• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2882-2884
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• 1999

본 연구는 시스템의 신뢰도(reliability)를 향상시키기 위해 사용되는 이중(Duplex) 시스템에서 EMI(전자기파 간섭현상) 같은 원인에 의한 동시 발생적(coincident) 고장의 영향을 최소화하는 기법을 제안하고 신뢰성 있는 고성능 컴퓨터를 위한 운영체계 및 H/W 구조의 설계와 최적 평가에 기여하는데 그 목적이 있다. 이중 시스템에 동시 발생적 고장이 일어나면 두 개의 모듈이 고장의 영향을 받게 되므로 고장 포용능력을 상실하게 된다. 이 같은 영향을 최소화하기 위해서 같은 작업들을 가능한 한 다른 시간대로 중복 수행하도록 시퀀싱(sequencing) 및 스케줄링(scheduling) 함으로써 동시발생적 고장으로 야기되는 전체 작업의 고장 결과를 피할 수 있다 또한 실시간 시스템에서 작업들은 기본적으로 수행이 완료되어야 할 시간적 제약(hard deadline)을 지니고 있으므로. 이러한 엄격한 마감시한 내에서 모든 작업을 완수하고 기본조건을 만족시키고자 한다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.553-555
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• 2003

e-Learning 컨텐트 설계에 있어 객체지향기법에 근간한 학습객체 기반 설계에 많은 관심이 모아지고 있다. 학습객체는 기존의 컨텐트가 하나의 커다란 덩어리로 이루어져 있어 동일한 내용에 관해서도 많은 코스들이 생성되었던 재사용성의 문제를 해결하며 상호운용성, 접근성. 내구성 등의 잇점을 제시하고 있다. 이러한 학습객체는 레고모형에 비유되어 각각의 학습자마다 서로 다른 조합의 코스를 제공한다고 하지만, 현재의 시퀀싱된 형태는 CBT 수준의 분기수준에 머물러 있다. 본 연구에서는 개별화 학습을 지원할 수 있는 시퀀싱 설계를 위하여 학습객체 구조의 관점에서 접근하며, 이러한 학습 설계에 기초가 되는 학습객체의 기본 구조를 제안하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.669-671
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• 2014

High Throughput Sequencing (HTS) 기술의 발달로 인해 시퀀싱 비용이 감소함에 따라 다양한 분야에서 이를 활용한 융합 연구가 활발하게 진행되고 있다. HTS 기술에서 가장 중요한 부분은 수백만개의 short read 들을 표준유전체 (reference genome)에 정렬시키는 것인데 RNA 시퀀싱 (RNA-Seq) 의 경우 RNA splicing 으로 인해 일반적인 aligner 로 처리가 불가능하다. 복잡한 RNA-Seq 정렬 문제를 해결하기 위해 그동안 다양한 알고리즘들이 제안되어 왔다. 본 논문에서는 RNA-seq 정렬분야에서 잘 알려진 알고리즘들과 최신 알고리즘들을 살펴봄으로써 RNA-seq 정렬 알고리즘의 동향을 살펴보고자 한다.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.511-526
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• 2020

Single-cell RNA-sequencing (scRNA-seq) data consists of each cell's RNA expression extracted from large populations of cells. One main purpose of using scRNA-seq data is to identify inter-cellular heterogeneity. However, scRNA-seq data pose statistical challenges when applying traditional clustering methods because they have many missing values and high level of noise due to technical and sampling issues. In this paper, motivated by analyzing scRNA-seq data, we propose a novel spectral-based clustering method by imposing different weights on genes when computing a similarity between cells. Assigning weights on genes and clustering cells are performed simultaneously in the proposed clustering framework. We solve the proposed non-convex optimization using an iterative algorithm. Both real data application and simulation study suggest that the proposed clustering method better identifies underlying clusters compared with existing clustering methods.

• Journal of Applied Biological Chemistry
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• v.59 no.4
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• pp.299-304
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• 2016

When used for amplicon sequencing, Illumina platforms produce more than hundreds of sequence artefacts, which affects operational taxonomic units based analyses such as differential abundance and network analyses. Nevertheless it has become a major tool for fecal microbial community analysis. In addition, results from sequence-based fecal microbial community analysis vary depending on conditions of samples (i.e., freshness, time of storage and quantity). We investigated if freeze-drying samples could improve quality of sequence data. Our results showed reduced number of possible artefacts while maintaining overall microbial community structure. Therefore, freeze-drying feces prior to DNA extraction is recommended for Illumina-based microbial community analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.12
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• pp.3009-3015
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• 2013

Next-generation sequencing (NGS) has enabled whole genome and transcriptome single nucleotide variant (SNV) discovery in cancer and method of the most fundamental being determining an individual's genotype from multiple aligned short read sequences at a position. Bayesian algorithm estimate parameter using posterior genotype probabilities and other method, EM algorithm, estimate parameter using maximum likelihood estimate method in observed data. Here, we propose a novel genotype-calling system and compare and analyze the effect of sample size(S = 50, 100 and 500) on posterior estimate of sequencing error rate, somatic mutation status and genotype probability. The result is that estimate applying Bayesian algorithm even for 50 of small sample size approached real parameter than estimate applying EM algorithm in small sample more accurately.

• Korean Journal of Food Science and Technology
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• v.50 no.1
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• pp.55-60
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• 2018

Microbial communities of four commercial Korean nuruks were analyzed by the 454 pyrosequencing method to correlate different characteristics of rice wine fermentation. The total and average sequencing reads of fungi in the four nuruks were 14,800 and 3,494, respectively. At the phylum level, Ascomycota was dominant in three nuruks, namely, SH, SS, and JJ, while Zygomycota was dominant in SJ. Saccharomycopsis was dominant in nuruks subjected to longer fermentation periods, such as SH and SS. The total and average sequence reads for bacteria were 31,485 and 7,871, respectively. Bacteria belonging to the phylum Firmicutes were dominant in all samples. SH showed several genera of lactic acid bacteria, such as Lactobacillus, Leuconostoc, Pediococcus, and other minor bacteria. Staphylococcus and Bacillus were the dominant bacteria in JJ and SJ, respectively.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2805-2812
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• 2018

The methylation score, expressed as a percentage of the methylation status data derived from the iterative sequencing process, has a value between 0 and 1. It is contrary to the assumption of normal distribution that simply applying the t-test to examine the difference in population-specific methylation scores in these data. In addition, since the result may vary depending on the number of repetitions of sequencing in the process of methylation score generation, a method that can analyze such errors is also necessary. In this paper, we introduce the symbolic data analysis and the interval K-S test method which convert observation data into interval data including uncertainty rather than one numerical data. In addition, it is possible to analyze the characteristics of methylation score by using Beta distribution without using normal distribution in the process of converting into interval data. For the data analysis, the nature of the proposed method was examined using sequencing data of actual patients and normal persons. While the t-test is only possible for the location test, it is found that the interval type K-S statistic can be used to test not only the location parameter but also the heterogeneity of the distribution function.

• The Journal of Korean Association of Computer Education
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• v.12 no.3
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• pp.53-62
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• 2009

As Information communication technology develops, requirements for new educational media and new contents gets bigger and bigger. Especially PDA is required for educational media on m-learning environment. We design and implement intra-educational contents sequencing model and educational contents sequencing model between educational contents for PDA as educational supplementary media for m-learning environment, and LMS supporting PDA. And we balanced the work load of PDA and LMS and constructed practical service platform for using PDA as educational supplementary device.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.25-30
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• 2018

It is extremely lacking and urgently required that the method of constructing the Gene Regulatory Network (GRN) from RNA-Sequencing data (RNA-Seq) because of Big-Data and GRN in Big-Data has obtained substantial observation as the interactions among relevant featured genes and their regulations. We propose newly the computational comparative feature patterns selection method by implementing a minimum-redundancy maximum-relevancy (MRMR) filter the support vector machine-recursive feature elimination (SVM-RFE) with Intensity-dependent normalization (DEGSEQ) as a preprocessor for emphasizing equal preciseness in RNA-seq in Big-Data. We found out the proposed algorithm might be more scalable and convenient because of all libraries in R package and be more improved in terms of the time consuming in Big-Data and minimum-redundancy maximum-relevancy of a set of feature patterns at the same time.