• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.420-423
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• 1999

In this paper, we propose the method which efficiently obtain information through edit and retrieval of video data easily and rapidly. To support this method, extract the candidate representative frame using existing scene change detection method and the user selects representative frame for video segmentation at his desire, and then visualization indexing methods supported by logical-links enable users to freely merge and split each scene.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.153-155
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• 2003

Laser scanning has become a viable technique for the collection of a large amount of accurate 3D point data densely distributed on the scanned object surface. The inherent 3D nature of the sub-randomly distributed point cloud provides abundant spatial information. To explore valuable spatial information from laser scanned data becomes an active research topic, for instance extracting digital elevation model, building models, and vegetation volumes. The sub-randomly distributed point cloud should be segmented and classified before the extraction of spatial information. This paper investigates some exist segmentation methods, and then proposes an octree-based split-and-merge segmentation method to divide lidar data into clusters belonging to 3D planes. Therefore, the classification of lidar data can be performed based on the derived attributes of extracted 3D planes. The test results of both ground and airborne lidar data show the potential of applying this method to extract spatial features from lidar data.

• Journal of The Korean Astronomical Society
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• v.52 no.4
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• pp.89-97
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• 2019

We demonstrate the subsurface origin of the observed evolution of the solar active region 10930 (AR10930) associated with merging and breakup of magnetic polarity regions at the solar surface. We performed a magnetohydrodynamic simulation of an emerging magnetic flux tube whose field-line twist is asymmetrically distributed along its axis, which is a key to merging and fragmentation in this active region. While emerging into the surface, the flux tube is subjected to partial splitting of its weakly twisted portion, forming separate polarity regions at the solar surface. As emergence proceeds, these separate polarity regions start to merge and then break up, while in the corona sigmoidal structures form and a solar eruption occurs. We discuss what physical processes could be involved in the characteristic evolution of an active region magnetic field that leads to the formation of a sunspot surrounded by satellite polarity regions.

• Journal of Species Research
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• v.10 no.2
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• pp.142-153
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• 2021

Algal genomics approaches provide a massive number of genome/transcriptome sequences and reveal the evolutionary history vis-à-vis primary and serial endosymbiosis events that contributed to the biodiversity of photosynthetic eukaryotes in the eukaryote tree of life. In particular, phylogenomic methods using several hundred or thousands of genes have provided new insights into algal taxonomy and systematics. Using this method, many novel insights into algal species diversity and systematics occurred, leading to taxonomic revisions. In addition, horizontal gene transfers (HGTs) of functional genes have been identified in algal genomes that played essential roles in environmental adaptation and genomic diversification. Finally, algal genomics data can be used to address the pangenome, including core genes shared among all isolates and partially shared strain-specific genes. However, some aspects of the pangenome concept (genome variability of intraspecies level) conflict with population genomics concepts, and the issue is closely related to defining species boundaries using genome variability. This review suggests a desirable future direction to merge algal pangenomics and population genomics beyond traditional molecular phylogeny and taxonomy.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.374-376
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• 2022

Community detection is meaningful research in social network analysis, and many existing studies use graph theory analysis methods to detect communities. This paper proposes a method to detect community by detecting maximal cliques and obtain the high influence cliques by high influence nodes, then merge the cliques with high similarity in social network.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.9
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• pp.2080-2089
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• 1997

Recent evolution in compute technology makesthe multimedia processing widely availiable. Conventional storage systems do not meet the requirements of multimedia data. Several approaches were suggested to improve disk storing methods for them. Bocheck proposed a disk partitioning technique for multiple steams assuming that all steams have same retrieval intervals with the same amount data for each access. While Bocheck's one provides a good method for same period, it does not consider the case of different periods of continous media streams. This paper proposes a new partitioning technique in which a fixed number of blocks are assigned for stresms with different retrieval periodicity. The analysis shows this problem is the same as the one scheduling the steams into a given sequence. The simulation was done to compare the proposed m-sequence merge method with the conventional Scan-EDF and Partitioning methods.

• International Journal of Contents
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• v.15 no.3
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• pp.32-38
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• 2019

We usually suffer from difficulties in treating or managing Big Data generated from various digital media and/or sensors using traditional mining techniques. Additionally, there are many problems relative to the lack of memory and the burden of the learning curve, etc. in an increasing capacity of large volumes of text when new data are continuously accumulated because we ineffectively analyze total data including data previously analyzed and collected. In this paper, we propose a general-purpose classifier and its structure to solve these problems. We depart from the current feature-reduction methods and introduce a new scheme that only adopts changed elements when new features are partially accumulated in this free-style learning environment. The incremental learning module built from a gradually progressive formation learns only changed parts of data without any re-processing of current accumulations while traditional methods re-learn total data for every adding or changing of data. Additionally, users can freely merge new data with previous data throughout the resource management procedure whenever re-learning is needed. At the end of this paper, we confirm a good performance of this method in data processing based on the Big Data environment throughout an analysis because of its learning efficiency. Also, comparing this algorithm with those of NB and SVM, we can achieve an accuracy of approximately 95% in all three models. We expect that our method will be a viable substitute for high performance and accuracy relative to large computing systems for Big Data analysis using a PC cluster environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1385-1402
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• 2019

Quickly picking up some valuable information from massive manufacturing event stream usually faces with the problem of long detection time, high memory consumption and low detection efficiency due to its stream characteristics of large volume, high velocity, many variety and small value. Aiming to solve the problem above for the current complex event processing methods because of not sharing detection during the detecting process for massive manufacturing event streams, an efficient complex event processing method based on multipattern sharing is presented in this paper. The achievement of this paper lies that a multipattern sharing technology is successfully used to realize the quick detection of complex event for massive manufacturing event streams. Specially, in our scheme, we firstly use pattern sharing technology to merge all the same prefix, suffix, or subpattern that existed in single pattern complex event detection models into a multiple pattern complex event detection model, then we use the new detection model to realize the quick detection for complex events from massive manufacturing event streams, as a result, our scheme can effectively solve the problems above by reducing lots of redundant building, storing, searching and calculating operations with pattern sharing technology. At the end of this paper, we use some simulation experiments to prove that our proposed multiple pattern processing scheme outperforms some general processing methods in current as a whole.

• Korean Journal of Remote Sensing
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• v.28 no.1
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• pp.39-54
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• 2012

The objective of this study is to propose efficient data fusion techniques feasible to the KOMPSAT-2 satellite images. The most widely used image fusion techniques, which are the high-pass filter (HPF), the intensity-hue-saturation-based (modified IHS), the pan-sharpened, and the wavelet-based methods, was applied to four KOMPSAT - 2 satellite images having different regional and seasonal characteristics. Each fusion result was compared and analyzed in spatial and spectral features, respectively. Quality evaluation of image fusion techniques was performed in both quantitative and visual analysis. The quantitative analysis methods used for this study were the relative global dimensional error (spatial and spectral ERGAS), the spectral angle mapper index (SAM), and the image quality index (Q4). The results of quantitative and visual analysis indicate that the pan-sharpened method among the fusion methods used for this study relatively has the suitable balance between spectral and spatial information. In the case of the modified IHS method, the spatial information is well preserved, while the spectral information is distorted. And also the HPF and wavelet methods do not preserve the spectral information but the spatial information.

• Korean Journal of Remote Sensing
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• v.14 no.2
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• pp.149-164
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• 1998

The main object of this study was to prove the effectiveness of different merging methods by using the high resolution IRS(Indian Remote Sensing Satellite)-1C panchromatic data and the multispectral Landsat TM data. The five methods used to merging the information contents of each of the satellite data were the intensity-hue-saturation(IHS), principal component analysis(PCA), high pass filter(HPF), ratio enhancement method and look-up-table(LUT) procedures. Two measures are used to evaluate the merging method. These measures include visual inspection and comparisons of the mean, standard deviation and root mean square error between merged image and original image data values of each band. The ratio enhancement method was well preserved the spectral characteristics of the data. From visual inspection, PCA method provide the best result, HPF next, ratio enhancement, IHS and LUT method the worst for the preservation of spatial resolution.