• Journal of Fashion Business
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• v.23 no.5
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• pp.1-18
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• 2019

The purpose of this study is to analyze the Muslim fashion that has recently appeared in the global fashion collection to see how the global fashion brand expresses Muslim's traditional costumes so as to provide references in design development to prepare for the larger Muslim fashion market in the future. In order to analyze Muslim fashion, keywords related to Muslims such as "Muslim," "Islamic fashion" and "hijab" were searched on Google, Samsung Design Net and Vogue websites, and a total of 370 fashion photos were selected for the final data, which was judged to reflect Muslim fashion styles after a review by four clothing experts. Muslim fashion styles have the following characteristics: Above all, the use of veils was most noticeable, with many T-shaped loose long tunic dresses. The hijab, which had the highest proportion of veils, was used to produce various images with wide range of materials and colors. Achromatic colors were the most common, but more than three colors were used to create an exotic image. There have also been cases of using direct religious images such as arabesque patterns and mosques and Muslim priests. As a final, Muslim fashion styles were studied follow: first, a unique style using a veil. Second, conservative style with minimal exposure, third, restrained long-and-lose fit style, fourth, exotic style by elaborate pattern. The domestic fashion industry is also expected to generate economic demand if it is designed with reference to such collection trends along with market research on Muslim consumers.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.10
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• pp.403-410
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• 2019

Image de-snowing aims at eliminating the negative influence by snow particles and improving scene understanding in images. In this paper, a perceptual generative adversarial network based a single image snow removal method is proposed. The residual U-Net is designed as a generator to generate the snow free image. In order to handle various sizes of snow particles, the inception module with different filter kernels is adopted to extract multiple resolution features of the input snow image. Except the adversarial loss, the perceptual loss and total variation loss are employed to improve the quality of the resulted image. Experimental results indicate that our method can obtain excellent performance both on synthetic and realistic snow images in terms of visual observation and commonly used visual quality indices.

• Steel and Composite Structures
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• v.33 no.3
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• pp.319-332
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• 2019

This study is aimed to predict the behaviour of channel shear connectors in composite floor systems at different temperatures. For this purpose, a soft computing approach is adopted. Two novel intelligence methods, including an Extreme Learning Machine (ELM) and a Genetic Programming (GP), are developed. In order to generate the required data for the intelligence methods, several push-out tests were conducted on various channel connectors at different temperatures. The dimension of the channel connectors, temperature, and slip are considered as the inputs of the models, and the strength of the connector is predicted as the output. Next, the performance of the ELM and GP is evaluated by developing an Artificial Neural Network (ANN). Finally, the performance of the ELM, GP, and ANN is compared with each other. Results show that ELM is capable of achieving superior performance indices in comparison with GP and ANN in the case of load prediction. Also, it is found that ELM is not only a very fast algorithm but also a more reliable model.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.245-245
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• 2015

In the recent years, due to long-lasting heavy rainfall events, a large number of landslides have been observed in the mountainous area of the world. Such landslides can also form a dam as it blocks the course of a river, which may burst and cause a catastrophic flood. Numerical analysis of landslide dam formation is rarely available, while laboratory experimental studies often use assumed shape to analyze the landslide dam failure and flood hydraulics in downstream. In this study, both experimental and numerical studies have been carried out to investigate the formation of landslide dam. Two case laboratory experiments were conducted in two flumes simultaneously. The first flume (2.0 m 0.6 m 0.5 m) was set at $22^{\circ}$ and $27^{\circ}$ slope to generate the landslide using rainfall intensity of 70.0 mm/hr. On the other hand, the second flume (1.5 m 0.25 m 0.3 m) was set perpendicularly at the downstream end of the first flume to receive the landslide mass forming landslide dam. The formation of landslide dam was observed at $15^{\circ}$ slope of the second flume. The whole processes including the landslide initiation and movement of the landslide mass into the second channel was captured by three digital cameras. In numerical analysis, a two-dimensional (2D) seepage flow model, a 2D slope stability model (Spencer method) and a 2D landslide dam-geometry evaluation model were coupled as a single unit. This developed model can determine the landslide occurrence time, the failure mass and the geometry of landslide dam deposited in the second channel. The data obtained from numerical simulation results has good agreement with the experimental measurements.

• Journal of Korean Society of Water and Wastewater
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• v.35 no.5
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• pp.335-349
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• 2021

As the frequency of seismic disasters in Korea has increased rapidly since 2016, interest in systematic maintenance and crisis response technologies for structures has been increasing. A data-based leading management system of Lifeline facilities is important for rapid disaster response. In particular, the water supply network, one of the major Lifeline facilities, must be operated by a systematic maintenance and emergency response system for stable water supply. As one of the methods for this, the importance of the structural health monitoring(SHM) technology has emerged as the recent continuous development of sensor and signal processing technology. Among the various types of SHM, because all machines generate vibration, research and application on the efficiency of a vibration-based SHM are expanding. This paper reviews a vibration-based pipeline SHM system for seismic disaster response of water supply pipelines including types of vibration sensors, the current status of vibration signal processing technology and domestic major research on structural pipeline health monitoring, additionally with application plan for existing pipeline operation system.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.65-74
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• 2021

Recently, studies on Korean spelling correction have been actively conducted based on machine translation and automatic noise generation. These methods generate noise and use as train and data set. This has limitation in that it is difficult to accurately measure performance because it is unlikely that noise other than the noise used for learning is included in the test set In addition, there is no practical error type standard, so the type of error used in each study is different, making qualitative analysis difficult. This paper proposes new 'error type classification' for deep learning-based Korean spelling correction research, and error analysis perform on existing commercialized Korean spelling correctors (System A, B, C). As a result of analysis, it was found the three correction systems did not perform well in correcting other error types presented in this paper other than spacing, and hardly recognized errors in word order or tense.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.10
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• pp.129-138
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• 2021

Several studies have revealed that information on borrower characteristics plays an important factor in approving their credit requests. Though the extent to which such characteritics are also applicable to the case of fintech lending remain uncertain. The aim of this study is, thus, to investigate the determinant factors that influence MSEs in obtaining credit through fintech lending. Here, we emphasize virtual trust in fintech lending encompasing the dimension of social network, economic attributes, and risk perception based on several indicators that are used as proxies. Primary data used in the study was gathered from an online survey to the respondents of MSEs in Java. The result of the study indicates that determinants of MSEs in obtaining credit from lender through fintech lending are statistically influenced by internet usage activities, borrowing history, loan utilization, annuity payment system, completeness of credit requirement documents and compatibility of loan size with the business need. These factors have a significant effect on credit approval because they can generate virtual trust of fintech lender to MSEs as potential borrowers. It concludes that the probability of obtaining fintech loans in accordance with their expectations are influenced by the dimensions of social network, economic attributes and risk perception.

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.496-502
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• 2021

Recently, deep speaker embedding approach has been widely used in text-independent speaker verification, which shows better performance than the traditional i-vector approach. In this work, to improve the deep speaker embedding approach, we propose a novel method called group-based speaker embedding which incorporates group information. We cluster all speakers of the training data into a predefined number of groups in an unsupervised manner, so that a fixed-length group embedding represents the corresponding group. A Group Decision Network (GDN) produces a group weight, and an aggregated group embedding is generated from the weighted sum of the group embeddings and the group weights. Finally, we generate a group-based embedding by adding the aggregated group embedding to the deep speaker embedding. In this way, a speaker embedding can reduce the search space of the speaker identity by incorporating group information, and thereby can flexibly represent a significant number of speakers. We conducted experiments using the VoxCeleb1 database to show that our proposed approach can improve the previous approaches.

• Journal of Korea Trade
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• v.24 no.8
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• pp.81-100
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• 2020

Purpose - Based on complexity theory, this study develops a configurational model to predict the profitability of Halal cosmetics firms in the Indonesian and Malaysian markets. The proposed research model involves two level configurations-industry context and selling strategies-to predict high and low scores of a firm's profitability. The industry context configuration model comprises industry stability, product homogeneity, price sensitivity, and switching cost. Selling strategies include customer-focused, competitor-focused, and margin-focused approaches. Design/methodology - This is the first empirical study that calculates causal models using a combination of industry context and selling strategy factors to predict profitability. Data obtained from the marketing managers of cosmetics firms are used to test the proposed configurational model using fuzzy-set qualitative comparative analysis (fsQCA). It contributes to the current knowledge of business marketing by identifying the factors necessary to achieve profitability using analysis of condition (ANC). Findings - The results revealed that unique and distinct models explain the conditions for high and low profitability in the Indonesian and Malaysian halal cosmetic markets. While customer-focused selling strategy is necessary to attain a higher profit in both the markets, margin-focused selling strategy appears to be an essential factor only in Malaysia. Complexity of the interactions of selling strategies with industry factors and differences between across two study markets confirmed that complexity theory can support the research configurational model. The theoretical and practical implications are also illustrated. Originality/value - Despite the rapid growth of the global halal industry, there is little knowledge about the halal cosmetic market. This study contributes to the current literature of the halal market by performing a set of asymmetric analytical approaches using a complex theoretical model. It also deepens our understating of how the Korean firms can approach the Muslim consumer's needs to generate more beneficial turnover/revenue.

• Journal of the Earthquake Engineering Society of Korea
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• v.25 no.2
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• pp.83-91
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• 2021

Seismic designs for Korean nuclear power plants (NPPs) under earthquakes' design basis are noticed due to the recent earthquake events in Korea and Japan. Japan has developed the technologies and experiences of the NPPs through theoretical research and experimental verification with extensively accumulated measurement data. This paper describes the main features of the design-time history complying with the Japanese seismic design standard. Proper seed motions in the earthquake catalog are used to generate one set of design time histories. A magnitude and epicentral distance specify the amplitude envelope function configuring the shape of the earthquake. Cumulative velocity response spectral values of the design time histories are compared and checked to the target response spectra. Spectral accelerations of the time histories and the multiple-damping target response spectra are also checked to exceed. The generated design time histories are input to the reactor building seismic analyses with fixed-base boundary conditions to calculate the seismic responses. Another set of design time histories is generated to comply with Korean seismic design procedures for NPPs and used for seismic input motions to the same reactor containment building seismic analyses. The responses at the dome apex of the building are compared and analyzed. The generated design time histories will be also applied to subsequent seismic analyses of other Korean standard NPP structures.