실제세계에서 데이터 수집의 비용과 한계를 고려할 때, 시뮬레이션 생성 환경은 데이터 생성 과 다양한 시도에 있어 효율적인 대안이다. 이 연구에서는 Unity ML Agent를 로그라이크 장 르에 적합한 강화학습 모델로 구현하였다. 간단한 게임에Agent를 이식하고, 이 Agent가 적을 인식하고 대응하는 과정을 코드로 작성하였다. 초기 모델은 조준사격의 한계를 보였으나 RayPerceptionSensor-Component2D를 통해 Agent의 센서 정보를 직접 제공함으로써, Agent가 적을 감지하고 조준 사격을 하는 능력을 관찰할 수 있었다. 결과적으로, 개선된 모델 은 평균3.81배 향상된 성능을 보여주었으며, 이는 Unity ML Agent가 로그라이크 장르에서 강화학습을 통한 데이터 수집이 가능함을 입증한다.

When the parent radionuclide decays, the progeny radionuclide is produced. Accordingly, the dose contribution of the progeny radionuclide should be considered when assessing dose. For this purpose, European Commission (EC) and International Atomic Energy Agency (IAEA) provide weighting factors for dose coefficient. However, these weighting factors have a limitation that does not reflect the latest nuclide data. Therefore, in this study, we analyzed the EC and IAEA methodology for derivation of weighting factor and used the latest nuclide data from ICRP 107 to derive weighting factors for dose coefficient. Weighting factor calculation is carried out through 1) selection of nuclide, 2) setting of evaluation period, and 3) derivation based on ICRP 107 radionuclide data. Firstly, in order to derive the weighting factor, we need to select the radionuclides whose dose contribution should be considered. If the half-life of progeny radionuclides sufficiently short compared to the parent radionuclide to achieve radioactive equilibrium, or if the dose coefficient is greater of similar to that of the parent radionuclide and cannot be ignored, the dose contribution of the progeny radionuclide should be considered. In order not to underestimate the dose contribution of progeny radionuclides, the weighting factors for the progeny nuclides are taken as the maximum activity ratio that the respective progeny radionuclides will reach during a time span of 100 years. Finally, the weighting factor can be derived by considering the radioactivity ratio and branch fraction. In order to calculate the weighting factor, decay data such as the half-life of the radionuclide, decay chain, and branch fraction are required. In this study, radionuclide data from ICRP 107 was used. As a result of the evaluation, for most radionuclides, the weighting factors were derived similarly to the existing EC and IAEA weighting factors. However, for some nuclides, the weighting factors were significantly different from EC and IAEA. This is judged to be a difference in the half-life and branch fraction of the radionuclide. For example, in the case of 95Zr, the weighting factor for 95mNb showed a 35.8% difference between this study and previous study. For ICRP 38, when 95Zr decays, the branch fraction for 95mNb is 6.98×10-3. In contrast, for ICRP 107, the branch fraction is 1.08×10-2, a difference of 54.7%. Therefore, the weighting factor for the dose coefficient based on ICRP 107 data may differ from existing studies depending on the half-life and decay information of the nuclide. This suggests the need for a weighting factor based on the latest nuclide data. The results of this study can be used as a basis for the consideration of dose contributions for progeny radionuclides in various dose assessments.

본 연구는 주변 환경의 차이에 따른 화분매개곤충의 유입 특성을 파악하기 위하여 국립수목원 내 진화속을걷 는정원과 부추속전문전시원에 식재된 울릉산마늘의 화분매개곤충을 조사하였다. 2023년 5월 22일부터 6월 2일 까지 꽃이 70% 이상 개화하였을 때 포충망을 활용하여 8일간 곤충을 채집하였고, 각 전시원 별 식생(피도), 기후 (온도·습도·조도)를 조사하였다. 조사 결과 진화속을걷는정원에서 피도 60% 온도 26.4℃, 습도 31.5%, 조도 40953.6lx, 화분매개곤충 20과 450개체, 부추속전문전시원은 피도 90%, 온도 25.6℃, 습도 31.6%, 조도 6387lx, 화분매개곤충 15과 196개체로 나타났다. 온도와 조도가 상대적으로 높은 진화속을걷는정원이 채집된 곤충의 다양성과 방문 빈도가 높았다. 시간대별 곤충의 방문 빈도를 비교해본 결과 온도와 조도는 개체수가 증가할 때 같이 증가하는 경향을 보였으며, 습도는 반대의 경향을 보였다.

Natural radionuclides-containing substances (NORM) contain natural radionuclides and cause radiation exposure. In Korea, safety management measures were needed to deal with and dispose of radon mattresses containing monazite in relation to such NORM. However, there is no clear safety management system related to NORM waste in Korea. In order to manage this reasonably and systematically, it is necessary to investigate and analyze standards and management measures related to the treatment and disposal of NORM waste. Therefore, this study investigated and analyzed the exemption and clearance level of NORM waste regulations in international organizations and foreign countries. IAEA GSR Part 3, 2013/59/Euratom, ANSI/HPS N13.53, CRCPD SSRCR Part N, and ARPANSA Publications 15 safety management regulations were analyzed to investigate safety management standards for NORM waste. The exemption and clearance level in international organizations and foreign countries were compared and analyzed based on radioactive concentration and dose. In addition, the management measures proposed for each literature were also investigated. As a result of the analysis, IAEA GSR Part 3 applied 1 mSv as a regulatory exemption level, 1 Bq/g for uranium and thorium series as a clearance level, and 10 Bq/g for K-40 nuclides. The IAEA recommends a differential approach to the potential and scale of exposure. The EU applied 1 Bq/g to uranium and thorium families and 10 Bq/g to K-40 nuclides for both regulatory exemption and clearance levels. The EU recommended that it be managed in proportion to the scale and likelihood of exposure as a result of the action. It is analyzed that this is similar to the IAEA’s management plan. In the United States, there was no single federal government radioactive concentration and dose for NORM management. The management plan differed in management status and level from state to state, and K-40 was excluded from regulation unless it was intentionally enriched. In the case of Australia, the radioactive concentration of uranium and thorium was 1 Bq/g as a standard for regulatory exemption and 1 mSv as a dose. As a management plan, it was suggested to dispose of waste by means of accumulation, dilution/dispersion, and reclamation. It was also suggested that the scale of exposure, like international organizations, take into account the possibility. The results of this study are believed to be used as basic data for presenting domestic NORM waste treatment and disposal methods in the future.

Currently, low and intermediate-level radioactive wastes and spent nuclear fuels are continuously generated in Korea. For the disposal of the radioactive wastes, the transport demand is expected to increase. Prior to transportation, it is necessary to evaluate the radiation risk of transportation to confirm that is not high. In Korea, there is no transportation risk assessment code that reflects domestic characteristics. Therefore, foreign assessment codes are used. In this study, before developing the overland transportation risk assessment code that reflects domestic characteristics, we analyzed the radiation risk assessment methodology in transportation accident codes developed in other countries. RADTRAN and RISKIND codes were selected as representative overland transportation risk assessment codes. For the two codes we analyzed accident scenarios, exposure pathways, and atmospheric diffusion. In RADTRAN, the user classifies accident severity for possible accident scenarios, and the user inputs the probability for each accident severity. On the other hand, in the case of RISKIND, the accident scenarios are classified and the probabilities are determined according to the NRC modal study (LLNL, 1987) in consideration of the cask impact velocity, cask impact angle, and fire temperature. In the case of RISKIND, the accident scenarios are applied only to transportation of spent nuclear fuel, and cannot be defined for low and intermediate-level radioactive waste. However, in the case of RADTRAN, since the severity and probability of accidents are defined by user, it can be applied to low and intermediate-level radioactive wastes. As the exposure pathways considered in transportation accident, both RADTRAN and RISKIND consider external exposure (cloudshine and groundshine), and internal exposure (inhalation, resuspension inhalation and ingestion). In the case of RADTRAN, additionally, external exposure due to loss of shielding (LOS) is considered. Atmospheric diffusion calculation is essential to determine the extent to which radioactive materials are diffused. In both RADTRAN and RISKIND, atmospheric diffusion calculations are based on Gaussian diffusion model. Users must input Pasquill stability class, release height, heat release, wind speed, temperature and mixing height, etc. Additionally, RADTRAN can input weather information relatively simply by inputting only the Pasquill stability class fraction and selecting the US average weather option. This study results will be used as a basis for developing radioactive waste overland transportation risk assessment code that reflects domestic characteristics.

Cement is widely used as representative industrial material. In Korea, about 50 million tons of cement are consumed every year. In the manufacture of cement, raw materials containing NORM such as fly ash and bauxite are used. Therefore, the workers can be subjected to radiation exposure. The major exposure pathway in NORM industries is internal exposure due to inhalation of aerosol. Internal radiation dose due to aerosol inhalation varies depending on physicochemical properties of the aerosol. Therefore, the objective of this study was to investigate aerosol properties influencing inhalation dose in cement industries. In this study, aerosol properties were measured for two cement manufacturers. A particulate size distribution and concentration at various processing areas in cement manufacturing industries in Korea were analyzed using a cascade impactor. The mass density of raw materials and byproducts were measured using pycnometer. Shape of particulates was analyzed using SEM. The radioactivity concentration of Ra-226, Ra-228 for U/Th decay series was measured using HPGe. Particulate concentration by size was distributed log-normally with maximum at particle size about 7.2 μm in manufacturer A and 5.2 μm in manufacturer B. The mass density of fly ash and cement were 2.3±0.06, 3.2±0.02 g/cm3 respectively in manufacturer A. In manufacturer B, the mass density of bauxite and cement were 3.4±0.02, 2.9±0.01 g/cm3 respectively. The shape of particulates appeared as spherical shape in manufacturer A and B regardless of sampling area. Thus, a shape factor of unity could be assumed. The radioactivity concentrations of Ra-226, Ra-228 were 82±9, 82±8 Bq/kg for fly ash, and 25±4, 23±3 Bq/kg for cement in manufacturer A. In manufacturer B, the radioactivity concentrations of Ra-226, Ra-228 were 344±34, 391±32 Bq/kg for bauxite, and 122±13, 145±12 Bq/kg for cement. The radioactivity concentrations of Ra-226, Ra-228 in cement were less than raw materials such as fly ash and bauxite. It is because the dilution of the radioactivity concentration occurred during mixing with other raw materials in cement production process. This study results will be used as database for accurate dose assessment due to airborne particulate inhalation by workers in cement industries.

Recently, the importance of preventive maintenance has been emerging since failures in a complex system are automatically detected due to the development of artificial intelligence techniques and sensor technology. Therefore, prognostic and health management (PHM) is being actively studied, and prediction of the remaining useful life (RUL) of the system is being one of the most important tasks. A lot of researches has been conducted to predict the RUL. Deep learning models have been developed to improve prediction performance, but studies on identifying the importance of features are not carried out. It is very meaningful to extract and interpret features that affect failures while improving the predictive accuracy of RUL is important. In this paper, a total of six popular deep learning models were employed to predict the RUL, and identified important variables for each model through SHAP (Shapley Additive explanations) that one of the explainable artificial intelligence (XAI). Moreover, the fluctuations and trends of prediction performance according to the number of variables were identified. This paper can suggest the possibility of explainability of various deep learning models, and the application of XAI can be demonstrated. Also, through this proposed method, it is expected that the possibility of utilizing SHAP as a feature selection method.

To increase the selection efficiency for new cultivars in cut roses during breeding programs, phenotypes of seedlings in two cross combinations, C1 (Rosa hybrida ‘Yellow King’ x ‘Aladin’) and C2 (R. hybrida ‘Evelien’ x ‘Sunny Isle’), were investigated. Although most seedlings showed classic-type flower shapes, some (0.4% - 0.7%) had tomato-shaped flowers, especially at the young plant stage (6 months old). The variation in petal color was high, and pink and pink-mixed flowers were dominant, accounting for 72% in C1 and 76% in C2 at the mature plant stage (13-month-old plants). However, the seedlings had changes in the expression of petal color at different growth stages. The number of petals at the young plant stage had a strong positive correlation with those at the mature plant stage in C1 (r = 0.84***) and C2 (r = 0.73***). Sizes of flowers and petals increased with the growth of the seedlings, and their traits at the young plant stage had positive correlations with those at the mature plant stage in both C1 (r = 0.32***) and C2 (r = 0.39***). Prickle expression on stems was mostly in level 5 at the young plant stage and then dispersed to various classes at the mature plant stage, which increased the variation and coefficient of variation. To select seedlings for new cultivars through breeding processes, breeders should consider the growth stage for targeted traits.

Recently, a study of prognosis and health management (PHM) was conducted to diagnose failure and predict the life of air craft engine parts using sensor data. PHM is a framework that provides individualized solutions for managing system health. This study predicted the remaining useful life (RUL) of aeroengine using degradation data collected by sensors provided by the IEEE 2008 PHM Conference Challenge. There are 218 engine sensor data that has initial wear and production deviations. It was difficult to determine the characteristics of the engine parts since the system and domain-specific information was not provided. Each engine has a different cycle, making it difficult to use time series models. Therefore, this analysis was performed using machine learning algorithms rather than statistical time series models. The machine learning algorithms used were a random forest, gradient boost tree analysis and XG boost. A sliding window was applied to develop RUL predictions. We compared model performance before and after applying the sliding window, and proposed a data preprocessing method to develop RUL predictions. The model was evaluated by R-square scores and root mean squares error (RMSE). It was shown that the XG boost model of the random split method using the sliding window preprocessing approach has the best predictive performance.