• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.307-316
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• 2011

The objective of this study is to figure out some specific features that were found during the adaptation process of migrant women by marriage to their local community, and also to elicit certain practical implications to facilitate their adaptation, based on the findings. For data collection, in-depth interviews were conducted with eight migrant women by marriage and the interviews were tape-recorded for transcription. For analysis, phenomenological method, particularly, Colaizzi method was adopted, by which meaningful statements in the data were categorized into themes and theme clusters. A total of 7 thematic unts, 17 themes and 47 meanings were elicited from the analysis and these 7 thematic units were "reinforcement of inner capability"; "cultural assimilation"; "to be a limited benefit receiver"; "no human network"; " impossible to be optimistic about future"; "hoping to reside in their local community"; "possible to leave Korea." Finally, this study suggested the implications for social welfare practices to facilitate the adaptation of immigrant women by marriage in their local community.

• Korean Journal of Cognitive Science
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• v.23 no.3
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• pp.295-322
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• 2012

In this paper, we cluster similar word senses applying vector space model and HAL (Hyperspace Analog to Language). HAL measures corelation among words through a certain size of context (Lund and Burgess 1996). The similarity measurement between a word pair is cosine similarity based on the vector space model, which reduces distortion of space between high frequency words and low frequency words (Salton et al. 1975, Widdows 2004). We use PCA (Principal Component Analysis) and SVD (Singular Value Decomposition) to reduce a large amount of dimensions caused by similarity matrix. For sense similarity clustering, we adopt supervised and non-supervised learning methods. For non-supervised method, we use clustering. For supervised method, we use SVM (Support Vector Machine), Naive Bayes Classifier, and Maximum Entropy Method.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.701-708
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• 2005

Structuring mechanism, important to serve messages in post-office box structure, is to construct the hierarchy of classes according to the contents of message objects. This Paper Proposes $\alpha$-cut based genealogy grouping method to cluster a lot of structured objects in application domain. The proposed method decides the relationship first by semantic similarity relation and fuzzy relation, and then performs the grouping by operations of search( ), insert() and hierarchy(). This hierarchy structure makes it easy to process group-related processing tasks such as answering queries, discriminating objects, finding similarities among objects, etc. The proposed post-office box structure may be efficiently used to serve and manage message objects by the creation of groups. The Proposed method is tested for 5500 message objects and compared with other methods such as non-grouping, BGM, RGM, OGM.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.2A
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• pp.105-112
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• 2012

Face detection at a distance faces is very challenging since images are often degraded by blurring and noise as well as low resolution. This paper proposes an improved face detection method with AdaBoost filtering and sequential testing stages with color and shape information. The conventional AdaBoost filter detects face regions but often generates false alarms. The face detection method is improved by adopting sequential testing stages in order to remove false alarms. The testing stages comprise skin-color test and variable edge-mask filtering. The skin-color filtering is composed of two steps, which involve rectangular window regions and individual pixels to generate binary face clusters. The size of the variable edge-mask is determined by the ellipse which is estimated from the face cluster. The validation of the horizontal and vertical ratio of the mask is also investigated. In the experiments, the efficacy of the proposed algorithm is proved by images captured by a CCTV and a smart-phone

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.453-459
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• 2017

Users tend to check in and post their statuses in location-based social networks (LBSNs) to describe that their interests are related to a point-of-interest (POI). While previous studies on discovering area-of-interests (AOIs) were conducted mostly on the basis of density-based clustering methods with the collection of geo-tagged photos from LBSNs, we focus on estimating a POI boundary, which corresponds to only one cluster containing its POI center. Using geo-tagged tweets recorded from Twitter users, this paper introduces a density-based low-complexity two-phase method to estimate a POI boundary by finding a suitable radius reachable from the POI center. We estimate a boundary of the POI as the convex hull of selected geo-tags through our two-phase density-based estimation, where each phase proceeds with different sizes of radius increment. It is shown that our method outperforms the conventional density-based clustering method in terms of computational complexity.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.444-450
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• 1999

This paper deals with several methods: the clustering method that uses k-means algorithm to abstract the area of characters on the image document and the distance function that suits for the HIS coordinate system to cluster the image. For the prepossessing step to recognize this, or the method of characters segmentate, the algorithm to abstract a discrete character is also proposed, using the linking picture element. This algorithm provides the feature that separates any character such as the touching or overlapped character. The methods of projecting and tracking the edge have so far been used to segment them. However, with the new method proposed here, the picture element extracts a discrete character with only one-time projection after abstracting the character string. it is possible to pull out it. dividing the area into the character and the rest (non-character). This has great significance in terms of processing color documents, not the simple binary image, and already received verification that it is more advanced than the previous document processing system.

• Journal of the Korea Society of Computer and Information
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• v.10 no.2 s.34
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• pp.49-57
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• 2005

In this paper. a CLB-based CPLD low power technology mapping algorithm for trade-off is proposed. To perform low power technology mapping for CPLD, a given Boolean network has to be represented to DAG. The proposed algorithm consists of three step. In the first step, TD(Transition Density) calculation have to be Performed. Total power consumption is obtained by calculating switching activity of each nodes in a DAG. In the second step, the feasible clusters are generated by considering the following conditions : the number of output. the number of input and the number of OR-terms for CLB within a CPLD. The common node cluster merging method, the node separation method, and the node duplication method are used to produce the feasible clusters. The proposed algorithm is examined by using benchmarks in SIS. In the case that the number of OR-terms is 5, the experiments results show reduction in the power consumption by 30.73$\%$ comparing with that of TEMPLA, and 17.11$\%$ comparing with that of PLAmap respectively

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.2
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• pp.150-156
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• 2008

Many papers have shown that the hierarchical clustering method takes good-performance, but is limited because of its quadratic time complexity. In contrast, with a large number of variables, K-means reduces a time complexity. Think of the factor of simplify, high-quality and high-efficiency, we combine the two approaches providing a new system named CONDOR system with hierarchical structure based on document clustering using K-means algorithm. Evaluated the performance on different hierarchy depth and initial uncertain centroid number based on variational relative document amount correspond to given queries. Comparing with regular method that the initial centroids have been established in advance, our method performance has been improved a lot.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.4
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• pp.489-498
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• 2018

Objective: Foot and mouth disease (FMD) and porcine reproductive and respiratory syndrome (PRRS) are major diseases that interrupt porcine production. Because they are viral diseases, vaccinations are of only limited effectiveness in preventing outbreaks. To establish an alternative multi-resistant strategy against FMD virus (FMDV) and PRRS virus (PRRSV), the present study introduced two genetic modification techniques to porcine cells. Methods: First, cluster of differentiation 163 (CD163), the PRRSV viral receptor, was edited with the clustered regularly interspaced short palindromic repeats-CRISPR-associated protein 9 technique. The CD163 gene sequences of edited cells and control cells differed. Second, short hairpin RNA (shRNAs) were integrated into the cells. The shRNAs, targeting the 3D gene of FMDV and the open reading frame 7 (ORF7) gene of PRRSV, were transferred into fibroblasts. We also developed an in vitro shRNA verification method with a target gene expression vector. Results: shRNA activity was confirmed in vitro with vectors that expressed the 3D and ORF7 genes in the cells. Cells containing shRNAs showed lower transcript levels than cells with only the expression vectors. The shRNAs were integrated into CD163-edited cells to combine the two techniques, and the viral genes were suppressed in these cells. Conclusion: We established a multi-resistant strategy against viral diseases and an in vitro shRNA verification method.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.185-191
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• 2007

Remote sensing images are multispectral image data collected from several band divided by wavelength ranges. The classification of remote sensing images is the method of classifying what has similar spectral characteristics together among each pixel composing an image as the important algorithm in this field. This paper presents a pattern classification method of remote sensing images by applying a possibilistic fuzzy c-means (PFCM) algorithm. The PFCM algorithm is a hybridization of a FCM algorithm, which adopts membership degree depending on the distance between data and the center of a certain cluster, combined with a PCM algorithm, which considers class typicality of the pattern sets. In this proposed method, we select the training data for each class and perform supervised classification using the PFCM algorithm with spectral signatures of the training data. The application of the PFCM algorithm is tested and verified by using Landsat TM and IKONOS remote sensing satellite images. As a result, the overall accuracy showed a better results than the FCM, PCM algorithm or conventional maximum likelihood classification(MLC) algorithm.