• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.125-133
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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. As fingerprinting method, k-nearest neighbor(KNN) has been widely applied for indoor location in wireless location area networks(WLAN), but its performance is sensitive to number of neighbors k and positions of reference points(RPs). So intuitive fuzzy c-means(IFCM) clustering algorithm is applied to improve KNN, which is the KNN/IFCM hybrid algorithm presented in this paper. In the proposed algorithm, through KNN, k RPs are firstly chosen as the data samples of IFCM based on signal to noise ratio(SNR). Then, the k RPs are classified into different clusters through IFCM based on SNR. Experimental results indicate that the proposed KNN/IFCM hybrid algorithm generally outperforms KNN, KNN/FCM, KNN/PFCM algorithm when the locations error is less than 2m.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.109-115
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• 2011

As fingerprinting method, k-nearest neighbor(KNN) has been widely applied for indoor location in wireless location area networks(WLAN), but its performance is sensitive to number of neighbors k and positions of reference points(RPs). So artificial neural network(ANN) clustering algorithm is applied to improve KNN, which is the KNN/ANN hybrid algorithm presented in this paper. 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. In the proposed algorithm, through KNN, k RPs are firstly chosen as the data samples of ANN based on SNR. Then, the k RPs are classified into different clusters through ANN based on SNR. Experimental results indicate that the proposed KNN/ANN hybrid algorithm generally outperforms KNN algorithm when the locations error is less than 2m.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.503-523
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• 2022

Nowadays, many attendance applications utilise biometric techniques such as the face, fingerprint, and iris recognition. Biometrics has become ubiquitous in many sectors. Due to the advancement of deep learning algorithms, the accuracy rate of biometric techniques has been improved tremendously. This paper proposes a web-based attendance system that adopts facial recognition using open-source deep learning pre-trained models. Face recognition procedural steps using web technology and database were explained. The methodology used the required pre-trained weight files embedded in the procedure of face recognition. The face recognition method includes two important processes: registration of face datasets and face matching. The extracted feature vectors were implemented and stored in an online database to create a more dynamic face recognition process. Finally, user testing was conducted, whereby users were asked to perform a series of biometric verification. The testing consists of facial scans from the front, right (30 - 45 degrees) and left (30 - 45 degrees). Reported face recognition results showed an accuracy of 92% with a precision of 100% and recall of 90%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.270-272
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• 2022

Safety is one of the factors that prevent clinical drugs from being distributed on the market. In the case of neurotoxicity, which is the main cause of safety problems caused by drug side effects, risk assessment of drugs and compounds is required in advance. Currently, experiments for testing drug safety are based on animal experimetns, which have the disadvantage of being time-consuming and expensive. Therefore in order to solve the above problem, a neurotoxic prediction model through an in silico experiment was suggested. In this study, the category of neurotoxicity was expanded using a unified medical language system and various related compound data were obtained based on an integrated database. The SMILES (Simplified Molecular Input Line Entry System) of the obtained compounds were converted into fingerprints and it is used as input of machine learning. The model finally predicts the presence or absence of neurotoxicity. The experiment proposed in this study can reduce the time and cost required for the in vivo experiment. Furthermore, it is expected to shorten the research period for new drug development and reduce the burden of suspension of development.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.4
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• pp.521-526
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• 2015

Research on how to use the biological characteristics of human to confirm the identity of the individual is being actively conducted. Electrocardiogram(: ECG) based biometric system is difficult to counterfeit and does not cause skin irritation on the subject. It can be easily combined with conventional biometrics such as fingerprint and face recognition to give multimodal biometric systems. In this thesis, biometric identification method analysing ECG waveform characteristics from Discrete Wavelet Transform(DWT) coefficients is suggested. Feature selection is performed on the 9 coefficients of DWT using the correlation analysis. The verification is achieved by using the error back propagation neural networks. Using the proposed approach on 24 subjects of MIT-BIH QT Database, 98.88% verification rate has been obtained.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2001.08a
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• pp.139-149
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• 2001

Since 1990, the National Cancer Institute(NCI) has screened more than 60.000 compounds against a panel of 60 human cancer cell lines. The 50-percent growth-inhibitory concentration (GI$_{50}$) values encode unexpectedly rich, detailed information on mechanisms of drug action and drug resistance. Each compound's pattern is like a fingerprint, essentially unique among the many billions of distinguishable possibilities. These activity patterns are being used in conjunction with molecular structural features of the tested agents to explore the NCI's database of more than 460, 000 compounds, and they are providing insight into potential target molecules and modulators of activity in the 60 cell lines. For example, the information is being used to search for candidate anticancer drugs that are not dependent on intact p53 suppressor gene function for their activity. It remains to be seen how effective this information-intensive strategy will be at generating new clinically active agents.s.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.1
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• pp.81-87
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• 2013

In this paper, we propose a music search algorithm for automotive infotainment system. The proposed method extracts fingerprints using the high peaks based on log-spectrum of the music signal, and the extracted music fingerprints store in cloud server applying a hash value. In the cloud server, the most similar music is retrieved by comparing the user's query music with the fingerprints stored in hash table of cloud server. To evaluate the performance of the proposed music search algorithm, we measure an accuracy of the retrieved results according to various length of the query music and measure a retrieval time according to the number of stored music database in hash table.

• Childhood Kidney Diseases
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• v.13 no.2
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• pp.170-175
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• 2009

Purpose : FSGS do not respond well to any kind of therapy and gradually progress to end-stage renal disease. This study was conducted to investigate the difference of protein expression between MCNS and FSGS as a preliminary study for understanding the pathophysiology of FSGS. Methods : Renal biopsy samples of MCNS and FSGS were obtained, which was diagnosed by one pathologist. They were solubilized with a conventional extraction buffer for protein extraction. The solution was applied on immobilized linear gradient strip gel (pH 4-7) using IPGphor system. Silver staining was carried out according to standard method. Protein identification was done by searching NCBI database using MASCOT Peptide Mass Fingerprint software. Results : The differences in protein expressions between MCNS and FSGS were shown by increased or decreased protein spots. Most prominently expressed spot among several spots in FSGS was isolated and analyzed, one of which was glutathione S-transferase (GST) P1-1, whereas it was not found in MCNS. So GSTP1-1 was considered as the one of the key biomarkers in pathogenesis of FSGS. Conclusion : This result would be helpful in diagnosing FSGS and researching FSGS. Further studies for glutathione S-transferase P1-1 might be necessary to elucidate the mechanisms regarding FSGS.

• Korean Journal of Microbiology
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• v.37 no.1
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• pp.72-79
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• 2001

The effect of phenol on the change of bacterial community in the effluent water from a wastewater treatment plant was analyzed by PCR and terminal restriction fragment length polymorphism (T-RFLP). The fragments of 16S rDNA were amplified by PCR with bacterial primers, where one of the primers was biotinylated at the 5'-end. After digestion with restriction enzymes, HaeIII and AluI, the biotinylated terminal restriction tragments (T-RFs) of the digested products were selectively isolated by using streptavidin paramagnetic particles. The single-stranded DNA of T-RFs was separated by electrophoresis on a polyacrylamide gel and detected by silver staining technique. When 10 standard strains were analyzed by our method, each strain had a unique T-RF which corresponded to the calculated size from the known sequences of RDP database. The T-RFLP fingerprint generated from the effluent water was very complex, and the predominant T-RFs corresponded to members of the genus Acinetobacter, Bacillus and Pseudomonas. In addition, the perturbation of bacterial community was observed when phenol was added to the sample at the final concentration of 250 $l^{-1}$. The number of T-RFs increased and the major bacterial population could be assigned to the genus Acinetobacter, Comamonas, Cytophaga and Pseudomonas. A intense band assigned to the putative genera of Acinetobacter and Cytophaga was eluted, amplified, and sequenced. The nucleotide sequence of the T-RF showed close relationship with the sequence of Acinetobacter junii.

• Korean Journal of Microbiology
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• v.49 no.4
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• pp.377-382
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• 2013

The bacterial community structures of the marine sponge, Dactylospongia metachromia, collected from Chuuk of Micronesia on February 2012, were analyzed by denaturing gradient gel electrophoresis (DGGE). The DGGE fingerprints of two individuals of D. metachromia, CH607 and CH840 showed the same band patterns. The sequences derived from DGGE bands revealed 93~100% similarities with known bacterial species in the public database and high similarity with uncultured bacterial clones. The bacterial community structures of both D. metachromia sponges (CH607, CH840) were composed of 6 phyla, 8 classes: Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, Acidobacteria, Actinobacteria, Chloroflexi, Cyanobacteria, Spirochaetes. DGGE fingerprint - based phylogenetic analysis revealed that the bacterial community profiles were identical in two individuals of the same sponge species collected from the same geographical location.