monitoring of patients who received invasive medical procedures and were rated with very low RASS and HAM-A scores.

• Journal of Gifted/Talented Education
• /
• v.21 no.1
• /
• pp.57-81
• /
• 2011

The purpose of this study was to explore the quality of life of musically talented students as measured by their external experiences (e.g., activities, companions) and internal experiences (e.g., flow, emotion). The participants in this study were 33 musically talented students (10 males, 23 females) aged 13 to 19. Study data were collected for 7 consecutive days using the Experience Sampling Method (ESM), which employs a cellular-phone as a signaling device. The results were as follows: First, in response to the 1625 random signals, musically talented students reported that 40.9% of their time was spent on productive activities. An additional 33.4% of time was used for maintenance activities and the rest of their time was spent on leisure/social activities. Also, musically talented students reported that 48.5% of their time was spent alone. When they were alone, they spent a lot of time engaging in productive activities (44.3%). Second, in order to measure the flow of their life, two methods were used. One used a 4-channel flow model (i.e. apathy, boredom, flow, anxiety) and the other used 8 dimensions and conditions of the flow experience (i.e. concentration, self-consciousness disappears, action and awareness merge, distorted sense of time, freedom from worry about failure, clear goals, immediate feedback, balance between challenges and skills). According to the former, when engaged in music-related activities, musically talented students usually reported flow (54.0%), while they felt apathy (41.3%) for daily routines activities. According to the latter method, musically talented students experienced flow for most productive activities, while they experienced flow least for maintenance activities. Emotional variables of ESF are comprised of 10 semantic scales (i.e. happy-sad, strong-weak, active-passive, sociablelonely, proud-ashamed, involved-detached, excited-bored, clear-confused, relaxed-worried, cooperative-competitive). Musically talented students reported experiencing the most positive emotion for social activities and experiencing the most negative emotion for maintenance activities. Results of this study assert that musically talented students had to trade off immediate enjoyment for developing their special gifts. They could not afford as much time for socializing with friends, and they had to spend more time alone compared to their peers without such gifts. Consequently, they were found to deprive themselves of the spontaneous good times that teenagers usually thrive on. They were helped in this respect by their autotelic personality traits, especially their strong need for achievement and endurance. The downside, however, is that the moment-to-moment quality of their moods suffered. The argument concerning musically talented students applies for all adolescents. The choices that talented students must make between immediate gratification and long-term development, and between solitude and companionship, are the same choices every young person must make, regardless of her or his level of talent. All of us have gifts that are potentially useful and worthy of being appreciated. But to develop these latent talents we must cultivate them, and this takes time and the investment of mental energy. The lifestyle that musically talented students develop can show us some of the choices all of us must make in order to cultivate our gifts.

• Journal of Animal Science and Technology
• /
• v.47 no.5
• /
• pp.703-710
• /
• 2005

Data from Hanwoo steers and bull calves were analyzed to see the phenotypic and genetic relationships between carcass traits from four different covariance models. Four models fit test station and test period as fixed effect of contemporary group and sire as random effect assuming paternal half-sib relationships among animals. Each model fits one of linear covariate (s) of different slaughter end points-age at slaughter in the first order, age at slaughter in the first and second order, slaughter weight or back fat thickness at 12-13th rib of cold carcass. Age at slaughter in its second order was not significant. Age at slaughter accounted for signifi- cant amount of genetic variances and covariances of carcass traits. Heritability estimates of back fat thickness, rib eye area, carcass weight, marbling score and dressing percentage were 0.34, 0.22, 0.24, 0.42 and 0.18, respectively at constant age basis. The genetic correlation between carcass weight and the other variables were all positive and low to high in magnitude. Genetic correlations between back fat thickness and rib eye area and between marbling score and dressing percentage were low but negative. Variance and covariance structure between these traits were shifted to a great extent when these variables were regressed on slaughter weight or on back fat thickness. These two covariates counteracted to each other but they adjusted each carcass variable or their interrelationship according to differential growth of body components, bone, muscle and fat. Slaughter weight tended to decrease genetic variances and covariances of carcass weight and between component traits and back fat thickness tended to increase those of rib eye area and between rib eye area and carcass weight.

• Health Policy and Management
• /
• v.12 no.2
• /
• pp.23-42
• /
• 2002

The purpose of this study was to analyze the results of 133 studies related to patient compliance published between 1980 and 2001 and to assess the effectiveness of intervention on compliance by using meta-analysis. We collected the existing literatures by using web and manual search 'patient compliance', 'sick role behavior', 'major clinical disease', and 'intervention' as key words and by reviewing content of journals related to medicine, nursing and public health. The compliance interventions were classified by theoretical focus into educational, behavioral, and affective categories within which specific intervention strategies were further distinguished. The compliance indicators broadly represent five classes of compliance-related assessments: (1) health outcomes (eg, blood pressure and hospitalization), (2) direct indicators (eg, urine and blood tracers and weight change), (3) indirect indicators (eg, pill count and refill records), (4) subjective report (eg, patients' or others' reports), (5) utilization (appointment making and keeping, use of preventive services). Quantitative meta-analysis was performed by MetaKorea program which was developed for meta-analysis in Korea. Among the 133 articles, 10 studies were selected through the qualitative meta-analysis process, and then only 6 studies were selected for the quantitative meta-analysis finally. The interventions produced significant effects for all the compliance indicators with the magnitude of common effect size (4.1192) than the non-intervention group in a random effect model. The largest effects were each study for patient of hypertension using health outcome such as blood pressure (0.4679) and diabetes mellitus using direct indicator such as glucose level in blood and urine (0.7753). These results suggest that strategic interventions showed clear advantage for improvement of patient compliance compared with non-intervention group.

• Korean Security Journal
• /
• no.42
• /
• pp.7-36
• /
• 2015

This study aims at analyzing difference of social Security network, Community unity and local government trust according to socio-demographical features, exploring the relation of social Security network, Community unity and local government trust according to socio-demographical features, presenting results between each variable as a model and verifying the property of mutual ones. This study sampled general citizens in Gwangju for about 15 days Aug. 15 through Aug. 30, 2014, distributed total 450 copies using cluster random sampling, gathered 438 persons, 412 persons of whom were used for analysis. This study verified the validity and credibility of the questionnaire through an experts' meeting, preliminary test, factor analysis and credibility analysis. The credibility of questionnaire was ${\alpha}=.809{\sim}{\alpha}=.890$. The inout data were analyzed by study purpose using SPSSWIN 18.0, as statistical techniques, factor analysis, credibility analysis, correlation analysis, independent sample t verification, ANOVA, multi-regression analysis, path analysis etc. were used. the findings obtained through the above study methods are as follows. First, building a social Security network has an effect on Community institution. That is, the more activated a, the higher awareness on institution. the more activated street CCTV facilities, anti-crime design, local government Security education, the higher the stability. Second, building a social Security network has an effect on trust of local government. That is, the activated local autonomous anti-crime activity, anti-crime design. local government's Security education, police public oder service, the more increased trust of policy, service management, busines performance. Third, Community unity has an effect on trust of local government. That is, the better Community institution is achieved, the higher trust of policy. Also the stabler Community institution, the higher trust of business performance. Fourth, building a social Security network has a direct or indirect effect on Community unity and local government trust. That is, social Security network has a direct effect on trust of local government, but it has a higher effect through Community unity of parameter. Such results showed that Community unity in Gwangju Region is an important factor, which means it is an important variable mediating building a social Security network and trust of local government. To win trust of local residents, we need to prepare for various cultural events and active communication space and build a social Security network for uniting them.

• 한국노년학
• /
• v.38 no.4
• /
• pp.811-835
• /
• 2018

Social relations and cognitive function in old age are closely related to each other, and social relation is classified into structural characteristics and qualitative characteristics reflecting cognitive and emotional evaluation. The concept of social isolation is the focus of attention in relation to the social relations of old age. Social isolation has a multidimensional theoretical structure that is divided into objective dimension such as social network, type of furniture, social participation, and subjective dimension such as lack of perceived social support and loneliness. There is also a close relationship between cognitive function and interpersonal conflict in old age. In this study, we examined the effect of subjective social isolation, which shows the structural characteristics of social relations, and subjective social isolation and interpersonal conflict on the dementia occurrence by age group in the elderly. The data were analyzed by applying a random effect panel logit model using 1,740 panel data from the first year to the third year of KSHAP. The results of the analysis are summarized as follows. First, the cognitive impairment increased sharply with age. Objective and subjective social isolation were both U-shaped distribution with an inflection point of 80 years old. Second, the main effect on the probability of cognitive impairment was statistically significant with objective and subjective social isolation, but the type of interpersonal conflict did not appear to be significant. Third, the results of two-way interaction effect analysis on the probability of cognitive impairment are as follows. The relationship between subjective social isolation and the probability of occurrence of cognitive impairment was significantly different according to the level of conflict with spouse. In addition, the higher the subjective social isolation, the higher the probability of cognitive impairment in the elderly(over 85) than in the young-old(65~74). In addition, as the level of conflict with spouses increases, the probability of cognitive impairment of the oldest-old(aged 85 or older) is drastically lower than that of the young-old(aged 65~74). Based on the results of this study, policy and practical implications for reducing the cognitive impairment of the elderly age group were suggested, and limitations of the study and suggestions for future research were discussed.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.175-197
• /
• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1371-1383
• /
• 2022

In this study, the amount of coal waste dump was calculated using six Digital Elevation Models (DEMs) produced between 2006 and 2018 in Jangseong-dong, Taebaek-si, Gangwon-do, and the subsidence was observed by applying the Persistent Scatterer Interferometric SAR (PSInSAR) technique on the Sentinel-1 SAR images. As a result of depositing activities using DEMs, a total of 1,668,980 m³ of coal waste was deposited over a period of about 12 years from 2006 to 2018. The observed subsidence rate from PSInSAR was -32.3 mm/yr and -40.2 mm/yr from the ascending and descending orbits, respectively. As the thickness of the waste pile increased, the rate of subsidence increased, and the more recent the completion of the deposit, the faster the subsidence tended to occur. The subsidence rates from the ascending and descending orbits were converted to vertical and horizontal east-west components, and 22 random reference points were set to compare the subsidence rate, the waste rock thickness, and the time of depositing completion. As a result, the subsidence rate of the reference point tended to increase as the thickness of the waste became thicker, similar to the PSInSAR results in relation to the waste thickness. On the other hand, there was no clear correlation between the completion time of the deposits and the rate Of subsidence at the reference points. This is because the time of completion of the deposits at all but 5 of the 22 reference points was too biased in 2010 and the correlation analysis was meaningless. As in this study, the use of DEM and PSInSAR is expected to be an effective alternative to compensate for the lack of field data in the safety management of coal waste deposits.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.6-7
• /
• 2021

Vegetation development in rivers is one of the important issues not only in academic fields such as geomorphology, ecology, hydraulics, etc., but also in river management practices. The problem of river vegetation is directly connected to the harmony of conflicting values of flood management and ecosystem conservation. In Korea, since the 2000s, the issue of river vegetation and land formation has been continuously raised under various conditions, such as the regulating rivers downstream of the dams, the small eutrophicated tributary rivers, and the floodplain sites for the four major river projects. In this background, this study proposes a method for classifying the distribution of vegetation in rivers based on remote sensing data, and presents the results of applying this to the Naeseong Stream. The Naeseong Stream is a representative example of the river landscape that has changed due to vegetation development from 2014 to the latest. The remote sensing data used in the study are images of Sentinel 1 and 2 satellites, which is operated by the European Aerospace Administration (ESA), and provided by Google Earth Engine. For the ground truth, manually classified dataset on the surface of the Naeseong Stream in 2016 were used, where the area is divided into eight types including water, sand and herbaceous and woody vegetation. The classification method used a random forest classification technique, one of the machine learning algorithms. 1,000 samples were extracted from 10 pre-selected polygon regions, each half of them were used as training and verification data. The accuracy based on the verification data was found to be 82~85%. The model established through training was also applied to images from 2016 to 2020, and the process of changes in vegetation zones according to the year was presented. The technical limitations and improvement measures of this paper were considered. By providing quantitative information of the vegetation distribution, this technique is expected to be useful in practical management of vegetation such as thinning and rejuvenation of river vegetation as well as technical fields such as flood level calculation and flow-vegetation coupled modeling in rivers.