Source localization technique using acoustic emission (AE) has been widely used to track the accurate location of the damaged structure. The principle of localization is based on signal velocity and the time difference of arrival (TDOF) obtained from different signals for the specific source. However, signal velocity changes depending on the frequency domain of signals. In addition, the TDOF is dependent on the signal threshold which affects the prediction accuracy. In this study, a convolutional neural network (CNN)-based approach is used to overcome the existing problem. The concrete block corresponding to 1.3×1.3×1.3 m size is prepared according to the mixing ratio of Wolseong low-to-intermediate level radioactive waste disposal concrete materials. The source is excited using an impact hammer, and signals were acquired through eight AE sensors attached to the concrete block and a multi-channel AE measurement system. The different signals for a specific source are time-synchronized to obtain TDOF information and are transformed into a time-frequency domain using continuous wavelet transform (CWT) for consideration of various frequencies. The developed CNN model is compared with the conventional TDOF-based method using the testing dataset. The result suggests that the CNN-based method can contribute to the improvement of localization performance.

Low and intermediate radioactive wastes in South Korea have been disposed in Wolsong Low and Intermediate Level Radioactive Waste Disposal Center (WLDC), Gyeongju. This repository structure is planned to be operated few hundred years while toxicity of the waste is sufficiently decayed. The structural integrity of the repository is required to protect the waste in safe. The integrity of the structure is commonly estimated using acoustic emission (AE) method. The integrity of the structure using AE is obtained by following process: 1) Estimation of maximum acoustic crack energy of the structure, 2) Acoustic signal measurement and filtering, and 3) Measurement of simultaneous acoustic cracking energy. The damage of the structure can be obtained from cumulative cracking energy from the structure divided by the predicted maximum cracking energy of the structure. Estimation of maximum cracking energy is gained by the specimens whose components are identical to the repository structure. The cracking energy of the different specimens are obtained during uniaxial compressive test and volume of the specimen is calculated. Then, the fractal coefficient for the structure is obtained and the maximum crack energy of the target structure can be calculated. The specimens whose diameters vary from 50 mm to 150 mm and heights are twice of the diameter are made with same recipe of WLDC silo concrete. The uniaxial compression test is conducted with loading rate of 0.1 mm·min−1. The fractal coefficient is obtained by least square method from the volume-cumulative energy relationship.

Low to intermediate radioactive waste disposal concrete structures are subjected to coupled hydromechanical conditions and the identification of structural damage is crucial to ensure safe long-term disposal. Different damage models for concrete and the surrounding rock can affect the damage characteristics of radioactive waste disposal structures. In this study, the effects of different rock damage models are applied to the hydro-mechanical-damage coupled structural analysis of the Wolseong Low and Intermediate Level Radioactive Waste Disposal Center silo. A two-dimensional model of the disposal silo was modeled using the finite element analysis software COMSOL and the Mazars’ damage model was applied to the silo concrete. The Mazars’ model parameters were obtained from uniaxial compression and tensile tests on cylindrical concrete specimens after 28 days of water curing and further 32 days of wet curing at 75°C). The COMSOL embedded Richards equation module was used to simulate hydraulic analysis. Structural loading due to waste disposal was applied at the bottom of the silo structure and the damage evolution characteristics were investigated. The non-linear mechanical rock behavior obtained from laboratory tests (Hoek-Brown criterion, resonant column test, Mazar’s damage model) and field tests (Goodman Jack) were input to assess the effects of different rock damage models. The results highlight the importance of structural damage consideration when assessing the long-term stability and safety of underground radioactive waste disposal structures under coupled hydro-mechanical conditions.

The buffer material plays a role in preventing the excessive rise in temperature generated from the high-level radioactive waste by dissipating the decay heat to the rock. For this reason, the buffer material must have thermal properties to ensure the performance of the deep geological repository. This study measured the thermal conductivity of sand-bentonite according to the mixing ratio to improve the thermal properties. The compacted buffer was manufactured with a sand-bentonite mixing ratio of 6:4, 7:3, and 8:2 with 9 to 12% water content. As a result, the thermal conductivity increases as the ratio of sand increases. As a further study, it is necessary to experiment on whether sand-bentonite’s hydraulic, mechanical, and chemical performance is suitable for the stable operation of a repository.

A geological repository system consists of a disposal canister with packed spent fuel, buffer material, backfill material, and intact rock. Among these, the bentonite buffer is one of the most important components to assure the safe disposal of high-level radioactive waste (HLW). As the bentonite buffer is installed as a block type, it is important to fabricate homogeneously. Generally, floating die method and cold isostatic press (CIP) method are used to fabricate bentonite blocks. In this paper, two bentonite blocks were produced using float die method at first, and CIP method was additionally applied to just one block. After that, several samples were cored from two blocks. The dry density and water content of several samples produced from two blocks were measured.

The purpose of this study is to monitor the pesticide residues in frozen fruits and vegetables distributed and sold in online and offline markets in Korea. For the study, 107 samples of 34 types of frozen fruits and vegetables were examined, and a total of 341 pesticide residues were analyzed by using multiclass pesticide multiresidue methods of the Korean Food Code. As a result, pesticide residues were detected from 16 of 64 frozen fruits samples and 15 of 43 frozen vegetables samples. Conclusively, residues were detected from 31 samples in total, showing a detection rate of 29.0%. Specifically, pyridaben exceeded the Maximum Residue Limits (MRLs) based on the Positive list system (PLS) in one of the frozen radish leaves, and the violation rate was 0.9%. Detection on frozen fruits and vegetables was made 23 times for 11 types and 36 times for 21 types. In total, 28 types of pesticide residues were detected 59 times. Fungicides were detected the most in frozen fruits, while insecticides were detected the most in frozen vegetables. The most detected pesticides were the insecticide, acaricide chlorfenapyr (5) and the fungicide boscalid (5). Chlorfenapyr was detected only in frozen vegetables, and boscalid was detected in frozen fruits except one.

본 연구에서는 총각무에 검출빈도가 높은 3성분의 농약 을 선택하여 농약 침지 후 잔류농약이 총각김치제조 과정에서 제거되는 정도를 측정하였다. 총각무의 절임과 세척 과정 후에는 초기 처리농도에 대비 잎은 diazinon, diniconazole 및 dimethomorph 각각 43.8%, 41.9% 및 89.8%가 제거되었으며, 뿌리는 59.5%, 54.7% 및 85.1%가 제거되었다. 4oC에서 김치를 숙성하는 과정 중 농약의 잔류량은 4주간의 숙성기간 동안 초기 처리농도 대비 잎은 diazinon 82.4%, diniconazole 77.1% 그리고 dimethomorph 98.9%가 제거되었고, 뿌리의 경우 diazinon 94.0%, diniconazole 91.8% 그리고 dimethomorph 90.0%가 제거되었다. 총각김치 잔류농약 제거율을 제조과정별 상대적인 백분율로 나타낸 결과 절임과정에서 가장 많은 잔류농약 제거율을 보였으며, 그 결과 농약 3종은 44.6%-66.5%가 제거되었다. 반면 뿌리에서 diazinon, diniconazole은 숙성과정에서 51.8%-55.8%로 가장 많은 잔류농약 제거율을 보였다. 3종의 농약이 잔류하는 김치를 0oC, 4oC에서 4주간 숙성시키면서 온도에 따른 농약제거율의 차이를 살펴본 결과, diazinon은 뿌리에서 4oC가 0oC에 비해 농약제거율이 2.7%-10.8%가 높은 것으로 확인되었다. 그 이외의 농약에서는 숙성온도 별 잔류농약 제거율의 차이는 미미한 것으로 확인되었다.

본 연구에서는 건조농산물의 잔류농약 안전성 조사를 위해 경기도에서 유통되는 건조농산물 110건을 수거하여 다종농약다성분 분석법으로 잔류농약 263종을 검사하였다. 조사 결과 총 10건의 시료에서 10종의 잔류농약이 검출되었으며, 건피마자에서 chlorpyrifos, 건취나물에서 chlorpyrifos, hexaconazole, pyridalyl, 무시래기에서 diniconazole, isoprothiolane, lufenuron, 건곤드레에서 hexaconazole, 건고춧 잎에서 bifenthrin, chlorothalonil, boscalid, pyraclostrobin이 각각 검출되었다. 검출률은 9.1%였으며, 이 중 건피마자 1 건에서 chlorpyrifos가 잔류허용기준을 초과하였고 부적합률은 0.9%로 나타났다. 유효성 검증 실험 결과 검출한계(LOD)는 0.002~0.027 mg/kg, 정량한계(LOQ)는 0.006~0.083 mg/kg 으로 나타났으며, 결정계수(R2)는 0.9964~1.0000으로 나타났다. 회수율은 전반적으로 74.8~118.9%로 나타났다. 2019 년부터 PLS가 시행됨에 따라 농산물에 대한 안전성 관리가 강화되었지만 건조농산물의 경우 잔류허용기준이 미비 하기 때문에 본 연구는 향후 기준 설정을 위한 기초자료로 제공하고자 한다.

증발산은 순복사 에너지를 사용하여 잠열의 형태로 수증기를 대기 중으로 수송함으로써 지구에너지 순환에 있어 중요한 요소 중의 하나이며, 증발산량은 지표유출의 두 배 정도로서 지구 물 수지에서 차지하는 비중이 매우 크다. 증발산의 지상관측은 지점에 국한되기 때문에 공간연속면 상에서의 증발산량 산출을 위하여 격자형 기상자료와 위성자료를 이용한 모델링이 오랫동안 이루어져왔다. PM(Penman-Monteith) 방정식에 기초한 METRIC(Mapping Evapotranspiration with Internalized Calibration) 모델이나 PT(Priestley-Taylor) 방정식을 이용한 MS-PT(Modified Satellite-based Priestley-Taylor) 모델 등이 주로 사용되어 왔으나, 또 하나의 대안으로서 본 연구에서는 최근 부각되고 있는 딥러닝 기법인 DNN(deep neural network)을 이용한 증발산 모델링을 수행하였다. 은닉층 구조, 손실함수, 옵티마이저, 활성화함수, L1/L2 정규화, 드롭아웃 비율 등의 최적화 과정을 거쳐서 수립한 DNN 모델은 RMSE = 0.326mm/day, 상관계수 = 0.975의 매우 양호한 정확도를 나타내었다. 이는 DNN 최적화와 함께, 국지예보모델과 위성자료로부터 증발산 기작에 관여하는 인자들을 선택하여 입력자료로 적절히 사용하였기 때문이기도 하다. 향후과제로서 훈련자료의 종류와 양을 증가시켜서 DNN 모델을 보다 정교화하는 것은 반드시 필요하다고 사료된다.