• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.2785-2796
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• 2023

How to generate the precise broad group cross section is important for the fast reactor design. In this study, a fast reactor multi-group cross-section generation code MGGC2.0 are developed in-house for processing ultrafine group MATXS format library. Validation and verification are performed for MGGC2.0 code by applying the benchmarks of ICSBEP handbook, and the results of MGGC2.0 agree well with that of MCNP. The consistent P_N method with critical buckling search is in good agreement that condensed with TWODANT flux and flux moment for the inner core and outer core region. For the radial blanket and reflector, two region approximation method has been applied in MGGC2.0 by using collision Probability Method neutron flux solver. The RBEC-M benchmark was used to verify the power distribution calculation, and the relative error of power distribution comparison with the reference are less than 0.8% in the fuel region and the maximum relative error is 5.58% in the reflector region. Therefore, the precise broad cross section can be generated by MGGC2.0 for fast reactor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.670-684
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• 2024

3D Cross-Modal Retrieval (3DCMR) is a task that retrieves 3D objects regardless of modalities, such as images, meshes, and point clouds. One of the most prominent methods used for 3DCMR is the Cross-Modal Center Loss Function (CLF) which applies the conventional center loss strategy for 3D cross-modal search and retrieval. Since CLF is based on center loss, the center features in CLF are also susceptible to subtle changes in hyperparameters and external inferences. For instance, performance degradation is observed when the batch size is too small. Furthermore, the Mean Squared Error (MSE) used in CLF is unable to adapt to changes in batch size and is vulnerable to data variations that occur during actual inference due to the use of simple Euclidean distance between multi-modal features. To address the problems that arise from small batch training, we propose a Noisy Center Loss (NCL) method to estimate the optimal center features. In addition, we apply the simple Siamese representation learning method (SimSiam) during optimal center feature estimation to compare projected features, making the proposed method robust to changes in batch size and variations in data. As a result, the proposed approach demonstrates improved performance in ModelNet40 dataset compared to the conventional methods.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2016.05a
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• pp.285-286
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• 2016

We aim to propose the prediction modeling method of ship's position with extracting ship's trajectory model through pattern recognition based on the data that are being collected in VTS centers at real time. Support Vector Machine algorithm was used for data modeling. The optimal parameters are calculated with k-fold cross validation and grid search. We expect that the proposed modeling method could support VTS operators' decision making in case of complex encountering traffic situations.

• The KIPS Transactions:PartD
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• v.10D no.4
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• pp.679-688
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• 2003

Motif databases are used in the function and structure prediction of proteins which appear on new and rapid release of raw data from genome sequencing projects. Recently, the frequency of use about these databases increases continuously. However, existing motif databases were developed and extended independently and were integrated mainly by using a web-based cross-reference, thus these databases have a heterogeneous search result problem, a complex query process problem and a duplicate database entry handling problem. Therefore, in this paper, we suppose physical motif resource integration and describe the integrated search method about a family-based protein prediction for solving above these problems. Finally, we estimate our implementation of the motif integration database and prediction system for predicting protein motifs.

• The Journal of the Acoustical Society of Korea
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• v.20 no.8
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• pp.58-66
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• 2001

In this paper, a computationally efficient time delay and doppler estimation algorithm is proposed for active sonar with Linear Frequency Modulated (LFM) signal. To reduce the computational burden of the conventional estimation algorithm, an algebraic equation is used which represents the relationship between the time delay and doppler in cross-ambiguity function of the LFM signal. The algebraic equation is derived based on the Fast maximum Likelihood (FML) method. Using this algebraic relation, the time delay and doppler are estimated with two 1-D search instead of the conventional 2-D search. The estimation errors of the proposed algorithm are analyzed for various SNR's. The simulation result demonstrates the good performance of the proposed algorithm.

• The Korean Journal of Applied Statistics
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• v.25 no.4
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• pp.669-680
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• 2012

Noise removal of document images is a necessary step during preprocessing to recognize characters effectively because it has influences greatly on processing speed and performance for character recognition. We have considered using the spatial filters such as traditional mean filters and Gaussian filters, and wavelet transformed based methods for noise deduction in natural images. However, these methods are not effective for the noise removal of document images. In this paper, we present noise removal of document images using support vector regression. The proposed approach consists of two steps which are SVR training step and SVR test step. We construct an optimal prediction model using grid search with cross-validation in SVR training step, and then apply it to noisy images to remove noises in test step. We evaluate our SVR based method both quantitatively and qualitatively for noise removal in Korean, English and Chinese character documents, and compare it to some existing methods. Experimental results indicate that the proposed method is more effective and can get satisfactory removal results.

• Nuclear Engineering and Technology
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• v.49 no.6
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• pp.1250-1258
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• 2017

The multigroup transport theory is the basis for many neutronics modules. A significant point of the cross-section (XS) generation procedure is the choice of the energy groups' boundaries in the XS libraries, which must be carefully selected as an unsuitable energy meshing can easily lead to inaccurate results. This decision can require considerable effort and is particularly difficult for the common user, especially if not well-versed in reactor physics. This work investigates a genetic algorithm-based tool which selects an appropriate XS energy structure (ES) specific for the considered problem, to be used for the condensation of a fine multigroup library. The procedure is accelerated by results storage and fitness calculation speedup and can be easily parallelized. The extension is applied to the coupled code SIMMER and tested on the European Sustainable Nuclear Industrial Initiative (ESNII+) Advanced Sodium Technological Reactor for Industrial Demonstration (ASTRID)-like reactor system with different fitness functions. The results show that, when the libraries are condensed based on the ESs suggested by the algorithm, the code actually returns the correct multiplication factor, in both reference and voided conditions. The computational effort reduction obtained by using the condensed library rather than the fine one is assessed and is much higher than the time required for the ES search.

• Journal of the Korean Society for Library and Information Science
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• v.31 no.3
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• pp.165-208
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• 1997

The purpose of this study is to evaluate the characteristics of user interface that affect the searching behavior of OPAC users. and then to propose how to design user-friendly interfaces of OPACS. An experiment was conducted on two systems with different interfaces to grasp the effect of user interface to search process and search outcome. A $2\times2$ cross-over design was used for the experiment. Sixty five searchers participated in the experiment. Several statistical techniques such as carry-over effect and system effect of a $2\times2$ cross-over design, $\chi^2$ test, t- test, McNemar test, test of marginal homogeneity through maximum likelihood method, factor analysis, regression analysis, and analysis of variance were applied according to the hypotheses tested and the data analyzed.

• Geomechanics and Engineering
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• v.18 no.5
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• pp.493-502
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• 2019

Internal stability is an important safety issue for levees, embankments, and other earthen structures. Since a large part of the world's population lives near oceans, lakes and rivers, floods resulting from breaching of dams can lead to devastating disasters with tremendous loss of life and property, especially in densely populated areas. There are some main factors that affect the internal stability of dams, levees and other earthen structures, such as the erodibility of the soil, the water velocity inside the soil mass and the geometry of the earthen structure, etc. Thus, the mechanism of internal erosion and stability of soils is very complicated and it is vital to investigate the assessment methods of internal stability of soils in embankment dams and their foundations. This paper presents an improved support vector machine (SVM) model to predict the internal stability of soils. The grid search algorithm (GSA) is employed to find the optimal parameters of SVM firstly, and then the cross - validation (CV) method is employed to estimate the classification accuracy of the GSA-SVM model. Two examples of internal stability of soils are presented to validate the predictive capability of the proposed GSA-SVM model. In addition to verify the effectiveness of the proposed GSA-SVM model, the predictions from the proposed GSA-SVM model were compared with those from the traditional back propagation neural network (BPNN) model. The results showed that the proposed GSA-SVM model is a feasible and efficient tool for assessing the internal stability of soils with high accuracy.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.48 no.5
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• pp.259-266
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• 2022

The relationship between obstructive sleep apnea (OSA) and diverse types of pain conditions have been proposed. However, no consensus on the relationship between OSA and painful temporomandibular disorders (TMDs) has been established. Therefore, this systematic review has been conducted to review the existing literatures and provide comprehensive synthesis of such literatures about OSA and painful TMDs using the evidence-based methodology. A literature search was conducted using two electronic databases, Scopus, and PubMed. Risk of bias was assessed using the risk-of-bias assessment tool for non-randomized study version 2.0. A total of 158 articles were screened from the initial search and eventually, 5 articles were included in this systematic review. One study adopted both the longitudinal prospective cohort and case-control designs and other 4 articles adopted the cross-sectional design. Two studies employed polysomnography (PSG) for the diagnosis of OSA and mentioned the results from the PSG. All cross-sectional studies demonstrated higher OSA prevalence among patients with TMD, and one cohort study suggested OSA as a risk factor for TMD. OSA appears to have potential influences on the development of TMD; however, the role of TMD in the development of OSA remains to be unknown owing to the lack of high-quality evidences.