• International Journal of Air-Conditioning and Refrigeration
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• 제14권1호
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• pp.14-18
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• 2006

This study was performed to investigate the heat transfer characteristics of the circular fin-tube heat exchanger. This paper contains the experimental data for the seven kinds of fin geometries. The correlation of Stasiulevicius agreed with the experimental data at high Reynolds number, however not well at low Reynolds number. The Nusselt number was well correlated with Graetz number, and showed a transition near Gz=10. An empirical correlation proposed in the present study agreed well with the experimental data.

• 설비공학논문집
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• 제16권6호
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• pp.584-588
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• 2004

This work was performed to investigate the heat transfer characteristics of the circular fin-tube heat exchanger. This paper contains the experimental data for the seven kinds of fin geometries. The correlation of Stasiulevicius agreed with the experimental data at high Reynolds number, however not well at low Reynolds number. The Nusselt number was well correlated with Graetz number, and showed a transition near Gz=10. An empirical correlation proposed in the present work agreed well with the experimental data.

• 한국물환경학회지
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• 제22권5호
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• pp.831-837
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• 2006

The present study applies wavelet transform for the extraction of various periodicities which are included in TOC and pH time series of water quality and rainfall data. The primary objective of the present study is to detect the relationships between the respective data through the correlation analysis using the approximation components which are decomposed by wavelet transform. The results reveal the approximation components of TOC and pH in the 5th level of wavelet transform can explain more than 99% of the whole energy for the raw data respectively and there are considerably high correlation between the approximation components of the respective data used for the study even through no significant correlation between the raw data has been detected.

• 한국멀티미디어학회논문지
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• 제25권2호
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• pp.363-373
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• 2022

Multi-omics data is difficult to interpret due to the heterogeneity of information by the volume of data, the complexity of characteristics of each data, and the diversity of omics platforms. There is not yet a system for interpreting to visualize research data on environmental diseases concerning environmental harmful substances. We provide MEE, a web-based visualization tool, to comprehensively explore the complexity of data due to the interconnected characteristics of high-dimensional data sets according to exposure to various environmental harmful substances. MEE visualizes omics data of correlation between omics data, subjects and samples by keyword searches of meta data, multi-omics data, and harmful substances. MEE has been demonstrated the versatility by two examples. We confirmed the correlation between smoking and asthma with RNA-seq and Methylation-Chip data, it was visualized that genes (P HACTR3, PXDN, QZMB, SOCS3 etc.) significantly related to autoimmune or inflammatory diseases. To visualize the correlation between atopic dermatitis and heavy metals, we selected 32 genes related immune response by integrated analysis of multi-omics data. However, it did not show a significant correlation between mercury in blood and atopic dermatitis. In the future, should continuously collect an appropriate level of multi-omics data in MEE system, will obtain data to analyze environmental substances and diseases.

• 융합신호처리학회논문지
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• 제15권2호
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• pp.55-63
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• 2014

본 논문에서는 대전상관기의 상관결과를 고속으로 저장하기 위한 데이터아카이브 시스템의 성능시험에 대해 기술한다. 대전상관기는 다양한 관측모드를 지원하고 있는데, 이 관측모드 중에서 적분시간에 따라 상관기로부터 출력되는 상관결과의 속도에 영향을 받는다. 대전상관기의 상관결과를 가장 빠른 속도로 출력하는 것은 최대 1.4GB/s를 출력하며, C1 관측모드일 경우 25.6ms의 상관적분인 경우이다. 본 연구에서는 대전상관기의 핵심부분인 VLBI상관서브시스템(VLBI Correlation Subsystem)과 4개의 광케이블로 연결되어 상관결과를 저장하는 데이터아카이브 시스템의 성능시험을 수행하였다. 데이터아카이브 시스템은 본 연구를 위해 2개 회사의 제품을 선정하여 벤치마크테스트(Benchmark Test)를 진행하였다. 본 논문에서는 벤치마크테스트를 위해 개발한 VCS의 상관출력 파일 생성 프로그램과 시험결과에 대해 기술한다.

• The Korean Journal of Ecology
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• 제9권3호
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• pp.135-147
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• 1986

The simplification and the searching trends of complex data which assumed relationship between predictor variables and object variables are one of primary objective of ecological research. This study was aimed to apply cononical analysis consisting of canonical correlation analysis and canonical variate analysis related to lichen vegetation and several environmental variables which are elevation, height on grond, exposure side and cover values. Data collected from the Duckyoo National Park in August 1985. Lichen species was ranked by eqivocation information theory with cover values. Canonical correlation analysis was applied to one data set both set both environmental variables and lichem family. In order to make two sets of data matrix the scale of position vector ordination was calculated from the vector scalar product for lichen species. Canonical variate analysis was applied to rearranged data which was made by interval class code for environmental variables. The sharpness values was calculated in frequency of cotingency tables and the dispersion profiles of each species in classes of environmental variables was designed to extract component values based on the decomposition of expected frequencies in contingency table. The results of canonical correlation analysis revealed canonical first correlation value 0.815(89%), and second correlation value 0.083(11%). Significance test showed that the hypothesis of joint mutuallity of canonical correlation is accepted (P>0.05). The relation between canonical score of vegetation variables and that of environmental variable indicated linear tendency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제3권3호
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• pp.331-343
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• 2009

Many types of sensor data exhibit strong correlation in both space and time. Both temporal and spatial suppressions provide opportunities for reducing the energy cost of sensor data collection. Unfortunately, existing clustering algorithms are difficult to utilize the spatial or temporal opportunities, because they just organize clusters based on the distribution of sensor nodes or the network topology but not on the correlation of sensor data. In this paper, we propose a novel clustering algorithm based on the correlation of sensor data. We modify the advertisement sub-phase and TDMA schedule scheme to organize clusters by adjacent sensor nodes which have similar readings. Also, we propose a spatio-temporal suppression scheme for our clustering algorithm. In order to show the superiority of our clustering algorithm, we compare it with the existing suppression algorithms in terms of the lifetime of the sensor network and the size of data which have been collected in the base station. As a result, our experimental results show that the size of data is reduced and the whole network lifetime is prolonged.

• 대한한의진단학회지
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• 제11권2호
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• pp.84-95
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• 2007

Background and Purpose: We have studied the trends of EEG signals in the voluntary breathing condition by applying the fractal analysis. According to chaos theory, irregularity of EEG signals can result from low dimensional deterministic chaos. A principal parameter to quantify the degree of Chaotic nonlinear dynamics is correlation dimension. The aim of this study was to analyze correlation between the correlation dimension of EEG and HRV(heart rate variability). We have studied the trends of EEG signals in the voluntary breathing condition by applying the fractal analysis. Methods: EEG raw data were measured by moving windows during 15 minutes. Then, the correlation dimension(D2) was calculated by each 40-seconds-segment in 15 minutes data, totally 36 segments. 8 channels EEG study on the Fp, F, T, P was carried out in 30 subjects. Results and Conclusion: Correlation analysis of HRV was calculated with deterministic non-linear data and stochastic non-linear data. 1. Ch1(Fp1), Ch4(F3), Ch4(F4) is positive correlated with In LF. 2. Ch1(Fp1), Ch3(F3) is positive correlated with In TF.

• Genomics & Informatics
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• 제16권4호
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• pp.33.1-33.9
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• 2018

Recently, there have been many studies in medicine related to genetic analysis. Many genetic studies have been performed to find genes associated with complex diseases. To find out how genes are related to disease, we need to understand not only the simple relationship of genotypes but also the way they are related to phenotype. Multi-block data, which is a summation form of variable sets, is used for enhancing the analysis of the relationships of different blocks. By identifying relationships through a multi-block data form, we can understand the association between the blocks in comprehending the correlation between them. Several statistical analysis methods have been developed to understand the relationship between multi-block data. In this paper, we will use generalized canonical correlation methodology to analyze multi-block data from the Korean Association Resource project, which has a combination of single nucleotide polymorphism blocks, phenotype blocks, and disease blocks.

• 웰빙융합연구
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• 제4권4호
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• pp.1-14
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• 2021

Purpose: This study aims to measure the level of satisfaction with the overall education of current students, who are major consumers of education by department at the Health Care Convergence University, and to establish development strategies such as improving education quality and satisfaction by deriving problems and improvements. Research design, data and methodology: This study drew conclusions through secondary data analysis based on data surveyed by the Educational Performance Management Center of a university in the academic year of 2020. The target of the education consumer satisfaction survey was 470 students of the College in the academic year 2020. Frequency analysis and correlation analysis were applied to the data using the statistical package program SPSS 25 version. Results: In the major curriculum satisfaction survey, the department with the highest score was optics and optics, 4.11, and the department with the lowest score was medical IT, 3.29. Conclusions: The department with the highest correlation in the correlation analysis result of the demand survey by department was the Department of Medical Management and the Department of Medical IT, with a correlation coefficient r=0.984, and the lowest correlation in the correlation analysis result of the demand survey by department was the Department of Medical IT and Dental Hygiene, with a correlation coefficient r= -0.085.