• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.251-253
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• 2019

This paper describes an implementation of a security SoC (System-on-Chip) prototype that interfaces a microprocessor with a block cipher crypto-core. The Cortex-M0 was used as a microprocessor, and a crypto-core implemented by integrating ARIA and AES into a single hardware was used as an intellectual property (IP). The integrated ARIA-AES crypto-core supports five modes of operation including ECB, CBC, CFB, CTR and OFB, and two master key sizes of 128-bit and 256-bit. The integrated ARIA-AES crypto-core was interfaced to work with the AHB-light bus protocol of Cortex-M0, and the crypto-core IP was expected to operate at clock frequencies up to 50 MHz. The security SoC prototype was verified by BFM simulation, and then hardware-software co-verification was carried out with FPGA implementation.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.51-60
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• 2021

Recent advances in network and hardware technologies have led to active research and multiple network technologies that fuse blockchain technologies with security. We propose a system model that analyzes technologies using existing blockchain and verifies the integrity of files using private blockchain in a limited environment. The proposed model can be written as a chain code of Hyperleisure Fabric, a private blockchain platform, and verified for integrity of files through Hyperleisure Explorer, a private blockchain integrated management platform. The system performance of the proposed model was analyzed from a developer perspective and from a user perspective. As a result of the analysis, there are compatibility problems according to the version of various modules to run the blockchain platform, and only limited elements such as chain code status and groups can be checked.

• Journal of Digital Convergence
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• v.19 no.1
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• pp.141-149
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• 2021

Due to the rapid rise of artificial intelligence technology around the world, SW education conducted in elementary and secondary schools is expanding including AI education. Therefore, this study aims to present an AI education system based on 4P(Play, Problem Solving, Product Making, Project) that can be applied from kindergarten to high school. The AI education system presented in this study is designed to be applied in 4P-based Play, Problem Solving, Product Making, and Project 4 stages so that it can be applied by school age and step by step. The level was presented by dividing it into two areas: AI literacy and AI development. In order to verify the validity of the developed AI education system, the Delphi method was applied to 15 experts who had experience in SW education or AI education. The AI education system derived as a result of the verification will be able to contribute to the development of a content system for AI education at each school level in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.1006-1014
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• 2020

Consumer expectations for drama VFX increased as the influx of overseas dramas increased due to the growth of OTT service, but the current production time of the drama production system has a negative effect on the quality and completion of work. This paper proposes a background rendering using a game engine and a subject rendering using background HDRI as environmental lighting. The proposed workflow is expected to reduce the production time of VFX and improve the quality of VFX. The proposed workflow was verified in 3 steps. Comparing game engine and rendering software according to the same lighting environment and camera distance, comparing rendering with existing rendering method and background HDRI generation, and validating the proposed workflow. For verification, quantitative evaluation was performed using Structual similarity index and histogram. This study is expected to be one of the options to improve the quality of VFX and improve the risk.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1411-1416
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• 2020

In this paper, we propose a method of extraction analysis of blood flow area on color doppler ultrasonography by using 4-directional contour tracking and K-Means algorithm. In the proposed method, ROI is extracted and a binarization method with maximum contrast as a threshold is applied to the extracted ROI. 4-directional contour algorithm is applied to extract the trapezoid shaped region which has blood flow area of brachial artery from the binarized ROI. K-Means based quantization is then applied to accurately extract the blood flow area of brachial artery from the trapezoid shaped region. In experiment, the proposed method successfully extracts the target area in 28 out of 30 cases (93.3%) with field expert's verification. And comparison analysis of proposed K-Means based blood flow area extraction on 30 color doppler ultrasonography and brachial artery blood flow ultrasonography provided by a specialist yielded a result of 94.27% accuracy on average.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.1
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• pp.41-55
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• 2005

A group key agreement protocol allows a group of user to share a key which may later be used to achieve certain cryptographic goals. In this paper, we propose a new scalable two-round ID-based group key agreement protocol which would be well fit to a Pay-TV system, additionally. to the fields of internet stock quotes, audio and music deliveries, software updates and the like. Our protocol improves the three round poop key agreement protocol of Nam et al., resulting in upgrading the computational efficiency by using the batch verification technique in pairing-based cryptography. Also our protocol simplifies the key agreement procedures by utilizing ID-based system. We prove the security of our protocol under the Computational Diffie-Hellman assumption and the Bilinear Decisional Diffie-Hellman assumption. Also we analyze its efficiency.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.3
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• pp.11-25
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• 2009

Fingerprint sample quality is one of major factors influencing the matching performance of fingerprint recognition systems. The error rates of fingerprint recognition systems can be decreased significantly by removing poor quality fingerprints. The purpose of this paper is to assess the effectiveness of individual sample quality measures on the performance of minutiae-based fingerprint recognition algorithms. Initially, the authors examined the various factors that influenced the matching performance of the minutiae-based fingerprint recognition algorithms. Then, the existing measures for fingerprint sample quality were studied and the more effective quality measures were selected and compared with two image quality software packages, (NFIQ from NIST, and QualityCheck from Aware Inc.) in terms of matching performance of a commercial fingerprint matcher (Verifinger 5.0 from Neurotechnologija). The experimental results over various Fingerprint Verification Competition (FVC) datasets show that even a single sample quality measure can enhance the matching performance effectively.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.2
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• pp.15-21
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• 2010

The user authentication using fingerprint information not only provides the convenience but also high security. However, the fingerprint information for user authentication can cause serious problems when it has been compromised. It cannot change like passwords, because the user only has ten fingers on two hands. Recently, there is an increasing research of the fuzzy fingerprint vault system to protect fingerprint information. The research on the problem of fingerprint alignment using geometric hashing technique carried out. This paper proposes the hardware architecture fuzzy fingerprint vault system based on geometric hashing. The proposed architecture consists of software and hardware module. The hardware module has charge of matching between enrollment hash table and verification hash table. Based on the experimental results, the execution time of the proposed system with 36 real minutiae is 0.2 second when 100 chaff minutiae, 0.53 second when 400 chaff minutiae.

• Animal Bioscience
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• v.34 no.4
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• pp.770-782
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• 2021

Objective: The aim of the present study was to compare the effectiveness of three approaches (the seasonal auto-regressive integrated moving average [SARIMA] model, the nonlinear autoregressive exogenous [NARX] artificial neural networks and Wood's model) to the prediction of milk yield during lactation. Methods: The dataset comprised monthly test-day records from 965 Polish Holstein-Friesian Black-and-White primiparous cows. The milk yields from cows in their first lactation (from 5 to 305 days in milk) were used. Each lactation was divided into ten lactation stages of approximately 30 days. Two age groups and four calving seasons were distinguished. The records collected between 2009 and 2015 were used for model fitting and those from 2016 for the verification of predictive performance. Results: No significant differences between the predicted and the real values were found. The predictions generated by SARIMA were slightly more accurate, although they did not differ significantly from those produced by the NARX and Wood's models. SARIMA had a slightly better performance, especially in the initial periods, whereas the NARX and Wood's models in the later ones. Conclusion: The use of SARIMA was more time-consuming than that of NARX and Wood's model. The application of the SARIMA, NARX and Wood's models (after their implementation in a user-friendly software) may allow farmers to estimate milk yield of cows that begin production for the first time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.57-64
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• 2020

Recently, due to the proliferation of IoT sensors, the development of big data and artificial intelligence, time series prediction research on fine dust pollution is actively conducted. However, because the data representing fine dust contamination changes rapidly, traditional time series prediction methods do not provide a level of accuracy that can be used in the field. In this paper, we propose a method that reflects the classification results of environmental conditions through CNN when predicting micro dust contamination using LSTM. Although LSTM and CNN are independent, they are integrated into one network through the interface, so this method is easier to understand than the application LSTM. In the verification experiments of the proposed method using Beijing PM2.5 data, the prediction accuracy and predictive power for the timing of change were consistently improved in various experimental cases.