The high-level nuclear waste (HLW) repository is a 500-1,000 m deep underground structure to dispose high-level nuclear waste. The waste has a very long half-time and is exposed to a number of stresses, including high temperatures, high humidity, high pressure These stresses cause the structure to deteriorate and create cracks. Therefore, structural health monitoring with monitoring sensors is required for safety. However, sensors could also fail due to the stresses, especially high temperature. Given that the sensors are installed in the bentonite buffer and the backfill tunnel, it is impossible to replace them if they fail. That’s why it is necessary to assess the sensors’ durability under the repository’s environmental conditions before installing them. Accelerated life test (ALT) can be used to assess durability or life of the sensors, and it is important to obtain the same failure mode for reliability tests including ALT. Before conducting the test, the proper stress level must be designed first to get reliable data in a short time. After that, acceleration of life reduction with increasing temperature and temperature-life model should be determined with some statistical methods. In this study, a methodology for designing stress levels and predicting the life of the sensor were described.

As the use of nuclear energy has been expanded, issues in a spent nuclear fuel management are raised. Several methods have been proposed and developed to manage spent fuels safely and efficiently. One method is to reduce environmental burden in disposal of spent fuels by decreasing volume of high-level waste. A nuclides management process (NMP) is one example. Through this novel process, it is able to separate highly mobile nuclides (ex. iodine, krypton), high thermal emission nuclides (ex. strontium, barium), and optionally, uranium from spent fuels. Since the NMP is a back-end fuel cycle technology, a reliable safeguards system should be employed in the facility. As international atomic energy agency (IAEA) recommends safeguards-by-design (SBD), it is desirable to investigate an appropriate safeguards approach at a step of technology development. Process monitoring (PM) is a complemental safeguards technology for traditional safeguards technologies which based on mass balance. PM traces nuclear materials indirectly but consecutively by using process parameters such as temperature, pressure, and flow of fluid. These parameters are obtainable by installing appropriate sensors. In a respect of SBD, PM is a promising approach to achieve the safeguards goal, the timely detection of diversion of a nuclear material. However, it is necessary to classify useful process parameters from all available signals which provided from PM in order to properly utilize PM. In this study, we investigated application methods of the PM approach to NMP. NMP consists of several unit processes in series. Firstly, we inspected a principle and a feature of each unit process. Based on the results, we evaluated applicability of the PM approach to each unit process according to effectiveness in enhancing safeguardability. Several unit processes were expected that their safeguards are able to be enhanced by using certain process parameters from PM.

Satellite imagery is an effective supplementary material for detecting and verifying nuclear activities and is helpful in areas where access and information are limited, such as nuclear facilities. This study aims to build training data using high-resolution KOMPSAT-3/3A satellite images to detect and identify key objects related to nuclear activities and facilities using a semantic segmentation algorithm. First, objects of interest, such as buildings, roads, and small objects, were selected, and the primary dataset was built by extracting them from the AI dataset provided by AIHub. In addition, to reflect the features of the area of interest (e.g., Yongbyon, Pyongsan), satellite images of the area were acquired, augmented, and annotated to construct an additional dataset (approximately 150,000). Finally, we conducted three stages of quality inspection to improve the accuracy of the training data. The training dataset of this study can be applied to semantic segmentation algorithms (e.g., U-Net) to detect objects of interest related to nuclear activities and facilities. Furthermore, it can be used for pixelbased object-of-interest change detection based on semantic segmentation results for multi-temporal images.

The swimming behavior of pacific bluefin tuna (Thunnus orientalis) in the offshore sea cage of the brass fishing net was observed and analyzed by imaging sonar techniques. The cultured fish spent most of the time swimming a circular path along the circular cage wall and continued to swim only clockwise direction without completely changing the swimming direction during the 23-hour observation time. In addition, changed swimming behaviors were divided into four categories: (a) the behavior of a large group temporarily swimming in the opposite (counter clockwise) direction, (b) the behavior of a small group temporarily swimming in a small circular path, (c) the behavior swimming small circular path in the center of the cage, and (d) the behavior of a large group swimming across the center of the cage. The maximum swimming speed of the cultured fish was from 3.5 to 3.8 TL/s, the mode was from 1.2 to 1.4 TL/s and the swimming speed during the day time was faster than at night time. It was confirmed the cultured fish swam not only on the surface but also near the bottom net of the cage during the day, but swam mainly at the upper part of the cage at night.

This study monitored temperature using electronic sensors and developed a prediction model for compost maturity. The experiment used swine manure in a mechanical composting facility equipped with a screw-type agitator, and the composting process was conducted for 60 d during the summer season in South Korea. Four electronic temperature sensors were installed on the inner wall between the compost piles on Days 7, 14, 21, and 28 for daily temperature monitoring. Compost samples were collected daily for 60 d, and compost maturity was analyzed using the Solvita method. Multiple comparisons, correlations, and modeling were performed using the stat package in R software. The average compost pile temperatures was 39.1±3.9, 36.4±4.3, 31.3±4.5, and 35.4±8.1 on days 7, 14, 21, and 28, respectively, after composting. The average compost maturity according to the composting date was 3.61±0.60, 4.13±0.59, 4.26±0.47, and 4.32 ±0.56 on days 7, 14, 21, and 28, respectively. A significant negative correlation was observed between the compost composting periods (seven, 14, 21, and 28 d) and the temperature of all compost piles (p<0.05), where the correlation coefficients were -0.329, -0.382, -0.507, and -0.634, respectively. A significant positive correlation was observed between the compost composting periods (seven, 14, 21, and 28 d) and the maturity of the compost (p<0.05), where the correlation coefficients were 0.410, 0.550, 0.727, and 0.840, respectively. The model for predicting the maturation of the 14 d average compost pile according to the compost composting period and the average temperature for 14 d was y=0.026 x d – 0.021 x mt.x_14 d (mean temperature for 14 d) + 4.336 (R2=0.7612, p<0.001). This study can be considered a basic reference for predicting compost maturity by the proposed model using electronic temperature sensors.