• Archives of design research
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• v.16 no.4
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• pp.185-196
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• 2003

The demand for an environment-friendly transportation system, equipped with low energy consumption, and low-or zero-pollution has been on the increase since the beginning of the World Trade Organization era. Simultaneously, the consistent growth of high-speed tram technology, combined with market share, has sparked a fierce competition among technologically-advanced countries like France, Germany, and Japan in an effort to keep the lead in high-speed train technology via extensive Research and development(R＆D) expenses. These countries are leaders in the race to implement the next-generation transportation system, build intercontinental rail way networks and export the high-speed train as a major industry commodity. The need to develop our own(Korean) 'high-speed train' technology and its core system technology layouts including original technology serves a few objectives: They boost the national competitive edge; they develop an environmental friendly rail road system that can cope with globalization and minimize the social and economic losses created by the growing traffic-congested delivery costs, environment pollution, and public discomforts. In turn, the 'G7 Project-Development of High Speed Railway Technology' held between 1996 and 2002 for a six-year period was focused on designing a domestic train capable of traveling at a speed of 350km/h combined and led to the actual implementation of engineering and producing the '2000 high-speed train:' This paper summarizes and introduces one of the G7 Projects-specifically, the design segment achievement within the development of train system engineering technology. It is true that the design aspect of the Korean domestic railway system program as a whole was lacking when compared with the advanced railroad countries whose early phase of train design emphasized the design aspect. However, having allowed the active participation of expert designers in the early phase of train design in the current project has led to a new era of domestic train development and the implementation of a way to meet demand flexibly with newly designed trains. The idea of a high-speed train in Korea and its design concept is well-conceived: a faster, more pleasant, and silent based Korean high-speed train that facilitates a new travel culture. A Korean-type of high-speed train is acknowledged by passengers who travel in such trains. The Korean high-speed prototype train has been born, combining aerodynamic air-cushioned design, which is the embodiment of Korean original design of forehead of power car minimized aerodynamic resistance using a curved car body profile, and the improvement of the interior design with ergonomics and the accommodation of the vestibule area through the study of passenger behavior and social culture that is based on the general passenger car.

• Proceedings of the Technology Innovation Conference
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• 1997.07a
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• pp.222-223
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• 1997

By analyzing the successful innovation case of potato microtuber mass production technology, a representative case of technology-push type creative innovation in an imitation oriented research culture, this paper attempts to figure out conceptual model of creative innovation that is initiated by the public laboratories in catching-up country, Stages of creative innovation can be divided into the internal R&D stage and the external commercialization stage. Success of the internal R&D stage depended on autonomy to secure creative research idea and commitment of individual researchers. Psychological pressure evoked from sportlights of mass media and commitment of sponsor increased the intensity of research efforts of the researcher Recognition of research problem and its significance was intensified by site visits of agricultural fields, and the recognized higher impacts of expected research results and knowledge creation achieved were a fundamental source of self-motivation. In the stage of commercialization stage, various legal, socio-economic, and psychological barriers were confronted. In a catching-up country lacking of experiences of creative innovation, creative innovation process can be regarded as a barrier elimination and cultural revolution process. Among the barriers, psychological refusal of farmers to corn-sized potato seeds was critical, which finally enforced to further researches to enlarge the size of potato seeds. In addition, the researcher has concentrated his research efforts in one specialized research area by getting a series of similar research project funds rather than diversification. It was lucky for him to have a chance to carry out a series of similar researches in one research area during the last 10 years. In getting research funds from government and private companies continuously in one research area, both internal and external promoters played significant roles.

• 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.