• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.3
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• pp.157-167
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• 2016

The purpose of this study is to research, such as the following. And to the empirical results that affect the potential growth factors in the organization and development of human resources through staff training for enterprises to grow into a competitive enterprise. Through the analysis we propose a systematic training of the human resource development needs of the company. The results are as follows. First, the number of courses, the degree completion has had a positive effect on job satisfaction. Second, the number of courses can have a partial mediating effect on financial job satisfaction. Third, corporate education funding ratio has a negative effect and Business support form has a positive effect on turnover intentions. Fourth, the control variables of marital status has a positive effect on psychological job satisfaction and company size had a negative impact on turnover intention. The implications of this study are as follows. Organizational commitment to act as a mediating effect can be maximized through realistic training plan and quality training. There is also a need to be made a high quality education content development through the advancement of learning styles.

• Journal of Technology Innovation
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• v.25 no.3
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• pp.115-154
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• 2017

This article offers a review of the major literature about sectoral innovation studies of Korea and its implications over the past 30 years. The literature on the sectoral innovation studies in Korea has focused on analysing successful technological catch-up from an evolutionary perspective and most of research has centered on the issues about entry strategies, learning mechanisms. Recently "Emerging economies" like Korea in the 2000s face major challenges as they make a transition from (a) a phase of economic development characterised by 'catching up' with the global technological frontier, involving technological "imitation", to (b) a phase of continuing development based on the development of new knowledge for globally leading (post catch-up) product and process innovation. This paper reviews those bodies of literature of patterns of sectoral innovation, technological capability accumulation and catch-up process, catch-up innovation and institutions, and patterns of growth dynamics. Finally, given the importance of sectoral innovation studies, we suggest that industrial upgrading, transition towards leadership, dark side of catch-up issues are needed for future research directions.

• Journal of Korea Technology Innovation Society
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• v.11 no.4
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• pp.565-591
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• 2008

Using theories of sectoral innovation system and supply chain management, this paper examines the status quo of Korean IT SoC industry's innovation system by comparing it with Taiwanese one. Taiwan IT SoC industry has accomplished a rapid growth on the basis of government policies that foster domestic firms after the establishment of Hsinchu Science Park. Cooperative networks between foundries firms and fablesses have been formed within the supply chain in this process. Therefore, Taiwan industry has possessed the possibility of the coevolution in sectoral innovation system. However, Korean IT SoC industry has failed to form cooperative networks, because of weak networks between related firms. In other words, there exists an interaction failure, which is a kind of the system failure, and it means a lack of linkage between actors as a result of insufficient use of complementarities and interactive learning. Therefore, Korean industry has little possibility of the coevolution in sectoral innovation system. The cooperative networks between actors are prerequisite towards evolution of innovation system of Korean IT SoC industry. Above all, the cooperative networks between fablesses and system companies need to be strengthened within the supply chain.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.7 no.3
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• pp.65-73
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• 2012

Microfinance has been an important tool for the economic growth and poverty alleviation. But the success factors and risk factors have not been synthesized in academic literature. This article has paid attention to success factors and potential risk of the Grameen Bank. Grameen Bank methodology is almost the reverse of the conventional banking methodology. Conventional banking is based on the principle that the more you have, the more you can get. Founder of Grameen Bank, Professor Yunus pointed out that, "The least you have the highest you have the priority to receive a loan". On the basis of theoretical literature, there have been different kinds of success factors of microfinance observed in this paper. Key success factors of Grameen Bank are like these: innovation, strict administrative structure, adaptation and learning practice, incentive system. Complementary services such as business consulting and brokerage will contribute to borrowers' economic performance development.

• Journal of Science Education
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• v.45 no.2
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• pp.172-186
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• 2021

The purpose of this study is to analyze the science lesson plan of pre-service elementary teachers about condensation. Pre-service elementary school teachers in A national university of education was included in this study. Through the analysis of prior research and expert review, a framework for analysis of science lesson plan of pre-service elementary teachers was derived. The results of the using the analysis frame are as follows: First, the ability to apply the instructional model in the science lesson plan about condensation differences in pre-service elementary teachers need to be enhanced due to deviations, and teaching on the exact understanding of condensation-related concepts of pre-service elementary teachers is also needed. Second, there is also a deviation of pre-service elementary teachers in the beginning, development, and finishing composition of lesson course, so feedback should be supplemented. Third, in the sub-domain of lesson environment, there was a demand for specific know-how on the lesson environment. Therefore, support is needed for related PCK growth. Fourth, the sub-domain of lesson evaluation have a variety of perspectives on timing and subjects, and some missing about learning objectives in the composition of evaluation content are found to require complementary teaching. In order to improve this situation, it was found that there was a need to prepare conditions for improving science teaching professionalism of pre-service elementary teachers through in-depth discussions on the teaching methods and organization related to science education in the university of education course.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.337-357
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• 2021

Because the growth of paddy rice is affected by meteorological factors, the selection of appropriate meteorological variables is essential to build a rice yield prediction model. This paper examines the suitability of multiple meteorological datasets for the rice yield modeling in South Korea, 1996-2019, and a hindcast experiment for rice yield using a machine learning method by considering the nonlinear relationships between meteorological variables and the rice yield. In addition to the ASOS in-situ observations, we used CRU-JRA ver. 2.1 and ERA5 reanalysis. From the multiple meteorological datasets, we extracted the four common variables (air temperature, relative humidity, solar radiation, and precipitation) and analyzed the characteristics of each data and the associations with rice yields. CRU-JRA ver. 2.1 showed an overall agreement with the other datasets. While relative humidity had a rare relationship with rice yields, solar radiation showed a somewhat high correlation with rice yields. Using the air temperature, solar radiation, and precipitation of July, August, and September, we built a random forest model for the hindcast experiments of rice yields. The model with CRU-JRA ver. 2.1 showed the best performance with a correlation coefficient of 0.772. The solar radiation in the prediction model had the most significant importance among the variables, which is in accordance with the generic agricultural knowledge. This paper has an implication for selecting from multiple meteorological datasets for rice yield modeling.

• Journal of KIISE
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• v.43 no.1
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• pp.87-95
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• 2016

Recently, due to the development of the Internet and electronic devices, there has been an enormous increase in the amount of available knowledge and information. As this growth has proceeded, studies on large-scale ontological reasoning have been actively carried out. In general, a machine learning program or knowledge engineer measures and provides a degree of confidence for each triple in a large ontology. Yet, the collected ontology data contains specific uncertainty and reasoning such data can cause vagueness in reasoning results. In order to solve the uncertainty issue, we propose an RDFS reasoning approach that utilizes confidence values indicating degrees of uncertainty in the collected data. Unlike conventional reasoning approaches that have not taken into account data uncertainty, by using the in-memory based cluster computing framework Spark, our approach computes confidence values in the data inferred through RDFS-based reasoning by applying methods for uncertainty estimating. As a result, the computed confidence values represent the uncertainty in the inferred data. To evaluate our approach, ontology reasoning was carried out over the LUBM standard benchmark data set with addition arbitrary confidence values to ontology triples. Experimental results indicated that the proposed system is capable of running over the largest data set LUBM3000 in 1179 seconds inferring 350K triples.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.675-681
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• 2021

With the recent growth of the AI speaker market, the demand for speech synthesis technology that enables natural conversation with users is increasing. Therefore, there is a need for a multi-speaker speech synthesis system that can generate voices of various tones. In order to synthesize natural speech, it is required to train with a large-capacity. high-quality speech DB. However, it is very difficult in terms of recording time and cost to collect a high-quality, large-capacity speech database uttered by many speakers. Therefore, it is necessary to train the speech synthesis system using the speech DB of a very large number of speakers with a small amount of training data for each speaker, and a technique for naturally expressing the tone and rhyme of multiple speakers is required. In this paper, we propose a technology for constructing a speaker encoder by applying the deep learning-based x-vector technique used in speaker recognition technology, and synthesizing a new speaker's tone with a small amount of data through the speaker encoder. In the multi-speaker speech synthesis system, the module for synthesizing mel-spectrogram from input text is composed of Tacotron2, and the vocoder generating synthesized speech consists of WaveNet with mixture of logistic distributions applied. The x-vector extracted from the trained speaker embedding neural networks is added to Tacotron2 as an input to express the desired speaker's tone.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.514-517
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• 2021

The purpose of this study is to discover the occupational identity by examining the narrative of the life of a vocational training teacher with self-esteem in programming fields. The following six types of occupational identity were found: 'a positive image of a vocational training teacher(fits oneself)', 'I feel proud of myself while doing vocational training activities.', 'a teacher who continues to develop him/herself as an expert in the subject class', 'a teacher who immerses him/herself as an expert on student change and growth', 'a teacher engaged in leading activities to create opportunities for vocational training', and 'a teacher of continuous pursuit'. This study has significance in exploring the structure of occupational identity recognition and experience of its formation of a self-esteemed vocational training teacher in programming fields, which have not been studied.

• Management & Information Systems Review
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• v.36 no.4
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• pp.33-51
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• 2017

With the advent of 'The Forth Industrial Revolution' and the growing demand for quality of life due to economic growth, needs for the quality of medical services are increasing. Artificial intelligence has been introduced in the medical field, but it is rarely used in chronic skin diseases that directly affect the quality of life. Also, atopic dermatitis, a representative disease among chronic skin diseases, has a disadvantage in that it is difficult to make an objective diagnosis of the severity of lesions. The aim of this study is to establish an intelligent severity recognition model of atopic dermatitis for improving the quality of patient's life. For this, the following steps were performed. First, image data of patients with atopic dermatitis were collected from the Catholic University of Korea Seoul Saint Mary's Hospital. Refinement and labeling were performed on the collected image data to obtain training and verification data that suitable for the objective intelligent atopic dermatitis severity recognition model. Second, learning and verification of various CNN algorithms are performed to select an image recognition algorithm that suitable for the objective intelligent atopic dermatitis severity recognition model. Experimental results showed that 'ResNet V1 101' and 'ResNet V2 50' were measured the highest performance with Erythema and Excoriation over 90% accuracy, and 'VGG-NET' was measured 89% accuracy lower than the two lesions due to lack of training data. The proposed methodology demonstrates that the image recognition algorithm has high performance not only in the field of object recognition but also in the medical field requiring expert knowledge. In addition, this study is expected to be highly applicable in the field of atopic dermatitis due to it uses image data of actual atopic dermatitis patients.