• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2733-2742
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• 2020

This paper presents an assessment of applicability of the multigroup cross sections generated with Monte Carlo tools to the fast reactor analysis based on transport calculations. 33-group cross section sets were generated for simple one- (1-D) and two-dimensional (2-D) sodium-cooled fast reactor problems using the SERPENT code and applied to deterministic steady-state and depletion calculations. Relative to the reference continuous-energy SERPENT results, with the transport corrected P₀ scattering cross section, the k-eff value was overestimated by 506 and 588 pcm for 1-D and 2-D problems, respectively, since anisotropic scattering is important in fast reactors. When the scattering order was increased to P₅, the 1-D and 2-D problem errors were increased to 577 and 643 pcm, respectively. A sensitivity and uncertainty analysis with the PERSENT code indicated that these large k-eff errors cannot be attributed to the statistical uncertainties of cross sections and they are likely due to the approximate anisotropic scattering matrices determined by scalar flux weighting. The anisotropic scattering cross sections were alternatively generated using the MC²-3 code and merged with the SERPENT cross sections. The mixed cross section set consistently reduced the errors in k-eff, assembly powers, and nuclide densities. For example, in the 2-D calculation with P3 scattering order, the k-eff error was reduced from 634 pcm to -223 pcm. The maximum error in assembly power was reduced from 2.8% to 0.8% and the RMS error was reduced from 1.4% to 0.4%. The maximum error in the nuclide densities at the end of 12-month depletion that occurred in ²³⁷Np was reduced from 3.4% to 1.5%. The errors of the other nuclides are also reduced consistently, for example, from 1.1% to 0.1% for ²³⁵U, from 2.2% to 0.7% for ²³⁸Pu, and from 1.6% to 0.2% for ²⁴¹Pu. These results indicate that the scalar flux weighted anisotropic scattering cross sections of SERPENT may not be adequate for application to fast reactors where anisotropic scattering is important.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2030-2052
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• 2023

Analysis of a bipartite (two-mode) network is a significant research area to understand the formation of social communities, economic systems, drug side effect topology, etc. in complex information systems. Most of the previous works talk about a projection-based model or latent feature model, which predicts the link based on singular similarity. The projection-based models suffer from the loss of structural information in the projected network and the latent feature is hardly present. This work proposes a novel method for link prediction in the bipartite network based on an ensemble of composite similarities, overcoming the issues of model-based and latent feature models. The proposed method analyzes the structure, neighborhood nodes as well as latent attributes between the nodes to predict the link in the network. To illustrate the proposed method, experiments are performed with five real-world data sets and compared with various state-of-art link prediction methods and it is inferred that this method outperforms with ~3% to ~9% higher using area under the precision-recall curve (AUC-PR) measure. This work holds great significance in the study of biological networks, e-commerce networks, complex web-based systems, networks of drug binding, enzyme protein, and other related networks in understanding the formation of such complex networks. Further, this study helps in link prediction and its usability for different purposes ranging from building intelligent systems to providing services in big data and web-based systems.

• The Research Journal of the Costume Culture
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• v.30 no.6
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• pp.799-813
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• 2022

The environment has increasingly attracted attention and fashion brands need to use new growth models by developing eco-friendly products, along with the drastic climate change. This study drew design guidelines from the factors of clothing disposal and reuse to propose ways to extend the longevity of clothing. It sets the design goals for the longevity extension of clothing as flexibility, originality, durability, and adjustability and drew a specific design guideline. The design methods used to achieve such goals are as follows. First, the design that is flexible in terms of physical changes needs to increase its activity and to be changeable, by applying pleats, rubber bands and elastic materials to the parts with many physical changes and movements. Second, it is necessary to reinforce the brand identity, create design that is flexible in terms of fashion and design very rare and attractive products, for the goal of original design beyond fashion. Third, it is necessary to increase the quality of clothing and improve the durability which can be decreased by washing and wearing. Fourth, it is necessary to create the design that can produce various styles, preserve the state of clothing and maintain its hygienic conditions by using removable detailed designs, shape-transformation designs and the designs which can be adjusted to climate changes and states, for the goal of adjustable design with better functionality. The findings provide ideas for fashion experts to pay more attention to the extending the longevity of clothing products and to develop eco-friendly designs and strategies.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.5
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• pp.891-906
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• 2023

The essence of fashion brands' marketing with metaverse-based virtual spaces is to capture more potential consumers and boost the sales of companies' virtual and physical products. However, existing research has not fully addressed customer responses and behavioral outcomes regarding fashion virtual brand spaces. This study uses flow theory to address this gap and explores the factors that lead to the flow experience in virtual brand spaces. It also establishes the causal relationships between the flow experience, satisfaction with virtual spaces, the intention to purchase virtual products, and the intention to purchase actual products. We chose "Ralph Lauren World" of Ralph Lauren on Zepeto as the virtual brand space for this study and analyzed 239 valid data sets. We tested the hypotheses using structural equation modeling and bootstrapping for the mediation analyses. The findings indicate that the flow experience in virtual brand spaces positively and indirectly affects the purchase intention of virtual products via satisfaction with virtual brand spaces. In addition, virtual space satisfaction had an indirect, positive effect on actual product purchase intention through virtual product purchase intention. The research emphasizes that the purchase intention of virtual and actual products has a positive causal relationship.

• Journal of the Korean Society of Radiology
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• v.14 no.6
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• pp.773-780
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• 2020

With the development of the 4th industrial, research is being conducted to prevent diseases and reduce damage in various fields of science and technology such as medicine, health, and bio. As a result, artificial intelligence technology has been introduced and researched for image analysis of radiological examinations. In this paper, we will directly apply a deep learning model for classification and detection of pneumonia using chest X-ray images, and evaluate whether the deep learning model of the Inception series is a useful model for detecting pneumonia. As the experimental material, a chest X-ray image data set provided and shared free of charge by Kaggle was used, and out of the total 3,470 chest X-ray image data, it was classified into 1,870 training data sets, 1,100 validation data sets, and 500 test data sets. I did. As a result of the experiment, the result of metric evaluation of the Inception V3 deep learning model was 94.80% for accuracy, 97.24% for precision, 94.00% for recall, and 95.59 for F1 score. In addition, the accuracy of the final epoch for Inception V3 deep learning modeling was 94.91% for learning modeling and 89.68% for verification modeling for pneumonia detection and classification of chest X-ray images. For the evaluation of the loss function value, the learning modeling was 1.127% and the validation modeling was 4.603%. As a result, it was evaluated that the Inception V3 deep learning model is a very excellent deep learning model in extracting and classifying features of chest image data, and its learning state is also very good. As a result of matrix accuracy evaluation for test modeling, the accuracy of 96% for normal chest X-ray image data and 97% for pneumonia chest X-ray image data was proven. The deep learning model of the Inception series is considered to be a useful deep learning model for classification of chest diseases, and it is expected that it can also play an auxiliary role of human resources, so it is considered that it will be a solution to the problem of insufficient medical personnel. In the future, this study is expected to be presented as basic data for similar studies in the case of similar studies on the diagnosis of pneumonia using deep learning.

• Journal of Magnetics
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• v.11 no.4
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• pp.195-207
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• 2006

The recently published and new results on design and fabrication of magnetophotonic crystals of different dimensionality are surveyed. Coupling of polarized light to 3D photonic crystals based on synthetic opals was studied in the case of low dielectric contrast. Transmissivity of opals was demonstrated to strongly depend on the propagation direction of light and its polarization. It was shown that in a vicinity of the frequency of a single Bragg resonance in a 3D photonic crystal the incident linearly polarized light excites inside the crystal the TE- and TM-eigen modes which passing through the crystal is influenced by Brags diffraction of electromagnetic field from different (hkl) sets of crystallographic planes. We also measured the faraday effect of opals immersed in a magneto-optically active liquid. It was shown that the behavior of the faraday rotation spectrum of the system of the opal sample and magneto-optically active liquid directly interrelates with transmittance anisotropy of the opal sample. The photonic band structure, transmittance and Faraday rotation of the light in three-dimensional magnetophotonic crystals of simple cubic and face centered cubic lattices formed from magneto-optically active spheres where studied by the layer Korringa-Kohn-Rostoker method. We found that a photonic band structure is most significantly altered by the magneto-optical activity of spheres for the high-symmetry directions where the degeneracies between TE and TM polarized modes for the corresponding non-magnetic photonic crystals exist. The significant enhancement of the Faraday rotation appears for these directions in the proximity of the band edges, because of the slowing down of the light. New approaches for one-dimensional magnetophotonic crystals fabrication optimized for the magneto-optical Faraday effect enhancement are proposed and realized. One-dimensional magnetophotonic crystals utilizing the second and the third photonic band gaps optimized for the Faraday effect enhancement have been successfully fabricated. Additionally, magnetophotonic crystals consist of a stack of ferrimagnetic Bi-substituted yttrium-iron garnet layers alternated with dielectric silicon oxide layers of the same optical thickness. High refractive index difference provides the strong spatial localization of the electromagnetic field with the wavelength corresponding to the long-wavelength edge of the photonic band gap.

• Journal of the Earthquake Engineering Society of Korea
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• v.14 no.1
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• pp.25-34
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• 2010

A method is presented for evaluating the seismic failure probability of bridge structures which show a nonlinear hysteretic dynamic behavior. Bridge structures are modeled as a bilinear dynamic system with a single degree of freedom. We regarded that the failure of bridges will occur when the displacement response of a deck level firstly crosses the predefined limit state during a duration of strong motion. For the estimation of the first-crossing probability of a nonlinear structural system excited by earthquake motion, we computed the average frequency of crossings of the limit state. We presented the non-Gaussian closure method for the approximation of the joint probability density function of response and its derivative, which is required for the estimation of the average frequency of crossings. The failure probabilities are estimated according to the various artificial earthquake acceleration sets representing specific seismic characteristics. For the verification of the accuracy and efficiency of presented method, we compared the estimated failure probabilities with the results evaluated from previous methods and the exact values estimated with the crude Monte-Carlo simulation method.

• Journal of Korean Society of Forest Science
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• v.104 no.4
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• pp.632-639
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• 2015

Working in the forest would require a wide range of skills and experience for specific tasks which involve with a high level of risks to worker's safety. However, there has been a concern on the current standard wage system for forest workers because it does not effectively reflect the characteristics of typical working conditions in the forest. In addition, the current standard wages for forestry workers was estimated based on the construction industry's wage system. Therefore, the purpose of this study is to assess a current wage system through the mail survey method and to develop a new wage system for forest worker which effectively reflects skill sets and experience required for successful completion of the work in the forest. We mailed the survey questionnaire consisting of 19 questions to 659 forest workers and received 188 responses resulting in a 28.5% response rate. The results showed that the current average optimal wages of forest worker, special worker and feller were 97,680won/day, 127,559won/day and 152,403won/day, respectively though there were variations depending on the regions. In developing the new standard wage system, this study suggest the current work types(worker, special worker and feller) could be divided into 5 work types (forest-environment workers, forest operations in beginner, forest operations in intermediate, forest operations in advanced and forest equipment operator) reflecting specialty of forest operation thereby stabilizing the new wage system for forest workers.

• Journal of the Korean Chemical Society
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• v.39 no.6
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• pp.446-452
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• 1995

Lithium intercalates, $Li_xFeMoO_4Cl$ ($1{\leq}X{\leq}2$) prepared by electrochemical lithiation of $FeMoO_4Cl$ crystallizes in monoclinic structure for all x values as revealed by x-ray diffraction and galvanostatic discharge experiments. According to the x-ray photoelectron spectroscopic study, Fe(III) is at first reduced to Fe(II) upon lithium intercalation with the x domain of $0{\leq}X{\leq}1$, where the crystal symmetry is changed from tetragonal to monoclinic. On the other hand, Mo(VI) is reduced to lower valent state upon further lithium intercalation ($1{\leq}X{\leq}2$), where no crystal symmetry transformation and reduction of Fe(II) to lower valent state are observed. The Mo 3d spectrum for $Li_2FeMoO_4Cl$ appears as a complex shape, but can be deconvoluted into the three sets of the doublet on the basis of Gaussian function, those which correspond to Mo(VI), Mo(V) and Mo(IV) states, respectively. The mixed valent states of molybdenum after further lithiation may be due to a competitive reaction between the formation of Mo(V) and its disproportionation to Mo(IV) and Mo(VI).

• The Journal of Bigdata
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• v.6 no.1
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• pp.63-81
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• 2021

The performance of natural language processing is rapidly improving due to the recent development and application of machine learning and deep learning technologies, and as a result, the field of application is expanding. In particular, as the demand for analysis on unstructured text data increases, interest in NLP(Natural Language Processing) is also increasing. However, due to the complexity and difficulty of the natural language preprocessing process and machine learning and deep learning theories, there are still high barriers to the use of natural language processing. In this paper, for an overall understanding of NLP, by examining the main fields of NLP that are currently being actively researched and the current state of major technologies centered on machine learning and deep learning, We want to provide a foundation to understand and utilize NLP more easily. Therefore, we investigated the change of NLP in AI(artificial intelligence) through the changes of the taxonomy of AI technology. The main areas of NLP which consists of language model, text classification, text generation, document summarization, question answering and machine translation were explained with state of the art deep learning models. In addition, major deep learning models utilized in NLP were explained, and data sets and evaluation measures for performance evaluation were summarized. We hope researchers who want to utilize NLP for various purposes in their field be able to understand the overall technical status and the main technologies of NLP through this paper.