• Journal of Broadcast Engineering
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• v.12 no.5
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• pp.460-479
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• 2007

When culture took large part in industrial area, every country has tried to utilize own cultural contents for educational or commercial purpose and the various cultures and histories are recognized as a main concept or subject so that a number of scholars who study history increase. In video game field, special characteristics of interface that audiences participate in the game to complete story-telling is considered as efficient material for learning process. As observed above, it is important to analyze the games that every country makes and export to the world in which the video games is understood as a play of human in general. This Paper has firstly analyzed the most favorite historical games developed in Korea, the USA, Japan, Taiwan and Germany from 1980 to 2005 and secondly, compared that wars and historical origin appears in game scenario, a world view and background story and finally after point out the preferable era and genre of the countries then propose the promising way of design for historical video games. In the process of analysis of a view and heroes in historical games, we compared the real persons, the real historical events and novel in which 11.8% only employed the real persons in 8 out of 68 games. Also the real history and background story are appeared in 37 games which is 54.4% of them. We discovered that the main material that is popular for each country is the historical backing rather than real persons where the favorite historical background is chosen at which they are proud of; 3-Throne era with strong ancient Gogurye for Korea, the 1st and 2nd World Wars and the Independence War for the USA, the tide of war around Middle age for Japan, ancient history of Europe for Germany. The favorite age for video games is Ancient times with 37 games for 54.4%, Middle Age with 7 games fer 10.3%, the prehistoric age with 5 games for 7.35%, remote age with 1 for 1.47%, while current historical games favor Ancient or Modern Age.

• Management & Information Systems Review
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• v.36 no.3
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• pp.131-150
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• 2017

The purpose of this study is to observe the spread pattern of open source software (OSS) while establishing relations with surrounding actors during its operation period. In order to investigate the change pattern of participants in the OSS, we use a netnography on the basis of online data, which can trace the change patterns of the OSS depending on the passage of time. For this, the cases of three OSSs (e.g. jQuery, MooTools, and YUI), which are JavaScript frameworks, were compared, and the corresponding data were collected from the open application programming interface (API) of GitHub as well as blog and web searches. This research utilizes the translation process of the actor-network theory to categorize the stages of the change patterns on the OSS translation process. In the project commencement stage, we identified the type of three different OSS-related actors and defined associated relationships among them. The period, when a master commences a project at first, is refined through the course for the maintenance of source codes with persons concerned (i.e. project growth stage). Thereafter, the period when the users have gone through the observation and learning period by being exposed to promotion activities and codes usage respectively, and becoming to active participants, is regarded as the 'leap of participants' stage. Our results emphasize the importance of promotion processes in participants' selection of the OSS for participation and confirm the crowding-out effect that the rapid speed of OSS development retarded the emergence of participants.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.5
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• pp.431-449
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• 2022

As many tunnels generally have been constructed, various experiences and techniques have been accumulated for tunnel design as well as tunnel construction. Hence, there are not a few cases that, for some usual tunnel design works, it is sufficient to perform the design by only modifying or supplementing previous similar design cases unless a tunnel has a unique structure or in geological conditions. In particular, for a tunnel blast design, it is reasonable to refer to previous similar design cases because the blast design in the stage of design is a preliminary design, considering that it is general to perform additional blast design through test blasts prior to the start of tunnel excavation. Meanwhile, entering the industry 4.0 era, artificial intelligence (AI) of which availability is surging across whole industry sector is broadly utilized to tunnel and blasting. For a drill and blast tunnel, AI is mainly applied for the estimation of blast vibration and rock mass classification, etc. however, there are few cases where it is applied to blast pattern design. Thus, this study attempts to automate tunnel blast design by means of machine learning, a branch of artificial intelligence. For this, the data related to a blast design was collected from 25 tunnel design reports for learning as well as 2 additional reports for the test, and from which 4 design parameters, i.e., rock mass class, road type and cross sectional area of upper section as well as bench section as input data as well as16 design elements, i.e., blast cut type, specific charge, the number of drill holes, and spacing and burden for each blast hole group, etc. as output. Based on this design data, three machine learning models, i.e., XGBoost, ANN, SVM, were tested and XGBoost was chosen as the best model and the results show a generally similar trend to an actual design when assumed design parameters were input. It is not enough yet to perform the whole blast design using the results from this study, however, it is planned that additional studies will be carried out to make it possible to put it to practical use after collecting more sufficient blast design data and supplementing detailed machine learning processes.

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

• The Mathematical Education
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• v.61 no.2
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• pp.339-357
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• 2022

This study explores pre-service secondary mathematics teachers (PSTs)' noticing competency. 17 PSTs participated in this study as a part of the mathematics teaching method class. Individual PST's essays regarding the question 'what effective mathematics teaching would be?' that they discussed and wrote at the beginning of the course were collected as the first data. PSTs' written analysis of an expert teacher's teaching video, colleague PSTs' demo-teaching video, and own demo-teaching video were also collected and analyzed. Findings showed that most PSTs' noticing level improved as the class progressed and showed a pattern of focusing on each key aspect in terms of the Teaching for Robust Understanding of Mathematics (TRU Math) framework, but their reasoning strategies were somewhat varied. This suggests that the TRU Math framework can support PSTs to improve the competency of 'what to attend' among the noticing components. In addition, the instructional reasoning strategies imply that PSTs' noticing reasoning strategy was mostly related to their interpretation of noticing components, which should be also emphasized in the teacher education program.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2008.11a
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• pp.270-276
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• 2008

The purpose of this study is to deal with fire fighting safety education primarily among the fields of child safety education. So, first of all, this study considered the theoretical background of fire fighting safety education. And this study analyzed the present state and cases of safety accidents in elementary schools. And by focusing on the fire fighting science class for elementary schools which Gyeonggi-do Goyang fire station is executing now, this study analyzed the educational outline, present state, educational goal, and content of fire fighting science class, and conducted the satisfaction survey through questionnaire over the elementary school students participating in fire fighting science class and the fire fighting officers in charge of fire fighting science class. On basis of this research, by developing the new field of fire fighting education and publicity into the program which can diffuse the fire fighting-related chemical experiment based on the science of chemistry and physics in the future and so provide the pleasure and surprise of experiencing directly not only natural fire fighting education and publicity but also learning and the common sense of fire fighting, this study tried to present the alternatives about the measures for activating the fire fighting safety education in elementary schools.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.7
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• pp.66-71
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• 2019

In this paper, a study was conducted on the method of using GPR data to predict rebar thickness inside a facility. As shown in the cases of poor construction, such as the use of rebars below the domestic standard and the construction of reinforcement, information on rebar thickness can be found to be essential for precision safety diagnosis of structures. For this purpose, the B-scan data of GPR was obtained by gradually increasing the diameter of rebars by making specimen. Because the B-scan data of GPR is less visible, the data was converted into the heatmap image data through migration to increase the intuition of the data. In order to compare the results of application of commonly used B-scan data and heatmap data to CNN, this study extracted areas for rebars from B-scan and heatmap data respectively to build training and validation data, and applied CNN to the deployed data. As a result, better results were obtained for the heatmap data when compared with the B-scan data. This confirms that if GPR heatmap data are used, rebar thickness can be predicted with higher accuracy than when B-scan data is used, and the possibility of predicting rebar thickness inside a facility is verified.

• Journal of The Korean Association For Science Education
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• v.37 no.1
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• pp.143-154
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• 2017

Science inquiry has long been emphasized in Korean science education. Scientific modeling is one of key practices in science inquiry with a potential to provide students with opportunities to develop their own explanations and knowledge thereafter. The purpose of this study is to investigate teacher's understanding of models in science and science teaching. A professional development program on Models (PDM) was developed and refined through three times of implementation while teachers' conceptions of models and modeling were examined. A total of 29 elementary and secondary teachers participated in this study. A survey based on model use of scientists in the history of science was developed and used to collect data and audio recordings of discussions among teachers and artifacts produced by the teachers during PDM were also collected. Three ways of ontological and two ways of epistemological understanding of models and modeling were found in teachers' ideas. After PDM, a quarter of the teachers changed their ontological understanding whereas very few changed their epistemological understanding. In contrast, more than two thirds of the teachers deepened and extended their ideas about using models and modeling in teaching. There were no clear relationships between teachers' understanding of models and ways and ideas about using models in science teaching. However, teachers' perceptions of school conditions were found to mediate their intention to use models in science teaching. The findings indicate possible approaches to professional development program content design and further research.

• Journal of Technology Innovation
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• v.26 no.2
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• pp.95-128
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• 2018

Since mid and late 2000s, the smartphones has been widely diffused and Korea ranked first in global smartphone market in 2011 thanks to its rapid adoption of Android operating system, technology capability accumulated in featurephone development, vertical integration on smartphone production and premium positioning. However, Korea fell behind because of the rise of another latecomer, China, in four years (2015) after it recorded the top position globally. How did the leadership change occurred in the smartphone industry so rapidly? In order to answer the question, we investigated three favorable windows of opportunity for the rise of China, which are technological, demand, and institutional, and the strategic responses of Chinese firms as well as the rigidity and complacency with the past success of Korean firms. Our findings contribute to the extension of 'catch-up cycle' theory as well as provide in-depth insights for strategies and policies settings to overcome the recent rise of China in information and communication technology sector for Korea.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.4 no.3
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• pp.1-22
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• 2009

A firm's competitive capabilities become greater when the firm has a specific knowledge. There are many studies have been examined how to accumulate the firm specific knowledge and to get the competitive capability on the various perspectives. This study suggest that the conceptual framework on the absorptive capability through reviews on the knowledge management theory. And it also suggests that the proposition about the technological capability building process through the in depth case study on a small and medium sized company in a LCD industry. This study found the following major characteristics about the absorptive capability building and knowledge creating process. First, it is required to building an absorptive capability rapidly that the harmony of local capabilities, integrative capabilities, and intensity of effort. And the most important factor is the intensity of effort in a small and medium sized firm with a weak knowledge base. Second, it is required to develop an innovative new product that the utilization of expeditious learning mechanism based on the exploration and exploitation process. Finally, complementary assets are needed to proactive exploration and exploitation. Based on the findings, the theoretical and managerial implications are derives and the further research directions are proposed.