• 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.

• Journal of Engineering Education Research
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• v.16 no.2
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• pp.78-85
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• 2013

An 'Introduction to Creative Engineering Design' course at College of Engineering at Chungnam National University is required for all freshmen. The objective of this course is to educate the freshmen with basic engineering design concepts and experiences in creative problem-solving approaches. It provides the students learning opportunities in solving engineering design problems through team efforts and creative approaches. Thus, this course emphasizes creative ideas and thinking, engineering design experiences to students over the course. This study presents the syllabus, the examples of PBL (problem based learning)-related activities as a team, and the results of the course evaluation and outcomes. Based on the results of this study, we can conclude that overall this course using PBL method had significant positive effects on the course outcomes and the creativity of the engineering freshmen in the department of environmental engineering at Chungnam National University. However, there are still efforts to be needed to improve the PBL-related activities in the course, including students' workload, financial supports, and team work.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.37-53
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• 2023

The AI based intelligent command and control system can automatically analyzes the properties of intricate battlefield information and tactical data. In addition, commanders can receive situation analysis results and battlefield awareness through the system to support decision-making. It is necessary to build a battlefield situation analysis dataset similar to the actual battlefield situation for learning AI in order to provide decision-making support to commanders. In this paper, we explain the next step of the dataset construction method of the existing previous research, 'A Virtual Battlefield Situation Dataset Generation for Battlefield Analysis based on Artificial Intelligence'. We proposed a method to build the dataset required for the final battlefield situation analysis results to support the commander's decision-making and recognize the future battlefield. We developed 'Dataset Generator SW', a software tool to build a learning dataset for battlefield situation analysis, and used the SW tool to perform data labeling. The constructed dataset was input into the Siamese Network model. Then, the output results were inferred to verify the dataset construction method using a post-processing ranking algorithm.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.6
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• pp.117-128
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• 2014

This is the case study of SATREC INITIATIVE company which is the unique domestic production of commercial satellites. We examined the path and pattern for accumulation of technological capability and technology learning process. This case study show that the process of technological innovation and their influencing factors. First, the technological learning of the satellite industry follows the stage of technological acquisition, absorption, improvement and is embodied by the technological capability. Second, accumulated technological capability of the satellite industry influences the technology innovation. Third, the top management team(TMT) affects the technological learning and technological capability. Fourth, TMT has a moderating role between the technological capability and the performance of technological innovation. Finally, technological innovations in the small and venture business would be the source of technological capability and technological learning. The implications of this study are as follows. TMT has the very important role for the technological innovation and affect the technology development and the production. Also technology-based companies must gain a competitiveness advantage through technological learning and technological innovations for sustainable growth.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.85-87
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• 2021

컴퓨터 전공자들의 프로그래밍 개발 중심의 팀 프로젝트 수업은 프로그래밍 개발 능력과 팀원들 간의 협업 능력을 키울 수 있는 수업으로서, 기존에는 대면수업을 통해 교수자와 학습자간 그리고 팀을 이룬 학습자들 간에 긴밀한 상호작용으로 진행되어왔다. 그러나 2020년 COVID-19의 확산으로 대부분의 대학 수업들이 비대면 방식으로 진행됨에 따라 팀 프로젝트 수업 역시 비 대면으로 운영되어야 했으며 팀 활동을 위해 다양한 온라인 커뮤니케이션 방법이 모색되어야 했다. 본 연구에서는 팀 프로젝트 수업을 기존의 대면 수업으로 운영하였을 경우와 비대면 방식으로 운영하였을 때 학습자들이 느끼는 학습 효과에 대해 비교해보고자 한다. 대면 수업과 비대면 수업에 참여했던 학습자들을 대상으로 설문을 실시하여 팀 프로젝트 수업을 통한 학습 이해도, 수업 흥미도, 학습자간의 소통의 원활성, 수업 참여의 적극성 그리고 전체적인 수업 만족도에 대한 설문 결과를 비교 분석하였다. 향후 포스트 코로나 시대에는 대면과 비대면의 하이브리드 커뮤니케이션 방식이 대세를 이룰 것으로 예측되고 있는 만큼 대학 수업 방식에도 변화가 필요할 것이며 본 연구는 이를 위한 기초 연구로 활용될 수 있다.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1771-1777
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• 2023

With the advancements of deep learning, many semantic segmentation-based methods for land cover classification have been proposed. However, existing deep learning-based models only use image information and cannot guarantee spatiotemporal consistency. In this study, we propose a land cover classification model using geographical coordinates. First, the coordinate features are extracted through the Coordinate Hash Encoder, which is an extension of the Multi-resolution Hash Encoder, an implicit neural representation technique, to the longitude-latitude coordinate system. Next, we propose an architecture that combines the extracted coordinate features with different levels of U-net decoder. Experimental results show that the proposed method improves the mean intersection over union by about 32% and improves the spatiotemporal consistency.

• Journal of Information Technology Services
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• v.23 no.3
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• pp.91-101
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• 2024

Based on the analysis of 174 Young Startups Project Papers from university students majoring in entrepreneurship, this study aimed to identify the thoughts and characteristics of prospective entrepreneurs and derive their main agendas. Detailed information was collected on their preparation and ambitions for entrepreneurship, market research in their fields of interest, analysis of potential target users, requirements, and team management. We refined, categorized, and interpreted 84,818 words across 396 digital pages on LMS, and visualized using word clouds program. Additionally, interviews about peer learning were conducted by Zoom. The analysis revealed unique differences in students' perspectives on entrepreneurship based on their major, academic year, and gender. Also we could find that they actively utilize AI tech with a strong spirit of challenge in the startups. Interestingly, they emphasized horizontal communication and collaboration capabilities to improve productivity. This suggests the need to consider diversity in the design of entrepreneurship incubating content and programs for generation Z technically and academically.

• Korean Journal of Adult Nursing
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• v.23 no.5
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• pp.503-513
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• 2011

Purpose: The purpose of this study was to identify a model based on f the learning objectives in adult health nursing curriculums. The model can be eventually reflected in Korean National Nurse's Licensing Examination (KNNLE). Methods: The model was developed through a series of practical analysis by the task force team from June to October, 2011. The research team conducted the data synthesis and analysis from the learning objectives of adult health nursing drawn from selected national and international universities, from the adult health nursing areas in KNNLE, and from the current disease prevalence from clinical data in the representative university hospitals in Korea. Results: The current disease prevalence and mortality rates are on the increase especially for malignant neoplasm, heart diseases, suicide and diabetes. The items on the KNNLE of adult nursing are categorized by body systems into digestive system (15%), introduction (13.8%), respiratory system (11.3%), and cardiac system (11.3%). While the current system-based learning objectives covers extensive areas of adult health nursing, the core items are required to be selected based on core competences and core learning objectives to restructure the items of KNNLE. The first revised model is to consolidate and restructure the items microscopically in the subjects of adult health nursing by system. The second proposed model from macroscopic perspective is to build a comprehensive scheme of nursing curriculum by encompassing 8 subjects under current KNNLE and adjust the number of items accordingly. Conclusion: The items of KNNLE need to be gradually redesigned by considering the intervention need at clinical practice and the modified learning objectives of adult health nursing. Based on the job analysis on core competences for newly employed nurses performed by the Korean Accreditation Board of Nursing, it is necessary to establish the core learning objectives of adult health nursing and set up standards for core items in KNNLE.

• Journal of Practical Engineering Education
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• v.14 no.1
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• pp.91-97
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• 2022

Recently, as per a representative education method to develop core capabilities (such as critical thinking, communication, collaboration, and creativity) problem based learning (PBL) has been widely adopted in universities. Two important features of PBL are 'collaboration between team members' and 'participation based self-directed learning'. These two features should be satisfied in online education, although it is difficult due to the limitation on space and time in the COVID-19 pandemic. This paper presents a new design of PBL class in Metaverse, based on improving the online PBL class operated in the previous semesters in the H university. In the proposed PBL class, students are able to display materials (e.g., image, pdf, video files) in 3D virtual space, that are related to problem solving. The 3D virtual space is called gallery in this paper. The concept of gallery allows for active participation of students. In addition, the gallery can be used as a tool for collaborative meeting or for final presentation. If possible, the new design of PBL class will be applied and its effectiveness will be analyzed.

• Journal of the Korea Convergence Society
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• v.8 no.1
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• pp.155-162
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• 2017

This paper suggests efficient learning improvement method of computer use instruction based on flipped learning. Traditional computer use classes were difficult to practice and collaborative with sufficient lectures. However, we used KOCW (Korea Open Courseware) as a footsteps in the class using the flipped learning method and learned in advance before entering the classroom. In the classroom, we conducted collaborative hands on class based on mutual discussion. After the instruction, we measured learning motivation and satisfaction by gender, grade, and major using the motivation test tool. The results showed that degree of attention awareness, perception of class relevance and perception of learning satisfaction were analyzed as 'very satisfied' and 'satisfied' more than 90%.