• The Korean Fashion and Textile Research Journal
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• v.23 no.5
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• pp.667-673
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• 2021

The purpose of this study was to obtain drape information and objective texture of fabrics easily and quickly by using a constructed fabric database. For the construction of the fabric database, 287 woven fabrics were examined by using the CLO fabric kit, KES-FB system, and drape test system. The k-means cluster analysis method was used to classify the fabrics into 7 grades. After correlation analysis of the fabric properties for each experiment, similar properties of the CLO fabric kit and KES-FB system were chosen, which were then designed to extract similar fabrics from the database. It was confirmed that inferring the drape information and objective hand feeling of fabrics was to some extent possible by extracting similar fabrics from the database. In this study, the primary hand and total hand value(THV) of KES-FB system, which was constructed by Kawabata and other experiments, were used to quantify the objective hand feeling, because they are the most widely used. However, these standards can be changed over time; in order to be applied within the clothing industry, these standards may have to be changed to some extent. Moreover, it is notable that although objective hand feeling cannot be expressed in the 3D virtual costume program, it can be easily derived from the constructed database. Additionally, it is expected that the existing 3D virtual costume program will express the costumes more realistically by improving these results.

• Journal of the Korean GEO-environmental Society
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• v.22 no.10
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• pp.13-19
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• 2021

Cracks in asphalt pavement occur due to changes in weather or impact from vehicles, and if cracks are left unattended, the life of the pavement may be shortened, and various accidents may occur. Therefore, studies have been conducted to detect cracks through images in order to quickly detect cracks in the asphalt pavement automatically and perform maintenance activity. Recent studies adopt machine-learning models for detecting cracks in asphalt road pavement using a Convolutional Neural Network. However, their practical use is limited because they require high-performance computing power. Therefore, this paper proposes a framework for detecting cracks in asphalt road pavement by applying a small deep learning model applicable to mobile devices. The small deep learning model proposed through the case study was compared with general deep learning models, and although it was a model with relatively few parameters, it showed similar performance to general deep learning models. The developed model is expected to be embedded and used in mobile devices or IoT for crack detection in asphalt pavement.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.85-110
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• 2018

A chatbot is an interactive assistant that utilizes many communication modes: voice, images, video, or text. It is an artificial intelligence-based application that responds to users' needs or solves problems during user-friendly conversation. However, the current version of the chatbot is focused on understanding and performing tasks requested by the user; its ability to generate personalized conversation suitable for relationship-building is limited. Recognizing the need to build a relationship and making suitable conversation is more important for social chatbots who require social skills similar to those of problem-solving chatbots like the intelligent personal assistant. The purpose of this study is to propose a text analysis method that evaluates relationships between chatbots and users based on content input by the user and adapted to the communication situation, enabling the chatbot to conduct suitable conversations. To evaluate the performance of this method, we examined learning and verified the results using actual SNS conversation records. The results of the analysis will aid in implementation of the social chatbot, as this method yields excellent results even when the private profile information of the user is excluded for privacy reasons.

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.25-37
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• 2019

Inspired by the rapid development of deep learning and online biometrics-based authentication, we propose a handwritten one-time password authentication system which employs deep learning-based handwriting recognition and writer verification techniques. We design a convolutional neural network to recognize handwritten digits and a Siamese network to compute the similarity between the input handwriting and the genuine user's handwriting. We propose the first application of the second edition of NIST Special Database 19 for a writer verification task. Our system achieves 98.58% accuracy in the handwriting recognition task, and about 93% accuracy in the writer verification task based on four input images. We believe the proposed handwriting-based biometric technique has potential for use in a variety of online authentication services under the FIDO framework.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.2
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• pp.417-424
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• 2019

This paper presents the design of a JPEG-LS codec for lossless image compression from AR/VR device. The proposed JPEG-LS(: LosSless) codec is mainly composed of a context modeling block, a context update block, a pixel prediction block, a prediction error coding block, a data packetizer block, and a memory block. All operations are organized in a fully pipelined architecture for real time image processing and the LOCO-I compression algorithm using improved 2D approach to compliant with the SBT coding. Compared with a similar study in JPEG-LS, the Block-RAM size of proposed STB-FLC architecture is reduced to 1/3 compact and the parallel design of the predication block could improved the processing speed.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.7-14
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• 2018

Salient information extraction from multi-view videos is a very challenging area because of inter-view, intra-view correlations, and computational complexity. There are several techniques developed for keyframes extraction from multi-view videos with very high computational complexities. In this paper, we present a keyframes extraction approach from multi-view videos using entropy and complexity information present inside frame. In first step, we extract representative shots of the whole video from each view based on structural similarity index measurement (SSIM) difference value between frames. In second step, entropy and complexity scores for all frames of shots in different views are computed. Finally, the frames with highest entropy and complexity scores are considered as keyframes. The proposed system is subjectively evaluated on available office benchmark dataset and the results are convenient in terms of accuracy and time complexity.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.4
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• pp.819-825
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• 2012

Nowadays many phishing sites contain HTTP links to victim web-site's contents such as images, bulletin board etc. to make the phishing sites look more real and similar to the victim web-site. We introduce a real-time phishing site detection system which makes use of the characteristic that the phishing sites' URLs flow into the victim web-site via the HTTP referer header field when the phishing site is visited. The detection system is designed to adopt an out-of-path network configuration to minimize effect on the running system, and a phishing site source code analysis technique to alert administrators in real-time when phishing site is detected. The detection system was installed on a company's web-site which had been targeted for phishing. As result, the detection system detected 40 phishing sites in 6 days of test period.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.214-220
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• 2021

In modern society, various digital communication equipments are being used due to the influence of the 4th industrial revolution, and accordingly, interest in removing noise generated in the data transmission process is increasing. In this paper, we propose a filtering algorithm to remove AWGN generated during digital image transmission. The proposed algorithm removes noise based on mask matching to preserve information such as the boundary of an image, and uses pixel values with similar patterns according to the pattern of the input pixel value and the surrounding pixels for output calculation. To evaluate the proposed algorithm, we simulated with existing AWGN removal algorithms, and analyzed using enlarged image and PSNR comparison. The proposed algorithm has superior AWGN removal performance compared to the existing method, and is particularly effective in images with strong noise intensity of AWGN.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.519-528
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• 2022

The purpose of the study was to further investigate the direction for one-person experience design based on visual shift due to the isolation one has experienced after the COVID-19 and the factors regarding it. The study involves eight female participants who are in their twenties via digital platform. The participants were instructed to choose digital image similar to COVID-19 and to write down facts based upon the image and the researcher will look into the result microscopically. The researchers found that the isolation factors include decreased face-to-face communication, reliance on social media, heavy usage of OTT platform, limited outdoor occasion and activity, limitation of untact technology and education program, fear over the pandemic and so on. The study has shown that the one-person experience design should be heading in a direction where it adopts space design that can crossover online and offline world, digital complex design to embody realness as well as the communication design to regain the relationships with others.

• Journal of Internet of Things and Convergence
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• v.8 no.2
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• pp.49-54
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• 2022

Immersive contents is content that provides a realistic experience by maximizing the user's five senses, and includes virtual reality, augmented reality, and mixed reality. In order to provide a sense of reality to users in immersive content, it is necessary to provide realistic visual images, hearing, and touch. However, due to the rapid change in the environment for developing immersive content, experts in training human resources are having difficulties in designing the curriculum. In this study, we propose a series of educational courses that use drones to acquire and process real-world measurement data and apply the derived data to VR, AR, and MR to help experts in training immersive content develop talent. The design of training process composes through demand survey and analysis of companies, students, and local communities. This study can be a useful resource for education experts who want to train immersive contents manpower.