• Proceedings of the Korean Society of Computer Information Conference
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• 2011.01a
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• pp.55-58
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• 2011

This paper proposes a method of occluded number recognition by matching interest points. Interest points of input pattern are found via SURF features extracting and matched to interest points of clusters in database following three steps: SURF matching, coordinate matching and SURF matching on coordinate matched points. Then the satisfied interest points are counted to compute matching rate of each cluster. The input pattern will be assigned to cluster having highest matching rate. We have experimented our method to different numerical fonts and got encouraging results.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.998-1007
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• 2018

The advent of Wireless Sensor Networks (WSN) has led to their use in numerous applications. Sensors are autonomous in nature and are constrained by limited resources. Designing an autonomous topology with criteria for economic and energy conservation is considered a major goal in WSN. The proposed honey-hive clustering consumes minimum energy and resources with minimal transmission delay compared to the existing approaches. The honey-hive approach consists of two phases. The first phase is an Intra-Cluster Min-Max Discrepancy (ICMMD) analysis, which is based on the local honey-hive data gathering technique and the second phase is Inter-Cluster Frequency Matching (ICFM), which is based on the global optimal data aggregation. The proposed data aggregation mechanism increases the optimal connectivity range of the sensor node to a considerable degree for inter-cluster and intra-cluster coverage with an improved optimal energy conservation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.1984-2002
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• 2020

With the appearance of wireless spectrum crisis in traditional cellular network, device-to-device (D2D) communication has been regarded as a promising solution to ease heavy traffic burden by enabling precise content delivery among mobile users. However, due to the channel sharing, the interference between D2D and cellular users can affect the transmission rate and narrow the throughput in the network. In this paper, we firstly present a weighted interference minimization cluster formation model involving both social attribute and physical closeness. The weighted-interference, which is evaluated under the susceptible-infected(SI) model, is utilized to gather user in social and physical proximity. Then, we address the cluster formation problem via spectrum clustering with iterative operation. Finally, we propose the stable matching theory algorithm in order to maximize rate oriented to accomplish the one-to-one resource allocation. Numerical results show that our proposed scheme acquires quite well clustering effect and increases the accumulative transmission rate compared with the other two advanced schemes.

• Journal of Labour Economics
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• v.24 no.1
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• pp.149-176
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• 2001

This paper analyzes job sequences of men and women using optimal matching in order to find patterns of intra-generational mobility in Korean society. Men and women differ in their job careers: men show long-lasting job sequences with few gaps, while women either have short careers or have interrupted careers with long gaps. Long gaps in men's career are limited to those cases in which men move from agricultural to other job. The results from a combination of optimal matching and cluster analysis show that men's job sequences cluster around major occupations while women's cluster in terms of sequence length. The interrupted careers characteristic of women are considered to be consequent on the burdens of house keeping and child raising together with the discrimination against women pursuing careers.

• Korean Journal of Remote Sensing
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• v.8 no.1
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• pp.15-26
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• 1992

This paper describes an implementation of adaptive stereo matching for DBM generation. The matching method of two stereo satellite images to find corresponding points used in this paper is area-based matching, which is usually used in the field of making DBM. Same window size and search area used as in the conventional matching methods and we propose adaptive stereo matching algorithm in this paper. We cluster three areas which are consist of mountainous areas, cultivated areas and cities, and rivers and lakes by using proposed linear feature extracting method. These classified areas are matched by adaptive window size and search area, but rivers and lakes is excluded in this experiment. The matching time is three times faster than conventional methods.

• The Journal of the Acoustical Society of Korea
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• v.15 no.1E
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• pp.29-33
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• 1996

A categorization for cluster is necessary when an unsupervised classfication is used for remote sensing image classification. It is desirable that this method is performed automatically, because manual categorization is a highly time consuming process. In this paper, several automatic determination methods were proposed and evaluated. They are four methods. a) maximum number method : which assigns the tharget cluster to the category which occupies the largest area of that cluster b) maximum percentage method : which assigns the target cluster to the category which shows the maximum percentage within the category in that cluster. c) minmun distance method : which assigns the target cluster to the category having minmum distance with that cluster d) element ratio matching method : which assigns local regions to the category having the most similar element ratio of that region From the results of the experiments, it was certified that the result of minimum distance method was almost the same as the result made by a human operator.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.928-931
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• 2002

Network based intrusion detection system is a computer network security tool. In this paper, we present an intrusion detection system based on Self-Organizing Maps (SOM) and Resilient Propagation Neural Network (RPROP) for visualizing and classifying intrusion and normal patterns. We introduce a cluster matching equation for finding principal associated components in component planes. We apply data from The Third International Knowledge Discovery and Data Mining Tools Competition (KDD cup'99) for training and testing our prototype. From our experimental results with different network data, our scheme archives more than 90 percent detection rate, and less than 5 percent false alarm rate in one SYN flooding and two port scanning attack types.

• The Bulletin of The Korean Astronomical Society
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• v.35 no.2
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• pp.42.1-42.1
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• 2010

We present ultraviolet (UV) photometric properties of dwarf galaxies in Fornax cluster and Ursa Major group in comparison with Virgo cluster using GALEX data. We construct UV color-magnitude relations (CMRs) of dwarf galaxies matching with available optical photometry and SDSS data. Majority of dwarf galaxies in Fornax cluster show sequence in UV CMRs consistent with that of dwarf elliptical (dEs) in Virgo cluster indicating similar age and metallicity properties of dEs in two clusters. The dS0 sequence in Fornax cluster is not distinct as much as that in Virgo cluster. Dwarf galaxies in outer region of the Fornax cluster show more bluer UV colors with a wide scatter in CMRs, which indicates recent star formation activity. We show that the UV colors of dwarf galaxies are related with the distribution and strength of the X-ray emission in the cluster. In contrast to the Fornax cluster, most dwarf galaxies in Ursa Major group are located in the blue cloud showing recent or on-going star formation, and few galaxies show characteristics of dEs. We discuss relationship between UV properties of dwarf galaxies and different environment of cluster.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.8
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• pp.744-750
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• 2010

To segment instances of vehicle classes in a sparse stereo-matching data set, this paper presents an algorithm for clustering based on DP (Dynamic Programming). The algorithm is agglomerative: it begins with each element in the set as a separate cluster and merges them into successively larger clusters according to similarity of two clusters. Here, similarity is formulated as a cost function of DP. The proposed algorithm is proven to be effective by experiments performed on various images acquired by a moving vehicle.

• Journal of Korea Technology Innovation Society
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• v.13 no.4
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• pp.738-757
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• 2010

Generally, it is known that the agglomeration economies appear in some industry clusters which were developed naturally. But, in Korea, most of industry clusters were built by government. This research was carried out to evaluate the performance of governments zoning investment, in other words, industry cluster policy. In this research, we use the data of manufacturing firms in Kyunggi-province. For the microeconomic analysis, we use the KIS-VALUE data of 2008. As the empirical test methods we use both multiple regressions and Propensity Score Matching. In conclusion, there is no evidences that firms in industry cluster have better performances. Surprisingly, in PSM analysis, we find the evidence that firms in industry cluster show less innovative performance.