2MW급 태양광 연계 에너지저장장치 용량 산정

이준영(Junyeong Lee) 대한전기학회 2021 대한전기학회 학술대회 논문집 Vol.2021 No.7

CERN EOS 분산 파일 시스템의 배포 환경에 따른 I/O 성능 비교

이준영(JunYeong Lee),김문현(Moonhyun Kim),김경준(KyeongJun Kim),노서영(Seo-Young Noh) 한국정보과학회 2021 한국정보과학회 학술발표논문집 Vol.2021 No.6

경영학 전공자들의 IT 실습 교과목에 대한 인식: 기대가치이론을 중심으로

이준영 ( Junyeong Lee ),신세인 ( Sein Shin ) 한국실천공학교육학회 2021 실천공학교육논문지 Vol.13 No.1

본 연구는 IT 비전공자인 경영학 전공 대학생들을 대상으로 IT 실습 교과목에 대한 인식을 기대 가치 이론에 기반하여 도출하고, 이를 개선하기 위한 방안을 제안하고자 한다. 이를 위해 IT 실습 교과목을 수강하는 102명의 학생들을 대상으로 온라인 개방형 설문조사를 실시하고, 질적 내용 분석을 활용하여 학생들의 인식을 선행연구와 연결하여 밝혔다. 분석 결과, 총 4개의 상위범주 (난이도 인식, 기대, 가치, 비용) 에 8개의 하위범주(생소한 용어, 생소한 소프트웨어, 수학적 개념과 사고의 어려움, 낮은 효능감, 내재적 가치, 성취 가치, 활용 가치, 많은 시간 소요)의 인식을 도출하였고, 이를 바탕으로 경영학 전공 대학생들의 IT 실습 교과목 학습을 도울 수 있는 개선 방안을 제안하였다. 본 연구는 경영학과 학생들을 중심으로 학습자 관점에서 비전공자의IT 실습 교과목에 대한 인식들을 실증적으로 살펴보았다는 점에서 학문적 의의가 있으며, 도출한 인식들을 바탕으로 실제 교육현장에 적용해 볼 수 있는 방안을 제시하였다는 점에서 실질적 시사점이 있다. This study attempted to explore the perception of business administration major students on course with IT practice based on expectancy-value theory, and suggested educational implications for improving course with IT practice for non-IT major students. Open-ended survey was conducted via online from 102 students who took course with IT practice, and response data was analyzed through qualitative content analysis. As a result, 4 main categories (perceived difficulty, expectation, value, cost) and 8 subcategories(unfamiliar terms, unfamiliar software, difficulty in mathematical concepts and thinking, low efficacy, intrinsic value, attainment value, utility value, long time required for learning) were revealed, and we provided educational suggestions that help to enhance IT practice learning for business administration major students (non-IT major students). This study has academic implication by empirically examining the perception of business administration major students based on expectancy value theory from the learner perspective, and also has practical implication via suggesting educational implications that could be applied to the substantive educational field based on the revealed students’ perceptions.

CERN EOS와 GlusterFS 분산파일시스템 성능비교

이준영(JunYeong Lee),김문현(MoonHyun Kim),노서영(SeoYoung Roh) 한국정보과학회 2020 한국정보과학회 학술발표논문집 Vol.2020 No.7

인공지능을 이용한 전력설비 분류

이준영(Junyeong Lee) 대한전기학회 2023 대한전기학회 학술대회 논문집 Vol.2023 No.7

개인의 흡수 역량이 프로세스 및 제품 혁신에 미치는 영향에 대한 연구

장재승 ( Jae Seung Jang ),이준영 ( Junyeong Lee ),곽찬희 ( Chanhee Kwak ),이희석 ( Heeseok Lee ) 한국지식경영학회 2016 지식경영연구 Vol.17 No.1

Absorptive capacity has been increasingly thought of as a potential source of innovation. From the knowledge management perspective, absorptive capacity is composed of a set of activities dealing with acquisition, assimilation, transformation, and exploitation of external and internal knowledge. This study investigates what relationship the absorptive capacity of individuals who have technical knowledge in the organization has with process innovation and product innovation. Mobile based survey was conducted from the employees working for the largest electronics manufacturer in Korea. The analyzed data was based on 156 responses from 199 participants. The analysis result shows that four phases of absorptive capacity such as acquisition, assimilation, transformation and exploitation have different effects on process innovation and product innovation, respectively. Specifically, transformation is found to be most critical in leading to innovation.

360 VR 기반 2D 이미지 활용 방안 연구

임소연(So-Yeon Im),이준영(Junyeong Lee),박철우(Cheol-woo Park),이영우(Young-woo-Lee) 한국정보통신학회 2022 한국정보통신학회 종합학술대회 논문집 Vol.26 No.2

딥러닝 오픈소스 프레임워크의 사례연구를 통한 도입 전략 도출

최은주(Eunjoo Choi),이준영(Junyeong Lee),한인구(Ingoo Han) 한국지능정보시스템학회 2020 지능정보연구 Vol.26 No.4

많은 정보통신기술 기업들은 자체적으로 개발한 인공지능 기술을 오픈소스로 공개하였다. 예를 들어, 구글의 TensorFlow, 페이스북의 PyTorch, 마이크로소프트의 CNTK 등 여러 기업들은 자신들의 인공지능 기술들을 공개하고 있다. 이처럼 대중에게 딥러닝 오픈소스 소프트웨어를 공개함으로써 개발자 커뮤니티와의 관계와 인공지능 생태계를 강화하고, 사용자들의 실험, 적용, 개선을 얻을 수 있다. 이에 따라 머신러닝 분야는 급속히 성장하고 있고, 개발자들 또한 여러가지 학습 알고리즘을 재생산하여 각 영역에 활용하고 있다. 하지만 오픈소스 소프트웨어에 대한 다양한 분석들이 이루어진 데 반해, 실제 산업현장에서 딥러닝 오픈소스 소프트웨어를 개발하거나 활용하는데 유용한 연구 결과는 미흡한 실정이다. 따라서 본 연구에서는 딥러닝 프레임워크 사례연구를 통해 해당 프레임워크의 도입 전략을 도출하고자 한다. 기술-조직-환경 프레임워크를 기반으로 기존의 오픈 소스 소프트웨어 도입과 관련된 연구들을 리뷰하고, 이를 바탕으로 두 기업의 성공 사례와 한 기업의 실패 사례를 포함한 총 3 가지 기업의 도입 사례 분석을 통해 딥러닝 프레임워크 도입을 위한 중요한 5가지 성공 요인을 도출하였다: 팀 내 개발자의 지식과 전문성, 하드웨어(GPU) 환경, 데이터 전사 협력 체계, 딥러닝 프레임워크 플랫폼, 딥러닝 프레임워크 도구 서비스. 그리고 도출한 성공 요인을 실현하기 위한 딥러닝 프레임워크의 단계적 도입 전략을 제안하였다: 프로젝트 문제 정의, 딥러닝 방법론이 적합한 기법인지 확인, 딥러닝 프레임워크가 적합한 도구인지 확인, 기업의 딥러닝 프레임워크 사용, 기업의 딥러닝 프레임워크 확산. 본 연구를 통해 각 산업과 사업의 니즈에 따라, 딥러닝 프레임워크를 개발하거나 활용하고자 하는 기업에게 전략적인 시사점을 제공할 수 있을 것이라 기대된다. 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 framewo

나노 입자 분리/분류를 위한 유전영동 칩 및 전극 패시베이션 기술 개발

박민수 ( Minsu Park ),노효웅 ( Hyowoong Noh ),강재운 ( Jaewoon Kang ),이준영 ( Junyeong Lee ),박홍식 ( Hongsik Park ) 한국센서학회 2021 센서학회지 Vol.30 No.2

Isolation and separation of biological nanoparticles, such as cells and extracellular vesicles, are important techniques for their characterization. Dielectrophoresis (DEP) based on microfluidic chips is an effective method to isolate and separate the nanoparticles. However, the electrodes of the DEP chips are electrolyzed by the electrical signals applied to the nanoparticles. Thus, the isolation/separation efficiency of the nanoparticles is reduced considerably. Through this study, we developed a microfluidic DEP chip for reliable isolation/separation of nanoparticles and developed a passivation technique for the protection of the DEP chip electrodes. The electrode passivation process was designed using a hydrogel and the stability of the hydrogel passivation layer was verified. The fabricated DEP chip and the proposed passivation technique were used for the collection and dispersion of the fluorescent polystyrene nanoparticles. The proposed chip and the technique for isolation and separation of nanoparticles can be leveraged in various bioelectronic applications.