The success of machine learning approach to identify key correlation in large database is critically controlled by the reliability and accuracy of the data. Here, we demonstrate that rigorous material properties of radioactive nuclear fuels can be obtained by integrated approach of first principles calculations and the machine learning approach. The reliable database is established by density functional theory and molecular dynamics simulations, which is the input of the machine learning to analyze any correlation among the database. The outcomes are applied to evaluate thermodynamic, kinetic and electrochemical properties, which plays a key role for safe management of spent nuclear fuels.

It is important to ensure worker’s safety from radiation hazard in decommissioning site. Real-time tracking of worker’s location is one of the factors necessary to detect radiation hazard in advance. In this study, the integrated algorithm for worker tracking has been developed to ensure the safety of workers. There are three essential techniques needed to track worker’s location, which are object detection, object tracking, and estimating location (stereo vision). Above all, object detection performance is most important factor in this study because the performance of tracking and estimating location is depended on worker detection level. YOLO (You Only Look Once version 5) model capable of real-time object detection was applied for worker detection. Among the various YOLO models, a model specialized for person detection was considered to maximize performance. This model showed good performance for distinguishing and detecting workers in various occlusion situations that are difficult to detect correctly. Deep SORT (Simple Online and Realtime Tracking) algorithm which uses deep learning technique has been considered for object tracking. Deep SORT is an algorithm that supplements the existing SORT method by utilizing the appearance information based on deep learning. It showed good tracking performance in the various occlusion situations. The last step is to estimate worker’s location (x-y-z coordinates). The stereo vision technique has been considered to estimate location. It predicts xyz location using two images obtained from stereo camera like human eyes. Two images are obtained from stereo camera and these images are rectified based on camera calibration information in the integrated algorithm. And then workers are detected from the two rectified images and the Deep SORT tracks workers based on worker’s position and appearance between previous frames and current frames. Two points of workers having same ID in two rectified images give xzy information by calculating depth estimation of stereo vision. The integrated algorithm developed in this study showed sufficient possibility to track workers in real time. It also showed fast speed to enable real-time application, showing about 0.08 sec per two frames to detect workers on a laptop with high-performance GPU (RTX 3080 laptop version). Therefore, it is expected that this algorithm can be sufficiently used to track workers in real decommissioning site by performing additional parameter optimization.

Deep geologic repositories (DGR) are designed to store spent nuclear fuel and to isolate it from the biosphere for an extended period of time as long as millions of years. The long-term performance of the DGR replies on the performance of the natural geologic barriers after the end of the lifetime for the engineered barrier systems. Typically, multiple analytical and numerical models are used to analyze and ensure the safety of the repositories along both engineered and natural barrier systems. Despite the immense advancement in computing power and modeling techniques over the last few decades, a series of models and their linkage often require many simplifying assumptions in this safety assessment. The degree of the reliability and confidence of the safety analysis is thus highly dependent on the validity of those tools used. Considering the significance of the DGR performance and public attention, the highest level of quality control is necessary for the models employed in the assessment. The performance of the ultimate long-term geologic barrier is determined by the expected travel time of the radioactive species of interest, the level of their dilution or radioactivity at compliance areas, and the uncertainty associated with them. As the species of interest can be carried away from the repository location by groundwater flow, the travel time is determined by groundwater velocity along the flow path from source to biosphere while the dilution is a function of the decay and production rates as well as the diffusion and dispersion. Due to the time scale and the complexity of the physicochemical processes and geologic media involved, the models used for safety evaluation will need to become more and more comprehensive, robust, and efficient which is difficult to achieve in principle. They will also need to be transparent and flexible to satisfy the regulatory quality control requirements. This study thus attempts to develop an accessible, transparent, and extensible integrated hydrologic models (IHM) which can be widely accepted by the regulators as well as scientific community and thus suitable for current and future safety assessment of the DGR systems. The IHM can be considered as a tool and a framework at the same time when it is designed to easily accommodate additional processes and requirements for the future as it is necessary. The IHM is capable of handling the atmospheric, land surface, and subsurface processes for simultaneously analyzing the regional groundwater driving force and deep subsurface flow, and repository scale safety features, providing an ultimate basis for seamless safety assessment in the DGR program. The applicability of the IHM to the DGR safety assessment is demonstrated using simple illustrative examples.

With the rapid improvement of hardware and software-related IT technology, applying A.I. to the private and public sectors, such as the Food Poisoning Prevention Program in Nevada and Smart City based on big data in Boston, is steadily increasing. However, the cases of application to the regulation sector of government are still insufficient. The Korea Institute of Nonproliferation and Control (KINAC) is studying to apply A.I. technology to the regulation to improve the objectivity, consistency, and efficiency of classification and export licensing review. The KINAC developed the Nation Nuclear Technology Information Collection and Analysis System using A.I. techniques such as machine learning and deep learning techniques. KINAC and FNC Technology are developing the Export Risk Assessment System using A.I. modules and Bayesian Networks. The KINAC and Korea Atomic Energy Research Institute (KAERI) are developing an inventory history management system subject to the Nuclear Cooperation Agreements. The Nuclear Safety and Security Commission (NSSC) and KINAC operate the Nuclear Import and Export Control System (NEPS) for application and export license review according to relevant laws such as the Foreign Trade Act. Therefore, preparing an integration plan for the existing NEPS and the new systems is necessary. Since the NEPS has to be operated and accessible at all times, so the stability of the NEPS is the most important when integration and linking. So, it is suggested that the Collection and Analysis System and the Risk Assessment System, which require a lot of data traffic, are configured in a server separate from the NEPS, and the new DB and the NEPS DB are only linked. An inventory history management system is also suggested to be integrated and configured into the NEPS. Third, it is recommended that each system lists the information provided to or received from the NEPS in advance, and one-way communication should be performed basically. Two-way communication should be performed when necessary. Finally, against various cyber accidents and information leakage, it is proposed to review security vulnerabilities and apply essential security measures and guidelines. Through the integration and linkage of these systems, it is expected that the objectivity, consistency, and efficiency of classification and export licensing review of the KINAC are strengthened, and national transparency of development, production, and use of nuclear material is enhanced. It can be satisfied with the increasingly strengthened demands of the international community on duty for strategic item management.

Nuclear security event involving nuclear and other radioactive materials outside of regulatory control (MORC) has the potential to cause severe consequences for public health, the environment, the economy and society. Each state has a responsibility to develop national nuclear security measures including nuclear forensics to respond to such events. In Japan, national nuclear forensics capability building efforts mainly based on research and development (R&D) have been conducted since 2010, in accordance with national statement of Japan at the Nuclear Security Summit in Washington DC. Most of that work is undertaken at the Integrated Support Center for Nuclear Non-proliferation and Nuclear Security (ISCN) of the Japan Atmic Energy Agency (JAEA) in close cooperation with other competent authorities. The ISCN has made increased contributions to the enhancement of international nuclear security by establishing technical capabilities in nuclear forensics and sharing the achievements with the international community. The ISCN has mainly engaged in R&Ds for establishing and enhancing nuclear forensics technical capability. As for the laboratory capability, several new pieces of analytical equipment have been introduced for nuclear forensics R&D purposes. High-precise measurement techniques validated in the past nuclear forensics incidents have been established, and novel techniques that can contribute to the more timely and confident nuclear forensics signature analysis have been developed. The ISCN has been also developed a proto-type nuclear forensics library based on the data of nuclear materials possessed for past nuclear fuel cycle research in JAEA. These technical capability developments have been conducted based on the cooperation with international partners such as the U.S. Department of Energy and EC Joint Research Center, as well as participation in exercises organized by Nuclear Forensics International Technical Working Group (NF-ITWG). Recent R&D works have been mainly based on the needs of domestic competent authorities, such as first responders and investigators, and aim to develop technologies covering the entire spectrum of nuclear forensics processes from crime scene investigation to laboratory analysis and interpretation. One important key issue is the enhancement of technical capability for post-dispersion nuclear forensics. For instance, the ISCN has carried out the development of radiation measurement equipment coupled with the low-cost and mobile radiation detectors that uses machine-learning algorithms for quick and autonomous radioisotope identification to support first responders during crime scene investigations. Laboratory measurement techniques for samples collected at a post-dispersion crime scene are also among the important technical issues studied at the ISCN. The application of emerging technologies to nuclear forensics has also been studied. This includes the application of deep leaning models to nuclear forensics signature interpretation that could provide more confident results, as well as the development of contamination imaging technology that could contribute to the analytical planning on the samples in collaboration with conventional forensics. Many analytical techniques have been developed and the capability to analyze nuclear and other radioactive materials for nuclear forensics purposes has been considerably matured over the past decade. The challenges of post-dispersion samples, collaboration with conventional forensics and the development of novel signatures will be more important in the near future. Therefore, the ISCN will promote the R&Ds to further enhance the technical capabilities solving these issues. In addition, the ISCN is also promoting to expand the nuclear forensics research into universities and other research institutes in Japan. This is expected to contribute to the establishment of a domestic nuclear forensics network that enables to respond timely and flexibly to the MORC incidents, and to the maturation of nuclear forensics as a new academic field.

The most common symptoms of COVID-19 are high fever, cough, headache, and fever. These symptoms may vary from person to person, but checking for “fever” is the government’s most basic measure. To confirm this, many facilities use thermographic cameras. Since the previously developed thermographic camera measures body temperature one by one, it takes a lot of time to measure body temperature in places where many people enter and exit, such as multi-use facilities. In order to prevent malfunctions and errors and to prevent sensitive personal information collection, this research team attempted to develop a facial recognition thermographic camera. The purpose of this study is to compensate for the shortcomings of existing thermographic cameras with disaster safety IoT integrated solution products and to provide quarantine systems using advanced facial recognition technologies. In addition, the captured image information should be protected as personal sensitive information, and a recent leak to China occurred. In order to prevent another case of personal information leakage, it is urgent to develop a thermographic camera that reflects this part. The thermal imaging camera system based on facial recognition technology developed in this study received two patents and one application as of January 2022. In the COVID-19 infectious disease disaster, ‘quarantine’ is an essential element that must be done at the preventive stage. Therefore, we hope that this development will be useful in the quarantine management field.

In this study, a study was conducted to improve the reliability of the valve by developing a valve leakage and reliability measurement system designed to secure the high quality and reliability of the butterfly valve. The system measuring the torque required for valve opening and closing operation, and was configured to operate after multiple opening and closing of the valve to check for leakage of the valve. Finally, a system that can perform efficient work in terms of productivity was developed by enabling leak inspection, torque measurement, and reliability inspection through one integrated system.

본 연구는 폭력가정에서 성장한 대학생이 대인관계 갈등과 학업 진로에 대한 불안으로 겪는 심리적인 고통과 혼란 감을 상담을 통해 회복하는 경험에 대한 단일 사례연구이다. 대학생 시기는 학업 성취와 이 시기에서의 발달 과제인 이성과의 친밀감을 형성하고, 독립적인 사회인이 되기 위한 준비 시기로서 정체성 형성에 기반이 되는 일과 관계를 맺는 방식과 삶의 방향을 설정하는 중요한 시기이다. 하지만 보건복지부 정신질환실태조사에 따르면 대학생 연령대의 30%에 가까운 젊은 청년들이 정신질환의 어려움을 호소하고 있으며 이의 해결이 절실한 시점이다. 본 연구는 자원 중심 상담과 감정 자유 기법 그리고 알아차림 호흡 명상을 활용하여 통합적으로 접근하였다. 연구대상은 대학 4학년인 여자 대학생이다. 본 연구에서는 상담에서 참여자가 호소하는 문제를 삶에서 이루기를 원하는 긍정적인 목표로 설정하였고, 이를 성취하기 위하여 내적 자원 활용, 개별적 수용, 변화로의 동기 부여, 이를 유지하고 확대하는 상담 과정과 정서의 수용과 알아차림 호흡 명상을 접목하여 마침내 생활상의 변화를 이루어 내는 과정을 구체적으로 살펴보았다. 참여자는 본 연구의 통합적 접근 상담을 통해 생활의 균형과 자기 통제력을 회복하게 되었으며 일과 관계를 맺는 방식에 변화를 가져왔다. 이와 같은 연구 결과를 통해 폭력가정에서 성장하며 경험했던 왜곡된 자기 지각, 부정 정서와 행동으로 고통 받는 참여자의 변화와 성장을 위해 본 연구에 적용된 기법들을 통합적으로 사용하면 효과적인 개입 법이 될 수 있음을 제시하였다

Intertidal mud crab (Macrophthalmus japonicus) is an organism with a hard chitinous exoskeleton and has function for an osmotic control in response to the salinity gradient of seawater. Crustacean exoskeletons change in their natural state in response to environmental factors, such as changes in the pH and water temperature, and the presence of pollutant substances and pathogen infection. In this study, the ecotoxicological effects of irgarol exposure and heavy metal distribution were presented by analyzing the surface roughness of the crab exoskeleton. The exoskeleton surface roughness and variation reduced in M. japonicus exposed to irgarol. In addition, it was confirmed that the surface roughness and variation were changed in the field M. japonicus crab according to the distribution of toxic heavy metals (Cd, Pb, Hg) in marine sediments. This change in the surface roughness of the exoskeleton represents a new end-point of the biological response of the crab according to external environmental stressors. This suggests that it may affect the functional aspects of exoskeleton protection, support, and transport. This approach can be utilized as a useful method for monitoring the aquatic environment as an integrated technology of mechanical engineering and biology.

Seasonal forecasting has numerous socioeconomic benefits because it can be used for disaster mitigation. Therefore, it is necessary to diagnose and improve the seasonal forecast model. Moreover, the model performance is partly related to the ocean model. This study evaluated the hindcast performance in the upper ocean of the Global Seasonal Forecasting System version 5-Global Couple Configuration 2 (GloSea5-GC2) using a multivariable integrated evaluation method. The normalized potential temperature, salinity, zonal and meridional currents, and sea surface height anomalies were evaluated. Model performance was affected by the target month and was found to be better in the Pacific than in the Atlantic. An increase in lead time led to a decrease in overall model performance, along with decreases in interannual variability, pattern similarity, and root mean square vector deviation. Improving the performance for ocean currents is a more critical than enhancing the performance for other evaluated variables. The tropical Pacific showed the best accuracy in the surface layer, but a spring predictability barrier was present. At the depth of 301 m, the north Pacific and tropical Atlantic exhibited the best and worst accuracies, respectively. These findings provide fundamental evidence for the ocean forecasting performance of GloSea5.

In the era of the 4th industrial revolution driven by the convergence of ICT(information and communication technology) and manufacturing, research on smart factories is being actively conducted. In particular, the manufacturing industry prefers smart factories that autonomously connect and analyze data. For the efficient implementation of smart factories, it is essential to have an integrated production system that vertically integrates separately operated production equipment and heterogeneous S/W systems such as ERP, MES. In addition, it is necessary to double-verify production data by using automatic data collection technology so that the production process can be traced transparently. In this study, we want to show a case of data-centered integration of a large aircraft parts processing factory that requires high precision, takes a long time, and has the characteristics of processing large raw materials. For this, the components of the data-oriented integrated production system were identified and the connection structure between them was explained. And we would like to share the experience gained through the design and implementation case. The integrated production system proposed in this study integrates internal components based on data, which is expected to serve as a basis for SMEs to develop into an advanced stage, and traces materials with RFID technology.