This study aims to classify R&D activities related to the nuclear fuel cycle using the deep learning methodology. First, R&D data of the Republic of Korea were collected from the National Science & Technology Information Service (NTIS) for the years 2021, 2022, and 2023. We use keywords such as ‘nuclear,’ ‘uranium,’ ‘plutonium,’ and ‘thorium’ to find nuclear-related R&D projects in the NTIS database. Among the numerous R&D projects found through keyword searches, overlapping and medical-related R&D projects were excluded. Finally, 495 R&D projects conducted in 2021, 430 R&D projects conducted in 2022, and 296 R&D projects conducted in 2023 were obtained for analysis. After that, Safeguards experts determine whether the R&D projects are subject to declaration under the AP. The values of the content validity index (CVI) and content validity ratio (CVR) were used to verify whether the experts’ judgments were valid. The 1,218 collected and labeled data were then divided 8:2 into training and test datasets to see if deep learning could be applied to classify nuclear fuel cycle-related R&D activities. We use the Python and TensorFlow packages, including RNN, GRU, and CNN methods. First, the collected text information was preprocessed to remove punctuation marks and then tokenized to make it suitable for deep learning. After 20 epochs of training to classify the nuclear fuel cycle-related R&D activities, the RNN model achieved 97.30% accuracy and a 5.85% error rate on the validation dataset. The GRU model achieved 96.53% accuracy and a 9.06% error rate on the validation dataset. In comparison, the CNN model achieved 94.61% accuracy and a 2.57% error rate on the validation dataset. When applying the test dataset to each model, the RNN model had a test accuracy of 83.20%, the GRU test accuracy of 82.79%, and the CNN model had a test accuracy of 85.66% for the same dataset. This study applied deep learning models to labeled data judged by various experts, and the CNN model showed the best results. In the future, this study will continue to develop an optimum deep learning model that can classify nuclear fuel cycle-related R&D activities to achieve the purpose of safeguards measures from open-source data such as papers and articles.

Since the National R&D Innovation Act was enacted in 2022, it became a crucial issue how to qualify or improve R&D activities and disseminate their outcomes. Many organizations have referred to various quality management standards such as the American National Standards Institute/American Society for Quality (ANSI/ASQ) Z1.13, International Organization for Standardization (ISO) 9001, and the American Society of Mechanical Engineers Nuclear Quality Assurance-1 (ASME NQA-1), as a means to set up their own quality system. ISO is the international standard for implementing a quality management system (QMS), which provides a framework and principles for managing an organization’s QMS, with the aim of ensuring that the organization consistently provide products or services that meet regulatory requirements. ISO 9001 can cover all aspects of an organization’s operations, and it can also be expanded to include R&D areas. The introduction of ISO 9001 to R&D aims to improve R&D practices and establish a standardized process framework for conducting R&D. ANSI/ASQ Z1.13 provides quality guidelines for research and consists of 10 sections covering various aspects of research quality, emphasizing ethical conduct, clear objectives, reliable data collection, and analysis. ASME NQA-1 is one of quality assurance standards for nuclear facility applications, but it has been extended and applied to R&D activities in the nuclear fields. It just focuses on planning, procedures, documentation, competence, equipment, and material control. KINAC has conducted extensive research on verifying and regulating nuclear activities while providing support for national nonproliferation technologies and policies. In addition to the quantitative growth achieved so far, efforts are being made to establish a qualitative and integrated management system. As a first step to achieve this goal, this study reviewed international standards and methodologies for research quality and derived the key components for R&D quality management. Moreover, the appropriate outline of quality management system framework was proposed for R&D as a regulatory support process, based on the ISO 9001. The implementation of quality management standards and procedures for R&D in KINAC, which could lead to improved research practices, more reliable data collection and analysis and increased efficiency in conducting R&D activities.

연구에서는 기술혁신 관점에서 창업기업 여부, 연구전담조직 보유, 국가연구개발사업 수혜 등의 특성이 한계기업의 정상기업 전환에 유의하게 기여하는지를 분석하였다. 분석결과, 한계기업 중 창업기업에게 국가연구개발사업을 지원하면, High-tech 산업을 중심으로 정상기업으로의 전환에 긍정적으로 기여하는 것으로 나타났다. 또한 한계기업의 연구전담조직의 보유여부가 정상기업으로의 전환에 긍정적으로 기여하였다. 이는 연구전담 조직이 체계적인 R&D를 수행할 수 있는 조직기반이 되고 있음을 시사한다. 상기 분석결과 는 한계기업에 대한 구조조정 위주의 기존 산업정책에서 정상기업으로의 전환 가능성이 높은 한계기업에 대한 선별적인 R&D 지원정책으로 패러다임 전환이 필요함과 동시에 한계기업의 정상기업으로의 전환을 지원하기 위한 국가연구개발사업의 전략성 강화가 필요함을 시사한다.

The purpose of this study is to present a business strategy plan to increase organizational effectiveness of small and medium-sized enterprises. The research investigated in the level of human resource activity, such as recruitment, education, evaluation, compensation and development for the employees and executives who are working at small and medium-sized enterprises where located in Busan and Gyongnam province. With this, the research carried out actual proof analysis on the level of human resource activity effects on organization effectiveness like job satisfaction and organizational commitment. The following implications can be acquired from the result of multiple regression analysis on the 201 employees of small and medium enterprises. First, small and medium-sized enterprises should carry out human resource management activities and improve research and development capacity to enhance organization effectiveness. Second, in order to improve job satisfaction of the members of small and medium-sized enterprises, the management should concentrate on recruitment activity and reward maintenance management activity and come up with strategies to enhance learning ability and external network ability. Third, in order to enhance organizational commitment of the members of small and medium-sized enterprises, recruitment activity, training activity, and reward maintenance management activity should be carried out and the management should come up with strategies to enhance learning ability and external network ability. In this research, the objective was only to find out antecedents of organization effectiveness, but considering that causality might arise among the antecedents, in the studies hereafter, the verification on the structural relationship of various factors will be needed.

다국적기업 자회사의 경쟁력을 좌우하는 중요 요인 중의 하나가 R&D 활동 수준이다. 최근까지 다국적기업 자 회사가 현지 시장에서 수행하는 R&D 관련 활동에 대한 연구는 자료 수집의 한계로 인해 매우 제한적이었다. 본 연구에서는 다국적기업 자회사가 위치하고 있는 현지국의 산업 기술 수준관련 요인들이 그 자회사의 R&D 활동 수준에 미치는 영향을 분석하고자 한다. 국내에 진출해 있는 131개 해외 자회사들을 대상으로 설문조사를 통해 분석하였다. 실증분석 결과, 현지국의 기술변화 속도와 다국적기업이 협력 가능한 연구기관의 수가 유의한 영향을 미치는 것으로 나타났다. 또한 다국 적기업 본사와 현지 자회사 간에 이루어지는 활발한 자원 공유가 유의한 영향을 미치는 것으로 조사되었다. 이는 국내 시장 내 해외자회사를 대상으로 한 실증 분석에서 R&D 활동 수준에 영향을 미치는 중요 요인을 확인하였 다는 점에서 그 의의가 있다.