• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2657-2675
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• 2013

A novel neighbor selection-based fingerprinting algorithm using matrix correlation (MC) for Wi-Fi localization is presented in this paper. Compared with classic fingerprinting algorithms that usually employ a single received signal strength (RSS) sample, the presented algorithm uses multiple on-line RSS samples in the form of a matrix and measures correlations between the on-line RSS matrix and RSS matrices in the radio-map. The algorithm makes efficient use of on-line RSS information and considers RSS variations of reference points (RPs) for localization, so it offers more accurate localization results than classic neighbor selection-based algorithms. Based on the MC algorithm, an error estimation method using artificial neural network is also presented to fuse available information that includes RSS samples and localization results computed by the MC algorithm and model the nonlinear relationship between the available information and localization errors. In the on-line phase, localization errors are estimated and then used to correct the localization results to reduce negative influences caused by a static radio-map and RP distribution. Experimental results demonstrate that the MC algorithm outperforms the other neighbor selection-based algorithms and the error estimation method can reduce the mean of localization errors by nearly half.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.536-544
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• 2017

Wireless RSSI (Received Signal Strength Indication) fingerprinting is one of the most popular methods for indoor positioning as it provides reasonable accuracy while being able to exploit existing wireless infrastructure. However, the process of radio map construction (aka fingerprint calibration) is laborious and time consuming as precise physical coordinates and wireless signals have to be measured at multiple locations of target environment. This paper proposes a method to build the map from a combination of RSSIs without location information collected in a crowdsourcing fashion, and a handful of labeled RSSIs using a semi-supervised self organizing map learning algorithm. Experiment on simulated data shows promising results as the method is able to recover the full map effectively with only 1% RSSI samples from the fingerprint database.

• The KIPS Transactions:PartD
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• v.15D no.5
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• pp.713-720
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• 2008

Fingerprinting scheme uses digital watermarks to trace originator of unauthorized or pirated copies, however, multiple users may collude and escape identification by creating an average or median of their individually watermarked copies. Previous research works are based on ACC (anti-collusion code) for identifying each user, however, ACC are shown to be resilient to average and median attacks, but not to LCCA and cannot support large number of users. In this paper, we propose a practical SACC (scalable anti-collusion code) scheme and its angular decoding strategy to support a large number of users from basic ACC (anti-collusion code) with LCCA (linear combination collusion attack) robustness. To make a scalable ACC, we designed a scalable extension of ACC codebook using a Gaussian distributed random variable, and embedded the resulting fingerprint using human visual system based watermarking scheme. We experimented with standard test images for colluder identification performance, and our scheme shows good performance over average and median attacks. Our angular decoding strategy shows performance gain over previous decoding scheme on LCCA colluder set identification among large population.

• The Korean Journal of Applied Statistics
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• v.15 no.1
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• pp.85-105
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• 2002

To explore the relationships among frequencies for sets of alleles, within or between loci, is one of the first analyses in population genetic study. The general question is whether the frequency of a set of alleles is the same as the product of each of the separate allele frequencies. For two alleles of a single locus, Hardy-Weinberg equilibrium is tested and for an allele from each of two loci, linkage disequilibrium is tested. However, it is more useful if we can quantify and graphically represent this information. In this study, we suggest graphical methods to find associations between alleles. We also analyze the STR data of Korean population as an illustration.

• Journal of Microbiology and Biotechnology
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• v.14 no.3
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• pp.599-606
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• 2004

Gram-positive antagonistic bacilli were isolated from agricultural soils for possible use in biocontrol of plant pathogenic fungi, Fusarium oxysporum, Rhizoctonia solani, and/or Pythium ultimum. Among the 65 antagonistic Gram-positive soil isolates, 22 strains were identified as Bacillus species by 16S rDNA sequence analyses. Four strains, including DF14, especially exhibited multiple antagonistic properties against the three damping-off fungi. Genotypic properties of the Bacillus isolates were characterized by rapid molecular fingerprinting methods using repetitive extragenic palindromic-PCR (REP-PCR), ribosomal intergenic spacer-length polymorphisms (RIS-LP), 16S rDNA PCR-restriction fragment length polymorphisms (PCR-RFLP), and strain-specific PCR assays. The results indicated that the REP-PCR method was more valuable than the RIS-LP and 16S rDNA PCR-RFLP analyses as a rapid and reliable approach for bacilli typing and identification. The use of strain-specific primers designed based on 16S rDNA sequence comparisons enabled it to be possible to selectively detect a strain, DF14, which is being used as a biocontrol agent against damping-off fungi.

• Journal of Integrative Natural Science
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• v.4 no.3
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• pp.238-242
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• 2011

For any pattern matching based algorithm in WLAN environment, the characteristics of signal to noise ratio(SNR) to multiple access points(APs) are utilized to establish database in the training phase, and in the estimation phase, the actual two dimensional coordinates of mobile unit(MU) are estimated based on the comparison between the new recorded SNR and fingerprints stored in database. The system that uses the artificial neural network(ANN) falls in a local minima when it learns many nonlinear data, and its classification accuracy ratio becomes low. To make up for this risk, the SVM/ANN hybrid algorithm is proposed in this paper. The proposed algorithm is the method that ANN learns selectively after clustering the SNR data by SVM, then more improved performance estimation can be obtained than using ANN only and The proposed algorithm can make the higher classification accuracy by decreasing the nonlinearity of the massive data during the training procedure. Experimental results indicate that the proposed SVM/ANN hybrid algorithm generally outperforms ANN algorithm.

• Biomolecules & Therapeutics
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• v.25 no.6
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• pp.559-568
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• 2017

Metabolomics has been used as a powerful tool for the analysis and quality assessment of the natural product (NP)-derived medicines. It is increasingly being used in the quality control and standardization of NP-derived medicines because they are composed of hundreds of natural compounds. The most common techniques that are used in metabolomics consist of NMR, GC-MS, and LC-MS in combination with multivariate statistical analyses including principal components analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). Currently, the quality control of the NP-derived medicines is usually conducted using HPLC and is specified by one or two indicators. To create a superior quality control framework and avoid adulterated drugs, it is necessary to be able to determine and establish standards based on multiple ingredients using metabolic profiling and fingerprinting. Therefore, the application of various analytical tools in the quality control of NP-derived medicines forms the major part of this review. $Veregen^{(R)}$ (Medigene AG, Planegg/Martinsried, Germany), which is the first botanical prescription drug approved by US Food and Drug Administration, is reviewed as an example that will hopefully provide future directions and perspectives on metabolomics technologies available for the quality control of NP-derived medicines.

• Korean Journal of Veterinary Research
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• v.39 no.4
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• pp.811-818
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• 1999

A total of 16 Staphylococcus epidermidis isolates collected from 14 dogs admitted to the Veterinary Medicial Teaching Hospital in Seoul National University over eleven months were examined for in vitro antibiotic susceptibility pattern with minimum inhibitory concentration (MIC) and slime production, a virulence-associated phenotype, and were genetically characterized by pulsed-field gel electrophoresis (PFGE). The frequency of resistance to antimicrobial agents tested was not high, with a susceptibility ranging from 56.3% to 100%. Three strains exhibited multiple drug resistance against amikacin (MIC, $32-64{\mu}g/ml$), ampicillin ($32{\mu}g/ml$), fosfomycin ($32-128{\mu}g/ml$) and gentamicin ($16{\mu}g/ml$). Vancomycin, ciprofloxacin and rifampin were effective antibiotics against the isolates. All isolates were slime producers ; strains isolated from dogs which died of bacteremia were more likely to produce slime than those isolated from dogs which survived. Chromosomal DNA fingerprinting of the isolates yielded 16 different genomic types with few common bands, indicating a variety of clones of S epidermidis were prevalent in the hospital. This study revealed that PFGE is an useful method for the genotype characterization of S epidermidis strains and this organism could probably be pathogenic in some dogs with severe disorders. Further works on a larger number of epidemiologically defined strains are required to assess these results.

• Archives of Pharmacal Research
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• v.28 no.7
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• pp.854-858
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• 2005

A Lactobacillus isolate was collected from the feces of a healthy Korean individual and named as Lactobacillus ruminus SPM0211. It was further characterized by subjecting it to an antibiotic resistance test and genetic analysis. In the antibiotic resistance test, all tested Lactobacillus spp. were classified as 'high resistance' for multiple antibiotics, such as isoniazid, ethambutol, cycloserine, and vancomycin. L. ruminus SPM0211 was classified as 'high resistance' for streptomycin also, while the other tested Lactobacillus spp. were classified as low resistance. This suggests that the antimicrobial spectra may be a good indicator in the discrimination of this strain among the tested Lactobacillus spp. In a polymerase chain reaction-random amplified polymorphic DNA (PCR-RAPD) analysis using the Microbial Uniprimer kit, L. ruminus SPM0211, and L. suebicus were clustered as a group with a 74.3% similarity level, suggesting that these two species are genetically related. Thus, our data suggest that the PCR-RADP method using the Microbial Uniprimer kit may be valuable in discriminating L. ruminus SPM0211 from other Lactobacillus spp.

• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.102-106
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• 2008

Forensic Marking is the technology that enables the service providers (SP) to identify the illegal digital contents distributors by first inserting markings (data indicating the user information and playback time) in realtime into the digital contents at time of playback of digital contents, and then later by extracting inserted markings from the contents which are illegally captured from the multimedia device such as IPTV STBs and distributed over the Internet. Digital Rights Management (DRM), which is a very popular content protection technology, has the security hole that can be vulnerable because the encrypted digital contents are transformed into their original plaintext forms after the decrypting process on the STBs. Therefore Forensic Marking (FM) has now become a companion content protection solution to DRM. This article describes a new way of tracking up to 4 illegal content users in FM implementation using the blue-difference chroma component of YCbCr color space. This FM technology has many advantages like fast processing time and easy portability to STB devices compared to that of the traditional watermarking processing in the frequency domain.