• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2086-2097
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• 2021

The current cultural assets are being restored depending on the opinions of experts (craftsmen). We intend to introduce digitalized artificial intelligence techniques, excluding the personal opinions of experts on reconstruction of such cultural properties. The first step toward restoring digitized cultural properties is separation. The restoration of cultural properties should be reorganized based on recorded documents, period historical backgrounds and regional characteristics. The cultural properties in the form of photographs or images should be collected by separating the background. In addition, when restoring cultural properties most of them depend a lot on the tendency of the restoring person workers. As a result, it often occurs when there is a problem in the accuracy and reliability of restoration of cultural properties. In this study, we propose a search method for learning stored digital cultural assets using AI technology. Pagoda was selected for restoration of Cultural Properties. Pagoda data collection was collected through the Internet and various historical records. The pagoda data was classified by period and region, and grouped into similar buildings. The collected data was learned by applying the well-known CNN algorithm for artificial intelligence learning. The pagoda search used Yolo Marker to mark the tower shape. The tower was used a total of about 100-10,000 pagoda data. In conclusion, it was confirmed that the probability of searching for a tower differs according to the number of pagoda pictures and the number of learning iterations. Finally, it was confirmed that the number of 500 towers and the epochs in training of 8000 times were good. If the test result exceeds 8,000 times, it becomes overfitting. All so, I found a phenomenon that the recognition rate drops when the enemy repeatedly learns more than 8,000 times. As a result of this study, it is believed that it will be helpful in data gathering to increase the accuracy of tower restoration.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.15-21
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• 2021

The type of crimes is diverse and the number of crimes is increasing as society changes. This phenomenon is showing a higher trend in places with higher population density. Accordingly, many organizations install CCTV to reduce crime and provide key evidence of crime. Nevertheless, it is still weak to deal with crimes such as video manipulation targeting CCTV. Although blockchain-based CCTV image integrity techniques are applied to prevent manipulation, they only guarantee the manipulation integrity of the entire video and can't explain how certain sections of the video has been manipulated. Therefore, in this research, we propose a system for supporting explainable CCTV video integrity based on a block chain.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.1
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• pp.125-133
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• 2009

Security of biometric data is particularly important as the compromise of the data will be permanent. To protect the biometric data, we need to store it in a non.invertible transformed version. Thus, even if the transformed version is compromised, its valid biometric data are securely remained. Fuzzy vault mechanism was proposed to provide cryptographic secure protection of critical data(e.g., encryption key) with the fingerprint data in a way that only the authorized user can access the critical data by providing the valid fingerprint. However, all the previous results cannot operate on the fingerprint image with a few minutiae, because they use fixed degree of the polynomial without considering the number of fingerprint minutiae. To solve this problem, we use adaptive degree of polynomial considering the number of minutiae. Also, we apply multiple polynomials to operate the fingerprint with a few minutiae. Based on the experimental results, we confirm that the proposed approach can enhance the security level and verification accuracy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.7 no.2
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• pp.55-72
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• 1997

This paper presents a digital signature scheme for facsimile document which directly embeds a signature onto the document. We use multiple reference lines which have been scanned just before and modify each distance of changing pels both on the reference line specified by key and on the coding line with a single bit of the signature data. The time to take in signature is reduced by spreading of signature. Non-repudiation in origin, the 3rd condition of digital signature is realized by proposed digital signature scheme. The transmitter embeds the signature secretly and transfers it, and the receiver makes a check of any forgery on the signature and the document. This scheme is compatible with the ITU-T.4(CCITT G3 or G4 facsimile standards). The total amount of data transmitted and the image quality are about the same to that of the original document, and thus a third party notices that no signature is embedded on the document.

• Fashion & Textile Research Journal
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• v.22 no.6
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• pp.716-726
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• 2020

This study presents methods and alternative examples for fashion brands to effectively use video-based communication channels to form brand identity that analyzes the definition, status and type of YouTube channel fashion films as well as enables the ability to derive brand identity characteristics. Literature studies focused on Christian Dior's official website and related previous studies. The temporal range of the case studies was from October 7, 2010, the date when the first fashion film was uploaded to current Christian Dior YouTube to July 17, 2020 (the survey date), and there are a total of 550 subjects for quantitative analysis. The succession of the couture spirit means that Christian Dior's craftsmanship was created and passed down by Musée Christian Dior to act as a contemporary key element of brand identity. The iconic expression of femininity is Dior's core design philosophy that began when the woman image of a new era was presented through a new look, and Dior's femininity means a woman that reflects the character of the times as is interpreted as her own personality from the perspective of modernism through the creative directors of future generations. The brand's core identity code 'Miss Dior' expresses the brand's vision and eternity through perfume as well as targets Z generation male consumers through an emotional approach based on forms that used emotional images such as movie-type films.

• Journal of Information Technology Services
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• v.21 no.2
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• pp.97-107
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• 2022

Recently, interest in smart factories is increasing. Investments to improve intelligence/automation are also being made continuously in manufacturing plants. Facility automation based on sensor data collection is now essential. In addition, we are operating our factories based on data generated in all areas of production, including production management, facility operation, and quality management, and an integrated standard information system. When producing LCD polarizer products, it is most important to link trace information between data generated by individual production processes. All systems involved in production must ensure that there is no data loss and data integrity is ensured. The large-capacity data collected from individual systems is composed of key values linked to each other. A real-time quality analysis processing system based on connected integrated system data is required. In this study, large-capacity data collection, storage, integration and loss prevention methods were presented for optimization of LCD polarizer production. The identification Risk model of inspection products can be added, and the applicable product model is designed to be continuously expanded. A quality inspection and analysis system that maximizes the yield rate was designed by using the final inspection image of the product using big data technology. In the case of products that are predefined as analysable products, it is designed to be verified with the big data knn analysis model, and individual analysis results are continuously applied to the actual production site to operate in a virtuous cycle structure. Production Optimization was performed by applying it to the currently produced LCD polarizer production line.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.27-33
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• 2021

With increasing interest in key areas of the 4th industrial revolution such as artificial intelligence, deep learning and big data, scientific approaches have developed in order to overcome the limitations of traditional decision-making methodologies. These scientific techniques are mainly used to predict the direction of financial products. In this study, the factors of apartment prices, which are of high social interest, were analyzed through SOM. For this analysis, we extracted the real prices of the apartments and selected a total of 16 input variables that would affect these prices. The data period was set from 1986 to 2021. As a result of examining the characteristics of the variables during the rising and faltering periods of the apartment prices, it was found that the statistical tendencies of the input variables of the rising and the faltering periods were clearly distinguishable. I hope this study will help us analyze the status of the real estate market and study future predictions through image learning.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.718-727
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• 2022

The Korean domestic cartoon industry has grown explosively by 65% compared to the previous year. Then the market size is expected to exceed KRW 1 trillion. However, excessive work results in health deterioration. Moreover, this working environment makes the production of human resources insufficient, repeating a vicious cycle. Although some tasks require creation activity during cartoon production, there are still a lot of simple repetitive tasks. Therefore, this study aimed to develop a method for creating a character pose through human pose estimation (HPE). The HPE is to detect key points for each joint of a user. The primary role of the proposed method was to make each joint of the character match that of the human. The proposed method enabled us to create the pose of the two-dimensional cartoon character through the results. Furthermore, it was possible to save the static image for one character pose and the video for continuous character pose.

• Smart Media Journal
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• v.11 no.11
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• pp.92-98
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• 2022

Identifying defects in textiles is a key procedure for quality control. This study attempted to create a model that detects defects by analyzing the images of the fabrics. The models used in the study were deep learning-based VGGNet and ResNet, and the defect detection performance of the two models was compared and evaluated. The accuracy of the VGGNet and the ResNet model was 0.859 and 0.893, respectively, which showed the higher accuracy of the ResNet. In addition, the region of attention of the model was derived by using the Grad-CAM algorithm, an eXplainable Artificial Intelligence (XAI) technique, to find out the location of the region that the deep learning model recognized as a defect in the fabric image. As a result, it was confirmed that the region recognized by the deep learning model as a defect in the fabric was actually defective even with the naked eyes. The results of this study are expected to reduce the time and cost incurred in the fabric production process by utilizing deep learning-based artificial intelligence in the defect detection of the textile industry.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.474-476
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• 2021

Glioblastoma is the most common brain malignancies arising from glial cells. Early diagnosis and treatment plan establishment are important, and cancer is diagnosed mainly through T1CE imaging through injection of a contrast agent. However, the risk of injection of gadolinium-based contrast agents is increasing recently. Region segmentation that marks cancer regions in medical images plays a key role in CAD systems, and deep neural network models for synthesizing new images are also being studied. In this study, we propose a model that simultaneously learns the generation of T1CE images and segmentation of cancer regions. The performance of the proposed model is evaluated using similarity measurements including mean square error and peak signal-to-noise ratio, and shows average result values of 21 and 39 dB.