• Proceedings of the Korean Nuclear Society Conference
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• 1995.10a
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• pp.29-34
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• 1995

The good features of the analytic function expansion nodal (AFEN) method are utilized to develop a practical scheme jot the multigroup diffusion problems, in combination with the polynomial expansion nodal (PEN) method. The thermal group fluxes exhibiting strong gradients are solved by the AFEN method[1-6], while the fast group fluxes that are smoother than the thermal group fuzes are solved by the PEN method[7-9]. The scheme is applied to a MOX-fuel loaded core with good results.

• Nuclear Engineering and Technology
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• v.49 no.3
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• pp.495-503
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• 2017

Operators of nuclear power plants may not be equipped with sufficient information during a loss-of-coolant accident (LOCA), which can be fatal, or they may not have sufficient time to analyze the information they do have, even if this information is adequate. It is not easy to predict the progression of LOCAs in nuclear power plants. Therefore, accurate information on the LOCA break position and size should be provided to efficiently manage the accident. In this paper, the LOCA break size is predicted using a cascaded fuzzy neural network (CFNN) model. The input data of the CFNN model are the time-integrated values of each measurement signal for an initial short-time interval after a reactor scram. The training of the CFNN model is accomplished by a hybrid method combined with a genetic algorithm and a least squares method. As a result, LOCA break size is estimated exactly by the proposed CFNN model.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4072-4079
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• 2021

This article presents a study on the state-of-the-art methods for automated radioactive material detection and identification, using gamma-ray spectra and modern machine learning methods. The recent developments inspired this in deep learning algorithms, and the proposed method provided better performance than the current state-of-the-art models. Machine learning models such as: fully connected, recurrent, convolutional, and gradient boosted decision trees, are applied under a wide variety of testing conditions, and their advantage and disadvantage are discussed. Furthermore, a hybrid model is developed by combining the fully-connected and convolutional neural network, which shows the best performance among the different machine learning models. These improvements are represented by the model's test performance metric (i.e., F1 score) of 93.33% with an improvement of 2%-12% than the state-of-the-art model at various conditions. The experimental results show that fusion of classical neural networks and modern deep learning architecture is a suitable choice for interpreting gamma spectra data where real-time and remote detection is necessary.

• Journal of Radiation Protection and Research
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• v.49 no.1
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• pp.1-18
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• 2024

Exponential growth has been observed in nuclear medicine procedures worldwide in the past decades. The considerable increase is attributed to the advance of positron emission tomography and single photon emission computed tomography, as well as the introduction of new radiopharmaceuticals. Although nuclear medicine procedures provide undisputable diagnostic and therapeutic benefits to patients, the substantial increase in radiation exposure to nuclear medicine patients raises concerns about potential adverse health effects and calls for the urgent need to monitor exposure levels. In the current article, model-based internal dosimetry methods were reviewed, focusing on Medical Internal Radiation Dose (MIRD) formalism, biokinetic data, human anatomy models (stylized, voxel, and hybrid computational human phantoms), and energy spectrum data of radionuclides. Key results from many articles on nuclear medicine dosimetry and comparisons of dosimetry quantities based on different types of human anatomy models were summarized. Key characteristics of seven model-based dose calculation tools were tabulated and discussed, including dose quantities, computational human phantoms used for dose calculations, decay data for radionuclides, biokinetic data, and user interface. Lastly, future research needs in nuclear medicine dosimetry were discussed. Model-based internal dosimetry methods were reviewed focusing on MIRD formalism, biokinetic data, human anatomy models, and energy spectrum data of radionuclides. Future research should focus on updating biokinetic data, revising energy transfer quantities for alimentary and gastrointestinal tracts, accounting for body size in nuclear medicine dosimetry, and recalculating dose coefficients based on the latest biokinetic and energy transfer data.

• Journal of Energy Engineering
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• v.26 no.3
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• pp.71-77
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• 2017

In the event of loss of coolant accident (LOCA) and station black out (SBO) in the primary system of a nuclear reactor, the coolant water should be injected to reactor coolant system (RCS) without any intervention of operators or active components. To satisfy the requirements, hybrid safety injection tank (Hybrid SIT) was suggested by Korea Atomic Energy Research Institute (KAERI). The pressure equalizing time of Hybrid SIT is an important parameter to determine the timing of coolant injection. To predict the pressure equalizing time of the Hybrid SIT, a separate effect test facility was constructed and sensitivity tests were conducted in various conditions. The main parameter determining the pressure equalizing time was obtained from separate effect test (SET) results. The wall of condensation on the inner wall of SIT and direct contact condensation on the water surface affected to the pressure equalizing time very much. In this study, the effect of each condensation phenomena on pressure equalizing time was quantitatively analyzed from results of SET and a prediction method of pressure equalizing time was proposed.

• Journal of Aquaculture
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• v.11 no.1
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• pp.77-81
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• 1998

Cytogenetic analyses were carried out with induced hybrid between common carp (Cyprinus carpio) female and crucian carp (Carassius auratus) male. The erythrocytic measurement revealed that cellular and nuclear size of induced hybrids were intermediate between those of paremtal species. The modal chromosome numbers of common carp, crucian carp and its hybid were same as 2n=100. The DNA content of induced hybrids determined based on flow cytometry was 3.7pg/cell which corresponding to intermediate value between the carp (3.6ph/cell) and crucian carp (3.8pg/cell)

• Advances in Computational Design
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• v.8 no.1
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• pp.37-59
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• 2023

Squat reinforced concrete (SRC) shear walls are a critical part of the structure for both office/residential buildings and nuclear structures due to their significant role in withstanding seismic loads. Despite this, empirical formulae in current design standards and published studies demonstrate a considerable disparity in predicting SRC wall shear strength. The goal of this research is to develop and evaluate hybrid and ensemble artificial neural network (ANN) models. State-of-the-art population-based algorithms are used in this research for hybrid intelligence algorithms. Six models are developed, including Honey Badger Algorithm (HBA) with ANN (HBA-ANN), Hunger Games Search with ANN (HGS-ANN), fitness-distance balance coyote optimization algorithm (FDB-COA) with ANN (FDB-COA-ANN), Averaging Ensemble (AE) neural network, Snapshot Ensemble (SE) neural network, and Stacked Generalization (SG) ensemble neural network. A total of 434 test results of SRC walls is utilized to train and assess the models. The results reveal that the SG model not only minimizes prediction variance but also produces predictions (with R²= 0.99) that are superior to other models.

• Nuclear Engineering and Technology
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• v.28 no.5
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• pp.488-499
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• 1996

We have extended the source projection analytic nodal discrete ordinates method (SPANDOM) for more flexible applicability in analysis of hexagonal assembly cores. The method (SPANDOM-FH) does not invoke transverse integration but instead solves the discrete ordinates equation analytically after the source term is projected and represented in hybrid form of high-order polynomials and exponential functions. SPANDOM-FH which treats a hexagonal node as one node is applied to two fast reactor benchmark problems and compared with TWOHEX. The results of comparison indicate that the present method SPANDOM-FH predicts accurately $k_eff$ and flux distributions in hexagonal assembly cores. In addition, SPANDOM-FH gives the continuous two dimensional intranodal scalar flux distributions in a hexagonal node. The reentering models between TWOHEX and SPANDOM were also compared and it was confirmed that SPANDOM's model is more realistic. Through the results of benchmark problems, we conclude that SPANDOM-FH has the sufficient accuracy for the nuclear design of fast breeder reactor (FBR) cores with hexagonal assemblies.

• Nuclear Medicine and Molecular Imaging
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• v.43 no.3
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• pp.222-228
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• 2009

Risk stratification and assessment of prognosis in patients with known or suspected CAD is of crucial important for the practice of contemporary medicine. Noninvasive testing such as myocardial perfusion scintigraphy, coronary artery calcium scoring or CT coronary angiography is increasingly being used to determine the need for aggressive medical therapy and to select patients for catheterization. The integrated anatomic and functional information may provide more additional information for the cardiologist or other clinician by the improved risk stratification and diagnostic accuracy of integrated techniques. The development of SPECT/CT or PET/CT hybrid systems is therefore of important value for the nuclear cardiology.

• Nuclear Engineering and Technology
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• v.30 no.3
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• pp.222-234
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• 1998

The Westinghouse OT$\Delta$T DNB protection logic heavily restricts the operation region by applying the same logic for a full range of operating pressure in order to maintain its simplicity. In this work, a fuzzy neural network method is used to estimate the DNB protection limit using the measured average temperature and pressure of a reactor core. Fuzzy system parameters are optimized by a hybrid learning method. This algorithm uses a gradient descent algorithm to optimize the antecedent parameters and a least-squares algorithm to solve the consequent parameters. The proposed method is applied to Yonggwang 3&4 nuclear power plants and the proposed method has 5.99 percent larger thermal margin than the conventional OT$\Delta$T trip logic. This simple algorithm provides a good information for the nuclear power plant operation and diagnosis by estimating the DNB protection limit each time step.