As the Fourth Industrial Revolution advances, smart factories have become a new manufacturing paradigm, integrating technologies such as Information and Communication Technology (ICT), the Internet of Things (IoT), Artificial Intelligence (AI), and big data analytics to overcome traditional manufacturing limitations and enhance global competitiveness. This study offers a comprehensive approach by evaluating both technological and economic performance of smart factory Research and Development (R&D) projects, addressing gaps in previous studies that focused narrowly on either aspect. The research combines Latent Dirichlet Allocation (LDA) topic modeling and Data Envelopment Analysis (DEA) to quantitatively compare the efficiency of various topics. This integrated approach not only identifies key research themes but also evaluates how effectively resources are utilized within each theme, supporting strategic decision-making for optimal resource allocation. Additionally, non-parametric statistical tests are applied to detect performance differences between topics, providing insights into areas of comparative advantage. Unlike traditional DEA methods, which face limitations in generalizing results, this study offers a more nuanced analysis by benchmarking efficiency across thematic areas. The findings highlight the superior performance of projects incorporating AI, IoT, and big data, as well as those led by the Ministry of Trade, Industry, and Energy (MOTIE) and small and medium-sized enterprises (SMEs). The regional analysis reveals significant contributions from non-metropolitan areas, emphasizing the need for balanced development. This research provides policymakers and industry leaders with strategic insights, guiding the efficient allocation of R&D resources and fostering the development of smart factories aligned with global trends and national goals.

This study introduces and experimentally validates a novel approach that combines Instruction fine-tuning and Low-Rank Adaptation (LoRA) fine-tuning to optimize the performance of Large Language Models (LLMs). These models have become revolutionary tools in natural language processing, showing remarkable performance across diverse application areas. However, optimizing their performance for specific domains necessitates fine-tuning of the base models (FMs), which is often limited by challenges such as data complexity and resource costs. The proposed approach aims to overcome these limitations by enhancing the performance of LLMs, particularly in the analysis precision and efficiency of national Research and Development (R&D) data. The study provides theoretical foundations and technical implementations of Instruction fine-tuning and LoRA fine-tuning. Through rigorous experimental validation, it is demonstrated that the proposed method significantly improves the precision and efficiency of data analysis, outperforming traditional fine-tuning methods. This enhancement is not only beneficial for national R&D data but also suggests potential applicability in various other data-centric domains, such as medical data analysis, financial forecasting, and educational assessments. The findings highlight the method's broad utility and significant contribution to advancing data analysis techniques in specialized knowledge domains, offering new possibilities for leveraging LLMs in complex and resource- intensive tasks. This research underscores the transformative potential of combining Instruction fine-tuning with LoRA fine-tuning to achieve superior performance in diverse applications, paving the way for more efficient and effective utilization of LLMs in both academic and industrial settings.

Rice ratooning is the cultural practice that easily produces secondary rice from the stubble left behind after harvesting the main crop. ‘Daol’ is an extremely early growing rice variety. Planting this variety early allows for an additional ratoon harvest after the primary rice harvest. The plant growth and yield of ratoon rice were very low compared to those of main rice. Protein, amylose content, and head rice rate were higher in ratoon rice than in main rice. The distribution by the rice flour particle size of main and ratoon rice was similar. The damaged starch content in ratoon rice was relatively high at 6.1%. Ratoon rice required a longer time and higher temperature for pasting than main rice. Compared to the original rice, peak viscosity (PV), hot paste viscosity (HPV), cool paste viscosity (CPV), and breakdown (BD) were very low, and setback (SB) was high. As a result of analyzing the gelatinization properties of main and ratoon rice using differential calorimetry, it was found that the onset (To), peak (Tp), and conclusion (Tc) of ratoon rice starch were processed at a lower temperature than those of main rice. The gelatinization enthalpy of both samples was similar. The distribution of amylopectin short chains in ratoon rice was higher than that in main rice.

In this study, we propose a novel approach to analyze big data related to patents in the field of smart factories, utilizing the Latent Dirichlet Allocation (LDA) topic modeling method and the generative artificial intelligence technology, ChatGPT. Our method includes extracting valuable insights from a large data-set of associated patents using LDA to identify latent topics and their corresponding patent documents. Additionally, we validate the suitability of the topics generated using generative AI technology and review the results with domain experts. We also employ the powerful big data analysis tool, KNIME, to preprocess and visualize the patent data, facilitating a better understanding of the global patent landscape and enabling a comparative analysis with the domestic patent environment. In order to explore quantitative and qualitative comparative advantages at this juncture, we have selected six indicators for conducting a quantitative analysis. Consequently, our approach allows us to explore the distinctive characteristics and investment directions of individual countries in the context of research and development and commercialization, based on a global-scale patent analysis in the field of smart factories. We anticipate that our findings, based on the analysis of global patent data in the field of smart factories, will serve as vital guidance for determining individual countries' directions in research and development investment. Furthermore, we propose a novel utilization of GhatGPT as a tool for validating the suitability of selected topics for policy makers who must choose topics across various scientific and technological domains.

Governments around the world are enacting laws mandating explainable traceability when using AI(Artificial Intelligence) to solve real-world problems. HAI(Human-Centric Artificial Intelligence) is an approach that induces human decision-making through Human-AI collaboration. This research presents a case study that implements the Human-AI collaboration to achieve explainable traceability in governmental data analysis. The Human-AI collaboration explored in this study performs AI inferences for generating labels, followed by AI interpretation to make results more explainable and traceable. The study utilized an example dataset from the Ministry of Oceans and Fisheries to reproduce the Human-AI collaboration process used in actual policy-making, in which the Ministry of Science and ICT utilized R&D PIE(R&D Platform for Investment and Evaluation) to build a government investment portfolio.

As the uncertainty of technology development and market needs increases due to changes in the global business environment, the interest and demand for R&D activities of individual companies are increasing. To respond to these environmental changes, technology commercialization players are paying great attention to enhancing the qualitative competitiveness of R&D. In particular, R&D companies in the marine and fishery sector face many difficulties compared to other industries. For example, the R&D environment is barren, it is challenging to secure R&D human resources, and it is facing a somewhat more difficult environment compared to other sectors, such as the difficulty in maintaining R&D continuity due to the turnover rate of researchers. In this study, based on the empirical data and patent status of private companies closely related to the R&D technology status, big data analysis, and simulation analysis methods were used to identify the relative position of individual companies' R&D capabilities and industrial perspectives. In this study, based on industrial evidence and patent applications closely related to the R&D technology status, the R&D capabilities of individual companies were evaluated using extensive data analysis and simulation analysis methods, and a statistical test was performed to analyze if there were differences in capabilities from an industrial point of view. At this time, the industries to be analyzed were based on all sectors, the maritime industry, the fisheries industry, and the maritime industry integration sector. In conclusion, it was analyzed that there was a certain level of difference in the R&D capabilities of individual companies in each industry sector, Therefore when developing a future R&D capability system, it was confirmed that it was necessary to separate the population for each industry and establish a strategy.

2011년 이후 농식품 산업에서 연구개발 기술 자산들에 대한 평가를 수행할 때주도적으로 활용되고 있는 방법이 현금흐름할인(DCF) 방법이었으며, 최근에 종자기술 등과같은 기술자산의 경우에는 기술료 사례 정보를 기반으로 하는 기술평가를 병행하여 수행해오고 있다. 지금까지 알려진 현금흐름할인 방법은 추정되어야 할 입력변수가 많아 기술평가시에 정교한 추정이 요구되고 있다. 또한 기술료 사례 정보를 근거로 하는 거래사례비교 방법이나 업종별 산업표준(industry norm)을 적용할 때에도 평가대상 기술자산과 보다 근사한거래사례가 적용되어야 하는 것은 농식품 산업분야에서도 동일하게 고려되어야 하는 문제이다. 농식품 산업에서 기술평가 시 활용되고 있는 주요입력변수는 경제적 수명주기, 농식품업종 관련 재무적 정보, 할인율, 기술기여율 등이며, 해당 평가기관에서는 기반 정보구축과자료 최신화를 주기적으로 수행해오고 있다. 본 연구에서는 농식품 산업에서의 기존의 기술평가 시 평가결과에 가장 중요한 영향 미치는 주요변수를 탐색하고, 기술평가 입력 정보의 최신화를 통해 도출된 참조 지원정보를 활용하여 기존의 대표 평가사례들의 평가결과가 어떤 변화가 발생하였는지를 분석하였다. 또한입력자료 최신화가 기술평가 결과에 미치는 단편적 정보제공을 보완하기 위한 방안을 제시하면서 기존평가 결과와 자료 현행화 이후의 기술평가 결과를 비교 분석하였다. 이러한 분석을 수행하기 위해서 과거 농식품 산업에서 수행되었던 기술평가 사례를 선별하고 이를 바탕으로 입력변수들에 대한 민감도 분석 방법과 주요 핵심변수를 활용한 시뮬레이션 분석방법을 적용하였다. 본 연구결과는 농식품 분야에서 기술평가 시에 활용되고 있는 입력변수들에대한 자료 최신화 필요성과 핵심입력변수 기반 구축에 대한 중요한 정책적 시사점, 그리고기술가치평가 결과에 대한 보다 유용한 정보를 제공할 수 있을 것으로 기대된다.

본 연구에서는 기술의 수명주기에 영향을 미치는 요인에 대해 분석하고, 기존 표준모델에서 활용되고 있는 평가지표를 근거로 개별기술의 수명에 영향을 미칠 평가지표를 분석해 이를 정량화하여, 피인용특허수명(CLT)을 기반으로 개별기술의 속성이 반영된 기술 수명주기를 산출하는 개선방법을 제안하였다. 본 연구에서 제안한 방법론은 기존 표준모델의 기술수명주기 산출방법인 한계점을 개선할 수 있는 방법으로 평가대상기술 관계자들에게 도출결과에 대한 설득의 용이성과 기존에 비해 보다 합리적인 기준을 제시함으로서 기술수명주기 도출결과의 타당성 및 활용성을 배가시킬 수 있을 것으로 기대된다.

The objective of this study was to investigate the optimal shapes and arrangements of sinkers attached to net cages to prevent their deformation in a current. A series of model experiments were conducted in a circulating water channel, using 5 different types of sinker(high-weighted ball, low-weighted ball, columntype, egg-shaped and iron bar-framed) and 2 types of square net cage constructed from both Nylon Raschel netting and Nylon knotted netting, on a 1/20th scale. The deflection of the model nets against the flow was smallest with the iron bar-framed weight compared to the other four types of sinker. It was expected that the optimal shapes of sinkers would be either the ball or egg-shape; however, iron bar-framed weight actually had larger drag forces. The dispersed deployment of sinkers on the bottom frames of model net cages performed better with relatively slow flows, while the concentrated deployment at 4 corners functioned better with relatively fast flows, in preventing the nets from becoming severely deformed. The deformation of the net cages was larger for the Nylon knotted netting than the Nylon Raschel netting. With respect to flow resistance, the Nylon Raschel netting, rather than the Nylon knotted netting, was more suitable for construction of net cages.

본 연구에서는 직접 제작된 전도유체(electrorheological fluid)용 수직진동 rhemeter 기기상의 구조 해석 및 실험을 실시하였다. 수직진동 rheomether는 간단하게 제작이 가능하고, 고전압 발생장치를 연결하므로 전동유체의 점탄성 특성을 비교적 쉽게 측정할 수 있다. Rheometer의 구조적 변수와 측정된 힘, 변형 등을 이용하여 복소 점도(complex viscosity), 복소 전단 변형률(complex shear modulus), loss tangent 등의 선형 점탄성 물질 함수를 직접 계산할 수 있으며, corn starch를 polybutene/kerosene에 분산시킨 전동유체를 이용하여 전기장하의 점탄성을 측정하였다.

제올라이트 분말을 기본재료로 하는 전기유변유체의 전기 및 유변학적 특성이 연구되었다. 전기장 인가시 높은 한계응력을 얻기 위하여 비교적 유전상슈가 큰 5종류의 유전유체를 선택하여 제올라이트 분말과 혼합하여 전기유변유체를 준비하였다. Couette형 rheometer를 이용하여 유변유체의 한계응력을 인가된 전기장 및 온도의 함수로서 측정하였다. 이중 chlorinate hydrocabon oil과 제올라이트 분말을 혼합한 전기유변유체의 한계응력은 6KPa(E=4KV/mm, T=25˚C)로서 최대치를 기록하였다. 측정된 한계응력은 온도가 상승하면 점차 감소하는 반면 전류밀도는 온도에 따라 증가하였다. 전류밀도에 대한 Arrhenius 그라프에서 전기전도에 대한 활성화 에너지는 약 0.7eV였으며 이는 제올라이트 분말에 포함된 Na+ 이온의 확산에 기인하는 것으로 분석되었다.