• 한국사회정책
• /
• v.25 no.1
• /
• pp.161-194
• /
• 2018

The purpose of this study is to investigate the factors that interrupt the use of social security of elderly North Korean refugees. As a result of qualitative content analysis using interview data, it is found that the elderly North Korean defectors have a lower access to the social security system. The main reason for hindering the use of social security system is low awareness due to limited information channels, psychological distance to South Korean society and residents. Moreover, this psychological distance is a factor that reduces the geographical accessibility of elderly North Korean defectors by preferring to use services in specific institutions for North Korean, while South Korean elderly people can use the service elsewhere. In addition, there are many cases in which the elderly in North Korean defectors do not receive sufficient social security due to low income. On the other hand, social security systems with high accessibility of older North Korean refugees are characterized by the fact that they are provided by experts who have formed sufficient rapport with North Korean refugees. Therefore, it seems possible to improve the accessibility of elderly defectors' friendly system by professional workers with cultural competence. In addition, a multidimensional approach is needed to fully cope with the complex desires of elderly North Korean defectors and a mechanism should be set up to reflect their opinion in system operation.

• Journal of Ginseng Research
• /
• v.43 no.2
• /
• pp.305-311
• /
• 2019

Background: Gintonin is a ginseng-derived exogenous ligand of the G protein-coupled lysophosphatidic acid (LPA) receptor. We previously reported that gintonin stimulates gliotransmitter release in primary cortical astrocytes. Astrocytes play key roles in the functions of neurovascular systems. Although vascular endothelial growth factor (VEGF) is known to influence the normal growth and maintenance of cranial blood vessels and the nervous system, there is little information about the effect of gintonin on VEGF regulation in primary astrocytes, under normal and hypoxic conditions. Methods: Using primary cortical astrocytes of mice, the effects of gintonin on the release, expression, and distribution of VEGF were examined. We further investigated whether the gintonin-mediated VEGF release protects astrocytes from hypoxia. Results: Gintonin administration stimulated the release and expression of VEGF from astrocytes in a concentration- and time-dependent manner. The gintonin-mediated increase in the release of VEGF was inhibited by the LPA1/3 receptor antagonist, Ki16425; phospholipase C inhibitor, U73122; inositol 1,4,5- triphosphate receptor antagonist, 2-APB; and intracellular $Ca^{2+}$ chelator, BAPTA. Hypoxia further stimulated astrocytic VEGF release. Gintonin treatment stimulated additional VEGF release and restored cell viability that had decreased due to hypoxia, via the VEGF receptor pathway. Altogether, the regulation of VEGF release and expression and astrocytic protection mediated by gintonin under hypoxia are achieved via the LPA receptor-VEGF signaling pathways. Conclusion: The present study shows that the gintonin-mediated regulation of VEGF in cortical astrocytes might be neuroprotective against hypoxic insults and could explain the molecular basis of the beneficial effects of ginseng on the central nervous system.

• Smart Structures and Systems
• /
• v.24 no.6
• /
• pp.769-781
• /
• 2019

Dynamic displacement response of civil structures is an important index for in-construction and in-service structural condition assessment. However, accurately measuring the displacement of large-scale civil structures such as high-rise buildings still remains as a challenging task. In order to cope with this problem, a vision-based system with the use of industrial digital camera and image processing has been developed for long-distance, remote, and real-time monitoring of dynamic displacement of supertall structures. Instead of acquiring image signals, the proposed system traces only the coordinates of the target points, therefore enabling real-time monitoring and display of displacement responses in a relatively high sampling rate. This study addresses the in-situ experimental verification of the developed vision-based system on the Canton Tower of 600 m high. To facilitate the verification, a GPS system is used to calibrate/verify the structural displacement responses measured by the vision-based system. Meanwhile, an accelerometer deployed in the vicinity of the target point also provides frequency-domain information for comparison. Special attention has been given on understanding the influence of the surrounding light on the monitoring results. For this purpose, the experimental tests are conducted in daytime and nighttime through placing the vision-based system outside the tower (in a brilliant environment) and inside the tower (in a dark environment), respectively. The results indicate that the displacement response time histories monitored by the vision-based system not only match well with those acquired by the GPS receiver, but also have higher fidelity and are less noise-corrupted. In addition, the low-order modal frequencies of the building identified with use of the data obtained from the vision-based system are all in good agreement with those obtained from the accelerometer, the GPS receiver and an elaborate finite element model. Especially, the vision-based system placed at the bottom of the enclosed elevator shaft offers better monitoring data compared with the system placed outside the tower. Based on a wavelet filtering technique, the displacement response time histories obtained by the vision-based system are easily decomposed into two parts: a quasi-static ingredient primarily resulting from temperature variation and a dynamic component mainly caused by fluctuating wind load.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.42 no.3
• /
• pp.343-350
• /
• 2022

High-resolution data are needed to understand water body mixing patterns at river junctions. In particular, in river analysis, hydrological and water quality characteristics are used as basic data for aquatic ecological health, so observation through continuous monitoring is necessary. In addition, since measurement is carried out through a one-dimensional and fixed measurement method in existing monitoring systems, a hydrological and water quality characteristics investigation of an entire river, except for in the immediate vicinity of the measurement point, is not undertaken. In order to obtain high-resolution measurement data, a measurer has to consider multiple factors, and the area or time that can be measured is limited. Although the resolution might be lowered, an appropriate interpolation method must be selected in order to acquire a wide range of data. Therefore, in this study, a high-elevation measurement method at a river junction was introduced, and the interpolation method according to the measurement results was compared. The overall hydraulic and water quality information of the river was indicated through the visualization of the prediction and interpolation method in the low-resolution measurement result. By comparing each interpolation method, Inverse Distance Weighting, Natural Neighbor, and Kriging techniques were applied in river mapping to improve the precision of river mapping through visualized data and quantitative evaluation. It is thought that this study will offer a new method for measuring rivers through spatial interpolation.

• Journal of Ginseng Culture
• /
• v.6
• /
• pp.80-104
• /
• 2024

Ginseng, a representative medicinal plant of South Korea, is also highly valued in North Korea. However, due to limited access to information about North Korea, the actual cultivation, research and development trends, and related industry status of ginseng in North Korea are not well known. In this study, we aimed to understand the current status and research trends of the ginseng industry in North Korea based on limited available literature. In the North Korean pharmacopoeia, ginseng is referred to as "Koryo ginseng" and is defined as the roots of 6-year-old ginseng cultivated in the Kaesong region. The pharmacopoeia includes 22 types of ginseng preparations. In addition, 10 ginseng preparations are included in North Korea's Essential Drug List, and various health supplements, cosmetics, and toothpastes containing ginseng have been developed, distributed, and sold. Since 2014, the ginseng industry and research in North Korea have become more active overall. During this period, the ginseng cultivation area in Kaesong has been significantly expanded, and the facilities have been renovated. The Kaesong Koryo Ginseng Processing Plant has been equipped with sterilized, modernized facilities since 2016 and has been in operation. Since 2017, there has been a growing interest in quality control research, leading to the introduction of quality management regulations and certification systems in 2019. In the 1990s, there was significant research on ginseng product development, and since the 2000s, studies on the pharmacological effects and clinical research of ginseng have been reported. Additionally, research on ginseng cultivation and ginseng processing industries to increase yield has been emphasized. Ginseng, as a representative medicinal crop of Korea, holds great importance for both South and North Korea. Given its significance and the potential for synergy through mutual cooperation, ginseng serves as an ideal subject for inter-Korean exchange and collaboration.

• Journal of Intelligence and Information Systems
• /
• v.21 no.4
• /
• pp.1-16
• /
• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Journal of Preventive Medicine and Public Health
• /
• v.11 no.1
• /
• pp.123-127
• /
• 1978

The major objective of this research is to identify those hospital characteristics that best explain cost variation among hospitals and to formulate linear models that can predict hospital costs. Specific emphasis is placed on hospital output, that is, the identification of diagnosis related patient groups (DRGs) which are medically meaningful and demonstrate similar patterns of hospital resource consumption. A casemix index is developed based on the DRGs identified. Considering the common problems encountered in previous hospital cost research, the following study requirements are estab-lished for fulfilling the objectives of this research: 1. Selection of hospitals that exercise similar medical and fiscal practices. 2. Identification of an appropriate data collection mechanism in which demographic and medical characteristics of individual patients as well as accurate and comparable cost information can be derived. 3. Development of a patient classification system in which all the patients treated in hospitals are able to be split into mutually exclusive categories with consistent and stable patterns of resource consumption. 4. Development of a cost finding mechanism through which patient groups' costs can be made comparable across hospitals. A data set of Medicare patients prepared by the Social Security Administration was selected for the study analysis. The data set contained 27,229 record abstracts of Medicare patients discharged from all but one short-term general hospital in Connecticut during the period from January 1, 1971, to December 31, 1972. Each record abstract contained demographic and diagnostic information, as well as charges for specific medical services received. The 'AUT-OGRP System' was used to generate 198 DRGs in which the entire range of Medicare patients were split into mutually exclusive categories, each of which shows a consistent and stable pattern of resource consumption. The 'Departmental Method' was used to generate cost information for the groups of Medicare patients that would be comparable across hospitals. To fulfill the study objectives, an extensive analysis was conducted in the following areas: 1. Analysis of DRGs: in which the level of resource use of each DRG was determined, the length of stay or death rate of each DRG in relation to resource use was characterized, and underlying patterns of the relationships among DRG costs were explained. 2. Exploration of resource use profiles of hospitals; in which the magnitude of differences in the resource uses or death rates incurred in the treatment of Medicare patients among the study hospitals was explored. 3. Casemix analysis; in which four types of casemix-related indices were generated, and the significance of these indices in the explanation of hospital costs was examined. 4. Formulation of linear models to predict hospital costs of Medicare patients; in which nine independent variables (i. e., casemix index, hospital size, complexity of service, teaching activity, location, casemix-adjusted death. rate index, occupancy rate, and casemix-adjusted length of stay index) were used for determining factors in hospital costs. Results from the study analysis indicated that: 1. The system of 198 DRGs for Medicare patient classification was demonstrated not only as a strong tool for determining the pattern of hospital resource utilization of Medicare patients, but also for categorizing patients by their severity of illness. 2. The wei틴fed mean total case cost (TOTC) of the study hospitals for Medicare patients during the study years was $11,27.02 with a standard deviation of $117.20. The hospital with the highest average TOTC ($1538.15) was 2.08 times more expensive than the hospital with the lowest average TOTC ($743.45). The weighted mean per diem total cost (DTOC) of the study hospitals for Medicare patients during the sutdy years was $107.98 with a standard deviation of $15.18. The hospital with the highest average DTOC ($147.23) was 1.87 times more expensive than the hospital with the lowest average DTOC ($78.49). 3. The linear models for each of the six types of hospital costs were formulated using the casemix index and the eight other hospital variables as the determinants. These models explained variance to the extent of 68.7 percent of total case cost (TOTC), 63.5 percent of room and board cost (RMC), 66.2 percent of total ancillary service cost (TANC), 66.3 percent of per diem total cost (DTOC), 56.9 percent of per diem room and board cost (DRMC), and 65.5 percent of per diem ancillary service cost (DTANC). The casemix index alone explained approximately one half of interhospital cost variation: 59.1 percent for TOTC and 44.3 percent for DTOC. Thsee results demonstrate that the casemix index is the most importand determinant of interhospital cost variation Future research and policy implications in regard to the results of this study is envisioned in the following three areas: 1. Utilization of casemix related indices in the Medicare data systems. 2. Refinement of data for hospital cost evaluation. 3. Development of a system for reimbursement and cost control in hospitals.

• Journal of Intelligence and Information Systems
• /
• v.27 no.1
• /
• pp.151-176
• /
• 2021

As the information technology industry develops, various kinds of data are being created, and it is now essential to process them and use them in the industry. Analyzing and utilizing various digital data collected online and offline is a necessary process to provide an appropriate experience for customers in the industry. In order to create new businesses, products, and services, it is essential to use customer data collected in various ways to deeply understand potential customers' needs and analyze behavior patterns to capture hidden signals of desire. However, it is true that research using data analysis and UX methodology, which should be conducted in parallel for effective service development, is being conducted separately and that there is a lack of examples of use in the industry. In thiswork, we construct a single process by applying data analysis methods and UX methodologies. This study is important in that it is highly likely to be used because it applies methodologies that are actively used in practice. We conducted a survey on the topic to identify and cluster the associations between factors to establish customer classification and target customers. The research methods are as follows. First, we first conduct a factor, regression analysis to determine the association between factors in the happiness data survey. Groups are grouped according to the survey results and identify the relationship between 34 questions of psychological stability, family life, relational satisfaction, health, economic satisfaction, work satisfaction, daily life satisfaction, and residential environment satisfaction. Second, we classify clusters based on factors affecting happiness and extract the optimal number of clusters. Based on the results, we cross-analyzed the characteristics of each cluster. Third, forservice definition, analysis was conducted by correlating with keywords related to happiness. We leverage keyword analysis of the thumb trend to derive ideas based on the interest and associations of the keyword. We also collected approximately 11,000 news articles based on the top three keywords that are highly related to happiness, then derived issues between keywords through text mining analysis in SAS, and utilized them in defining services after ideas were conceived. Fourth, based on the characteristics identified through data analysis, we selected segmentation and targetingappropriate for service discovery. To this end, the characteristics of the factors were grouped and selected into four groups, and the profile was drawn up and the main target customers were selected. Fifth, based on the characteristics of the main target customers, interviewers were selected and the In-depthinterviews were conducted to discover the causes of happiness, causes of unhappiness, and needs for services. Sixth, we derive customer behavior patterns based on segment results and detailed interviews, and specify the objectives associated with the characteristics. Seventh, a typical persona using qualitative surveys and a persona using data were produced to analyze each characteristic and pros and cons by comparing the two personas. Existing market segmentation classifies customers based on purchasing factors, and UX methodology measures users' behavior variables to establish criteria and redefine users' classification. Utilizing these segment classification methods, applying the process of producinguser classification and persona in UX methodology will be able to utilize them as more accurate customer classification schemes. The significance of this study is summarized in two ways: First, the idea of using data to create a variety of services was linked to the UX methodology used to plan IT services by applying it in the hot topic era. Second, we further enhance user classification by applying segment analysis methods that are not currently used well in UX methodologies. To provide a consistent experience in creating a single service, from large to small, it is necessary to define customers with common goals. To this end, it is necessary to derive persona and persuade various stakeholders. Under these circumstances, designing a consistent experience from beginning to end, through fast and concrete user descriptions, would be a very effective way to produce a successful service.

• The Journal of Korean Academy of Sensory Integration
• /
• v.19 no.1
• /
• pp.69-82
• /
• 2021

Objective : This recent work intended to provide basic information for researchers and practitioners related to occupational therapy about Developmental Coordination Disorder (DCD) in South Korea. The previous research of screening DCD and the effects of intervention programs were reviewed. Methods : Peer-reviewed papers relating to DCD and published in Korea from January 1990 to December 2020 were systematically reviewed. The search terms "developmental coordination disorder," "development coordination," and "developmental coordination" were used to identify previous Korean research in this area from three representation database, the Research Information Sharing Service, Korean Studies Information Service System, and Google Scholar. We found a total of 4,878 articles identified through the three search engines and selected seventeen articles for analysis after removing those that corresponded to the overlapping or exclusion criteria. We adopted "the conceptual model" to analyze the selected articles about DCD assessment and intervention. Results : We found that twelve of the 17 studies showed the qualitative level of Level 2 using non-randomized approach between the two groups. The Movement Assessment Battery for Children and its second edition were the most frequently used tools in assessing children for DCD. Among the intervention studies, the eight articles (47%) were adopted a dynamic systems approach; a normative functional skill framework and cognitive neuroscience were each used in 18% of the pieces; and 11% of the articles were applied neurodevelopmental theory. Only one article was used a combination approach of normative functional skill and general abilities. These papers were mainly focused on the movement characteristics of children with DCD and the intervention effect of exercise or sports programs. Conclusion : Most of the reviewed studies investigated the movement characteristics of DCD or explore the effectiveness of particular intervention programs. In the future, it would be useful to investigate the feasibility of different assessment tools and to establish the effectiveness of various interventions used in rehabilitation for better motor performance in children with DCD.

• Journal of Intelligence and Information Systems
• /
• v.29 no.3
• /
• pp.57-78
• /
• 2023

COVID-19, which started in Wuhan, China in November 2019, spread beyond China in 2020 and spread worldwide in March 2020. It is important to prevent a highly contagious virus like COVID-19 in advance and to actively treat it when confirmed, but it is more important to identify the confirmed fact quickly and prevent its spread since it is a virus that spreads quickly. However, PCR test to check for infection is costly and time consuming, and self-kit test is also easy to access, but the cost of the kit is not easy to receive every time. Therefore, if it is possible to determine whether or not a person is positive for COVID-19 based on the sound of a cough so that anyone can use it easily, anyone can easily check whether or not they are confirmed at anytime, anywhere, and it can have great economic advantages. In this study, an experiment was conducted on a method to identify whether or not COVID-19 was confirmed based on a cough sound. Cough sound features were extracted through MFCC, Mel-Spectrogram, and spectral contrast. For the quality of cough sound, noisy data was deleted through SNR, and only the cough sound was extracted from the voice file through chunk. Since the objective is COVID-19 positive and negative classification, learning was performed through XGBoost, LightGBM, and FCNN algorithms, which are often used for classification, and the results were compared. Additionally, we conducted a comparative experiment on the performance of the model using multidimensional vectors obtained by converting cough sounds into both images and vectors. The experimental results showed that the LightGBM model utilizing features obtained by converting basic information about health status and cough sounds into multidimensional vectors through MFCC, Mel-Spectogram, Spectral contrast, and Spectrogram achieved the highest accuracy of 0.74.