• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1141-1162
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• 2024

Humidity is an important parameter in meteorology and is closely related to weather, human health, and the environment. Due to the limitations of the number of observation stations and other factors, humidity data are often not as good as expected, so high-resolution humidity fields are of great interest and have been the object of desire in the research field and industry. This study presents a novel super-resolution algorithm for humidity fields based on the Wasserstein generative adversarial network(WGAN) framework, with the objective of enhancing the resolution of low-resolution humidity field information. WGAN is a more stable generative adversarial networks(GANs) with Wasserstein metric, and to make the training more stable and simple, the gradient cropping is replaced with gradient penalty, and the network feature representation is improved by sub-pixel convolution, residual block combined with convolutional block attention module(CBAM) and other techniques. We evaluate the proposed algorithm using ERA5 relative humidity data with an hourly resolution of 0.25°×0.25°. Experimental results demonstrate that our approach outperforms not only conventional interpolation techniques, but also the super-resolution generative adversarial network(SRGAN) algorithm.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.23 no.9
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• pp.1151-1162
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• 1999

The effects of radial heat and $H_2O$ diffusion on the evolution of silica particles in coflow diffusion flames have been studied experimentally. The evolution of silica aggregate particles in coflow diffusion flames has been measured experimentally using light scattering and thermophoretic sampling techniques. The measurements of scattering cross section from $90^{\circ}$ light scattering have been utilized to calculate the aggregate number density and volume fraction using with combination of measuring the particle size and morphology through the localized sampling and a TEM image analysis. Aggregate or particle number densities and volume fractions were calculated using Rayleigh-Debye-Gans and Mie theory for fractal aggregates and spherical particles, respectively. Flame temperatures and volumetric differential scattering cross sections have been measured for different flame conditions such as inert gas species, $H_2$ flow rates, and burner injection configurations to examine the relation between the formation of particles and radial $H_2O$ diffusion. The comparisons of oxidation and flame hydrolysis have also been made for various $H_2$ flow rates using $N_2$ or $O_2$ as a carrier gas. Results indicate that the role of oxidation becomes dominant as both carrier gas($O_2$) and $H_2$ flow rates increases since the radial heat diffusion precedes $H_2O$ diffusion in coflow flames used in this study. The effect of carrier gas flow rates on the evolution of silica particles have also been studied. When using $N_2$ as a carrier gas, the particle volume fraction has a maximum at a certain carrier gas flow rate and as the flow rate is further increased, the hydrolysis reaction Is delayed and the spherical particles finally evolves into fractal aggregates due to decreased flame temperature and residence time.

• Journal of the Korean Society of Costume
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• v.62 no.8
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• pp.28-44
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• 2012

The aim of this research is to examine the tendency of national tastes reflected in global SPA brands based on the theories of Gans' taste culture and Bourdieu's cultural capital. In this study, the leading global SPA brands such as H&M, ZARA, GAP and UNIQLO can be considered as a representative taste culture as well as an icon of popular culture in the $21^{st}$ century global fashion and also the aesthetic taste of each brand differentiated from their national aesthetic values based on socio-cultural backgrounds. H&M represents fashionableness, practicality and environmental friendliness based on naturalism, democratic humanism, and functional practicality of Sweden. ZARA emerges as the most trend oriented brand as well as customer centered on the basis of cultural diversity, passion and glamorous artistic sensibility of Spain. GAP shows American iconic style, which is the functional sports casual wear, originated from American leisure culture and mass production. Lastly, UNIQLO represents high-tech functionalism and practical simplicity based on Japanese delicate workmanship and simple, concise lifestyle while relatively does not much follow the fashion forward trends. Consequently, the national taste has been proved as a solid foundation to identify each global brand. This should be the key component also applied to Korean global brands for developing their concepts and strategies more successfully based on our own national aesthetic taste.

• The Journal of the Acoustical Society of Korea
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• v.39 no.4
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• pp.292-302
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• 2020

Machine learning algorithms have made immense contributions in various fields including sonar and radar applications. Recently developed Cycle-Consistency Generative Adversarial Network (CycleGAN), a variant of GAN has been successfully used for unpaired image-to-image translation. We present a modified CycleGAN for translation of underwater ship engine sounds with high perceptual quality. The proposed network is composed of an improved generator model trained to translate underwater audio from one vessel type to other, an improved discriminator to identify the data as real or fake and a modified cycle-consistency loss function. The quantitative and qualitative analysis of the proposed CycleGAN are performed on publicly available underwater dataset ShipsEar by evaluating and comparing Mel-cepstral distortion, pitch contour matching, nearest neighbor comparison and mean opinion score with existing algorithms. The analysis results of the proposed network demonstrate the effectiveness of the proposed network.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.1-7
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• 2019

Recently, various approaches to detect network attacks using machine learning have been studied and are being applied to detect new attacks and to increase precision. However, the machine learning method is dependent on feature extraction and takes a long time and complexity. It also has limitation of performace due to learning data imbalance. In this study, we propose a method to solve the degradation of classification performance due to imbalance of learning data among the limit points of detection system. To do this, we generate data using Generative Adversarial Networks (GANs) and propose a classification method using Convolutional Neural Networks (CNNs). Through this approach, we can confirm that the accuracy is improved when applied to the NSL-KDD and UNSW-NB15 datasets.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1588-1594
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• 2020

In this paper, we discuss a data balancing method for learning a neural network that generates digital holograms using a deep neural network (DNN). Deep neural networks are based on deep learning (DL) technology and use a generative adversarial network (GAN) series. The fringe pattern, which is the basic unit of a hologram to be created through a deep neural network, has very different data types depending on the hologram plane and the position of the object. However, because the criteria for classifying the data are not clear, an imbalance in the training data may occur. The imbalance of learning data acts as a factor of instability in learning. Therefore, it presents a method for classifying and balancing data for which the classification criteria are not clear. And it shows that learning is stabilized through this.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.3
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• pp.21-30
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• 2021

Architectural cultural properties suffer a lot of damage due to various environmental factors. In order to preserve damaged cultural properties, preventive preservation and long-term preservation management are becoming more important. Therefore, research on a scientific non-destructive testing method applicable to regular inspection is required. For related research, DangGan with a high flag-pole shape was selected as the subject of study among various cultural properties. Among the preserved DangGans, a basic study was conducted on the analysis technique to evaluate the structural stability by selecting Treasure No. 49 Naju SeokDangGan. An idealized model was presented and a multi-degree of freedom equation of motion was derived. In addition, an equation for estimating the critical stiffness value for each joint position is presented.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.867-871
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• 2022

Amid the recent rapid trend change, the change in design has a great impact on the sales of fashion companies, so it is inevitable to be careful in choosing new designs. With the recent development of the artificial intelligence field, various machine learning is being used a lot in the fashion market to increase consumers' preferences. To contribute to increasing reliability in the development of new products by quantifying abstract concepts such as preferences, we generate new images that do not exist through three adversarial generative neural networks (GANs) and numerically compare abstract concepts of preferences using pre-trained convolution neural networks (CNNs). Deep convolutional generative adversarial networks (DCGAN), Progressive growing adversarial networks (PGGAN), and Dual Discriminator generative adversarial networks (DANs), which were trained to produce comparative, high-level, and high-level images. The degree of similarity measured was considered as a preference, and the experimental results showed that D2GAN showed a relatively high similarity compared to DCGAN and PGGAN.

• The Journal of the Acoustical Society of Korea
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• v.41 no.3
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• pp.351-358
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• 2022

Recently, Generative Adversarial Networks (GAN) and Variational AutoEncoders (VAE) have been applied to voice conversion that can make use of non-parallel training data. Especially, Conditional Cycle-Consistent Generative Adversarial Networks (CC-GAN) and Cycle-Consistent Variational AutoEncoders (CycleVAE) show promising results in many-to-many voice conversion among multiple speakers. However, the number of speakers has been relatively small in the conventional voice conversion studies using the CC-GANs and the CycleVAEs. In this paper, we extend the number of speakers to 100, and analyze the performances of the many-to-many voice conversion methods experimentally. It has been found through the experiments that the CC-GAN shows 4.5 % less Mel-Cepstral Distortion (MCD) for a small number of speakers, whereas the CycleVAE shows 12.7 % less MCD in a limited training time for a large number of speakers.

• Journal of Platform Technology
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• v.11 no.6
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• pp.34-46
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• 2023

The design of non-pneumatic tires, which are created by filling the space between the wheel and the tread with elastomeric compounds or polygonal spokes, has become an important research topic in the automotive and aerospace industries. In this study, a system was designed for the design of non-pneumatic tires through the implementation of a generative adversarial network. We specifically examined factors that could impact the design, including the type of non-pneumatic tire, its intended usage environment, manufacturing techniques, distinctions from pneumatic tires, and how spoke design affects load distribution. Using OpenCV, various shapes and spoke configurations were generated as images, and a GAN model was trained on the projected GANs to generate shapes and spokes for non-pneumatic tire designs. The designed non-pneumatic tires were labeled as available or not, and a Vision Transformer image classification AI model was trained on these labels for classification purposes. Evaluation of the classification model show convergence to a near-zero loss and a 99% accuracy rate confirming the generation of non-pneumatic tire designs.