• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.331-341
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• 2017

This study analyzed cases of teaching consulting in the cyber university field and activated it in a remote university in the right direction to contribute to the development of high quality contents. This study analyzed the instructional consulting data of the Center for Content Quality Management at A Cyber University in Chungnam area and interviewed researchers working at the center using the phenomenological approach based on the data. This study showed that the professors participating in instructional consulting had many problems, but they were relatively active in improving the quality of the lectures. They wanted to know the teaching methods in the unfamiliar environment of remote universities. In addition, the researchers had difficulty in delivering feedback to the instructor through the process of exploring images taken several times with the objective framework of lecture evaluation. To allow better communication, it was necessary to form rapport between the instructor and researcher. Unlike general universities, cyber universities have features and limitations in that they only take cameras in the classroom without learners and proceed with class consulting. Therefore, the teachers have a feeling of burden about shooting and recording, but they are less than general university teachers, and are more willing to engage in class consultation more actively. In this study, the results of the research was discussed and the proposal of cyber universities' instruction consulting and the effective plan is proposed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.409-416
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• 2020

Artificial intelligence, which is in the spotlight as the core driving force of the 4th industrial revolution, is expanding its scope to various industrial fields such as smart factories and autonomous driving with the development of high-performance hardware, big data, data processing technology, learning methods and algorithms. In the field of defense, as the security environment has changed due to decreasing defense budget, reducing military service resources, and universalizing unmanned combat systems, advanced countries are also conducting technical and policy research to incorporate artificial intelligence into their work by including recognition systems, decision support, simplification of the work processes, and efficient resource utilization. For this reason, the importance of technology-driven planning and investigation is also increasing to discover and research potential defense future technologies. In this study, based on the research data that was collected to derive future defense technologies, we analyzed the characteristic evaluation indicators for future technologies in the field of artificial intelligence and conducted empirical studies. The study results confirmed that in the future technologies of the defense AI field, the applicability of the weapon system and the economic ripple effect will show a significant relationship with the prospect.

• Journal of Science and Technology Studies
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• v.15 no.1
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• pp.181-214
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• 2015

This study analyzed the characteristics of the politics of technoscience and governance in South Korea, taking advantage of the policy changes on the stem cell research after Hwang's affair. In spite of generally accepted conventional wisdom that stem cell research had been suffering 'crisis' after the Hwang's affair, South Korea succeeded in developing the first and the largest stem cell product in the world. However, considering the fact that the stem cell research capabilities and technological competitiveness of Korea have been assessed as relatively low compared to the development performance, there is a need to extrapolate how such result could be achieved. To answer these questions, we analyzed changes in the R&D expenditure before and after the scandal and verified the 'crisis of stem cell research' following the reduction of financial support from government. From the analysis of literature on the policy reports and news, we described the process of discourse changes in policy and analyzed the characteristics of the politics of technoscience and governance of stem cell research. This study emphasized that the government R&D and regulation policy play the key roles in the development of stem cell research rather than in the technological competitiveness in South Korea. Furthermore, this study argued that democratic governance still does not work under the policy conditions that technocratic decision-making of stem cell research fails to learn from the Hwang's affairs.

• Journal of The Korean Association For Science Education
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• v.19 no.4
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• pp.653-664
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• 1999

This study was designed to develope an in-service training programme using Interactive $Physics^{TM}$ simulation for science teachers and to evaluate the effect of programme. The purposes of training programme are the improvement of scientific inquiry teaching ability as well as enhancement of the understanding of scientific concepts, inquiry skills, and the computer manipulation skills. The developed programme was implemented four times with 15 hours for each courses. The questionnaire for evaluating the programme after the last course showed that many teachers (1) voluntarily participated in this programme with internal motivation, (2) were satisfied with the level of programme difficulty, professionality of lecturer, and classroom environment, (3) gave positive responses about the achievement of the purposes of this programme, (4) showed strong intention for applying simulation to their school teaching. And future studies were proposed.

• Journal of Korean Elementary Science Education
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• v.25 no.4
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• pp.430-453
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• 2006

The purpose of this study was to explore how an elementary school teacher developed PCK by utilizing her knowledge domains in teaching practice, regarding the specific science topic of 'animals:' A case study approach was adopted with the participation of a 1st grade teacher, in a poor urban neighborhood elementary school in NYC. Data was collected through interview and the participant observation method in order to investigate: a) the teacher's existing knowledge base in terms of subject matter knowledge, pedagogical knowledge and contextual knowledge; b) how she develops PCK during classroom practice, centering on the relationship between knowledge domains. The findings illustrate the ways in which the three knowledge domains are closely related and developed as PCK through the whole teaching process. In particular, the findings indicate that the teacher's contextual knowledge plays a critical role in shaping and developing PCK. Before instruction, her contextual knowledge regarding the administrative policies and the school test system in the district enabled her to make decisions and plans about teaching science. During classroom teaching, her knowledge of students' sociocultural backgrounds and living conditions in the urban setting helped her to identify specific teaching strategies and resources suitable to the students' needs and interests. The study results imply that science instruction can be more feasible in meeting the demands of particular groups of students if teachers make an effort to become knowledgeable about their own teaching context and utilize it in developing their PCK.

• International Commerce and Information Review
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• v.14 no.2
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• pp.249-269
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• 2012

The focus of our work is to identify and understand the drivers of alliance performance so that businesses can maximize their chances of a successful alliance-an area that has received little attention in empirical modeling. Although both conceptual and applied research on alliances has increased, an empirically tested comprehensive theoretical model that explains alliance performance has yet to be developed. Using five salient perspective, namely market power theory, transaction cost theory, the resource-based view, institutional theory, real option theory, this paper attempts to provide a theoretical rationale linking motives of global alliance networks on firm's capabilities, partner's capabilities, operating structures, and performances of Korean companies. The key contribution of this study is that it paints a picture of what matters in driving alliance performance. Our work shows the complex nature of driving performance and the interplay of firm's capabilities, partner's capabilities, and operating structures for understanding alliance performances. This study has given us a small but significant step forward towards understanding the intricacies of alliance performance. We are now better able to understand the respective roles played by various alliance factors and derive insights that lead to improved alliance performance.

• Journal of the Economic Geographical Society of Korea
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• v.17 no.1
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• pp.45-68
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• 2014

This research addressed improvement directions and problems of regional science and technology(S&T) policies in Daegu and Gyungbuk regions in terms of 'Creative Economy' which was a new paradigm of Park Gunhye Government. Creative Economy stressed the construction of an ecosystem in regional research and development activities, and thus it was deeply associated with building a regional innovation system(RIS). There were several problems to strengthen RIS with regional S&T policies of the regions as follows: limits in meeting regional needs due to excessive attraction of central government's projects into regions; the high ratio of programs for future basic research potentials; the lack of programs assessing and coordinating the policies; and the lack of experiences and expenditure of research institutes and firm supporting organizations. Due to these problems, the role of the policies in building RIS did not seem to be effective. Therefore, the policies need to be improved through the following measures: the expansion of regional own policies focusing regional needs; the enhancement of policy coordination by shifting to systematic approach; the expansion of soft supporting programs for constructing innovation systems; and the enhancement of stability and ability of research institutes and firm supporting organizations.

• The KIPS Transactions:PartB
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• v.18B no.3
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• pp.147-156
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• 2011

This paper proposes a 3-stage (preprocessing, feature extraction, and classification) fault detection and classification algorithm for induction motors. In the first stage, a low-pass filter is used to remove noise components in the fault signal. In the second stage, a discrete cosine transform (DCT) and a statistical method are used to extract features of the fault signal. Finally, a back propagation neural network (BPNN) method is applied to classify the fault signal. To evaluate the performance of the proposed algorithm, we used one second long normal/abnormal vibration signals of an induction motor sampled at 8kHz. Experimental results showed that the proposed algorithm achieves about 100% accuracy in fault classification, and it provides 50% improved accuracy when compared to the existing fault detection algorithm using a cross-covariance method. In a real-world data acquisition environment, unnecessary noise components are usually included to the real signal. Thus, we conducted an additional simulation to evaluate how well the proposed algorithm classifies the fault signals in a circumstance where a white Gaussian noise is inserted into the fault signals. The simulation results showed that the proposed algorithm achieves over 98% accuracy in fault classification. Moreover, we developed a testbed system including a TI's DSP (digital signal processor) to implement and verify the functionality of the proposed algorithm.

• Science of Emotion and Sensibility
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• v.20 no.4
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• pp.31-40
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• 2017

This research aims to investigate 'Hallyu' contents consumption tendency of consumers from Korea, Japan, and the United States by analyzing their emotional responses. With the development of social media, research on emotion analysis by reviewing text materials has grown. Whereas environmental variables affect consumer demand towards 'Hallyu' contents, little comparative analyses have been conducted on the emotional responses of consumers from different countries. In this research, the emotional prototype model proposed by Russell(1980) used to extract and distinguish emotional words to clarify how people in the three countries differently perceive the Korean drama "Goblin". First of all, the SNS reviews were collected during a two-month period (February 12 to April 12). Second, significant factors were identified in the collected data according to Russell's emotion model. Third, random forest was applied to organize the selected variables in the order of variable importance. Fourth, the correlations among the emotional words were compared. Lastly, the accuracy of the trained model was measured using the test dataset. The results show that "Happy" was found to be the greatest factor in Korea and in the United States and "Pleased" in Japan. Emotional words correlations showed that when watching the drama "Goblin", "passive unpleasure" was the main factor associated with individual's interest in Korea whereas "passive pleasure" was associated with individual's interest in Japan and in the United States. Based on the results, this research suggests the possibility of developing evaluation guidelines for emotional responses of different countries towards 'Hallyu' contents.

• 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.