• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.4
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• pp.49-63
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• 2015

Keyword indexed in multidisciplinary journals show trends about science and technology innovation. Nature and Science were selected as multidisciplinary journals for our analysis. In order to reduce the effect of plurality of keyword, stemming algorithm were implemented. After this process, we fitted growth curve of keyword (stem) following bass model, which is a well-known model in diffusion process. Bass model is useful for expressing growth pattern by assuming innovative and imitative activities in innovation spreading. In addition, we construct keyword co-occurrence network and calculate network measures such as centrality indices and local clustering coefficient. Based on network metrics and yearly frequency of keyword, time series analysis was conducted for obtaining statistical causality between these measures. For some cases, local clustering coefficient seems to Granger-cause yearly frequency of keyword. We expect that local clustering coefficient could be a supportive indicator of emerging science and technology.

• Nuclear Engineering and Technology
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• v.49 no.6
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• pp.1157-1164
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• 2017

The Monte Carlo criticality simulation of decoupled systems, as for instance in large reactor cores, has been a challenging issue for a long time. In particular, due to limited computer time resources, the number of neutrons simulated per generation is still many order of magnitudes below realistic statistics, even during the start-up phases of reactors. This limited number of neutrons triggers a strong clustering effect of the neutron population that affects Monte Carlo tallies. Below a certain threshold, not only is the variance affected but also the estimation of the eigenvectors. In this paper we will build a time-dependent diffusion equation that takes into account both spatial correlations and population control (fixed number of neutrons along generations). We will show that its solution obeys a traveling wave dynamic, and we will discuss the mechanism that explains this biasing of local tallies whenever leakage boundary conditions are applied to the system.

• Journal of Korean Society on Water Environment
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• v.37 no.5
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• pp.335-343
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• 2021

Water quality prediction is essential for the proper management of water supply systems. Increased suspended sediment concentration (SSC) has various effects on water supply systems such as increased treatment cost and consequently, there have been various efforts to develop a model for predicting SSC. However, SSC is affected by both the natural and anthropogenic environment, making it challenging to predict SSC. Recently, advanced machine learning models have increasingly been used for water quality prediction. This study developed an ensemble machine learning model to predict SSC using the XGBoost (XGB) algorithm. The observed discharge (Q) and SSC in two fields monitoring stations were used to develop the model. The input variables were clustered in two groups with low and high ranges of Q using the k-means clustering algorithm. Then each group of data was separately used to optimize XGB (Model 1). The model performance was compared with that of the XGB model using the entire data (Model 2). The models were evaluated by mean squared error-ob servation standard deviation ratio (RSR) and root mean squared error. The RSR were 0.51 and 0.57 in the two monitoring stations for Model 2, respectively, while the model performance improved to RSR 0.46 and 0.55, respectively, for Model 1.

• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.109-116
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• 2007

To reduce the effect of color distortions on reading colors, it is more desirable to statistically process as many pixels in the individual color region as possible. This process may require segmentation, which usually requires edge detection. However, edges in color codes can be disconnected due to various distortions such as dark current, color cross, zipper effect, shade and reflection, to name a few. Edge linking is also a difficult process. In this paper, k-means clustering was performed on the images where edge detectors failed segmentation. Experiments were conducted on 311 images taken in different environments with different cameras. The primary and secondary colors were randomly selected for each color code region. While segmentation rate by edge detectors was 89.4%, the proposed method increased it to 99.4%. Color recognition was performed based on hue, a*, and b* components, with the accuracy of 100% for the successfully segmented cases.

• Smart Structures and Systems
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• v.21 no.4
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• pp.421-433
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• 2018

Carbon fiber reinforced polymer (CFRP) cable has good mechanical properties and corrosion resistance. However, the anchorage of CFRP cable is a big issue due to the anisotropic property of CFRP material. In this article, a high-efficient bonding anchorage with novel configuration is developed for CFRP cables. The acoustic emission (AE) technique is employed to evaluate the performance of anchorage in the fatigue test and post-fatigue ultimate bearing capacity test. The obtained AE signals are analyzed by using a combination of unsupervised K-means clustering and supervised K-nearest neighbor classification (K-NN) for quantifying the performance of the anchorage and damage evolutions. An AE feature vector (including both frequency and energy characteristics of AE signal) for clustering analysis is proposed and the under-sampling approaches are employed to regress the influence of the imbalanced classes distribution in AE dataset for improving clustering quality. The results indicate that four classes exist in AE dataset, which correspond to the shear deformation of potting compound, matrix cracking, fiber-matrix debonding and fiber fracture in CFRP bars. The AE intensity released by the deformation of potting compound is very slight during the whole loading process and no obvious premature damage observed in CFRP bars aroused by anchorage effect at relative low stress level, indicating the anchorage configuration in this study is reliable.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3295-3311
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• 2020

With the wide application of hyperspectral images, it becomes more and more important to compress hyperspectral images. Conventional recursive least squares (CRLS) algorithm has great potentiality in lossless compression for hyperspectral images. The prediction accuracy of CRLS is closely related to the correlations between the reference bands and the current band, and the similarity between pixels in prediction context. According to this characteristic, we present an improved CRLS with adaptive band selection and adaptive predictor selection (CRLS-ABS-APS). Firstly, a spectral vector correlation coefficient-based k-means clustering algorithm is employed to generate clustering map. Afterwards, an adaptive band selection strategy based on inter-spectral correlation coefficient is adopted to select the reference bands for each band. Then, an adaptive predictor selection strategy based on clustering map is adopted to select the optimal CRLS predictor for each pixel. In addition, a double snake scan mode is used to further improve the similarity of prediction context, and a recursive average estimation method is used to accelerate the local average calculation. Finally, the prediction residuals are entropy encoded by arithmetic encoder. Experiments on the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) 2006 data set show that the CRLS-ABS-APS achieves average bit rates of 3.28 bpp, 5.55 bpp and 2.39 bpp on the three subsets, respectively. The results indicate that the CRLS-ABS-APS effectively improves the compression effect with lower computation complexity, and outperforms to the current state-of-the-art methods.

• The Journal of Bigdata
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• v.5 no.1
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• pp.125-138
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• 2020

After the spread of COVID-19 in 2020, the country's Crisis Alert Level went up to the highest level, Level 4. Respond of COVID-19 pandemic, Governments, and cities secured each province's duty for the citizens. The government provided health assistance first and stepped forward to support the necessary resources for the citizens. Busan City proposed policy response to prepare and implement the Corona support for each county as well. The high occupant rate of self-business owners lost basic incomes, and the effect varies on industries. In our paper, to avoid any crisis in such an epidemic, we propose a clustering analysis for the guidance of policy support for Busan City. By analyzing patterns and clustering on districts and Sectors, we would like to provide reference materials for determining the direction of support and guiding preemptive response in the event of a similar epidemic.

• Korean Circulation Journal
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• v.53 no.1
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• pp.17-30
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• 2023

Background and Objectives: To evaluate the impact of smoking in young adults on the risk of cardiovascular disease (CVD) and the clustering effect of behavioral risk factors such as smoking, obesity, and depression. Methods: A Korean nationwide population-based cohort of a total of 3,280,826 participants aged 20-39 years old who underwent 2 consecutive health examinations were included. They were followed up until the date of CVD (myocardial infarction [MI] or stroke), or December 2018 (median, 6 years). Results: Current smoking, early age of smoking initiation, and smoking intensity were associated with an increased risk of CVD incidence. Even after quitting smoking, the risk of MI was still high in quitters compared with non-smokers. Cigarette smoking, obesity, and depression were independently associated with a 1.3-1.7 times increased risk of CVD, and clustering of 2 or more of these behavioral risk factors was associated with a 2-3 times increased risk of CVD in young adults. Conclusions: In young adults, cigarette smoking was associated with the risk of CVD, and the clustering of 2 or more behavioral risk factors showed an additive risk of CVD.

• Proceedings of the Materials Research Society of Korea Conference
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• 2007.11a
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• pp.19.2-19.2
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• 2007

• Composites Research
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• v.30 no.5
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• pp.323-330
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• 2017

In this study, multiscale analysis in which the information obtained from molecular dynamics simulation is applied to the continuum mechanics level is conducted to investigate the effects of clustering of silicon carbide nanoparticles reinforced into polypropylene matrix on mechanical behavior of nanocomposites. The elastic behavior of polymer nanocomposites is observed for various states of nanoparticulate agglomeration according to the model reflecting the degradation of interphase properties. In addition, factors which mainly affect the mechanical behavior of the nanocomposites are identified, and new index 'clustering density' is defined. The correlation between the clustering density and the elastic modulus of nanocomposites is understood. As the clustering density increases, the interfacial effect decreased and finally the improvement of mechanical properties is suppressed. By considering the random distribution of the nanoparticles, the range of elastic modulus of nanocomposites for same value of clustering density can be investigated. The correlation can be expressed in the form of exponential function, and the mechanical behavior of the polymer nanocomposites can be effectively predicted by using the nanoparticulate clustering density.