• KIPS Transactions on Computer and Communication Systems
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• v.10 no.11
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• pp.305-310
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• 2021

Artificial intelligence technology in the medical field initially focused on analysis and algorithm development, but it is gradually changing to web application development for service as a product. This paper describes a Urinary Stone segmentation model in abdominal CT images and an artificial intelligence web application based on it. To implement this, a model was developed using U-Net, a fully-convolutional network-based model of the end-to-end method proposed for the purpose of image segmentation in the medical imaging field. And for web service development, it was developed based on AWS cloud using a Python-based micro web framework called Flask. Finally, the result predicted by the urolithiasis segmentation model by model serving is shown as the result of performing the AI web application service. We expect that our proposed AI web application service will be utilized for screening test.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1691-1707
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• 2022

Cheonji Lake, the caldera of Baekdu Mountain, located on the border of the Korean Peninsula and China, alternates between melting and freezing seasonally. There is a magma chamber beneath Cheonji, and variations in the magma chamber cause volcanic antecedents such as changes in the temperature and water pressure of hot spring water. Consequently, there is an abnormal region in Cheonji where ice melts quicker than in other areas, freezes late even during the freezing period, and has a high-temperature water surface. The abnormal area is a discharge region for hot spring water, and its ice gradient may be used to monitor volcanic activity. However, due to geographical, political and spatial issues, periodic observation of abnormal regions of Cheonji is limited. In this study, the degree of ice change in the optimal region was quantified using a Landsat -5/-7/-8 optical satellite image and a Modified U-Net regression model. From January 22, 1985 to December 8, 2020, the Visible and Near Infrared (VNIR) band of 83 Landsat images including anomalous regions was utilized. Using the relative spectral reflectance of water and ice in the VNIR band, unique data were generated for quantitative ice variability monitoring. To preserve as much information as possible from the visible and near-infrared bands, ice gradient was noticed by applying it to U-Net with two encoders, achieving good prediction accuracy with a Root Mean Square Error (RMSE) of 140 and a correlation value of 0.9968. Since the ice change value can be seen with high precision from Landsat images using Modified U-Net in the future may be utilized as one of the methods to monitor Baekdu Mountain's volcanic activity, and a more specific volcano monitoring system can be built.

• Journal of Broadcast Engineering
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• v.24 no.4
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• pp.592-601
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• 2019

For coronary procedures, X-ray angiogram images are useful for diagnosing and assisting procedures. It is challenging to accurately segment a coronary artery using only a single segmentation model in 2D X-ray images due to a complex structure of three-dimensional coronary artery, especially from phenomenon of vessels being broken in the middle or end of coronary artery. In order to solve these problems, the initial segmentation is performed using an existing single model, and the candidate regions for the sophisticate correction is estimated based on the initial segment, and the local patch-based correction is performed in the candidate regions. Through this research, not only the broken coronary arteries are re-connected, but also the distal part of coronary artery that is very thin is additionally correctly found. Further, the performance can be much improved by combining the proposed correction method with any existing coronary artery segmentation method. In this paper, the U-net, a fully convolutional network was chosen as a segmentation method and the proposed correction method was combined with U-net to demonstrate a significant improvement in performance through X-ray images from several patients.

• Nuclear Engineering and Technology
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• v.51 no.1
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• pp.170-175
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• 2019

Displacement cross-sections for an advanced assessment of radiation damage rates were obtained for a number of materials using the arc-dpa model at neutron incident energies from $10^{-5}eV$ to 10 GeV. Evaluated data files, CEM03 and ECIS codes, and an approximate approach were applied for the calculation of recoil energy distributions in neutron induced reactions. Three sets of displacement cross-sections based on the use of low-energy data from JEFF-3.3, ENDF/B-VIII.0, and JENDL-4.0u were prepared. Files contain also cross-sections calculated using the standard NRT model. Special efforts were made to estimate the uncertainty of obtained displacement cross-sections.

• Nuclear Engineering and Technology
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• v.56 no.5
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• pp.1895-1901
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• 2024

The fission model, G4ParaFissionModel, was enhanced in this study, mainly focusing on refining the energy dependence of the peak-to-valley ratio in the mass distribution and the energy dependence of the average total kinetic energy (TKE). The enhanced model was employed to investigate the characteristics of fission products from ²³⁵U(n, f) reaction. The calculated results, including fission yield, TKE distribution, prompt fission neutron and gamma spectra, were compared with both evaluated and experimental data. The comparison shows that these physical observables related nuclear data, which are of importance for developments of the nuclear power and physics, can be reasonably well reproduced.

• Nuclear Engineering and Technology
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• v.41 no.8
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• pp.1109-1114
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• 2009

The evolution of a high burnup structure (HBS) in a light water reactor (LWR) $UO_2$ fuel was simulated using the Potts model. A simulation system for the Potts model was defined as a two-dimensional triangular lattice, for which the stored energy was calculated from both the irradiation damage of the $UO_2$ matrix and the formation of a grain boundary in the newly recrystallized small HBS grains. In the simulation, the evolution probability of the HBS is calculated by the system energy difference between before and after the Monte Carlo simulation step. The simulated local threshold burnup for the HBS formation was 62 MWd/kgU, consistent with the observed threshold burnup range of 60-80 MWd/kgU. The simulation revealed that the HBS was heterogeneously nucleated on the intergranular bubbles in the proximity of the threshold burnup and then additionally on the intragranular bubbles for a burnup above 86 MWd/kgU. In addition, the simulation carried out under a condition of no bubbles indicated that the bubbles played an important role in lowering the threshold burnup for the HBS formation, thereby enabling the HBS to be observed in the burnup range of conventional high burnup fuels.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.100-108
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• 2023

In this study, a neural network model inspired by a one-dimensional convolution U-net is developed to automatically accelerate edge localized mode (ELM) detection from big diagnostic data of fusion devices and increase the detection accuracy regardless of the hyperparameter setting. This model recognizes the input signal patterns and overcomes the problems of existing detection algorithms, such as the prominence algorithm and those of differential methods with high sensitivity for the threshold and signal intensity. To train the model, 10 sets of discharge radiation data from the KSTAR are used and sliced into 11091 inputs of length 12 ms, of which 20% are used for validation. According to the receiver operating characteristic curves, our model shows a positive prediction rate and a true prediction rate of approximately 90% each, which is comparable to the best detection performance afforded by other algorithms using their optimized hyperparameters. The accurate and automatic ELM-burst detection methodology used in our model can be beneficial for determining plasma properties, such as the ELM frequency from big data measured in multiple experiments using machines from the KSTAR device and ITER. Additionally, it is applicable to feature detection in the time-series data of other engineering fields.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2867-2873
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• 2020

In reprocessed uranium, derived from an impoverished fuel of light-water moderated reactors, there are isotopes of ^{232, 234, 236}U, which make its recycling remarkably difficult. A method of concentration of ²³⁵U target isotope in cascade's additional product was proposed to recover the isotopic composition of reprocessed uranium. A general calculation procedure is presented and a parameters' optimization of multi-flow cascades with additional products. For the first time a numeric model of a cascade that uses the cuts of partial flows of stages with relatively high separation factors was applied in this procedure. A novel computing experiment is carried out on separation of reprocessed uranium hexafluoride with providing a high concentration of ²³⁵U in cascade's additional product with subsequent dilution. The parameters of cascades' stages are determined so as to allow reducing the ^{232, 234, 236}U isotope content up to the acceptable. It was demonstrated that the dilution of selected products by the natural waste makes it possible to receive a low enriched uranium hexafluoride that meets the ASTM C996-15 specification for commercial grade.

• Nuclear Engineering and Technology
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• v.52 no.7
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• pp.1471-1480
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• 2020

This research presents a case study on the remediation of a radioactive waste (uranium: U) utilizing a multi-objective fuzzy optimization in an electrocoagulation process for the iron-stainless steel and aluminum-stainless steel anode/cathode systems. The incorporation of the cumulative uncertainty of result, operational cost and energy consumption are essential key elements in determining the feasibility of the developed model equations in satisfying specific maximum contaminant level (MCL) required by stringent environmental regulations worldwide. Pareto-optimal solutions showed that the iron system (0 ㎍/L U: 492 USD/g-U) outperformed the aluminum system (96 ㎍/L U: 747 USD/g-U) in terms of the retained uranium concentration and energy consumption. Thus, the iron system was further carried out in a multi-objective analysis due to its feasibility in satisfying various uranium standard regulatory limits. Based on the 30 ㎍/L MCL, the decision-making process via fuzzy logic showed an overall satisfaction of 6.1% at a treatment time and current density of 101.6 min and 59.9 mA/㎠, respectively. The fuzzy optimal solution reveals the following: uranium concentration - 5 ㎍/L, cumulative uncertainty - 25 ㎍/L, energy consumption - 461.7 kWh/g-U and operational cost based on electricity cost in the United States - 60.0 USD/g-U, South Korea - 55.4 USD/g-U and Finland - 78.5 USD/g-U.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.239-247
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• 2022

Cities are becoming more complex due to rapid industrialization and population growth in modern times. In particular, urban areas are rapidly changing due to housing site development, reconstruction, and demolition. Thus accurate road information is necessary for various purposes, such as High Definition Map for autonomous car driving. In the case of the Republic of Korea, accurate spatial information can be generated by making a map through the existing map production process. However, targeting a large area is limited due to time and money. Road, one of the map elements, is a hub and essential means of transportation that provides many different resources for human civilization. Therefore, it is essential to update road information accurately and quickly. This study uses Semantic Segmentation algorithms Such as LinkNet, D-LinkNet, and NL-LinkNet to extract roads from drone images and then apply hyperparameter optimization to models with the highest performance. As a result, the LinkNet model using pre-trained ResNet-34 as the encoder achieved 85.125 mIoU. Subsequent studies should focus on comparing the results of this study with those of studies using state-of-the-art object detection algorithms or semi-supervised learning-based Semantic Segmentation techniques. The results of this study can be applied to improve the speed of the existing map update process.