Physical protection education was legislated by the Ministry of Education, Science and Technology (MEST) in November 2010. KINAC (Korea Institute of Nuclear Nonproliferation and Control) was designated as the exclusive institution for physical protection education and training by MEST in October 2011, and it has since functioned as the sole institution responsible for this critical aspect of nuclear security education in the country. Over the past decade, KINAC has undertaken a variety of training initiatives aimed at enhancing the capabilities of nuclear operators’ physical protection personnel. Furthermore, it has consistently pursued annual curriculum revisions based on insights gleaned from surveys and workshops. In conventional curriculum assessments, general surveys often rely on Likert scale or short-answer questions as primary indicators, mainly due to their ease of data processing. Descriptive questions, while capable of capturing diverse opinions, have historically been relegated to a secondary role owing to the inherent challenges associated with data analysis. While physical protection education has made concerted efforts to solicit diverse opinions through descriptive questions, difficulties in organizing and leveraging this valuable data have resulted in it primarily serving as reference material. This study introduces a novel approach by employing ChatGPT, a chatbot, to conduct a comprehensive analysis of the descriptive questions from the physical protection education survey administered in the first half of 2023. The primary objective is to formulate a robust plan for curriculum enhancement based on a wide spectrum of opinions. Following the completion of physical protection training by 2,014 individuals in the first half of 2023, a survey was distributed, yielding an impressive response rate of 95.7% with 1,927 respondents. Chatbots were harnessed to extract major keywords and perform frequency analyses on approximately 360 responses to descriptive questions in the survey. The analysis revealed that certain keywords emerged with notable frequency, in the following order: “drone” (mentioned 51 times), “access management” (mentioned 28 times), “inspection and search” (mentioned 27 times), and “cybersecurity” (mentioned 20 times). Further analysis of these major keywords and related content revealed a consensus among trainees that there is a pressing need to incorporate topics addressing drone threats and responses, as well as strategies to fortify access management into the curriculum. This study underscores the potential to harness standardized data analysis techniques to synthesize and integrate trainees’ subjective opinions, thereby providing a solid foundation for the refinement of the curriculum.

To conduct numerical simulation of a disposal repository of the spent nuclear fuel, it is necessary to numerically simulate the entire domain, which is composed on numerous finite elements, for at least several tens of thousands of years. This approach presents a significant computational challenge, as obtaining solutions through the numerical simulation for entire domain is not a straightforward task. To overcome this challenge, this study presents the process of producing the training data set required for developing the machine learning based hybrid solver. The hybrid solver is designed to correct results of the numerical simulation composed of coarse elements to the finer elements which derive more accurate and precise results. When the machine learning based hybrid solver is used, it is expected to have a computational efficiency more than 10 times higher than the numerical simulation composed of fine elements with similar accuracy. This study aims to investigate the usefulness of generating the training data set required for the development of the hybrid solver for disposal repository. The development of the hybrid solver will provide a more efficient and effective approach for analyzing disposal repository, which will be of great importance for ensuring the safe and effective disposal of the spent nuclear fuel.

An effective method for produce munitions effectiveness data is to calculate weapon effectiveness indices in the US military’s Joint Munitions Effectiveness Manuals (JMEM) and take advantage of the damage evaluation model (GFSM) and weapon Effectiveness Evaluation Model (Matrix Evaluator). However, a study about the Range Safety that can be applied in the live firing exercises is very insufficient in the case of ROK military. The Range Safety program is an element of the US Army Safety Program, and is the program responsible for developing policies and guidance to ensure the safe operation of live-fire ranges. The methodology of Weapon Danger Zone (WDZ) program is based on a combination of weapon modeling/simulation data and actual impact data. Also, each WDZ incorporates a probability distribution function which provides the information necessary to perform a quantitative risk assessment to evaluate the relative risk of an identified profile. A study of method to establish for K-Range Safety data is to develop manuals (pamphlet) will be a standard to ensure the effective and safe fire training at the ROK military education and training and environmental conditions. For example, WDZs are generated with the WDZ tool as part of the RMTK (Range Managers Tool Kit) package. The WDZ tool is a Geographic Information System-based application that is available to operational planners and range safety manager of Army and Marine Corps in both desktop and web-based versions. K-Range Safety Program based on US data is reflected in the Korean terrain by operating environments and training doctrine etc, and the range safety data are made. Thus, verification process on modified variables data is required. K-Range Safety rather than being produced by a single program, is an package safety activities and measures through weapon danger zone tool, SRP (The Sustainable Range Program), manuals, doctrine, terrain, climate, military defence M&S, weapon system development/operational test evaluation and analysis to continuously improving range safety zone. Distribution of this K-range safety pamphlet is available to Army users in electronic media only and is intended for the standing army and army reserve. Also publication and distribution to authorized users for marine corps commands are indicated in the table of allowances for publications. Therefore, this study proposes an efficient K-Range Safety Manual producing to calculate the danger zones that can be applied to the ROK military’s live fire training by introducing of US Army weapons danger zone program and Range Safety Manual

In the ground environment, mobile robot research uses sensors such as GPS and optical cameras to localize surrounding landmarks and to estimate the position of the robot. However, an underwater environment restricts the use of sensors such as optical cameras and GPS. Also, unlike the ground environment, it is difficult to make a continuous observation of landmarks for location estimation. So, in underwater research, artificial markers are installed to generate a strong and lasting landmark. When artificial markers are acquired with an underwater sonar sensor, different types of noise are caused in the underwater sonar image. This noise is one of the factors that reduces object detection performance. This paper aims to improve object detection performance through distortion and rotation augmentation of training data. Object detection is detected using a Faster R-CNN.