• Journal of vocational education research
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• v.29 no.3
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• pp.309-334
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• 2010

Under the global economic competition, company competitiveness depends on creating high performance through effective human development. It is also a tendency to analyze organization members'competencies and outcomes, develop competency model, and apply the model to developing and managing human resource in each organization. Therefore, this study figures out the present condition of development of competency model and its application to examine the degree to which the competency model, which has been introduced to Korean companies for the use of competency diagnosis of members, is being utilized. For this purpose, survey items were created by expert council and advisory committee and survey was conducted for Korean corporations. The result shows that most of the companies, regardless of size and type, develop their own model by referring to outside models, or use models developed by consulting company lead. Therefore, it is urgent for the companies to develop model appropriate for the peculiarity of each company. Second, while the main reason to introduce competency model was to reinforce workers' competency, models developed have not widely utilized in HRM overall. There is a need to overcome a limit of utilization of models. Third, Majority responded that they, regardless of size and type, upgraded models or did not upgrade at all due to change of work environment. There is also a need to systematize follow-up care of the models. This is a primary research to examine the present condition of development of competency model and its application in company so that it can be used as springboard to study in-depth inside condition of Korean company using competency model and condition of particular positions of company members.

• 한국노년학
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• v.30 no.4
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• pp.1359-1375
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• 2010

Expecting the expansion of the elderly population under long-term home care with the coming of the aged society, this study purposed to propose a prevention and self-reliance support model and to get practical implications for minimizing dependency on care benefits and enhancing the effectiveness of prevention and self-reliance support. Research methods employed for this study were: first, reviewing theoretical literature for clarifying the concept of prevention and self-reliance support in providing long-term care benefits for the elderly; second, identifying factors hindering prevention and self-reliance support through analyzing standard long-term care use plans and documents related to long-term care benefits at elderly welfare centers to which the research subjects belonged; and third, surveying care benefit users on factors hindering their use of prevention and self-reliance support and their needs in the use of care benefits. Based on the results of the three types of qualitative research, we proposed directions for prevention and self-reliance support modeling and suggested practical implications for enhancing the effectiveness of prevention and self-reliance support. For this study, we collected documentary materials and conducted in-depth interviews with the participants with the consents and cooperation of managers and professional social workers at day care centers and elderly welfare centers in D City. According to the results of this study, literature review suggested that long-term care prevention and self-reliance support should be provided in a way of 'strengthening user-centered support systems,' which support elderly long-term care beneficiaries' right to lead a life as the subject of their own life. Document analysis found the absence of benefits related to health and medicine and lack of social support systems for prevention and self-reliance support, and the results of in-depth interviews suggested the necessity to strengthen services related to elderly long-term care beneficiaries' prevention and self-reliance, and the keen needs of the long-term care elders for prevention and self-reliance included: ① loneliness, anxiety, fear; ② missing for and worry about children and people; ③ moving, outing; ④ health and medical services, rehabilitation programs; ⑤ desire to use day care; ⑥ inconvenience of house structure; ⑦desire for meal menus; and ⑧ the occurrence of disuse syndrome. Based on these results, we suggested the base of prevention and self-reliance support modeling with three axes: ① strengthening user-centered support systems; ② strengthening support systems connected to health and medicine; and ③ strengthening social support systems.

• 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.