• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.2
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• pp.97-101
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• 2022

In this paper, we propose a method of measuring Fine dust in images using K-means and Sobel-mask based edge detection techniques using CCTV. The proposed algorithm collects images using a CCTV camera and designates an image range through a region of interest. When clustering is completed by applying the K-means algorithm, outline is detected through Sobel-mask, edge strength is measured, and the concentration of fine dust is determined based on the measured data. The proposed method extracts the contour of the mountain range using the characteristics of Sobel-mask, which has an advantage in diagonal measurement, and shows the difference in detection according to the concentration of fine dust as an experimental result.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.6
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• pp.383-394
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• 2013

As a way of augmenting constrained resources of mobile devices such as CPU and memory, many works on mobile cloud computing (MCC), where mobile devices utilize remote resources of cloud services or PCs, have been proposed. Typically, in MCC, many nodes with different operating systems and platform and diverse mobile applications or services are located, and a central manager autonomously performs several management tasks to maintain a consistent level of MCC overall quality. However, as there are a larger number of nodes, mobile applications, and services subscribed by the mobile applications and their interactions are extremely increased, a traditional management method of MCC reveals a fundamental problem of degrading its overall performance due to overloaded management tasks to the central manager, i.e. a bottle neck phenomenon. Therefore, in this paper, we propose a clustering-based optimization method to solve performance-related problems on large-scaled MCC and to stabilize its overall quality. With our proposed method, we can ensure to minimize the management overloads and stabilize the quality of MCC in an active and autonomous way.

• Journal of Korean Society for Atmospheric Environment
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• v.21 no.3
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• pp.357-365
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• 2005

The typical patterns of surface $O_3$ pollution and their dependence on meteorology were studied in the Greater Seoul Area (GSA) during warm season (April-September) from 1998 to 2002. In order to classify the $O_3$ pollution patterns, two-stage (average linkage then k-means) clustering technique was employed based on daily maximum $O_3$ concentrations obtained from 53 monitoring sites during high $O_3$ events (118 days). The clustering technique identified four statistically distinct $O_3$ pollution patterns representing the different horizontal distributions and levels of $O_3$ in GSA. The prevailed pattern (93 days, $49.5\%$) distinctly showed the gradient of $49.5\%$ concentrations going from west to east in GSA. Very high $49.5\%$ concentrations throughout GSA (24 days, $12.8\%$) were also found as a significant pattern of severe $O_3$ pollution. In order to understand the characteristics of $O_3$ pollution patterns, the relationship between $O_3$ pollution patterns and meteorological conditions were analyzed using both synoptic charts and surface/upper air data. Each pattern was closely associated with surface wind interacted with synoptic background flow allowing to transport and accumulate $O_3$ and its precursor. In particular, the timing and inland penetration of sea-breeze were apparently found to play very important role in determining $O_3$ distributions.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.22 no.1
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• pp.15-28
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• 2018

Recently Machine Learning algorithms are widely used to process Big Data in various applications and a lot of these applications are executed in run time. Therefore the speed of Machine Learning algorithms is a critical issue in these applications. However the most of modern iteration Machine Learning algorithms use a successive iteration technique well-known in Numerical Linear Algebra. But this technique has a very low convergence, needs a lot of iterations to get solution of considering problems and therefore a lot of time for processing even on modern multi-core computers and clusters. Tchebychev iteration technique is well-known in Numerical Linear Algebra as an attractive candidate to decrease the number of iterations in Machine Learning iteration algorithms and also to decrease the running time of these algorithms those is very important especially in run time applications. In this paper we consider the usage of Tchebychev iterations for acceleration of well-known K-Means and SVM (Support Vector Machine) clustering algorithms in Machine Leaning. Some examples of usage of our approach on modern multi-core computers under Apache Spark framework will be considered and discussed.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.1
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• pp.52-59
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• 2014

We survey 3,857 journal articles published on the four domestic academic journals in the industrial engineering field during 1975~2012. Titles, abstracts, and keywords of the papers are searched by means of text mining technique to draw the information on the methodologies and techniques adopted in the papers, and then we aggregate and merge similar ones to obtain final 38 representative methodologies and techniques. Trends of these methodologies and techniques are studied by analyzing frequencies, clustering, and finding association rules among them. Results of the paper can shed a light to choose tools in the future education and research in the industrial engineering related area.

• Journal of the Korea Convergence Society
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• v.8 no.8
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• pp.33-40
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• 2017

In this paper, we propose a text extraction method by pattern analysis on contour for effective text detection in image. Text extraction algorithms using edge based methods show good performance in images with simple backgrounds, The images of complex background has a poor performance shortcomings. The proposed method simplifies the color of the image by using K-means clustering in the preprocessing process to detect the character region in the image. Enhance the boundaries of the object through the High pass filter to improve the inaccuracy of the boundary of the object in the color simplification process. Then, by using the difference between the expansion and erosion of the morphology technique, the edges of the object is detected, and the character candidate region is discriminated by analyzing the pattern of the contour portion of the acquired region to remove the unnecessary region (picture, background). As a final result, we have shown that the characters included in the candidate character region are extracted by removing unnecessary regions.

• Korean Journal of Remote Sensing
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• v.22 no.3
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• pp.199-209
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• 2006

The Global Land Cover 2000 (GLC 200) project, as a most recent issue, is to provide for the year 2000 a harmonized land cover database over the whole globe. The classifications were performed according to continental or regional scales by corresponding organization using the data of VEGETATION sensor onboard the SPOT4 Satellite. Even if the global land cover classification for Asia provided by Chiba University showed a good accuracy in whole Asian area, some problems were detected in Korean region. Therefore, the construction of new land cover database over Korea is strongly required using more recent data set. The present study focuses on the development of a new upgraded land cover map at 1 km resolution over Korea considering the widely used K-means clustering, which is one of unsupervised classification technique using distance function for land surface pattern classification, and the principal components transformation. It is based on data sets from the Earth observing system SPOT4/VEGETATION. Newly classified land cover was compared with GLC 2000 for Korean peninsula to access how well classification performed using confusion matrix.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.326-354
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• 2014

In this paper, a new green clustering algorithm is proposed to be as a first approach in the framework of an energy efficient strategy for centralized enterprise high-density WLANs. Traditionally, in order to maintain the network coverage, all the APs within the WLAN have to be powered-on. Nevertheless, the new algorithm can power-off a large proportion of APs while the coverage is maintained as its always-on counterpart. The two main components of the new approach are the faster procedure based on K-means and the more accurate procedure based on Evolutionary Algorithm (EA), respectively. The two procedures are processes in parallel for different designed requirements and there is information interaction in between. In order to implement the new algorithm, EA is applied to handle the optimization of multiple objectives. Moreover, we adapt the method for selection and recombination, and then introduce a new operator for mutation. This paper also presents simulations in scenarios modeled with ray-tracing method and FDTD technique, and the results show that about 67% to 90% of energy consumption can be saved while it is able to maintain the original network coverage during periods when few users are online or the traffic load is low.

• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.8-16
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• 2021

In this paper, locations of vertiports were selected and evaluated to operate urban air mobility (UAM) in the Seoul metropolitan area. Demand data were analyzed using the data from the survey of commuting population and were marked on a map using MATLAB. To cluster the data, the K-means algorithm function built in MATLAB was used to identify the center of the cluster to as the location of vertiports, and using the silhouette technique, the accuracy and reliability of the clustering were evaluated. The locations of the selected vertiports were also identified using satellite maps to ensure that the locations of the selected vertiports were suitable for the actual vertiport location, and, if the location was not appropriate, final vertiports were selected through the repositioning process.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.436-444
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• 2010

In this study, we introduce and discuss a concept of a granular-oriented radial basis function neural networks (GRBF NNs). In contrast to the typical architectures encountered in radial basis function neural networks(RBF NNs), our main objective is to develop a design strategy of GRBF NNs as follows : (a) The architecture of the network is fully reflective of the structure encountered in the training data which are granulated with the aid of clustering techniques. More specifically, the output space is granulated with use of K-Means clustering while the information granules in the multidimensional input space are formed by using a so-called context-based Fuzzy C-Means which takes into account the structure being already formed in the output space, (b) The innovative development facet of the network involves a dynamic reduction of dimensionality of the input space in which the information granules are formed in the subspace of the overall input space which is formed by selecting a suitable subset of input variables so that the this subspace retains the structure of the entire space. As this search is of combinatorial character, we use the technique of genetic optimization to determine the optimal input subspaces. A series of numeric studies exploiting some nonlinear process data and a dataset coming from the machine learning repository provide a detailed insight into the nature of the algorithm and its parameters as well as offer some comparative analysis.