• Korean Security Journal
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• no.42
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• pp.7-36
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• 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
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• v.38 no.4
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• pp.811-835
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• 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
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• v.27 no.3
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• pp.175-197
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• 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
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• v.38 no.6_1
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• pp.1371-1383
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• 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
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• 2021.06a
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• pp.6-7
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• 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.

• Journal of Venture Innovation
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• v.3 no.2
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• pp.41-76
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• 2020

This study confirmed factors affecting smart factory technology acceptance through empirical analysis. It is a study on what factors have an important influence on the introduction of the smart factory, which is the core field of the 4th industry. I believe that there is academic and practical significance in the context of insufficient research on technology acceptance in the field of smart factories. This research was conducted based on the Unified Theory of Acceptance and Use of Technology (UTAUT), whose explanatory power has been proven in the study of the acceptance factors of information technology. In addition to the four independent variables of the UTAUT : Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions, Government Assistance Expectancy, which is expected to be an important factor due to the characteristics of the smart factory, was added to the independent variable. And, in order to confirm the technical factors of smart factory technology acceptance, the Task Technology Fit(TTF) was added to empirically analyze the effect on Behavioral Intention. Trust is added as a parameter because the degree of trust in new technologies is expected to have a very important effect on the acceptance of technologies. Finally, empirical verification was conducted by adding Innovation Resistance to a research variable that plays a role as a moderator, based on previous studies that innovation by new information technology can inevitably cause refusal to users. For empirical analysis, an online questionnaire of random sampling method was conducted for incumbents of domestic small and medium-sized enterprises, and 309 copies of effective responses were used for empirical analysis. Amos 23.0 and Process macro 3.4 were used for statistical analysis. For accurate statistical analysis, the validity of Research Model and Measurement Variable were secured through confirmatory factor analysis. Accurate empirical analysis was conducted through appropriate statistical procedures and correct interpretation for causality verification, mediating effect verification, and moderating effect verification. Performance Expectancy, Social Influence, Government Assistance Expectancy, and Task Technology Fit had a positive (+) effect on smart factory technology acceptance. The magnitude of influence was found in the order of Government Assistance Expectancy(β=.487) > Task Technology Fit(β=.218) > Performance Expectancy(β=.205) > Social Influence(β=.204). Both the Task Characteristics and the Technology Characteristics were confirmed to have a positive (+) effect on Task Technology Fit. It was found that Task Characteristics(β=.559) had a greater effect on Task Technology Fit than Technology Characteristics(β=.328). In the mediating effect verification on Trust, a statistically significant mediating role of Trust was not identified between each of the six independent variables and the intention to introduce a smart factory. Through the verification of the moderating effect of Innovation Resistance, it was found that Innovation Resistance plays a positive (+) moderating role between Government Assistance Expectancy, and technology acceptance intention. In other words, the greater the Innovation Resistance, the greater the influence of the Government Assistance Expectancy on the intention to adopt the smart factory than the case where there is less Innovation Resistance. Based on this, academic and practical implications were presented.

• Journal of Korean Academy of Nursing
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• v.12 no.2
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• pp.1-26
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• 1982

The main purpose of this study was to test the effect of the structured information on the sleep amount of the patients undergoing open heart surgery. This study has specifically addressed to the Following two basic research questions: (1) Would the structed in formation influence in the reduction of sleep disturbance related to anxiety and Physical stress before and after the operation? and (2) that would be the effects of the structured information on the level of preoperative state anxiety, the hormonal change, and the degree of behavioral change in the patients undergoing an open heart surgery? A Quasi-experimental research was designed to answer these questions with one experimental group and one control group. Subjects in both groups were matched as closely as possible to avoid the effect of the differences inherent to the group characteristics, Baseline data were also. collected on both groups for 7 days prior to the experiment and found that subjects in both groups had comparable sleep patterns, trait anxiety, hormonal levels and behavioral level. A structured information as an experimental input was given to the subjects in the experimental group only. Data were collected and compared between the experimental group and the control group on the sleep amount of the consecutive pre and post operative days, on preoperative state anxiety level, and on hormonal and behavioral changes. To test the effectiveness of the structured information, two main hypotheses and three sub-hypotheses were formulated as follows; Main hypothesis 1: Experimental group which received structured information will have more sleep amount than control group without structured information in the night before the open heart surgery. Main hypothesis 2: Experimental group with structured information will have more sleep, amount than control group without structured information during the week following the open heart surgery Sub-hypothesis 1: Experimental group with structured information will be lower in the level of State anxiety than control group without structured information in the night before the open heart surgery. Sub-hypothesis 2 : Experimental group with structured information will have lower hormonal level than control group without stuctured information on the 5th day after the open heart surgery Sub-hypothesis 3: Experimental group with structured information will be lower in the behavioral change level than control group without structured information during the week after the open heart surgery. The research was conducted in a national university hospital in Seoul, Korea. The 53 Subjects who participated in the study were systematically divided into experimental group and control group which was decided by random sampling method. Among 53 subjects, 26 were placed in the experimental group and 27 in the control group. Instruments; (1) Structed information: Structured information as an independent variable was constructed by the researcher on the basis of Roy's adaptation model consisting of physiologic needs, self-concept, role function and interdependence needs as related to the sleep and of operational procedures. (2) Sleep amount measure: Sleep amount as main dependent variable was measured by trained nurses through observation on the basis of the established criteria, such as closed or open eyes, regular or irregular respiration, body movement, posture, responses to the light and question, facial expressions and self report after sleep. (3) State anxiety measure: State Anxiety as a sub-dependent variable was measured by Spi-elberger's STAI Anxiety scale, (4) Hormornal change measure: Hormone as a sub-dependent variable was measured by the cortisol level in plasma. (5) Behavior change measure: Behavior as a sub-dependent variable was measured by the Behavior and Mood Rating Scale by Wyatt. The data were collected over a period of four months, from June to October 1981, after the pretest period of two months. For the analysis of the data and test for the hypotheses, the t-test with mean differences and analysis of covariance was used. The result of the test for instruments show as follows: (1) STAI measurement for trait and state anxiety as analyzed by Cronbachs alpha coefficient analysis for item analysis and reliability showed the reliability level at r= .90 r= .91 respectively. (2) Behavior and Mood Rating Scale measurement was analyzed by means of Principal Component Analysis technique. Seven factors retained were anger, anxiety, hyperactivity, depression, bizarre behavior, suspicious behavior and emotional withdrawal. Cumulative percentage of each factor was 71.3%. The result of the test for hypotheses show as follows; (1) Main hypothesis, was not supported. The experimental group has 282 minutes of sleep as compared to the 255 minutes of sleep by the control group. Thus the sleep amount was higher in experimental group than in control group, however, the difference was not statistically significant at .05 level. (2) Main hypothesis 2 was not supported. The mean sleep amount of the experimental group and control group were 297 minutes and 278 minutes respectively Therefore, the experimental group had more sleep amount as compared to the control group, however, the difference was not statistically significant at .05 level. Thus, the main hypothesis 2 was not supported. (3) Sub-hypothesis 1 was not supported. The mean state anxiety of the experimental group and control group were 42.3, 43.9 in scores. Thus, the experimental group had slightly lower state anxiety level than control group, howe-ver, the difference was not statistically significant at .05 level. (4) Sub-hypothesis 2 was not supported. . The mean hormonal level of the experimental group and control group were 338 ㎍ and 440 ㎍ respectively. Thus, the experimental group showed decreased hormonal level than the control group, however, the difference was not statistically significant at .05 level. (5) Sub-hypothesis 3 was supported. The mean behavioral level of the experimental group and control group were 29.60 and 32.00 respectively in score. Thus, the experimental group showed lower behavioral change level than the control group. The difference was statistically significant at .05 level. In summary, the structured information did not influence the sleep amount, state anxiety or hormonal level of the subjects undergoing an open heart surgery at a statistically significant level, however, it showed a definite trends in their relationships, not least to mention its significant effect shown on behavioral change level. It can further be speculated that a great degree of individual differences in the variables such as sleep amount, state anxiety and fluctuation in hormonal level may partly be responsible for the statistical insensitivity to the experimentation.