• Journal of Korea Entertainment Industry Association
• /
• v.15 no.3
• /
• pp.231-241
• /
• 2021

This study intended to present strategies and implications for attracting new students and customized education to university officials through research on the participation of workers' academic aspirations in university education. Thus, variables were derived by analyzing prior data, and causal settings between variables and questionnaires were developed. Subject to the survey, 331 workers interested in participating in university education were collected through interpersonal interviews. The collected data were dataized, and reliability and feasibility verification and frequency analysis were conducted. Finally, we validate the fit of the structural equation model and the causal relationship for each concept. Therefore, the results of the validation show the following implications. First, university officials should be motivated by a mentor and mentee system with experienced people who have switched to a suitable vocational group through university education. It will also be necessary to develop and disseminate programs so that they can continue to develop themselves for the future. To this end, it will be necessary to help them understand their aptitude and strengths through consultation with experts. Second, university officials should strengthen public relations so that prospective students can know the cases and information of the job transformation of the admitted workers through recommendations. It will also be necessary to develop university education programs that can self-develop, accept various ideas through "public contest", and provide accurate information about university education to workers through re-processing. Third, university officials should provide workers with a program that allows them to catch two rabbits: job transformation and self-improvement through university education. In other words, it is necessary to stimulate the motivation of workers by providing various information such as visiting advanced overseas companies, obtaining various certificates, moving between departments of blue-collar and white-collar, and transfer opportunities. Fourth, university officials should actively promote university education programs related to this by participating in university education and receiving systematic education and the flow of social environment. Finally, university officials will need to consult and promote workers so that they can self-develop when they participate in college education, and they will have to figure out what they need for self-development through demand surveys and analysis.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_3
• /
• pp.925-938
• /
• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Journal of the Korean Society of Earth Science Education
• /
• v.15 no.2
• /
• pp.235-248
• /
• 2022

With the changes in the future educational environment, such as the rapid decline of the school-age population and the expansion of students' choice of curriculum, changes are also required in PCK, the expertise of science teachers. In other words, the categories constituting the existing 'consensus-PCK' and the characteristics of 'science PCK' are not fixed, so more categories and characteristics can be added. The purpose of this study is to explore the potential area of science PCK required to cope with changes in the future educational environment in the form of 'Family Resemblance Science PCK (Family Resemblance-PCK, hereafter)' through Wittgenstein's family resemblance approach. For this purpose, in-depth interviews were conducted with three focus groups. In the focus group in-depth interview, participants discussed how the science PCK required for science teachers in future schools in 2030-2045 will change due to changes in the future society and educational environment. Qualitative analysis was performed based on the in-depth interview, and semantic network analysis was performed on the in-depth interview text to analyze the characteristics of 'Family Resemblance-PCK' differentiated from the existing 'consensus-PCK'. In results, the characteristics of Family Resemblance-PCK, which are newly requested along with changes in role expectations of science teachers, were examined by PCK area. As a result of semantic network analysis of Family Resemblance-PCK, it was found that Family Resemblance-PCK expands its boundaries from the existing consensus-PCK, which is the starting point, and new PCK elements were added. Looking at the aspects of Family Resemblance-PCK, [AI-Convergence Knowledge-Contents-Digital], [Community-Network-Human Resources-Relationships], [Technology-Exploration-Virtual Reality-Research], [Self-Directed Learning-Collaboration-Community], etc., form a distinct network cluster, and it is expected that future science teacher expertise will be formed and strengthened around these PCK areas. Based on the research results, changes in the professionalism of science teachers in future schools and countermeasures were proposed as a conclusion.

• Journal of Korea Entertainment Industry Association
• /
• v.13 no.2
• /
• pp.27-36
• /
• 2019

Today, the Korean wave headed by K-pop is newly named as 'New Korean Wave' in that it has been extended to United States, Europe and Russia. K-POP, the main player of the new Korean wave, has been successful in SNS marketing channels. Furthermore, the content of K-pop has attracted the attention of the global audience. The media and public attention on the Korean Wave is meaningful because it is not merely a cultural export. It also makes Korean people feel national pride, seeing the mental influence of its culture on other regions. Moreover, the development of the cultural industry in our society, which is different from industrial or material development, is a proof that Korean society is at the center of globalization. Until the 20th century, Korean culture had been rather receptive than dominant. In other words, it was focused more on acceptance of other cultures than active creation or outflow of its own. Now, however, K-POP is not anymore copying Western culture. It is creating its own unique characters, which makes K-pop very competitive. Korean culture has been formed for a long time in Korea's unique historical background. Korean popular culture also has to establish a solid foothold in world markets through its distinctive and traditional feature. The positive consumer response to Korean pop culture will create the added value of Korean contents and their derivatives, which will heighten Korea's national image also. In other words, if traditional art and K-POP are converged and equipped with our own unique and highly artistic culture, they will take the lead in the global cultural art market. In this study, we will recognize the possibility, growth and development of K-pop culture and analyze the cases of combining K-pop and Korean traditional art. First, we have to blend traditional art and other various genres to create diverse contents, and we have to actively utilize media channels. Second, we must improve people's awareness of the copyrights of traditional art. Also, we have to mitigate the copyrights of creative dance to expand the disclosure of contents which can be utilized. Third, we have to learn about traditional arts from younger age. Fourth, we will expand traditional arts to the whole of Korean cultural policies, which can enhance the nation's cultural value and create economic benefits. These four are expected to be effective ways to preserve the identity of traditional art and at the same time, globalize Korean culture.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.18 no.1
• /
• pp.203-223
• /
• 2023

Recently, the platform industry is rapidly growing in the global market, and competition is intensifying at the same time. Therefore, in order for domestic platform companies to have global competitiveness in the platform market, it is necessary to study the platform business ecosystem and success factors. However, most of the recent platform-related studies have been theoretical studies on the characteristics of platform business status analysis, platform economy, and indirect network externalities of platforms. Therefore, this study comprehensively analyzed the success factors of Silicon Valley's business ecosystem proposed in previous studies, and at the same time analyzed the success factors extracted from stakeholders in the actual Silicon Valley platform business ecosystem. And based on these factors, an IPA analysis was conducted as a way to propose a success plan to stakeholders in the platform business ecosystem. As a result of the analysis, among the success factors collected through previous studies, manpower, capital, and challenge culture were identified as factors that are relatively well maintained in both importance and satisfaction in Silicon Valley. In the end, it can be seen that the creation of an environment and culture in which Silicon Valley can use it to challenge itself based on excellent human resources and abundant capital contributes the most to the success of Silicon Valley's platform business. On the other hand, although it is of high importance to Silicon Valley's platform corporate ecosystem, the factors that show relatively low satisfaction among stakeholders are 'learning and benchmarking among active companies' and 'strong ties and cooperation between members', and it is analyzed that interest and effort are needed to strengthen these factors in the future. Finally, the systems and policies necessary for market autonomous competition, 'business support service industry', 'name value', and 'spin-off start-up' were important factors in literature research, but the importance and satisfaction of these factors were lowered due to changes in the times and environment. This study has academic implications in that it comprehensively analyzes the success factors of Silicon Valley's business ecosystem proposed in previous studies, and at the same time analyzes the success factors extracted from stakeholders in the actual Silicon Valley platform business ecosystem. In addition, there is another academic implications that importance and satisfaction were simultaneously examined through IPA analysis based on these various extracted factors. As for academic implications, it is meaningful in that it contributed to the formation of the domestic platform ecosystem by providing the government and companies with concrete information on the success factors of the platform business ecosystem and the theoretical grounds for the growth of domestic platform businesses.

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.45 no.5
• /
• pp.746-756
• /
• 2016

The purpose of this study was to investigate food safety knowledge, food safety attitudes, and handling behavior in the elderly. The survey was conducted on 358 individuals over 65 years old in urban and rural areas. Data were analyzed with descriptive analysis and ${\chi}^2$ test analysis of variance using SPSS. From the results on elderly's food safety knowledge, the item 'tangerines should be washed before eating' was correctly answered by urban subjects (75.4%) than rural subjects (49.7%). 'Is it okay to cook meat left on the sink since afternoon in the evening' showed the lowest correct answer rate in both urban (23.1%) and rural (31.9%) subjects. For the item related to food keeping, 'Bacterial cells do not multiply in Samgyetang when it is kept in a refrigerator right after boiling thoroughly', 58.5% of urban and 54.6% of rural elderly answered correctly. Most elderly people showed a tendency to think that boiled foods might be safe to eat. Secondly, for food safety attitudes, urban elderly had more proper attitude regarding the item, 'Namul is very tasty only when mixed with bare hands' (disagree rate 34.9%) than rural elderly (P<0.05)'. On the other hand, rural elderly had more positive attitudes regarding the store principle "first in, first out" compared to urban elderly (P<0.001). Thirdly, regarding food safety behaviors, only 67.9% of urban and 58.7% of rural elderly responded that they washed their hands right after answering the telephone while cooking. Exactly 33.8% of urban and 39.6% of rural older people replied 'defrost meat on top of sink or table' as the defrost method for frozen foods, showing that elderly did not recognize the risk of foodborne illness during improper defrosting at room temperature.

• Journal of Intelligence and Information Systems
• /
• v.26 no.3
• /
• pp.71-90
• /
• 2020

Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.

• Journal of Intelligence and Information Systems
• /
• v.26 no.4
• /
• pp.27-65
• /
• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.