Fueled by international efforts towards AI standardization, including those by the European Commission, the United States, and international organizations, this study introduces a AI-driven framework for analyzing advancements in drone technology. Utilizing project data retrieved from the NTIS DB via the “drone” keyword, the framework employs a diverse toolkit of supervised learning methods (Keras MLP, XGboost, LightGBM, and CatBoost) enhanced by BERTopic (natural language analysis tool). This multifaceted approach ensures both comprehensive data quality evaluation and in-depth structural analysis of documents. Furthermore, a 6T-based classification method refines non-applicable data for year-on-year AI analysis, demonstrably improving accuracy as measured by accuracy metric. Utilizing AI’s power, including GPT-4, this research unveils year-on-year trends in emerging keywords and employs them to generate detailed summaries, enabling efficient processing of large text datasets and offering an AI analysis system applicable to policy domains. Notably, this study not only advances methodologies aligned with AI Act standards but also lays the groundwork for responsible AI implementation through analysis of government research and development investments.

At a time when the demand for drones is increasing, a plan to utilize drone images was sought for efficient promotion of cadastral resurvey. To achieve the purpose of this study, the technical and legal status of drone images was reviewed, and through this, the possibility of using it for cadastral resurvey was primarily reviewed. subsequently, an experiment was conducted targeting the project district to examine whether drone images were applied to boundary extraction, which is the primary process of cadastral resurvey. As a result of the experiment, it was found that boundary extraction from images is possible. However, in some cases, it is impossible due to field conditions or image quality. Therefore, it is necessary first to apply cases where boundary extraction is possible to cadastral resurvey and seek solutions for some impossible cases. In particular, the image quality problem may have problems with the current technology, but it will also have problems with the existing drone equipment. So, standard for drone calibration should also be established. Finally, the cadastral resurvey surveying procedure using drones was also presented.

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.

Explainable AI (XAI) is an approach that leverages artificial intelligence to support human decision-making. Recently, governments of several countries including Korea are attempting objective evidence-based analyses of R&D investments with returns by analyzing quantitative data. Over the past decade, governments have invested in relevant researches, allowing government officials to gain insights to help them evaluate past performances and discuss future policy directions. Compared to the size that has not been used yet, the utilization of the text information (accumulated in national DBs) so far is low level. The current study utilizes a text mining strategy for monitoring innovations along with a case study of smart-farms in the Honam region.

Nanotechnology has been growing constantly and it is becoming the leading technology in scientific research and development. Although nanotechnology has important applications in broad variety of fields without boundary of any particular industrial area, the study of nanotechnology related to its commercialization has been conducted in a few ways. To put that figure in context, this study investigates public and expert perceptions about negative potentials of nanotechnology. Through a series of surveys with public (N = 541) and experts (N = 62), we analyzed about public willingness to pay for nano-applied products. Survey results showed that public and experts preferred nano-applied products in the order of electronics, cosmetics, and food and medicine. Experts express high payment intention to electronics rather than public intention. In addition, the survey results showed the purchasing intention of both public and expert group was affected by the attributes of nano-applied products in the order of risk fatality, risk chance, certification, and labeling. But experts put more importance in risk fatality than risk chance comparing to public. Through the case analysis of the effects of labeling and certification, we revealed either labeling or certification can induce both public and experts to buy the nano-applied products with high risk chance and low risk fatality. However, for the nano-applied product with high risk fatality and low risk chance, both labeling and certification are simultaneously required to make customers have positive purchasing intention. The result of this study could be utilized for the nanotechnology-based company to get the consumer behavior information about nano-based product and to establish their marketing strategy.

The study aims at quantifying the effect of nano technology in the fields of economics and social aspects by using the methodology of system dynamics. A case study which using selenium oxide nanoparticles as additive agent in order to enhance fuel efficiency was selected as an example of nano technology in economic and societal benefits. Additionally, models for exhaust gas from combustion of fuel (diesel) and related issues are developed to evaluate real-time assessment of the effect of nano technology. It was found that the selenium oxide nanoparticles increase fuel efficiency, and it also affects on the amount of exhaust gas and the respiratory disease related issues. The results of this study which give quantitative value for the effect of nano technology can be used as objective references in development of national policy.

It is difficult to make an accurate estimate of the economic value and effects on societal economy of Nano-technologies. This research measures an economic value of Nano-technologies quantitatively and analyzes its influences on societal economy. This paper chooses some major industries as analysis targets and adapts the DEFRA comparative methodology model which has been developed in the UK and recommended by OECD. For this reason, some industries which are in range of economic value assessment were investigated and related data were collected. Also, the economic value and societal influences of Nano-technologies were calculated, through the procedure of the model. In addition, this study conducts a questionnaire to experts for the validity of measurement results and procedures. This paper suggests a guideline for economic value and effects on societal economy of Nano-technologies assessments through quantitative Defra comparative methodologies.