• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.57-60
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• 2020

순위다중패턴매칭문제는 길이가 n인 텍스트 T와 패턴들의 집합 P' = {P₁,P₂…,P_k}가 주어졌을 때, P'에 속하는 패턴들과 상대적인 순위가 일치하는 T의 모든 부분문자열들의 위치를 찾는 문제이다. P'에서 가장 짧은 패턴의 길이가 m, 가장 긴 패턴의 길이를 $\bar{m}$, 모든 패턴들의 길이의 합을 M, q개의 연속된 문자들을 q-그램이라 할 때, 기존에 텍스트의 핑거프린트를 이용하여 순위다중패턴매칭문제를 $O(q!+nqlogq+Mlog\bar{m}+nM)$ 시간에 해결하는 알고리즘이 제시되었다. 본 논문에서는 텍스트의 핑거프린트를 활용하여 O(max(q!,M,n))개의 스레드를 이용하여 순위다중패턴매칭문제를 평균적으로 $O(\bar{m}+qlogq+n/q!)$ 시간에 해결하는 병렬 구현 방법을 제시한다. 실험 결과, n = 1,000,000, k = 1,000, m = 5, q = 3일 때, 본 논문에서 제시하는 병렬 구현 방법은 기존의 순차 알고리즘보다 약 19.8배 빠르게 수행되었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.458-459
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• 2021

Recently, researches using deep learning technology based on Wi-Fi fingerprints have been conducted for accurate services in indoor location-based services. Among the deep learning models, an RNN model that can store information from the past can store continuous movements in indoor positioning, thereby reducing positioning errors. At this time, continuous sequential data is required as training data. However, since Wi-Fi fingerprint data is generally managed only with signals for a specific location, it is inappropriate to use it as training data for an RNN model. This paper proposes a path generation method through prediction of a moving path based on Wi-Fi fingerprint data extended to region data through clustering to generate sequential input data of the RNN model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.456-457
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• 2021

Recently, in order to more accurately provide indoor location-based services, technologies using Wi-Fi fingerprints and deep learning are being studied. Among the deep learning models, an RNN model that can store information from the past can store continuous movements in indoor positioning, thereby reducing positioning errors. When using an RNN model for indoor positioning, the collected training data must be continuous sequential data. However, the Wi-Fi fingerprint data collected to determine specific location information cannot be used as training data for an RNN model because only RSSI for a specific location is recorded. This paper proposes a region clustering technique for sequential input data generation of RNN models based on Wi-Fi fingerprint data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.490-491
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• 2021

Recently, technologies using Wi-Fi fingerprints and deep learning are being studied to provide accurate location-based services in an indoor environment. At this time, the composition of learning data is very important, and it is essential to collect sufficient data necessary for learning. However, the number of specific points for the collection of radio signal data within the area requiring positioning is infinite, and it is impossible to collect all of these data. Therefore, there is a need for a way to make up for insufficient learning data. This study proposes a method of constructing a sufficient number of location data necessary for learning based on insufficiently collected location data.

• Convergence Security Journal
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• v.22 no.4
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• pp.141-146
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• 2022

The iris, a biometric information, is safe, unique, and reliable, such as fingerprints, and is personal information that can significantly lower the misrecognition rate than other biometric authentication. However, due to the nature of biometric authentication, it is impossible to replace it if it is stolen. There is a case in which an actual iris photo is taken and 3d printed so that the eyes work as if they were in front of the camera. As such, there is a possibility of iris leakage through high-definition images and photos. In this paper, we propose to improve iris masking performance by supplementing iris region masking research based on existing blurring techniques. Based on the results derived in this study, it is expected that it can be used for the security of video conference programs and electronic devices.

• Journal of Microbiology
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• v.44 no.6
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• pp.591-599
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• 2006

To address the diversity of cyclodextrin-producing P. graminis strains isolated from wheat roots and rhizospheres of maize and sorghum sown in Australia, Brazil, and France, restriction fragment length polymorphism analysis of part of genes encoding RNA polymerase (rpoB-RFLP) and DNA gyrase subunit B (gyrB-RFLP) was used to produce genetic fingerprints. A phylogenetic tree based on rpoB gene sequences was also constructed. The isolates originated from Brazil could be separated from those from Australia and France, when data from the rpoB-based phylogenetic tree or gyrB-RFLP were considered. These analyses also allowed the separation of all P. graminis strains studied here into four clusters; one group formed by the strains GJK201 and $RSA19^T$, second group formed by the strains MC22.02 and MC04.21, third group formed by the strains TOD61, TOD 221, TOD302, and TOD111, and forth group formed by all strains isolated from plants sown in Cerrado soil, Brazil. As this last group was formed by strains isolated from sorghum and maize sown in the same soil (Cerrado) in Brazil, our results suggest that the diversity of these P. graminis strains is more affected by the soil type than the plant from where they have been isolated.

• Journal of Digital Convergence
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• v.12 no.4
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• pp.319-326
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• 2014

According to the increasing mobile security users who have experienced authentication failure by forgetting passwords, user names, or a response to a knowledge-based question have preference for biological information such as hand geometry, fingerprints, voice in personal identification and authentication. Therefore biometric verification of personal identification and authentication for mobile security provides assurance to both the customer and the seller in the internet. Our study focuses on human hand biometric information recognition system for personal identification and personal Authentication, including its shape, palm features and the lengths and widths of the fingers taken from mobile phone photographs such as iPhone4 and galaxy s2. Our hand biometric information recognition system consists of six steps processing: image acquisition, preprocessing, removing noises, extracting standard hand feature extraction, individual feature pattern extraction, hand biometric information recognition for personal identification and authentication from input images. The validity of the proposed system from mobile phone image is demonstrated through 93.5% of the sucessful recognition rate for 250 experimental data of hand shape images and palm information images from 50 subjects.

• Asian-Australasian Journal of Animal Sciences
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• v.17 no.9
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• pp.1296-1302
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• 2004

This study was conducted to evaluate the possible application of 2 D-SDS-PAGE (2 DE)-based proteome analysis techniques to the assessment of extreme proteolysis in postmortem skeletal muscle. Eight Hanwoo longissimus muscles were incubated immediately after slaughter for 24 h at 5$^{\circ}C$, 15$^{\circ}C$ or 36$^{\circ}C$. Warner Bratzler (WB)-shear force and ultrastructural configuration were determined at 24 h, and rate of proteolysis to 24 h was determined by 1 D-SDS-PAGE (1 DE) and 2 DE. In addition, tentative protein identification was performed from peptide mass fingerprints of MALDI-ToF analysis of major protein groups on 2 DE profiles. The result showed that although ultrastructural configuration was similar between the 5$^{\circ}C$ and 36$^{\circ}C$ treatments, meat at 5$^{\circ}C$ had higher WBshear force (approximately 5 kg greater). A higher rate of protein degradation at 36$^{\circ}C$ was observed based on Troponin-T degradation, 1 DE, and 2 DE analysis. This indicates that proteolysis during the early postmortem period was a significant determinant of shear force at 24 h. Little difference in proteolysis between 5$^{\circ}C$ and 15$^{\circ}C$ treatments was found based on classic 1 DE profile assessment. Meanwhile, considerable differences in the 2 DE profiles between the two treatments were revealed, with substantially higher rate of proteolysis at 15$^{\circ}C$ compared to 5$^{\circ}C$. Nuclease treatment improved 2 DE profile resolution. 400 ${\mu}$g and 600 ${\mu}$g of sample loading appeared to be appropriate for 24 cm pH 3-10 and pH 5-7 IPG strips, respectively. Protein detection and quantification of the 5$^{\circ}C$, 15$^{\circ}C$ and 36$^{\circ}C$ 2 DE profiles revealed 78, 163 and 232 protein spots respectively that were differentially modified in terms of their electrophoretic properties between approximately pI 5.3-7.7 with the molecular weight range of approximately 71-12 kDa. The current results demonstrated that 2 DE was a superior tool to 1 DE for characterising proteolysis in postmortem skeletal muscle.

• The Transactions of the Korea Information Processing Society
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• v.1 no.2
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• pp.215-224
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• 1994

In this paper, the rotation and size invariant fingerprint recognition using the neural network EART (Extended Adaptive Resonance Theory) is studied ($515{\times}512$) gray level fingerprint images are converted into the binary thinned images based on the adaptive threshold and a thinning algorithm. From these binary thinned images, we extract the ending points and the bifurcation points, which are the most useful critical feature points in the fingerprint images, using the $3{\times}3$ MASK. And we convert the number of these critical points and the interior angles of convex polygon composed of the bifurcation points into the 40*10 critical using the weighted code which is invariant of rotation and size as the input of EART. This system produces very good and efficient results for the rotation and size variations without the restoration of the binary thinned fingerprints.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.1
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• pp.241-259
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• 2014

A PUF is a technology for distinguishing a device from other devices like biological information such as humans' iris or fingerprints. Over the past decade, many researchers studied various methods for implementing PUFs and utilizing them in identification, random number generation, key distribution and authentication. However, various attacks on the PUFs are the major reason to inhibiting the proliferation of PUF. For the reasons, various technologies are being studied to enhance safety of PUFs. In this paper, we will see several PUF implementations and various attacks on PUFs, and suggest guidelines for securely implementing PUFs. We expect our guidelines would be the foundation for implementing the secure and reliable PUFs.