• Nuclear Engineering and Technology
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• 제54권12호
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• pp.4671-4678
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• 2022

The purpose of this comprehensive research is to observe the impact of scintillator crystal type on entire detection process. For this aim, MCNPX (version 2.6.0) is used for designing of a physical plastic scintillation detector available in our laboratory. The modelled detector structure is validated using previous studies in the literature. Next, different types of plastic scintillation crystals were assessed in the same geometry. Several fundamental detector properties are determined for six different plastic scintillation crystals. Additionally, the deposited energy quantities were computed using the MCNPX code. Although six scintillation crystals have comparable compositions, the findings clearly indicate that the crystal composed of PVT 80% + PPO 20% has superior counting and detecting characteristics when compared to the other crystals investigated. Moreover, it is observed that the highest deposited energy amount, which is a result of the highest collision number in the crystal volume, corresponds to a PVT 80% + PPO 20% crystal. Despite the fact that plastic detector crystals have similar chemical structures, this study found that performing advanced Monte Carlo simulations on the detection discrepancies within the structures can aid in the development of the most effective spectroscopy procedures by ensuring maximum efficiency prior to and during use.

• Nuclear Engineering and Technology
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• 제54권12호
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• pp.4809-4818
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• 2022

The decommissioning project of a nuclear facility is a large-scale process that is expected to take about 15 years or longer. The range of risks to be considered is large and complex, then, it is expected that various risks will arise in decision-making by area during the project. Therefore, in this study, the risk family derived from the Decommissioning Risk Management (DRiMa) project was reconstructed into a decommissioning project risk profile suitable for the Kori Unit 1. Two criteria of uncertainty and importance are considered in order to prioritize the selected 26 risks of decommissioning project. The uncertainty is scored according to the relevant laws and decommissioning plan preparation guidelines, and the project importance is scored according to the degree to which it primarily affects the triple constraints of the project. The results of risks are divided into high, medium, and low. Among them, 10 risks are identified as medium level and 16 risks are identified as low level. 10 risks, which are medium levels, are classified in five categories: End state of decommissioning project, Management of waste and materials, Decommissioning strategy and technology, Legal and regulatory framework, and Safety. This study is a preliminary assessment of the risk of the decommissioning project that could be considered in the preparation stage. Therefore, we expect that the project risks considered in this study can be used as an initial data for reevaluation by reflecting the detail project progress in future studies.

• Nuclear Engineering and Technology
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• 제55권4호
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• pp.1485-1494
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• 2023

The bow of the nuclear fuel assembly is a well-known phenomenon. One of the vital criteria during the history of nuclear fuel development has been fuel assembly's mechanical stability. Once present, the fuel assembly bow can lead to safety issues like excessive water gap and power redistribution or even incomplete rod insertion (IRI). The extensive bow can result in assembly handling and loading problems. This is why the fuel assembly's bow is one of the most often controlled geometrical factors during periodic fuel inspections for VVER when compared e.g. to on-site fuel rod gap measurements or other instrumental measurements performed on-site. Our proposed screening method uses existing video records for fuel inspection. We establish video frames normalization and aggregation for the purposes of bow measurement. The whole process is done by digital image processing algorithms which analyze rotations of video frames, extract angles whose source is the fuel set torsion, and reconstruct torsion schema. This approach provides results comparable to the commonly utilized method. We tested this new approach in real operation on 19 fuel assemblies with different campaign numbers and designs, where the average deviation from other methods was less than 2 % on average. Due to the fact, that the method has not yet been validated during full scale measurements of the fuel inspection, the preliminary results stand for that we recommend this method as a complementary part of standard bow measurement procedures to increase measurement robustness, lower time consumption and preserve or increase accuracy. After completed validation it is expected that the proposed method allows standalone fuel assembly bow measurements.

• 디지털융복합연구
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• 제20권3호
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• pp.265-273
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• 2022

본 연구는 COVID-19 팬데믹 하에서 사회적경제기업의 상황을 살펴보고 미래과제를 제시하는데 있다. 2019년말 시작된 COVID-19 팬데믹이 현재까지 건강, 경제, 사회, 네트워킹 등 우리 사회의 다양한 영역에 영향을 미치고 삶을 황폐하게 하고 있다. 바이러스가 호흡기를 통해 전염되는 것으로, 사람 중심의 사회적경제에 장애요인으로서 작용될 것이며, 주요하게는 재정적인 부분에서 기업가들의 활동을 규제하는 모양새로 나타나고 있다. 다만, 팬데믹이 지속되는 과정 속에서도 사회적경제의 규모는 확대되었으며, 청소위생 및 보건업을 중심으로 경제적인 성과와 함께 지역사회 속에서 사회적 성과를 달성하고 있다는 것에서 호혜와 협력에 기반한 사회적경제기업의 협업을 통해 COVID-19 이라는 문제를 해결하고 있다는 것을 확인할 수 있다. 사회적경제의 건강한 생태계 조성을 위해서는 COVID-19에 일차원적으로 대응하기 보다는 경제사회적 안전망 구축차원에서 사회적경제의 지속가능성을 담보할 수 있는 보편적이고 구체적인 미래 과제를 선정, 추진해야 할 것이다.

• Nuclear Engineering and Technology
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• 제55권6호
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• pp.2256-2262
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• 2023

The electrochemical corrosion behaviors of SA106 Grade B (SA106B) carbon steel in H₂SO₄4-N₂H₄ and H₂SO₄-N₂H₄-CuSO₄ solutions at 95 ℃ have been investigated with the addition of commercial corrosion inhibitors (CI#30 and No. 570S), to determine the stability of SA106B in the hydrazine-based reductive metal ion decontamination (HyBRID) process. The potentiodynamic polarization experiment revealed that the corrosion inhibitors were capable of lowering the corrosion rate of SA106B in H₂SO₄-N₂H₄ solution. It was found that the corrosion inhibitors induced formation of fixed surface layer on the carbon steel upon the corrosion. This corrosion inhibition performance was reduced in the presence of CuSO₄ in the solution owing to the chemical reactions between organic compounds in the corrosion inhibitors and CuSO₄. CI#30 showed a better corrosion inhibition effect in the H₂SO₄-N₂H₄-CuSO₄ solution. Although the corrosion inhibitors can provide better stability to SA106B in the HyBRID solution, their application should be carefully considered because it may result in reduced decontamination performance and increased secondary waste generation.

• Nuclear Engineering and Technology
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• 제55권6호
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• pp.2096-2106
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• 2023

Rotating machinery is widely applied in important equipment of nuclear power plants (NPPs), such as pumps and valves. The research on intelligent fault diagnosis of rotating machinery is crucial to ensure the safe operation of related equipment in NPPs. However, in practical applications, data-driven fault diagnosis faces the problem of small and imbalanced samples, resulting in low model training efficiency and poor generalization performance. Therefore, a deep convolutional conditional generative adversarial network (DCCGAN) is constructed to mitigate the impact of imbalanced samples on fault diagnosis. First, a conditional generative adversarial model is designed based on convolutional neural networks to effectively augment imbalanced samples. The original sample features can be effectively extracted by the model based on conditional generative adversarial strategy and appropriate number of filters. In addition, high-quality generated samples are ensured through the visualization of model training process and samples features. Then, a deep convolutional neural network (DCNN) is designed to extract features of mixed samples and implement intelligent fault diagnosis. Finally, based on multi-fault experimental data of motor and bearing, the performance of DCCGAN model for data augmentation and intelligent fault diagnosis is verified. The proposed method effectively alleviates the problem of imbalanced samples, and shows its application value in intelligent fault diagnosis of actual NPPs.

• Nuclear Engineering and Technology
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• 제55권5호
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• pp.1644-1650
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• 2023

In spent fuel reprocessing, UO₂(DBP)₂ (U-DBP) can be deposited in stainless steel equipment. U-DBP must be removed by dissolution and the process must not cause corrosion to stainless steel. This study was conducted to find the best scheme for dissolution. U-DBP was manufactured by the titrimetric sedimentation method. The effects of different factors on the dissolution of U-DBP were investigated. For example, solid-liquid ratio, hydrazine carbonate solutions with different mass components, mixed solutions containing different concentrations of H₂O₂, and different carbonates. The results indicated that U-DBP does not have a regular crystal morphology. With the increase of the solid-liquid ratio and the mass fraction of hydrazine carbonate, the concentration of U(VI) at the dissolution equilibrium increases gradually. The addition of H₂O₂ has a great promotion effect on the dissolution. However, when the concentration of H₂O₂ is greater than 0.5 M, the dissolution solution may have an erosive effect on the stainless steel. (NH₄)₂CO₃ can increase the dissolution capacity of dissolved U-DBP, but it may also accelerate the corrosion of stainless steel.

• Nuclear Engineering and Technology
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• 제55권7호
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• pp.2349-2355
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• 2023

In the TRITIUM project, an on-site monitoring system is being developed to measure tritium (³H) levels in water near nuclear power plants. The quite low-energy betas emitted by ³H have a very short average path in water (5 ㎛ as shown by simulations for 18 keV electrons). This path would be further reduced by impurities present in the water, resulting in a significant reduction of the detection efficiency. Therefore, one of the essential requirements of the project is the elimination of these impurities through a filtration process and the removal of salts in solution. This paper describes a water treatment system developed for the project that meets the following requirements: the water produced should be of near-pure water quality according to ISO 3696 grade 3 standard (conductivity < 10 µS/cm); the system should operate autonomously and be remotely monitored.

• 대한임베디드공학회논문지
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• 제18권6호
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• pp.267-275
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• 2023

This paper presents the development of specialized software for annotating volume-of-interest on ¹⁸F-FDG PET/CT images with the goal of facilitating the studies and diagnosis of head and neck cancer (HNC). To achieve an efficient annotation process, we employed the SE-Norm-Residual Layer-based U-Net model. This model exhibited outstanding proficiency to segment cancerous regions within ¹⁸F-FDG PET/CT scans of HNC cases. Manual annotation function was also integrated, allowing researchers and clinicians to validate and refine annotations based on dataset characteristics. Workspace has a display with fusion of both PET and CT images, providing enhance user convenience through simultaneous visualization. The performance of deeplearning model was validated using a Hecktor 2021 dataset, and subsequently developed semi-automatic annotation functionalities. We began by performing image preprocessing including resampling, normalization, and co-registration, followed by an evaluation of the deep learning model performance. This model was integrated into the software, serving as an initial automatic segmentation step. Users can manually refine pre-segmented regions to correct false positives and false negatives. Annotation images are subsequently saved along with their corresponding ¹⁸F-FDG PET/CT fusion images, enabling their application across various domains. In this study, we developed a semi-automatic annotation software designed for efficiently generating annotated lesion images, with applications in HNC research and diagnosis. The findings indicated that this software surpasses conventional tools, particularly in the context of HNC-specific annotation with ¹⁸F-FDG PET/CT data. Consequently, developed software offers a robust solution for producing annotated datasets, driving advances in the studies and diagnosis of HNC.

• 대한의용생체공학회:의공학회지
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• 제45권2호
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• pp.90-94
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• 2024

Diagnosing wounds presents a significant challenge in clinical settings due to its complexity and the subjective assessments by clinicians. Wound deep learning algorithms quantitatively assess wounds, overcoming these challenges. However, a limitation in existing research is reliance on specific datasets. To address this limitation, we created a comprehensive dataset by combining open dataset with self-produced dataset to enhance clinical applicability. In the annotation process, machine learning based on Gradient Vector Flow (GVF) was utilized to improve objectivity and efficiency over time. Furthermore, the deep learning model was equipped U-net with residual blocks. Significant improvements were observed using the input dataset with images cropped to contain only the wound region of interest (ROI), as opposed to original sized dataset. As a result, the Dice score remarkably increased from 0.80 using the original dataset to 0.89 using the wound ROI crop dataset. This study highlights the need for diverse research using comprehensive datasets. In future study, we aim to further enhance and diversify our dataset to encompass different environments and ethnicities.