As an initial part of Kori-1 & Wolsung-1 Unit decommissioning planning, a characterization plan is developed to define the nature, extent and location of contaminants, determine sampling locations and protocols, determine quality assurance objectives for characterization, and define documentation requirements. The actual characterization of a facility is an iterative process that involves initial sampling according to the characterization plan, field management (such as labeling, packaging, storing, and transport) of the samples, laboratory analysis, conformance to the data quality objectives (DQOs), and then identifying any additional sampling required, refining the DQOs, and modifying the characterization plan accordingly. The final product of the facility characterization is a document that describes the type, amount, and location of contaminants that will require consideration and removal during the decommissioning operations sufficient to prepare a decommissioning plan. In this study, implementing a characterization plan, developed in accordance with this standard, will result in obtaining or deriving the above information.

Kori Nuclear Power Plant Unit 1, which began operating in 1978, is Korea’s oldest commercial nuclear reactor. The reactor was permanently shut down in June 2017, and now the decommissioning process has begun. The decommissioning process will generate a significant amount of waste that requires appropriate management to minimize the impact on the environment and human health. And the waste routing, i.e. the activities and logistics for managing the material generated, is a key point in a decommissioning project. It determines the routes from the material inventory to the envisaged material end states. In this study, we review on several factors for the selection of the waste routes in a decommissioning project. In terms of sustainability, the ‘waste hierarchy’ should be applied to routing materials from nuclear facilities. According to the waste hierarchy, the preferred end state is reuse or recycling of the waste as material or, more preferably, the avoidance of waste generation. In addition, treatments (such as decontamination and thermal treatment) that can reduce the volumes requiring disposal as radioactive waste should be considered. Another important parameter is the need to secure availability and capacity of waste routes. Short-term bottlenecks or any delay in the removal of the waste from the site often has an impact on other site activities. If possible, at least two alternative waste routes should be identified for the main categories of waste and kept available throughout the decommissioning project. All routes should be direct to the material end state if possible, but it is more important that waste is removed from the site so that other site operations are not impeded.

Prevention of radiation hazards to workers and the environment in the event of decommissioning nuclear power plants is a top priority. To this end, it is essential to continuously perform radiation characterization before and during decommissioning. In operating nuclear power plants, various detectors are used depending on the purpose of measurement. Portable detectors used in power plants have excellent portability, but there is a limit to the use of a single measuring device alone to quantify radioactive contamination, nuclide analysis, and ensure representation of measurement results. In foreign countries, gamma-ray visualization detectors are being actively used for operating and decommissioning nuclear power plants. KHNP is also conducting research on the development of gamma-ray visualization detectors for multipurpose field measurement at decommissioning nuclear power plants. It aims to develop detectors capable of visualizing radioactive contamination, analyzing nuclides, estimating radioactivity, and estimating dose rates. To this end, we are developing related software according to the development process by purchasing sensors from H3D, which account for more than 75% of the US gamma-ray visualization detector market. In addition, field tests are planned in the order of Wolsong Unit 1 and Kori Unit 1 with Research reactor in Gongneung-dong in accordance with the progress of development. The detector will be optimized by analyzing the test results according to various gamma radiation field environments. The development detector will be used for various measurement purposes for Kori unit 1 and Wolsong

The inclusion of conductive carbon materials into lithium-ion batteries (LIBs) is essential for constructing an electrical network of electrodes. Considering the demand for cells in electric vehicles (e.g., higher energy density and lower cell cost), the replacement of the currently used carbon black with carbon nanotubes (CNTs) seems inevitable. This review discusses how CNTs can contribute to the development of advanced LIBs for EVs. First, the reason for choosing CNTs as a conducting agent for the cathode is discussed in terms of energy density. Second, the reinforcing effect of CNTs on the anode is described with respect to the choice of silicon as the active material. Third, the development of water-based cathode fabrication as well as dry electrode fabrication with aid of CNTs is discussed. Fourth, three technical hurdles, that is, the price, dispersion issue, and entrapped metal impurities, for widespread use of CNTs in LIBs are discussed.

Recently, the development of computer vision with deep learning has made object detection using images applicable to diverse fields, such as medical care, manufacturing, and transportation. The manufacturing industry is saving time and money by applying computer vision technology to detect defects or issues that may occur during the manufacturing and inspection process. Annotations of collected images and their location information are required for computer vision technology. However, manually labeling large amounts of images is time-consuming, expensive, and can vary among workers, which may affect annotation quality and cause inaccurate performance. This paper proposes a process that can automatically collect annotations and location information for images using eXplainable AI, without manual annotation. If applied to the manufacturing industry, this process is thought to save the time and cost required for image annotation collection and collect relatively high-quality annotation information.