• Nuclear Engineering and Technology
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• v.51 no.1
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• pp.163-169
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• 2019

The robust controller synthesis and analysis of the water level process in the U-tube system generator (UTSG) is addressed in this paper. The parameter uncertainties of the steam generator (SG) are modeled as multiplicative perturbations which are normalized by designing suitable weighting functions. The relative errors of the nominal SG model with respect to the other operating power level models are employed to specify the weighting functions for normalizing the plant uncertainties. Then, a robust controller is designed based on ${\mu}$-synthesis and D-K iteration, and its stability robustness is verified over the whole range of power operations. A gain-scheduled controller with $H_{\infty}$-synthesis is also designed to compare its robustness with the proposed controller. The stability analysis is accomplished and compared with the previous QFT design. The ${\mu}$-analysis of the system shows that the proposed controller has a favorable stability robustness for the whole range of operating power conditions. The proposed controller response is simulated against the power level deviation in start-up and shutdown stages and compared with the other concerning controllers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2191-2208
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• 2022

Motion blur in PET (Positron emission tomography) images induced by respiratory motion will reduce the quality of imaging. Although exiting methods have positive performance for respiratory motion correction in medical practice, there are still many aspects that can be improved. In this paper, an improved 3D unsupervised framework, Res-Voxel based on U-Net network was proposed for the motion correction. The Res-Voxel with multiple residual structure may improve the ability of predicting deformation field, and use a smaller convolution kernel to reduce the parameters of the model and decrease the amount of computation required. The proposed is tested on the simulated PET imaging data and the clinical data. Experimental results demonstrate that the proposed achieved Dice indices 93.81%, 81.75% and 75.10% on the simulated geometric phantom data, voxel phantom data and the clinical data respectively. It is demonstrated that the proposed method can improve the registration and correction performance of PET image.

• Smart Structures and Systems
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• v.32 no.4
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• pp.219-233
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• 2023

Crack width is an important indicator to evaluate the health condition of the concrete structure. The crack width is measured by manual using crack width gauge commonly, which is time-consuming and laborious. In this paper, we have proposed a fast and simplified crack width quantification method via deep Q learning and geometric calculation. Firstly, the crack edge is extracted by using U-Net network and edge detection operator. Then, the intelligent decision of is made by the deep Q learning model. Further, the geometric calculation method based on endpoint and curvature extreme point detection is proposed. Finally, a case study is carried out to demonstrate the effectiveness of the proposed method, achieving high precision in the real crack width quantification.

• Nuclear Engineering and Technology
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• v.55 no.4
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• pp.1460-1467
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• 2023

A robust approach was conducted to determining the absolute oral bioavailable (f_ab) fractions of ²³⁸U and ²³²Th in rats exposed to contaminated soil along with their hematotoxicity and nephrotoxicity. The soil sample is the International Atomic Energy Agency-312 (IAEA-312) certified reference material, whereas blood, bones, and kidneys of in vivo female Sprague-Dawley (SD) rats estimate ²³⁸U- and ²³²Th-f_ab fractions post-exposure. We predict the bioavailable concentration (C_ab) and f_ab values of ²³⁸U and ²³²Th after acute soil ingestion. The blood ²³⁸U (0.750%) and ²³²Th (0.028%) reach their maximum f_ab values after 48 h. The ²³⁸U (f_ab: 0.169-0.652%) accumulates mostly in the kidney, whereas the ²³²Th (f_ab: 0.004-0.021%) accumulates primarily in the bone. Additionally, ²³⁸U is more bioavailable than ²³²Th. Post 48 h acute ingestion demonstrates noticeable histopathological and hematological alterations, implying that intake of ²³⁸U in co-contaminated soil can lead to erythrocytes and proximal tubules damage, whereas, ²³²Th intake can harm erythrocytes. Our study provides new directions for future research into the health implications of acute oral exposures to ²³⁸U and ²³²Th in co-contaminated soils. The findings offer significant insight into the utilization of in vivo SD rat testing to estimate ²³⁸U and ²³²Th bioavailability and toxicity in exposure assessment.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.111-126
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• 2022

Land use and land cover (LULC) mapping is an important factor in geospatial analysis. Although highly precise ground-based LULC monitoring is possible, it is time consuming and costly. Conversely, because the synthetic aperture radar (SAR) sensor is an all-weather sensor with high resolution, it could replace field-based LULC monitoring systems with low cost and less time requirement. Thus, LULC is one of the major areas in SAR applications. We developed a LULC model using only KOMPSAT-5 single co-polarized data and digital elevation model (DEM) data. Twelve HH-polarized images and 18 VV-polarized images were collected, and two HH-polarized images and four VV-polarized images were selected for the model testing. To train the LULC model, we applied the conditional generative adversarial network (cGAN) method. We used U-Net combined with the residual unit (ResUNet) model to generate the cGAN method. When analyzing the training history at 1732 epochs, the ResUNet model showed a maximum overall accuracy (OA) of 93.89 and a Kappa coefficient of 0.91. The model exhibited high performance in the test datasets with an OA greater than 90. The model accurately distinguished water body areas and showed lower accuracy in wetlands than in the other LULC types. The effect of the DEM on the accuracy of LULC was analyzed. When assessing the accuracy with respect to the incidence angle, owing to the radar shadow caused by the side-looking system of the SAR sensor, the OA tended to decrease as the incidence angle increased. This study is the first to use only KOMPSAT-5 single co-polarized data and deep learning methods to demonstrate the possibility of high-performance LULC monitoring. This study contributes to Earth surface monitoring and the development of deep learning approaches using the KOMPSAT-5 data.

• Nuclear Engineering and Technology
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• v.41 no.2
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• pp.199-214
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• 2009

High burn-up transformation process in low temperature nuclear fuel oxides material was observed in the early sixties in LWR $UO_2$ fuels, but not studied in depth. Increasing progressively the fuel discharge burn-up in PWR power plants, this material transformation was again observed in 1985 and identified as an important process to be accounted for in the fuel simulations due to its expected consequence on fuel heat transfer and therefore on the fission gas release. Fission gas release was one of the major concerns in PWR fuels, mainly during transient or accidents events. The behaviour of such a material in case of rod failure was also an important aspect to analyse. Therefore several national and international programs were launched during the last 25 years to understand the mechanisms leading to the high burn-up structure formation and to evaluate the physical properties of the final material. A large observations database has been acquired, using the more sophisticated techniques available in hot cells. This large database is discussed in this paper, providing basis to build an engineering-model, which is based on phenomenological description data and information accumulated. In addition this paper has the ambition to construct the best logical model to understand restructuring.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2454-2463
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• 2021

The objective of this research is to the use of americium (AmO₂) as a burnable absorber effectively instead of conventional gadolinium (Gd₂O₃) for VVER-1200 reactor by analyzing its impacts on reactivity, power peaking factor (PPF), safety factor, and quality of the spent fuel. The assembly is burned to 60 GWd/t by using SRAC-2006 code and JENDL-4.0 data library for finding the optimum amount and effective way of using AmO₂ as a burnable absorber. From these studies, it is found that AmO₂ can decrease the excess reactivity like Gd₂O₃ without changing the criticality life span and enrichment of ²³⁵U. A homogeneous mixture of the 0.20% AmO₂+ 4.95% enriched UO₂ fuel rod (model MF-4) decreases the PPF than the reference assembly. The use of AmO₂+UO₂ in the integral burnable absorber (IBA) rod or the outer layer could also decrease the PPF up to 10 GWd/t but increases rapidly after 30 GWd/t, which could be a safety threat. The fuel temperature coefficient and void coefficient of the model MF-4 are the same as the reference assembly. In addition, 22% of initially loaded Am are burning effectively and contributing to the power production.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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• pp.265-272
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• 2022

As the risk of lung cancer has increased, early-stage detection and treatment of cancers have received a lot of attention. Among various medical imaging approaches, computer tomography (CT) has been widely utilized to examine the size and growth rate of lung nodules. However, the process of manual examination is a time-consuming task, and it causes physical and mental fatigue for medical professionals. Recently, many computer-aided diagnostic methods have been proposed to reduce the workload of medical professionals. In recent studies, encoder-decoder architectures have shown reliable performances in medical image segmentation, and it is adopted to predict lesion candidates. However, localizing nodules in lung CT images is a challenging problem due to the extremely small sizes and unstructured shapes of nodules. To solve these problems, we utilize atrous spatial pyramid pooling (ASPP) to minimize the loss of information for a general U-Net baseline model to extract rich representations from various receptive fields. Moreover, we propose mixed-up attention mechanism of reverse, boundary and convolutional block attention module (CBAM) to improve the accuracy of segmentation small scale of various shapes. The performance of the proposed model is compared with several previous attention mechanisms on the LIDC-IDRI dataset, and experimental results demonstrate that reverse, boundary, and CBAM (RB-CBAM) are effective in the segmentation of small nodules.

• Journal of Animal Science and Technology
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• v.66 no.1
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• pp.167-177
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• 2024

Iris segmentation is an initial step for identifying the biometrics of animals when establishing a traceability system for livestock. In this study, we propose a deep learning framework for pixel-wise segmentation of bovine iris with a minimized use of annotation labels utilizing the BovineAAEyes80 public dataset. The proposed image segmentation framework encompasses data collection, data preparation, data augmentation selection, training of 15 deep neural network (DNN) models with varying encoder backbones and segmentation decoder DNNs, and evaluation of the models using multiple metrics and graphical segmentation results. This framework aims to provide comprehensive and in-depth information on each model's training and testing outcomes to optimize bovine iris segmentation performance. In the experiment, U-Net with a VGG16 backbone was identified as the optimal combination of encoder and decoder models for the dataset, achieving an accuracy and dice coefficient score of 99.50% and 98.35%, respectively. Notably, the selected model accurately segmented even corrupted images without proper annotation data. This study contributes to the advancement of iris segmentation and the establishment of a reliable DNN training framework.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2082-2102
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• 2024

Accurate segmentation of magnetic resonance (MR) images is crucial for providing doctors with effective quantitative information for diagnosis. However, the presence of weak boundaries, intensity inhomogeneity, and noise in the images poses challenges for segmentation models to achieve optimal results. While deep learning models can offer relatively accurate results, the scarcity of labeled medical imaging data increases the risk of overfitting. To tackle this issue, this paper proposes a novel fuzzy c-means (FCM) model that integrates a deep learning approach. To address the limited accuracy of traditional FCM models, which employ Euclidean distance as a distance measure, we introduce a measurement function based on the skewed normal distribution. This function enables us to capture more precise information about the distribution of the image. Additionally, we construct a regularization term based on the Kullback-Leibler (KL) divergence of high-confidence deep learning results. This regularization term helps enhance the final segmentation accuracy of the model. Moreover, we incorporate orthogonal basis functions to estimate the bias field and integrate it into the improved FCM method. This integration allows our method to simultaneously segment the image and estimate the bias field. The experimental results on both simulated and real brain MR images demonstrate the robustness of our method, highlighting its superiority over other advanced segmentation algorithms.