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Yuncherl Choi; Taeyoung Ha; Jongmin Han; Sewoong Kim; Doo Seok Lee. Turing instability and dynamic phase transition for the Brusselator model with multiple critical eigenvalues. Discrete & Continuous Dynamical Systems 2021, 41, 4255 .
AMA StyleYuncherl Choi, Taeyoung Ha, Jongmin Han, Sewoong Kim, Doo Seok Lee. Turing instability and dynamic phase transition for the Brusselator model with multiple critical eigenvalues. Discrete & Continuous Dynamical Systems. 2021; 41 (9):4255.
Chicago/Turabian StyleYuncherl Choi; Taeyoung Ha; Jongmin Han; Sewoong Kim; Doo Seok Lee. 2021. "Turing instability and dynamic phase transition for the Brusselator model with multiple critical eigenvalues." Discrete & Continuous Dynamical Systems 41, no. 9: 4255.
In the age of knowledge-based economies, open innovation has increasing importance. This study aimed to explore the architectural design approaches that can revitalize innovation activities in the era of knowledge-based economies. This paper investigated global case research campuses, manufacturing systems, and innovation districts where architectural design supports innovation activities. This study developed a research framework of architectural design for innovation and applied it in the selected case studies to generate insights. First, the research campuses selected as case studies included Panopticon, DGIST Education and Research Campuses, and Apple Park. Second, the open innovation of manufacturing system architecture was analyzed through the case studies of the Ford Motor Company, Toyota Motor Corporation, and Rolls-Royce Motor Cars. Third, this paper studied the clustered open innovation architectures of Macquarie Park, One North, and Strijp-S Innovation Districts. The findings revealed how tacit knowledge motivates open innovation through the design of manufacturing systems, research campuses, and innovation districts through real examples and mathematical or concept model building.
JinHyo Joseph Yun; Xiaofei Zhao; Tan Yigitcanlar; DooSeok Lee; Heungju Ahn. Architectural Design and Open Innovation Symbiosis: Insights from Research Campuses, Manufacturing Systems, and Innovation Districts. Sustainability 2018, 10, 4495 .
AMA StyleJinHyo Joseph Yun, Xiaofei Zhao, Tan Yigitcanlar, DooSeok Lee, Heungju Ahn. Architectural Design and Open Innovation Symbiosis: Insights from Research Campuses, Manufacturing Systems, and Innovation Districts. Sustainability. 2018; 10 (12):4495.
Chicago/Turabian StyleJinHyo Joseph Yun; Xiaofei Zhao; Tan Yigitcanlar; DooSeok Lee; Heungju Ahn. 2018. "Architectural Design and Open Innovation Symbiosis: Insights from Research Campuses, Manufacturing Systems, and Innovation Districts." Sustainability 10, no. 12: 4495.
The purpose of this study is to address the following research question: What is the relationship between open innovation and firm performance? The study built up a research framework with three factors—i.e., open innovation strategy, time scope, and industry condition—to find out the concrete open innovation effects on firm performance. This study adopted four different research methods. Firstly, we applied the aforementioned factors to a game of life simulation in order to identify the concrete differences of open innovation effects on firm performance. Secondly, the study examined the real dynamics of open innovation effects on firm performance in the aircraft industry—one of the oldest modern industries—through a quantitative patent analysis. It then looked into the effects of major factors that impact open innovation effects. Thirdly, this study developed a mathematical model and tried to open the black box of open innovation effects on firm performance. Lastly, the study logically compiled research on open innovation effects on firm performance through the presentation of a causal loop model and derived the possible implications.
JinHyo Joseph Yun; Dongkyu Won; Euiseob Jeong; KyungBae Park; DooSeok Lee; Tan Yigitcanlar. Dismantling of the Inverted U-Curve of Open Innovation. Sustainability 2017, 9, 1423 .
AMA StyleJinHyo Joseph Yun, Dongkyu Won, Euiseob Jeong, KyungBae Park, DooSeok Lee, Tan Yigitcanlar. Dismantling of the Inverted U-Curve of Open Innovation. Sustainability. 2017; 9 (8):1423.
Chicago/Turabian StyleJinHyo Joseph Yun; Dongkyu Won; Euiseob Jeong; KyungBae Park; DooSeok Lee; Tan Yigitcanlar. 2017. "Dismantling of the Inverted U-Curve of Open Innovation." Sustainability 9, no. 8: 1423.
Yuncherl Choi; Taeyoung Ha; Jongmin Han; Doo Seok Lee. Bifurcation and final patterns of a modified Swift-Hohenberg equation. Discrete & Continuous Dynamical Systems - B 2017, 22, 2543 -2567.
AMA StyleYuncherl Choi, Taeyoung Ha, Jongmin Han, Doo Seok Lee. Bifurcation and final patterns of a modified Swift-Hohenberg equation. Discrete & Continuous Dynamical Systems - B. 2017; 22 (7):2543-2567.
Chicago/Turabian StyleYuncherl Choi; Taeyoung Ha; Jongmin Han; Doo Seok Lee. 2017. "Bifurcation and final patterns of a modified Swift-Hohenberg equation." Discrete & Continuous Dynamical Systems - B 22, no. 7: 2543-2567.
What do we need for sustainable artificial intelligence that is not harmful but beneficial human life? This paper builds up the interaction model between direct and autonomous learning from the human’s cognitive learning process and firms’ open innovation process. It conceptually establishes a direct and autonomous learning interaction model. The key factor of this model is that the process to respond to entries from external environments through interactions between autonomous learning and direct learning as well as to rearrange internal knowledge is incessant. When autonomous learning happens, the units of knowledge determinations that arise from indirect learning are separated. They induce not only broad autonomous learning made through the horizontal combinations that surpass the combinations that occurred in direct learning but also in-depth autonomous learning made through vertical combinations that appear so that new knowledge is added. The core of the interaction model between direct and autonomous learning is the variability of the boundary between proven knowledge and hypothetical knowledge, limitations in knowledge accumulation, as well as complementarity and conflict between direct and autonomous learning. Therefore, these should be considered when introducing the interaction model between direct and autonomous learning into navigations, cleaning robots, search engines, etc. In addition, we should consider the relationship between direct learning and autonomous learning when building up open innovation strategies and policies.
JinHyo Joseph Yun; DooSeok Lee; Heungju Ahn; KyungBae Park; Tan Yigitcanlar. Not Deep Learning but Autonomous Learning of Open Innovation for Sustainable Artificial Intelligence. Sustainability 2016, 8, 797 .
AMA StyleJinHyo Joseph Yun, DooSeok Lee, Heungju Ahn, KyungBae Park, Tan Yigitcanlar. Not Deep Learning but Autonomous Learning of Open Innovation for Sustainable Artificial Intelligence. Sustainability. 2016; 8 (8):797.
Chicago/Turabian StyleJinHyo Joseph Yun; DooSeok Lee; Heungju Ahn; KyungBae Park; Tan Yigitcanlar. 2016. "Not Deep Learning but Autonomous Learning of Open Innovation for Sustainable Artificial Intelligence." Sustainability 8, no. 8: 797.