• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.31-38
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• 2023

Style transfer is one of deep learning-based image processing techniques that has been actively researched recently. These research efforts have led to significant improvements in the quality of result images. Style transfer is a technology that takes a content image and a style image as inputs and generates a transformed result image by applying the characteristics of the style image to the content image. It is becoming increasingly important in exploiting the diversity of digital content. To improve the usability of style transfer technology, ensuring stable performance is crucial. Recently, in the field of natural language processing, the concept of Transformers has been actively utilized. Attention maps, which forms the basis of Transformers, is also being actively applied and researched in the development of style transfer techniques. In this paper, we analyze the representative techniques SANet and AdaAttN and propose a novel attention map-based structure which can generate improved style transfer results. The results demonstrate that the proposed technique effectively preserves the structure of the content image while applying the characteristics of the style image.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.4
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• pp.135-142
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• 2023

Recently, many companies are using public cloud services or building their own data center because digital transformation is expanding. The software-defined storage is a key solution for storing data on the cloud platform and its use is expanding worldwide. Software-defined storage has the advantage of being able to virtualize and use all storage resources as a single storage device and supporting flexible scale-out. On the other hand, since the size of an object is variable, an imbalance occurs in the use of the disk and may cause a failure. In this study, a method of redistributing objects by optimizing disk weights based on storage state information was proposed to solve the imbalance problem of disk use, and the experimental results were presented. As a result of the experiment, it was confirmed that the maximum utilization rate of the disk decreased by 10% from 89% to 79%. Failures can be prevented, and more data can be stored by optimizing the use of disk.

• Design Convergence Study
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• v.14 no.2
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• pp.151-168
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• 2015

As the market of music download stores moved into the mobile epoch the market was growing explosively. Recently, the digital music market tends to move from the mp3 download market to streaming service and the users can use the service on their mobile devices without reference to any inconvenient download and limited storage capacity. It was found that they mainly use the recommendation music playlist, instant player, main player and sharing of the functions of the streaming service from the user behavior research. This is noticeable features that set apart from the mp3 download service. However, the interface design of the streaming app followed the previous service and it needs the optimization of its UI design. In this study, the usability of high ranked three mobile streaming apps was evaluated. The result of the test was that Naver music and Bugs had high scores overall in four sections of the streaming service features. On the other hand, the Melon had primarily high score in color application on the service. The aim of this study is to suggest the direction of the UI design of music streaming service through the understanding of essence of streaming service and evaluation the usability test.

• Journal of Fashion Business
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• v.27 no.5
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• pp.60-77
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• 2023

The scale of the industry utilizing the Metaverse platform is gradually growing around the world. Fashion brands are also starting to utilize the Metaverse platform as a new market to replace the next e-commerce platform by targeting new consumers, MZ generation, and even Alpha generation. In this study, a real K-fashion designer's outfit was made into a 3D outfit using CLO 3D program to express it in a new market, the Metaverse 3D platform. It was then compared with a real outfit. An avatar prototype was completed using Max program to simulate the 3D digital fashion outfit and produce an avatar through an optimization process. The 3D outfits showed the same level of results as the actual outfits in terms of fabric surface, material texture, drapability, overall outfit, details, and trimmings. In addition, we proposed a 2D work on total styling suggestion and modeling to secure data sets for future AI-based styling services. In conclusion, this study revealed that actual outfits and 3D outfits had the same results. It is significant that it can be a sample work to build a styling data set through styling suggestion and content production as a significant amount of styling DB construction will be required before AI styling automation services.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.99-109
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• 2023

The darknet is frequently referred to as the hub of illicit online activity. In order to keep track of real-time applications and activities taking place on Darknet, traffic on that network must be analysed. It is without a doubt important to recognise network traffic tied to an unused Internet address in order to spot and investigate malicious online activity. Any observed network traffic is the result of mis-configuration from faked source addresses and another methods that monitor the unused space address because there are no genuine devices or hosts in an unused address block. Digital systems can now detect and identify darknet activity on their own thanks to recent advances in artificial intelligence. In this paper, offer a generalised method for deep learning-based detection and classification of darknet traffic. Furthermore, analyse a cutting-edge complicated dataset that contains a lot of information about darknet traffic. Next, examine various feature selection strategies to choose a best attribute for detecting and classifying darknet traffic. For the purpose of identifying threats using network properties acquired from darknet traffic, devised a hybrid deep learning (DL) approach that combines Recurrent Neural Network (RNN) and Bidirectional LSTM (BiLSTM). This probing technique can tell malicious traffic from legitimate traffic. The results show that the suggested strategy works better than the existing ways by producing the highest level of accuracy for categorising darknet traffic using the Black widow optimization algorithm as a feature selection approach and RNN-BiLSTM as a recognition model.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.135-155
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• 2024

Urban public health monitoring in smart cities focuses on the control of conditions and health challenges in urban environments. Considering the rapid spread of diseases and pandemics, it is important for health authorities to trace people carrying the virus. In smart cities, this tracing must be interoperable and intelligent, especially in indoor surfaces characterized by small distances between people. Therefore, to fight pandemics, it is necessary to start with the already-existing digital equipment of the Internet of Things, such as connected objects and smartphones. In this study, the developed system is employed to provide a social IoT network and suggest a strategy which allows reliable traceability without threatening the privacy of users. This IoT-based system allows respecting the social distance between persons sharing public services in smart cities without applying smartphone applications or severe confinement. It also permits a return to normal life in case of viral pandemic and ensures the much-desired balance between economy and health. The present study analyses previous proposed social distance systems then, unlike these studies, suggests an intelligent and distributed IoT based strategy for positioning students. Two scenarios of static and dynamic optimization-based placement of Bluetooth Low Energy devices are proposed and an experimental study shows the contribution and complementarity of the introduced contact tracing strategy with the applications on smartphones.

• Journal of Platform Technology
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• v.11 no.6
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• pp.89-105
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• 2023

With the development of information technology, most of the information has been converted into digital information, leading to the Big Data era. The demand for search platform has increased to enhance accessibility and usability of information in the databases. Big data search software platforms consist of two main components: (1) an indexing component to generate and store data indices for a fast and efficient data search and (2) a searching component to look up the given data fast. As an amount of data has explosively increased, data indexing performance has become a key performance bottleneck of big data search platforms. Though many companies adopted big data search platforms, relatively little research has been made to improve indexing performance. This research study employs Elasticsearch platform, one of the most famous enterprise big data search platforms, and builds physical clusters of 3 nodes to investigate optimal indexing performance configurations. Our comprehensive experiments and studies demonstrate that the proposed optimal Elasticsearch configuration achieves high indexing performance by an average of 3.13 times.

• Journal of Service Research and Studies
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• v.13 no.4
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• pp.97-112
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• 2023

Background: In modern society, the importance of Public Design has become increasingly significant in contributing to the enhancement of urban functionality and the quality of life of citizens. Smart Public Facilities have played a pivotal role in enriching user experience by improving accessibility, convenience, and safety, and in elevating the value of the city. This research recognizes the importance of Public Facilities and explores the potential of Smart Public Facilities in solving urban challenges and progressing towards sustainable and Inclusive cities. Method: The literature review comprehensively examines existing theories and research results on Smart Public Facilities. The case study analyzes actual examples of Smart Public Facilities implemented in cities both domestically and internationally, drawing out effects, user satisfaction, and areas for improvement. Through analysis and discussion, the results of the case studies are evaluated, discussing the potential development of Smart Public Facilities. Results: Smart Public Facilities have been found to bring positive changes in various aspects such as urban management, energy efficiency, safety, and information accessibility. In terms of urban management, they play a crucial role in optimization, social Inclusiveness, environmental protection, fostering citizen participation, and promoting technological innovation. These changes create a new form of urban space, combining physical space and digital technology, enhancing the quality of life in the city. Conclusion: This research explores the implications, current status, and functions of Smart Public Facilities in service and design aspects, and their impact on the urban environment and the lives of citizens. In conclusion, Smart Public Facilities have brought about positive changes in the optimization of urban management, enhancement of energy efficiency, increased information accessibility, User-Centric design, increased interaction, and social Inclusiveness. Technological innovation and the integration of Public Facilities have made cities more efficient and proactive, enabling data-based decision-making and optimized service delivery. Such developments enable the creation of new urban environments through the combination of physical space and digital technology. The advancement of Smart Public Facilities indicates the direction of urban development, where future cities can become more intelligent, proactive, and User-Centric. Therefore, they will play a central role in Public Design and greatly contribute to improving the urban environment and the quality of life of citizens.

• Journal of the Korean Society of Radiology
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• v.11 no.5
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• pp.379-385
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• 2017

This study identifies the optimal tube voltages depending on the changes in the patient's body type for limb tests using a digital radiography (DR) system. For the upper-limp test, the dose area product (DAP) was fixed at $5.06dGy{\ast} cm^2$, and for the lower-limb test, the DAP was fixed at $5.04dGy{\ast} cm^2$. Afterwards, the tube voltage was changed to four different stages and the images were taken three times at each stage. The thickness of the limbs was increased by 10 mm to 30 mm to change in the patient's body type. For a quantitative evaluation, Image J was used to calculate the contrast to noise ratio (CNR) and signal to noise ratio (SNR) among the four groups, according to the tube voltage. For statistical testing, the statistically significant differences were analyzed through the Kruskal-Wallis test at a 95% confidence level. For the qualitative analysis of the images, the pre-determined items were evaluated based on a 5-point Likert scale. In both upper-limb and lower-limb tests, the more the tube voltage increased, the more the CNR and SNR of the images decreased. The test on the changes depending on the patient's body shape showed that the more the thickness increased, the more the CNR and SNR decreased. In the qualitative evaluation on the upper limbs, the more the tube voltage increased, the more score increased to 4.6 at the maximum of 55kV and 3.6 at 40kV, respectively. The mean score for the lower limbs was 4.4, regardless of the tube voltage. The more either the upper or lower limbs got thicker, the more the score generally decreased. The score of the upper limps sharply dropped at 40kV, whereas that of the lower limps sharply dropped at 50kV. For patients with a standard thickness, the optimized images can be obtained when taken at 45kV for the upper limbs, and at 50kV for the lower limbs. However, when the thickness of the patient's limbs increases, it is best to set the tube voltage at 50 kV for the upper limbs and at 55 kV for the lower limbs.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.29-37
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• 2019

Textile quality management targets the physical properties of fabrics and the subjective discriminations of color and fitting. Color is the most representative quality factor that consumers can use to evaluate quality levels without any instruments. For this reason, quantification using a color discrimination device has been used for statistical quality management in the textile industry. However, small and medium-sized domestic textile manufacturers use only visual inspection for color discrimination. As a result, color discrimination is different based on the inspectors' individual tendencies and work procedures. In this research, we want to develop a textile industry-friendly quality management method, evaluating the possibility of rapid color discrimination using a digital imaging device, which is one of the office-automation instruments. The results show that an imaging process-based color discrimination method is highly correlated with conventional color discrimination instruments ($R^2=0.969$), and is also applicable to field discrimination of the manufacturing process, or for different lots. Moreover, it is possible to recognize quality management factors by analyzing color components, ${\Delta}L$, ${\Delta}a$, ${\Delta}b$. We hope that our rapid discrimination method will be a substitute technique for conventional color discrimination instruments via elaboration and optimization.