• Journal of Fashion Business
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• v.23 no.3
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• pp.85-100
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• 2019

This study examines changes in consumer perceptions of fashion shows, which are critical elements in the apparel industry and a means to represent a brand's image and originality. For this purpose, big data in clothing marketing, text mining, semantic network analysis techniques were applied. This study aims to verify the effectiveness and significance of fashion shows in an effort to give directions for their future utilization. The study was conducted in two major stages. First, data collection with the key word, "fashion shows," was conducted across websites, including Naver and Daum between 2015 and 2018. The data collection period was divided into the first- and second-half periods. Next, Textom 3.0 was utilized for data refinement, text mining, and word clouding. The Ucinet 6.0 and NetDraw, were used for semantic network analysis, degree centrality, CONCOR analysis and also visualization. The level of interest in "models" was found to be the highest among the perception factors related to fashion shows in both periods. In the first-half period, the consumer interests focused on detailed visual stimulants such as model and clothing while in the second-half period, perceptions changed as the value of designers and brands were increasingly recognized over time. The findings of this study can be utilized as a tool to evaluate fashion shows, the apparel industry sectors, and the marketing methods. Additionally, it can also be used as a theoretical framework for big data analysis and as a basis of strategies and research in industrial developments.

• Journal of Climate Change Research
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• v.8 no.4
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• pp.393-399
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• 2017

According to the United Nations Framework Convention on Climate Change (UNFCCC), all parties have to submit the national GHG inventory report. Estimating carbon stocks and changes in Land Use, Land-Use Changes and Forestry (LULUCF) needs an activity data and emission factors. So this study was conducted to develop carbon emission factor for Robinia pseudoacacia L., Betula platyphylla var. japonica, and Liriodendron tulipifera. As a result, the basic wood density ($g/cm_3$) was 0.64 for R. pseudoacacia, 0.55 for B. platyphylla, and 0.46 for L. tulipifera. Biomass expansion factor was 1.47 for R. pseudoacacia, 1.30 for B. platyphylla, and 1.24 for L. tulipifera. Root to shoot ratio was 0.48 for R. pseudoacacia, 0.29 for B. platyphylla, and 0.23 for L. tulipifera. Uncertainty of estimated emission factors on three species ranged from 3.39% to 27.43% within recommended value (30%) by IPCC. We calculated carbon stock and change using these emission factors. Three species stored carbon in forest and net $CO_2$ removal was $1,255,398\;t\;CO_2/yr$ during 5 years. So we concluded that our result could be used as emission factors for national GHG inventory report on forest sector.

• Convergence Security Journal
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• v.20 no.4
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• pp.197-208
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• 2020

Based on the network infrastructure advanced in the information knowledge society, the structure of computer net work is operated by establishing the composition of network in various forms that have secured the security. In case of computer network of national/public organizations, it is necessary to establish the technical and managerial securit y environment even considering the characteristics of each organization and connected organizations. For this, the im portance of basic researches for cyber training by analyzing the technical/managerial vulnerability and cyber threats based on the classification and map of cyber threats according to the characteristics of each organization is rising. T hus, this study aims to analyze each type of external/internal cyber threats to computer network of national/public o rganizations established based on the dualistic infrastructure network of internet and national information network, a nd also to present the cyber threat framework for drawing the elements of cyber security training, by drawing and analyzing the actual elements of cyber threats through the case-based scenario.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.214-222
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• 2022

Convolutional Neural networks (CNNs) are a category of deep learning networks that have proven very effective in computer vision tasks such as image classification. Notwithstanding, not much has been seen in its use for remote sensing image classification in developing countries. This is majorly due to the scarcity of training data. Recently, transfer learning technique has successfully been used to develop state-of-the art models for remote sensing (RS) image classification tasks using training and testing data from well-known RS data repositories. However, the ability of such model to classify RS test data from a different dataset has not been sufficiently investigated. In this paper, we propose a deep CNN model that can classify RS test data from a dataset different from the training dataset. To achieve our objective, we first, re-trained a ResNet-50 model using EuroSAT, a large-scale RS dataset to develop a base model then we integrated Augmentation and Ensemble learning to improve its generalization ability. We further experimented on the ability of this model to classify a novel dataset (Nig_Images). The final classification results shows that our model achieves a 96% and 80% accuracy on EuroSAT and Nig_Images test data respectively. Adequate knowledge and usage of this framework is expected to encourage research and the usage of deep CNNs for land cover mapping in cases of lack of training data as obtainable in developing countries.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4809-4818
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• 2022

The decommissioning project of a nuclear facility is a large-scale process that is expected to take about 15 years or longer. The range of risks to be considered is large and complex, then, it is expected that various risks will arise in decision-making by area during the project. Therefore, in this study, the risk family derived from the Decommissioning Risk Management (DRiMa) project was reconstructed into a decommissioning project risk profile suitable for the Kori Unit 1. Two criteria of uncertainty and importance are considered in order to prioritize the selected 26 risks of decommissioning project. The uncertainty is scored according to the relevant laws and decommissioning plan preparation guidelines, and the project importance is scored according to the degree to which it primarily affects the triple constraints of the project. The results of risks are divided into high, medium, and low. Among them, 10 risks are identified as medium level and 16 risks are identified as low level. 10 risks, which are medium levels, are classified in five categories: End state of decommissioning project, Management of waste and materials, Decommissioning strategy and technology, Legal and regulatory framework, and Safety. This study is a preliminary assessment of the risk of the decommissioning project that could be considered in the preparation stage. Therefore, we expect that the project risks considered in this study can be used as an initial data for reevaluation by reflecting the detail project progress in future studies.

• Nuclear Engineering and Technology
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• v.55 no.4
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• pp.1439-1447
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• 2023

The fuel cycle characteristics of the IVG.1M reactor were studied within the framework of the research reactor conversion program to modernize the IVG.1M reactor. Optimum use of the nuclear fuel and reactor was achieved through routine methods which included partial fuel reloading combined with scheduled maintenance operations. Since, the additional problem in planning the fuel cycle of the IVG.1M reactor was the poisoning of the beryllium parts of the core, reflector, and control system. An assessment of the residual power and composition of spent fuel is necessary for the selection and justification of the technology for its subsequent management. Computational studies were performed using the MCNP6.1 program and the neutronics model of the IVG.1M reactor. The proposed scheme of annual partial fuel reloading allows for maintaining a high reactor reactivity margin, stabilizing it within 2-4 β_eff for 20 years, and achieving a burnup of 9.9-10.8 MW × day/kg U in the steady state mode of fuel reloading. Spent fuel immediately after unloading from the reactor can be placed in a transport packaging cask for shipping or safely stored in dry storage at the research reactor site.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.939-946
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• 2023

Comprehensive Research Facility for Fusion Technology (CRAFT) is an integration of different demonstrating or testing facilities, which aim to develop the critical technology or composition system towards the fusion reactor. Due to the importance and challenge of the negative ion based neutral beam injection (NNBI), a NNBI test facility is included in the framework of CRAFT. The initial object of CRAFT NNBI test facility is to obtain a H⁰ beam power of 2 MW at the energy of 200-400 keV for the pulse duration of 100 s. Inside the negative ion accelerator of NNBI system, the interactions of the negative ions with the background gas and electrodes can generate abundant stray electrons. The stray electrons can be further accelerated and dumped on the electrodes or eject from the accelerator. The stray electrons, including the ejecting electrons, cause the unwanted particle and heat flux onto the electrodes and the inner components of beamline (especially the temperature sensitive cryopump). The suppression of the stray electrons from the CRAFT accelerator is carried out via a series of design and simulation works. The paper focuses the influence of different magnetic field configurations on the stray electrons and the character of the ejecting electrons.

• Wind and Structures
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• v.36 no.6
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• pp.405-421
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• 2023

Strong wind is the main factors of wind-damage of high-rise buildings, which often creates largely economical losses and casualties. Wind pressure plays a critical role in wind effects on buildings. To obtain the high-resolution wind pressure field, it often requires massive pressure taps. In this study, two traditional methods, including bilinear and bicubic interpolation, and two deep learning techniques including Residual Networks (ResNet) and Generative Adversarial Networks (GANs), are employed to reconstruct wind pressure filed from limited pressure taps on the surface of an ideal building from TPU database. It was found that the GANs model exhibits the best performance in reconstructing the wind pressure field. Meanwhile, it was confirmed that k-means clustering based retained pressure taps as model input can significantly improve the reconstruction ability of GANs model. Finally, the generalization ability of k-means clustering based GANs model in reconstructing wind pressure field is verified by an actual engineering structure. Importantly, the k-means clustering based GANs model can achieve satisfactory reconstruction in wind pressure field under the inputs processing by k-means clustering, even the 20% of pressure taps. Therefore, it is expected to save a huge number of pressure taps under the field reconstruction and achieve timely and accurately reconstruction of wind pressure field under k-means clustering based GANs model.

• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2556-2566
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• 2023

A first-principle approach within the framework of density functional theory was employed to study the effect of vacancy defects and fission products (FPs) doping on the mechanical, electronic, and thermodynamic properties of uranium monocarbide (UC). Firstly, the calculated vacancy formation energies confirm that the C vacancy is more stable than the U vacancy. The solution energies indicate that FPs prefer to occupying in U site rather than in C site. Zr, Mo, Th, and Pu atoms tend to directly replace U atom and dissolve into the UC lattice. Besides, the results of the mechanical properties show that U vacancy reduces the compressive and deformation resistance of UC while C vacancy has little effect. The doping of all FPs except He has a repairing effect on the mechanical properties of U_1-xC. In addition, significant modifications are observed in the phonon dispersion curves and partial phonon density of states (PhDOS) of UC_1-x, Zr_xU_1-xC, Mo_xU_1-xC, and Rh_xU_1-xC, including narrow frequency gaps and overlapping phonon modes, which increase the phonon scattering and lead to deterioration of thermal expansion coefficient (α_V) and heat capacity (C_p) of UC predicted by the quasi harmonic approximation (QHA) method.

• Nuclear Engineering and Technology
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• v.56 no.3
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• pp.973-979
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• 2024

In the framework of the Generation IV research and development project, in which the French Commission of Alternative and Atomic Energies (CEA) is involved, a main objective for the design of Sodium-cooled Fast Reactor (SFR) is to meet the safety goals for severe accidents. Among the severe ones, the Unprotected Transient OverPower (UTOP) accidents can lead very quickly to a global melting of the core. UTOP accidents can be considered either as slow during a Control Rod Withdrawal (CRW) or as fast. The paper focuses on fast UTOP accidents, which occur in a few milliseconds, and three different scenarios are considered: rupture of the core support plate, uncontrolled passage of a gas bubble inside the core and core mechanical distortion such as a core flowering/compaction during an earthquake. Several levels and rates of reactivity insertions are also considered and the thermal-mechanical behavior of an ASTRID fuel pin from the ASTRID CFV core is simulated with the GERMINAL code. Two types of fuel pins are simulated, inner and outer core pins, and three different burn-up are considered. Moreover, the feedback from the CABRI programs on these type of transients is used in order to evaluate the failure mechanism in terms of kinetics of energy injection and fuel melting. The CABRI experiments complete the analysis made with GERMINAL calculations and have shown that three dominant mechanisms can be considered as responsible for pin failure or onset of pin degradation during ULOF/UTOP accident: molten cavity pressure loading, fuel-cladding mechanical interaction (FCMI) and fuel break-up. The study is one of the first step in fast UTOP accidents modelling with GERMINAL and it has shown that the code can already succeed in modelling these type of scenarios up to the sodium boiling point. The modeling of the radial propagation of the melting front, validated by comparison with CABRI tests, is already very efficient.