• 한국노년학
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• v.36 no.3
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• pp.905-926
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• 2016

Although the concept of the elderly varies depending on scholars and laws, as consumption expenditure is deeply associated with income due to the nature of this study, 55 years old was set as the low limit standard for the elderly according to Prohibition of Discrimination on Age in Employment and Employment Promotion for the Aged Act and the elderly households were limited to single-elderly person household and an elderly couple family household for this study. It is considered consumption characteristics as a significant analysis subject in terms of social welfare because it could be understood as an expressed need which was a reflection of desire. Therefore, the present study aimed to investigate the consumption characteristics of the elderly households by stereotyping the consumption pattern of the elderly households, and find the determining factors for consumption patterns and thus contribute to the establishment of related policies through the expressed needs of the elderly households. K-means of cluster analysis was performed by putting the consumption expenditure of the elderly households to investigate inherent structural type of consumption pattern of the elderly households, which were the investigation subjects. As a result, four groups were stereotyped and named as below: 'health care-centered type', 'saving-centered type', 'livelihood-centered type', and 'food expenses-centered type' Binary Logistic Regression analysis was used to identify the factors that influence the decision of consumption pattern of the elderly households. The result of study showed that the elderly households faced all different needs and problems and thus there is a need for various approach plans to solve this situation. In particular, although the elderly have been viewed as economically poor people so far, the study showed that there were also kind of prepared households through saving. Overall, livelihoodcentered type accounted for the highest portion and, as a factor that influenced this, marital state and household income played an important role. Therefore, it is considered that more active efforts to increase the income of the elderly households are needed. In addition, age, owning of house and subjective health state were found to also have significant influence. Through these results of the study, the elderly's own improvement of awareness on health, presentation of overall standard for health state of the elderly, securement of the elderly's access to cultural life, and financial management coordination for improvement of quality of life, development and dissemination of jobs suitable for the elderly, and dissemination of communal life household, which is a cooperation residential type, were presented as institutional task in the conclusion.

• Journal of Christian Education in Korea
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• v.64
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• pp.289-321
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• 2020

Without overcoming the fragmented characteristics of the postmodern era and solving many difficulties as it is, our society is passing through a time of crisis more than ever because of Corona 19, a more rapid social disaster. As the crisis caused by the pandemic is prolonged, our society is becoming more diverse than before the coronavirus, and efforts in various fields of society are required to cultivate new capabilities to overcome social conflicts. This study started with an awareness of the necessity of reinforcing Christian civic education to fulfill the public responsibilities of Christians by recognizing the social and situational problems of this era as a public task amid in the crisis and change of the pendemic. Therefore, a meaningful work was undertaken to find an educational ministry practice frame for essential core competencies and transformative transformation competencies responding to changes in the times as education to cultivate and reinforce competencies as Christian citizens. First, the theoretical basis for competency education for Christian citizens was reviewed through the theological and Christian education theories that were studied in the situation of public theology about the public perception and social responsibility of Christians on the issues of the times. Furthermore, through this study, education to establish and cultivate the public identity of disciples-citizens as a Introductory education is explored in a multifaceted method of educational ministry, and educational methods were searched for cultivating communication competencies of Christian citizens with practical capacity of public faith. In conclusion, through this study, an educational ministry frame of identity cultivation, the core competency of recognizing the position of the public mission as a Christian citizen while living as a disciple of God's kingdom in the world and an educational frame to cultivate the ability to communicate as a transformative Christian citizen's transformative competency to carry out public tasks was systematically established, and an educational ministry convergence frame was proposed for cultivating core competencies and transformation competencies for Christian citizenship education.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.6
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• pp.27-42
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• 2020

Startup accelerators have emerged as new investment entities that help early startups, which are not easy to survive continuously due to lack of funds, commercialization capabilities, and experiences. As their positive performance on early startups and the ecosystem has been proven, the number of early startups which want to receive their investment is also increasing. However, they are vaguely preparing to attract accelerators' investment because they do not have any information on what factors the accelerators consider important. In addition, researches on startup accelerators are also at an early level, so there are no remarkable prior studies on factors that decide on investment. Therefore, this study aims to help startups prepare for investment attraction by looking at what factors are important for accelerators to invest, and to provide meaningful implications to academia. In the preceding study, we derived five upper level categories, 26 lower level accelerators' investment determinants through the qualitative meta-synthesis method, secondary data analysis, observation on US accelerators and in-depth interviews. In this study, we want to derive important implications by deriving priorities of the accelerators' investment determinants. Therefore, we used AHP that are evaluated as the suitable methodology for deriving importance and priority. The analysis results show that accelerators value market-related factors most. This means that startups that are subject to investment by accelerators are early-stage startups, and many companies have not fully developed their products or services. Therefore, market-related factors that can be evaluated objectively seem to be more important than products (or services) that are still ambiguous. Next, it was found that the factors related to the internal workforce of startups are more important. Since accelerators want to develop their businesses together with start-ups and team members through mentoring, ease of collaboration with them is very important, which seems to be important. The overall priority analysis results of the 26 investment determinants show that 'customer needs' and 'founders and team members' understanding of customers and markets' (0.62) are important and high priority factors. The results also show that startup accelerators consider the customer-centered perspective very important. And among the factors related to startups, the most prominent factor was the founder's openness and execution ability. Therefore, it can be confirmed that accelerators consider the ease of collaboration with these startups very important.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.6
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• pp.727-736
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• 2021

Road tunnel lengths are increasing. Some 1,300 tunnels with 1,102 km in length had been increased till 2019 from 2010. There are 64 tunnels over 3,000 m in length, with their total length adding up to 276.7 km. Safety facilities in the event of a tunnel fire are critical so as to prevent large-scale casualties. Standards for installing disaster prevention facilities are being proposed based on the guidelines of the Ministry of Land, Infrastructure and Transport, but they may be limited to deep underground tunnels. This study was undertaken to provide guidelines for the spacing of evacuation connection passages and the widths of evacuation connection doors. Evacuation with various spacing and widths was simulated in regards to evacuation time, which is the measure of safety, using the evacuation analysis simulation software EXODUS Ver.6.3 and the fire/smoke analysis software SMARTFIRE Ver.4.1. Evacuation connection gates with widths of 0.9 m and 1.2 m, and spacings of 150 m to 250 m, were set to every 20 m. In addition, longitudinal slopes of 6 % and 0 % were considered. It was determined to be safe when the evacuation completion time was shorter than the delay diffusion time. According to the simulation results, all occupants could complete evacuation before smoke spread regardless of the width of the evacuation connection door when the longitudinal slope was 6 % and the interval of evacuation connection passage was 150 m. When the evacuation connection passage spacing was 200 m and the evacuation connection gate width was 1.2 m, all occupants could evacuate when the longitudinal slope was 0 %. Due to difference in evacuation speed according to the longitudinal slope, the evacuation time with a 6 % slope was 114 seconds shorter (with the 190 m connection passage) than with a 0 % slope. A shorter spacing of evacuation connection passages may reduce the evacuation time, but this is difficult to implement in practice because of economic and structural limitations. If the width of the evacuation junction is 1.2 m, occupants could evacuate faster than with a 0.9 m width. When the width of a connection door is 1.2 m with appropriate connection passage spacing, it might provide a means to increase economic efficiency and resolve structural limitations while securing evacuation safety.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.2
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• pp.69-77
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• 2022

The 3D spatial model is an analysis framework for solving urban problems and is used in various fields such as urban planning, environment, land and housing management, and disaster simulation. The utilization of drones that can capture 3D images in a short time at a low cost is increasing for the construction of 3D spatial model. In terms of building a virtual city and utilizing simulation modules, high location accuracy of aerial survey and precision of 3D spatial model function as important factors, so a method to increase the accuracy has been proposed. This study analyzed location accuracy of aerial survey and precision of 3D spatial model by each condition of aerial survey for urban areas where buildings are densely located. We selected Daerim 2-dong, Yeongdeungpo-gu, Seoul as a target area and applied shooting angle, shooting altitude, and overlap rate as conditions for the aerial survey. In this study, we calculated the location accuracy of aerial survey by analyzing the difference between an actual survey value of CPs and a predicted value of 3D spatial Model. Also, We calculated the precision of 3D spatial Model by analyzing the difference between the position of Point cloud and the 3D spatial Model (3D Mesh). As a result of this study, the location accuracy tended to be high at a relatively high rate of overlap, but the higher the rate of overlap, the lower the precision of 3D spatial model and the higher the shooting angle, the higher precision. Also, there was no significant relationship with precision. In terms of baseline-height ratio, the precision tended to be improved as the baseline-height ratio increased.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.707-723
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• 2022

Although satellite-based sea surface temperature (SST) is advantageous for monitoring large areas, spatiotemporal data gaps frequently occur due to various environmental or mechanical causes. Thus, it is crucial to fill in the gaps to maximize its usability. In this study, daily SST composite fields with a resolution of 4 km were produced through a two-step machine learning approach using polar-orbiting and geostationary satellite SST data. The first step was SST reconstruction based on Data Interpolate Convolutional AutoEncoder (DINCAE) using multi-satellite-derived SST data. The second step improved the reconstructed SST targeting in situ measurements based on light gradient boosting machine (LGBM) to finally produce daily SST composite fields. The DINCAE model was validated using random masks for 50 days, whereas the LGBM model was evaluated using leave-one-year-out cross-validation (LOYOCV). The SST reconstruction accuracy was high, resulting in R² of 0.98, and a root-mean-square-error (RMSE) of 0.97℃. The accuracy increase by the second step was also high when compared to in situ measurements, resulting in an RMSE decrease of 0.21-0.29℃ and an MAE decrease of 0.17-0.24℃. The SST composite fields generated using all in situ data in this study were comparable with the existing data assimilated SST composite fields. In addition, the LGBM model in the second step greatly reduced the overfitting, which was reported as a limitation in the previous study that used random forest. The spatial distribution of the corrected SST was similar to those of existing high resolution SST composite fields, revealing that spatial details of oceanic phenomena such as fronts, eddies and SST gradients were well simulated. This research demonstrated the potential to produce high resolution seamless SST composite fields using multi-satellite data and artificial intelligence.

• Korean journal of applied entomology
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• v.61 no.4
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• pp.605-614
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• 2022

Two major problems currently threaten human survival on Earth: climate change and the rapid aging of the population in developed countries. Climate change is a result of the increase in greenhouse gas (GHG) concentrations in the atmosphere due to the increase in the use of fossil fuels owing to economic and transportation development. The rapid increase in the age of the population is a result of the rise in life expectancy due to the development of biomedical science and technology and the improvement of personal hygiene in developed countries. To avoid irreversible global climate change, it is necessary to quickly transition from the current fossil fuel-based economy to a zero-carbon renewable energy-based economy that does not emit GHGs. To achieve this goal, the dairy and livestock industry, which generates the most GHGs in the agricultural sector, must transition to using low-carbon emission production methods while simultaneously increasing consumers' preference for low-carbon diets. Although 77% of currently available arable land globally is used to produce livestock feed, only 37% and 18% of the proteins and calories that humans consume come from dairy and livestock farming and industry. Therefore, using edible insects as a protein source represents a good alternative, as it generates less GHG and reduces water consumption and breeding space while ensuring a higher feed conversion rate than that of livestock. Additionally, utilizing the functionality of medicinal insects, such as silkworms, which have been proven to have certain health enhancement effects, it is possible to develop functional foods that can prevent or delay the onset of currently incurable degenerative diseases that occur more frequently in the elderly. Insects are among the first animals to have appeared on Earth, and regardless of whether humans survive, they will continue to adapt, evolve, and thrive. Therefore, the use of various edible and medicinal insects, including silkworms, in industry will provide an important foundation for human survival and prosperity on Earth in the near future by resolving the current two major problems.

• Journal of Chest Surgery
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• v.39 no.7 s.264
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• pp.511-519
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• 2006

Background: Loss of cardiomyocytes in the myocardial infarction leads to regional contractile dysfunction, and necrotized cardiomyocytes in infracted ventricular tissues are progressively replaced by fibroblasts forming scar tissue. Although cardiomyoplasty, or implantation of ventricular assist device or artificial heart was tried in refractory heart failure, the cardiac transplantation was the only therapeutic modality because these other therapeutic strategies were not permanent. Cell transplantation is tried instead of cardiac transplantation, especially bone marrow is the most popular donated organ. But because bone marrow aspiration procedure is invasive and painful, and it had the fewer amounts of cellular population, the adipose tissue is recommended for harvesting of mesenchymal stem cells. Material and Method: After adipose tissues were extracted from abdominal subcutaneous adipose tissue and intra-abdominal adipose tissue individually, the cellular components were obtained by same method. These cellular components were tried to transformation with the various titers of 5-azacytidine to descript the appropriate concentration of 5-azacytidine and possibility of transformation ability of adipose tissue. Group 1 is abdominal subcutaneous adipose tissue and Group 2 is intra-abdominal adipose tissue-retroperitoneal adipose tissue and omentum. Cellular components were extracted by collagenase and $NH_4Cl$ et al, and these components were cultured by non-induction media - DMEM media containing 10% FBS and inducted by none, $3{\mu}mol/L,\;6{\mu}mol/L,\;and\;9{\mu}mol/L$ 5-azacytidine after the 1st and 2nd subculture. After 4 weeks incubation, tile cell blocks were made, immunostaining was done with the antibodies of CD34, heavy myosin chain, troponin T, and SMA. Result: Immunostaining of the transformed cells for troponin T was positive in the $6{\mu}mol/L\;&\;9{\mu}mol/L$ 5-azacytidine of Group 1 & 2, but CD34 and heavy myosin chain antibodies were negative and SMA antibody was positive in the $3{\mu}mol/L\;&\;6{\mu}mol/L$ 5-azacytidne of Group 2. Conclusion: These observations confirm that adult mesenchymal stem cells isolated from the abdominal subcutaneous adipose tissues and intra-abdominal adipose tissues can be chemically transformed into cardiomyocytes. This can potentially be a source of autologous cells for myocardial repair.

• Journal of Internet Computing and Services
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• v.16 no.1
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• pp.67-74
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• 2015

In recent years, frequent itemset mining for considering the importance of each item has been intensively studied as one of important issues in the data mining field. According to strategies utilizing the item importance, itemset mining approaches for discovering itemsets based on the item importance are classified as follows: weighted frequent itemset mining, frequent itemset mining using transactional weights, and utility itemset mining. In this paper, we perform empirical analysis with respect to frequent itemset mining algorithms based on transactional weights. The mining algorithms compute transactional weights by utilizing the weight for each item in large databases. In addition, these algorithms discover weighted frequent itemsets on the basis of the item frequency and weight of each transaction. Consequently, we can see the importance of a certain transaction through the database analysis because the weight for the transaction has higher value if it contains many items with high values. We not only analyze the advantages and disadvantages but also compare the performance of the most famous algorithms in the frequent itemset mining field based on the transactional weights. As a representative of the frequent itemset mining using transactional weights, WIS introduces the concept and strategies of transactional weights. In addition, there are various other state-of-the-art algorithms, WIT-FWIs, WIT-FWIs-MODIFY, and WIT-FWIs-DIFF, for extracting itemsets with the weight information. To efficiently conduct processes for mining weighted frequent itemsets, three algorithms use the special Lattice-like data structure, called WIT-tree. The algorithms do not need to an additional database scanning operation after the construction of WIT-tree is finished since each node of WIT-tree has item information such as item and transaction IDs. In particular, the traditional algorithms conduct a number of database scanning operations to mine weighted itemsets, whereas the algorithms based on WIT-tree solve the overhead problem that can occur in the mining processes by reading databases only one time. Additionally, the algorithms use the technique for generating each new itemset of length N+1 on the basis of two different itemsets of length N. To discover new weighted itemsets, WIT-FWIs performs the itemset combination processes by using the information of transactions that contain all the itemsets. WIT-FWIs-MODIFY has a unique feature decreasing operations for calculating the frequency of the new itemset. WIT-FWIs-DIFF utilizes a technique using the difference of two itemsets. To compare and analyze the performance of the algorithms in various environments, we use real datasets of two types (i.e., dense and sparse) in terms of the runtime and maximum memory usage. Moreover, a scalability test is conducted to evaluate the stability for each algorithm when the size of a database is changed. As a result, WIT-FWIs and WIT-FWIs-MODIFY show the best performance in the dense dataset, and in sparse dataset, WIT-FWI-DIFF has mining efficiency better than the other algorithms. Compared to the algorithms using WIT-tree, WIS based on the Apriori technique has the worst efficiency because it requires a large number of computations more than the others on average.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.1-23
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• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.