• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.8A
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• pp.847-857
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• 2007

For the implementation of a RFID system, an anti-collision algorithm is required to identify multiple tags within the range of a RFID Reader. A Bit-by-Bit algorithm is defined by Auto ID Class 0. In this paper, we propose a SBBB(Stack Bit-by-Bit) algorithm. The SBBB algorithm save the collision position and makes a query using the saved data. SBBB improve the efficiency of collision resolution. We show the performance of the SBBB algorithm by simulation. The performance of the proposed algorithm is higher than that of BBB algorithm. Especially, the more each tag bit streams are the duplicate, the higher performance is.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.6A
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• pp.63-73
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• 2008

RFID(Radio Frequency IDentification) system is an automated identification system that basically consists of tags and readers and Back-End-Databases. Tags and Readers communicate with each other by RF signal. As a reader can identify many tags in contactless manner using RF signal, RFID system is expected to do a new technology to replace a bar-code system in supply-chain management and payment system and access control and medical record and so on. However, RFID system creates new threats to the security of systems and privacy of individuals, Because tags and readers communicate with each other in insecure channel using RF signal. So many people are trying to study various manners to solve these problems against attacks, But they are difficult to apply to RFID system based on EPCglobal UHF Class-1 Generation-2 tags. Recently, Chien and Chen proposed a mutual Authentication protocol for RFID conforming to EPCglobal UHF Class-1 Generation-2 tags. we discover vulnerabilities of security and inefficiency about their protocol. Therefore, We analyze vulnerabilities of their protocol and propose an efficient mutual authentication protocol that improves security and efficiency.

• Journal of Gifted/Talented Education
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• v.23 no.2
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• pp.257-273
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• 2013

The purpose of this study was to explore a more useful identification method by comparing diverse selection approaches for the gifted education programs at the each elementary school. Diverse selection methods examined in the study include 'written examinations', 'mixed evaluation', 'achievement test scores', and 'self-recommendation'. For the study, each identification group's gifted students' characteristics, such as intelligence, creativity, motivation and self-regulated learning strategies, were compared. The subjects of the study were a total of 594 gifted and normal students. The results of this study were as follows: First, there were no statistically significant differences between students in each gifted education class and gifted students who belong to the regional gifted education programs which are considered higher level of gifted education programs. While, there were statistically significant differences between two groups of gifted students and general students in all aspects examined, such as intelligence, creativity, motivation and learning strategies. In addition and most importantly, diverse identification method utilized in each school showed differences in gifted students' characteristics. Especially, students who were selected through the self-recommendation showed significantly lower intelligence, creativity, motivation and learning strategies. The implications of the study related to the identification and education for the gifted at each elementary school were discussed in depth.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.233-238
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• 2017

Automatic identification of disease in plants from their leaves is one of the most challenging task to researchers. Diseases among plants degrade their performance and results into a huge reduction of agricultural products. Therefore, early and accurate diagnosis of such disease is of the utmost importance. The advancement in deep Convolutional Neural Network (CNN) has change the way of processing images as compared to traditional image processing techniques. Deep learning architectures are composed of multiple processing layers that learn the representations of data with multiple levels of abstraction. Therefore, proved highly effective in comparison to many state-of-the-art works. In this paper, we present a plant disease identification methodology from their leaves using deep CNNs. For this, we have adopted GoogLeNet that is considered a powerful architecture of deep learning to identify the disease types. Transfer learning has been used to fine tune the pre-trained model. An accuracy of 85.04% has been recorded in the identification of four disease class in Apple plant leaves. Finally, a comparison with other models has been performed to show the effectiveness of the approach.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8A
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• pp.794-804
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• 2008

In this paper, we analyze the performance of probabilistic slotted anti-collision algorithm used in EPCglobal Class-1 Generation-2 (Gen2). To increase throughput and system efficiency, and to decrease tag identification time and collision ratio, we propose new tag anti-collision algorithms, which are FAFQ (fired adjustable flamed Q) algorithm and AAFQ (adaptive adjustable framed Q) algorithm, by using QueryAdjust command. We also propose grouping method based on Gen2 to improve the efficiency of tag identification. The simulation results show that all the proposed algorithms outperform Q algorithm, and AAFQ algorithm performs the best. That is, AAFQ has an increment of 5% of system efficiency and a decrement of 4.5% of collision ratio. For FAFQ and AAFQ algorithm, the performance of grouping method is similar to that of ungrouping method. However, for Q algorithm in Gen2, grouping method can increase throughput and system efficiency, and decrease tag identification time and collision ratio compared with ungrouping method.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.7
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• pp.4167-4175
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• 2013

A najor current challenge and constraint in cervical cancer research is the development of vaccines against human papilloma virus (HPV) epitopes. Although many studies are done on epitope identification on HPVs, no computational work has been carried out for high risk forms which are considered to cause cervical cancer. Of all the high risk HPVs, HPV 16, HPV 18 and HPV 45 are responsible for 94% of cervical cancers in women worldwide. In this work, we computationally predicted the promiscuous epitopes among the E6 proteins of high risk HPVs. We identified the conserved residues, HLA class I, HLA class II and B-cell epitopes along with their corresponding secondary structure conformations. We used extremely precise bioinformatics tools like ClustalW2, MAPPP, NetMHC, Epi,Jen, EpiTop 1.0, ABCpred, BCpred and PSIPred for achieving this task. Our study identified specific regions 'FAFR(K)DL' followed by 'KLPD(Q)LCTEL' fragments which proved to be promiscuous epitopes present in both human leukocyte antigen (HLA) class I, class II molecules and B cells as well. These fragments also follow every suitable character to be considered as promiscuous epitopes with supporting evidences of previously reported experimental results. Thus, we conclude that these regions should be considered as the important for design of specific therapeutic vaccines for cervical cancer.

• Proceedings of the Korean Aquaculture Society Conference
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• 2003.10a
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• pp.44-44
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• 2003

Gene expression in five tissues of the abalone (Haliotis discus hannai) was investigated using an expressed sequence tag (EST) analysis. Randomly selected clones were obtained from cDNA libraries constructed with gill (GI), digestive diverticula(DD), hepatopancreas (HP), foot/mucus (FM) and rectangular muscle (RM). Of 1,235 clonesanalyzed (288 clones for GI, DD, HP each,166 for FM, and 205 for RM), 741 (60.0%) clones in total turned out to share significant similarity with the sequences from NCBI GenBank (less than 10/sup -3/ of e-values), 423 sequences showed poor similarity (> 10/sup -3/), and 71 sequences didn't match with any sequences in GenBank. The percent unique sequence (singleton) was ranged from 56.1% (RM) to 74.7% (FM) among libraries. On the other hand, overall percent singleton was 55.3% when all the ESTs from five libraries were assembled into contigs. Analysis of the organisms represented by the best hit for each EST (e-values < 10/sup -3/) showed that 23.8% matched with mammalian entries, 24.0% with mollusks, 14.4% with insects, 11.6% with fish and 26.2% with others. The expressed patterns differed among the tissues when judged by the categorization of the sequences from each library into 10 broad functional classes. In all the libraries, the class I (no hit o. poor similarity) was the largest category with an average of 40.1%. This largest class was followed by class V (general metabolisms) in DD (21.9%), GI (14.6%) and HP (16.7%), while the 'cell structure and motility'(class VI) was the second largest class in remaining two libraries (31.2% for RM and 9.6% for FM). The class IX (cell division and proliferation) was the smallest class in all the libraries (less than 3%). This report provides the first tissue-specific lists of expressed abalone genes, which could be a fundamental basis for genomics program of abalone species.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.19-28
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• 2024

This paper proposes a method to improve the performance of ship identification through lofargram analysis of ship noise by applying the Hough Transform to a Convolutional Neural Network (CNN) model. When processing the signals received by a passive sonar, the time-frequency domain representation known as lofargram is generated. The machinery noise radiated by ships appears as tonal signals on the lofargram, and the class of the ship can be specified by analyzing it. However, analyzing lofargram is a specialized and time-consuming task performed by well-trained analysts. Additionally, the analysis for target identification is very challenging because the lofargram also displays various background noises due to the characteristics of the underwater environment. To address this issue, the Hough Transform is applied to the lofargram to add lines, thereby emphasizing the tonal signals. As a result of identification using CNN models on both the original lofargrams and the lofargrams with Hough transform, it is shown that the application of the Hough transform improves lofargram identification performance, as indicated by increased accuracy and macro F1 scores for three different CNN models.

• Journal of Preventive Medicine and Public Health
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• v.38 no.4
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• pp.443-448
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• 2005

Objectives : This study was done to evaluate the reliability of education and occupational class between using the health survey and the death certificate data. Methods : The 1998 National Health and Nutrition Examination Survey (NHANES) was conducted on a cross-sectional probability sample of South Korean households, and it contained unique 13-digit personal identification numbers that were linked to the data on mortality from the Korean National Statistical Office. The data from 263 deaths were used to estimate the agreement rates and the Kappa indices of the education and occupational class between using the NHANES data and the death certificate data. Results : The simple and weighted Kappa indices for education were 0.60 (95% CI=0.53-0.68) and 0.73 (95% CI=0.67-0.79) respectively, if the educational level was grouped into five categories: no-formal-education, elementary-school, middle-school, high-school and college or over. The overall agreement rate was 71.9% for these educational groups. The magnitude of reliability, as measured by the overall agreement rates and Kappa indices, tended to increase with a decrease in the educational class. The number of non-educated people with using the death certificate data was smaller than that with using the NHANES data. For the occupational class (manual workers, non-manual workers and others), the Kappa index was 0.40 (95% CI=0.30-0.51), which was relatively lower than that for the educational class. Compared with the NHANES, the number of non-manual workers for the deceased who were aged 30-64 tended to be increased (8 to 12) when using the death certificate data, whereas the number of manual workers tended to be decreased (59 to 41). Conclusions : The socioeconomic inequalities in the mortality rates that were based on the previous unlinked studies in South Korea were not due to a numerator/denominator bias. The mortality rates for the manual workers and the no-education groups might have been underestimated.

• Korean Journal of Environmental Biology
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• v.38 no.3
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• pp.433-449
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• 2020

Nine fish and one clam species were collected from freshwater environments in Korea, including four lakes, two streams, and the Nakdong River, to investigate the host-associated bacteria. Hundreds of bacterial strains were isolated from the samples using a cell sorter and a dilution plating method. After identification of the bacterial strains using 16S rRNA gene sequences, 42 strains with greater than 98.7% sequence similarity with validly published species were determined to be unrecorded bacterial species in Korea. These strains were phylogenetically diverse and assigned to four phyla, six classes, 17 orders, 27 families, and 32 genera. At the genus level, the unrecorded species were classified as Corynebacterium, Mycobacterium, Mycolicibacterium, Gordonia, Williamsia, Modestobacter, Brachybacterium, Sanquibacter, Arthrobacter, and Mycolicibacterium of the class Actinobacteria; Empedobacter, and Flavobacterium of the class Flavobacteriia; Fictibacillus, Psychrobacillus, Cohnella, Paenibacillus, Rummeliibacillus, Enterococcus, and Vagococcus of the class Bacilli; Aquamicrobium, Paracoccus, and Sphingomonas of the class Alphaproteobacteria; Achromobacter, Delftia, and Deefgea of the class Betaproteobacteria; and Aeromonas, Providencia, Yersinia, Marinomonas, Acinetobacter, and Pseudomonas of the class Gammaproteobacteria. The 42 unrecorded species were subjected to further taxonomic characterization using gram staining, cellular and colony morphological determination, biochemical analyses, and phylogenetic analyses. This paper provides detailed descriptions of the 42 previously unrecorded bacterial species.