Recently, the spent fuel pools withdrawn from nuclear power plants in Korea have been saturated. Therefore, specific regulations on the management of spent fuel pools, such as transportation and intermediate storage are needed. The burnup history is directly related to the management of spent nuclear fuel. This is because the decision to handle nuclear fuel may vary depending on the initial concentration of nuclear fuel, the degree to which nuclear fuel is irradiated and radioisotope nuclides are decayed, and the cooling state in the spent nuclear fuel storage tank. The purpose of this study is to determine the burnup of fuel based on the value obtained by scanning the surface of spent nuclear fuel through a neutron detector. Conversely, a database of neutron signals that scan bundles of spent nuclear fuel with an instrument with an already identified combustion history needs to be completed. First of all, the correlation between burnup history and nuclides was identified in previous studies. By setting the burnup history as the input value in the ORIGEN-ARP code, it was possible to identify the radioactive isotopes remaining in the bundle of nuclear fuel. Neutrons can finally be measured based on the amount of nuclide inventory that constitutes spent nuclear fuel. Through MCNP, the neutron detector was simulated and signals were measured to confirm how it correlates with the previously acquired burnup history database. In addition, the M (sub-critical multiplication) value, which is essential for neutron measurement, was checked to confirm the degree to which additional neutrons were generated in spent nuclear fuel in a subcritical state. The target nuclear fuel assembly was CE16×16, WH14×14, and WH17×17, which confirmed the correlation (1) between burnup, enrichment, and cooling time with the previous research topic, TNSI (Total neutron source intensity). 􀜤􀜷􁈺􀜩􀜹􀝀/􀜯􀜶􀜷􁈻 = 0.83􁈺􀜵􀯇􁈻􀬴.􀬶􀬷􀬼 ∙ 􁈺􀜫􀜧􁈻􀬴.􀬸􀬺􀬶􀬻 ∙ 􀝁􀬴.􀬴􀬴􀬼􀬷∙􀯧 􁈺1􁈻 A neutron signal will be obtained from the case according to each burnup history constituting this database. In particular, PAR=SF, a function that calculates the production amount of the fission product, was used. To confirm the computational logic of SF, it was confirmed whether a reasonable calculation was made by calculating with a nuclide spectrum.

As a result of active geological investigation of faults in Korea, many Quaternary faults have been identified and some of them were judged to have potential to generate earthquakes. Those faults need to be considered as additional seismic sources in the seismic hazard analysis. When a fault is introduced as a new source, the earthquakes generated by the fault should be removed from the area sources that include any part of the fault, to avoid double counting. In practice, however, double counting cannot completely be avoided as the complete separation of the fault-generated earthquakes from the area sources is impossible due to uncertainties related to the earthquake location, subsurface structures of faults, etc. When a new fault source is introduced, the only constraint is the invariance of earthquake frequency. The maximum earthquake and the Richter-b value should also be subject to change, but there are no competent approaches to estimate the change due to incomplete separation of earthquakes. To gain insight into the effect of a new fault source, an example calculation of the seismic hazard were carried out. The example calculation shows that addition of a new fault source centers seismic hazard around the fault source.

Radioactivity of radiostrontiums, Sr-89 and Sr-90, which are both pure beta-emitters, are generally measured via Cherenkov counting. However, the determination of Cherenkov counting efficiencies of radiostrontiums requires a complicated procedure due to the presence of Y-90 (also a pure betaemitter) which is the daughter nuclide of Sr-90. In this study, we have developed a machine learning approach using a linear regression model which allows an easier and simultaneous determination of the Cherenkov counting efficiencies of the radiostrontiums. The linear regression model was employed because total net Cherenkov count (Ct) from the three beta-emitters at time t after the separation of Y- 90, can be expressed as a linear combination of their respective time-varying radioactivities with their respective coefficients (parameters) being their counting efficiencies: Ct = εSr-90[ASr-90·exp(–λSr-90·t)] + εSr-89[ASr-89·exp(–λSr-89·t)] + εY-90[ASr-90·exp(1–λSr-90·t)], where ε is a counting efficiency, A is an initial activity, λ is a decay constant and t is time after the separation of Y-90, Thus, if we train the model with multiple Cherenkov counts measured from the three beta emitters, then we can obtain their estimates for counting efficiencies (so-called parameters) straightforward. For this, the model has been trained by two methods: Ordinary Least Squares (OLS) and Bayesian linear regression (BLR), for which two software packages, PyMC3 and Stan were employed to compare their performances. The results showed that the accuracy of the OLS was worse than that of the BLR. Particularly, the counting efficiency of Sr-90 was estimated to be smaller than 0, which is an unrealistic value. On the other hand, the estimates of the BLR gave realistic values which are close to the true values. Additionally, the BLR was able to provide a distribution for each counting efficiency (so-called “posterior”) from which various types of inference can be made including median and credible interval in the Bayesian statistics which is analogous to, but different from confidence interval in the Frequentist statistics. In the results of the BLR, the Stan package gave more accurate estimates than the PyMC3 package. Therefore, it is expected that counting efficiencies of the radiostrontiums including radioyttrium can be determined at the same time, more easily and accurately, by using the BLR with the Stan package and that the activities of radiostrontium also can be determined more easily by using the BLR if we know their counting efficiencies in advance. It is worth noting that the usage of the linear regression model in this study was different from the usual one where the trained model is used to predict a response value (count) from a set of unseen regressor values (activities).

For the peaceful use of nuclear energy, the international community has devoted itself to fulfilling its obligations under the Safeguards Agreement with IAEA. In this regard, uranium in a radioactive waste drum should be analyzed and reported in terms of mass and 235U enrichment. In order to characterize radioactive wastes, gamma spectroscopy techniques can be effectively applied. In the case of high-resolution gamma spectroscopy, because an HPGe detector can provide excellent energy resolution, it can be applied to analyze a mixture having a complicated isotopic composition. However, other substances such as wood, concrete, and ash are mixed in radioactive waste with various form factors; hence, the efficiency calibration is difficult. On the other hand, In Situ Object Counting System (ISOCS) has a capability of efficiency calibration without standard materials, making it possible to analyze complex radioactive wastes. In this study, the analysis procedure with the ISOCS was optimized for quantification of radioactive waste. To this end, a standard radioactive waste drum at KEPCO NF and low-level radioactive waste drums at Korea Radioactive Waste Agency (KORAD) were measured. The performance of the ISOCS was then evaluated by Monte Carlo simulations, Multi-Group Analysis for Uranium (MGAU) code, and destructive analysis. As a result, the ISOCS showed good performance in the quantification of uranium for a drum with the homogenized simple geometry and long measurement time. It is confirmed that the ISOCS gamma spectroscopy technique could be used for control and accountancy of nuclear materials contained in a radioactive waste drum.

Because of their attractive and colorful flowers, many species from the genus Aster serve as garden plants. Chrysanthemum owes its popularity to its ornamental and medicinal herb value. It can be used as a cut flower, potted plant, vegetable, and herbal tea. Plant breeders have attempted to identify the available species and produce new cultivars to improve the quality of chrysanthemum for commercial purposes. The use of cytogenetic studies has paved the way for identifying compatibility, ancestry, and other useful information for this undertaking. Thus, an investigation was conducted into the chromosome numbers of 23 wild Asteraceae species in Republic of Korea to determine their genetic characteristics and variations. The somatic chromosome spread has been used for chromosome counting. The results revealed that Asteraceae species have a chromosome range from 18 (diploid) to 54 (hexaploid). These findings provide primary and important information on the chromosome numbers in chrysanthemum plants that can be used to select the right variety for cultivation.

논문에서는 머신비전을 이용하여 컨베이어 시스템에서 이동하는 객체를 계수하는 알고리즘을 제안하였다. 영상처리를 이용한 객체 계수 시스템은 유동인구나 교통량 파악 등의 다양한 산업현장에서 사용되고 있으며, 주로 템플릿 매칭이나 기계학습의 방 법으로 검출하여 추적 후 계수한다. 하지만 빠르게 움직이는 컨베이어 벨트위의 물체를 검출하기 위해서는 연산에 소요되는 시간이 짧 아야 하므로 영역기반의 방법으로 영상처리를 하였다. 본 연구에서는 모양과 크기, 그리고 색깔이 비슷한 전복 치패를 계수하였다. 컨 베이어 시스템은 한 방향으로 동작하는 특성을 이용하여 첫 번째 영역에서 치패를 검출하여 정보를 얻은 것을 기반으로 다음 프레임 에서의 물체의 위치 범위를 계속적으로 변화하여 치패를 검출하고 각각의 획득한 정보를 비교하여 계수하였다. 치패가 간격을 두고 이 동 시에는 정확하게 계수됨을 확인하였으며, 치패가 붙어서 오는 경우에는 크기정보를 이용하여 계수하여 중복되거나 누락됨을 방지 하였다. 본 논문에서 제안한 알고리즘은 컨베이어 시스템 위에서 움직이는 다양한 객체 계수 제어에 적용할 수 있을 것이다.

Since it has been reported that asbestos fibers cause serious health problems such as lung cancer, malignant mesothelioma and other related diseases, it turns into social issue leading to a number of studies for characterizing asbestos found in the indoor environment. Among the established methods for detecting asbestos fibers, phase contrast microscopy (PCM) method is widely used as it dose not require complicated process nor high-priced equipments. However, PCM method is hard to define a sort of asbestos and to detect tiny asbestos fibers. We developed an image-based high-throughput microscopy (HTM) for automated counting of asbestos fibers which were distinguishable from the spherical particles. HTM method enabled us to analyze asbestos fibers both automatically and quantitatively. Test samples of chrysotile, amosite and crocidolite, which are frequently detected in Korea, were used in this study and comparisons were made between concentrations of asbestos fibers measured by manual counting method and HTM method. Application of HTM system can be extended to various areas such as malaria diagnosis, rare cell detection and bacterial colony counting.

When a new counting experiment is proposed, it is crucial to predict whether the desired source signal will be detected, or how much observation time is required in order to detect the signal at a certain significance level. The concept of the a priori prediction of the detection limit in a newly proposed experiment should be distinguished from the a posteriori claim or decision whether a source signal was detected in an experiment already performed, and the calculation of statistical significance of a measured source signal. We formulate precise definitions of these concepts based on the statistical theory of hypothesis testing, and derive an approximate formula to estimate quickly the a priori detection limit of expected Poissonian source signals. A more accurate algorithm for calculating the detection limits in a counting experiment is also proposed. The formula and the proposed algorithm may be used for the estimation of required integration or observation time in proposals of new experiments. Applications include the calculation of integration time required for the detection of faint emission lines in a newly proposed spectroscopic observation, and the detection of faint sources in a new imaging observation. We apply the results to the calculation of observation time required to claim the detection of the surface thermal emission from neutron stars with two virtual instruments.