• Journal of the Korea Convergence Society
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• v.10 no.12
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• pp.315-325
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• 2019

The purpose of this study is to design and construct a data mart that anyone can easily analyze subway OD movement patterns. Subway OD data of the year 2017 was downloaded from the Seoul Open Data Plaza and used as the source data. A multidimensional model was designed, and Gaussian mixed cluster analysis and visualization analysis using Tableau were performed. Interestingly, movement between suburban and Seoul accounts for 23% of the total traffic. The passengers of Suwon Station move to the suburbs much more than Seoul, while Pangyo Station mostly moves to Seoul. As a result of Gaussian mixed cluster, eight clusters of OD segments were found, and the characteristics of each cluster were characterized by segment distance and passenger size.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.12-20
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• 2011

In this paper, we propose an efficient object detection and classification algorithm for video surveillance applications. Previous researches mainly concentrated either on object detection or classification using particular type of feature e.g., Scale Invariant Feature Transform (SIFT) or Speeded Up Robust Feature (SURF) etc. In this paper we propose an algorithm that mutually performs object detection and classification. We combinedly use heterogeneous types of features such as texture and color distribution from local patches to increase object detection and classification rates. We perform object detection using spatial clustering on interest points, and use Bag of Words model and Naive Bayes classifier respectively for image representation and classification. Experimental results show that our combined feature is better than the individual local descriptor in object classification rate.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.5
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• pp.113-119
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• 2008

Conventional 1-1-1 cooperative protocol offers path-loss gain as advantage of multi-hop and spatial diversity which is equivalent to MIMO system. This protocol is enable to get higher reliability and reduction of power consumption than those of the single-hop or multi-hop. But the 1-1-1 cooperative protocol get only the diversity order 2 and limited path-loss reduction gain because this protocol has a single cooperative relay. We propose 1-2-1 cooperative protocol using two cooperative relays R1, R2. The 1-2-1 cooperative protocol can improve path-loss reduction and increase diversity order 3. Moreover, the cooperative relay R2 attains diversity order 2. The signaling method in transmission uses DF (Decode and Forward) or DR (Decode and Reencode) and 1-2-1 DF/DR cooperative protocol are applied to clustering based wireless sensor networks (WSNs). Simulations are performed to evaluate the performance of the protocols under Rayleigh fading channel plus AWGN (Additive White Gaussian Noise).

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.4
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• pp.139-144
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• 2011

This study proposed a classification method of LIDAR data by using simultaneously the color information (R, G, B) and reflection intensity information (I) obtained from terrestrial LIDAR and by analyzing the association between these data through the use of statistical classification methods. To this end, first, the factors that maximize variance were calculated using the variables, R, G, B, and I, whereby the factor matrix between the principal factor and each variable was calculated. However, although the factor matrix shows basic data by reducing them, it is difficult to know clearly which variables become highly associated by which factors; therefore, Varimax method from orthogonal rotation was used to obtain the factor matrix and then the factor scores were calculated. And, by using a non-hierarchical clustering method, K-mean method, a cluster analysis was performed on the factor scores obtained via K-mean method as factor analysis, and afterwards the classification accuracy of the terrestrial LiDAR data was evaluated.

• The Journal of the Korea Contents Association
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• v.8 no.5
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• pp.29-37
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• 2008

To transmit video bit stream over low bandwidth such as wireless channel, high compression algorithm like H.264 codec is exploited. In transmitting high compressed video bit-stream over low bandwidth, packet loss causes severe degradation in image quality. In this paper, a new algorithm for recovery of missing or erroneous motion vector is proposed. Considering that the missing or erroneous motion vectors in blocks are closely correlated with those of neighboring blocks. Motion vector of neighboring blocks are clustered according to average linkage algorithm clustering and a representative value for each cluster is determined to obtain the candidate motion vector sets. As a result, simulation results show that the proposed method dramatically improves processing time compared to existing H.264/AVC. Also the proposed method is similar to existing H.264/AVC in terms of visual quality.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.6
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• pp.724-732
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• 2001

In this paper, a new method of feature extraction of major objects to represent an image using Vector Quantization(VQ) is proposed. The principal features of the image, which are used in a content-based image retrieval system, are color, texture, shape and spatial positions of objects. The representative color and texture features are extracted from the given image using VQ(Vector Quantization) clustering algorithm with a general feature extraction method of color and texture. Since these are used for content-based image retrieval and searched by objects, it is possible to search and retrieve some desirable images regardless of the position, rotation and size of objects. The experimental results show that the representative feature extraction time is much reduced by using VQ, and the highest retrieval rate is given as the weighted values of color and texture are set to 0.5 and 0.5, respectively, and the proposed method provides up to 90% precision and recall rate for 'person'query images.

• Journal of the Korean association of regional geographers
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• v.21 no.2
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• pp.394-410
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• 2015

This study aims to classify climate zones for extreme climate indices over the Republic of Korea. First, frequencies and magitudes of extreme high temperature, spatial distributions for extreme low temperature, and extreme precipitation are analysed. Frequencies of summer days in inland region show more than coastal region. In frequencies of frost days, the characteristics of altitude and longitude are appeared. Heavy precipitation days show many frequencies in the southern coastal region and Jeju island, but little in Gyeongsangbuk-do region. The classification of climate zone for extreme climate indices by principal component analysis and cluster analysis is conducted for the first half, second half of study period, and climatology period for 1981-2010. Summer days are classified according to latitude. In case of frost days, the eastern and the southern coastal region and Jeju island are classified as same region. Heavy precipitation days are classified according to longitude in south region of Gyeonggi-do and Gangwon-do. This study will help to prepare adaptation and mitigation system for climate change in wide range of fields.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.665-670
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• 2014

A SVM is a kind of binary classifier in order to find optimal hyperplane which separates training data into two groups. Due to its remarkable performance, the SVM is applied in various fields such as inductive inference, binary classification or making predictions. Also it is a representative black box model; there are plenty of actively discussed researches about analyzing trained SVM classifier. This paper conducts a study on a method that is automatically detecting the line-shaped echoes, sun strobe echo and radial interference echo, using the SVM algorithm because the line-shaped echoes appear relatively often and disturb weather forecasting process. Using a spatial clustering method and corrected reflectivity data in the weather radar, the training data is made up with mean reflectivity, size, appearance, centroid altitude and so forth. With actual occurrence cases of the line-shaped echoes, the trained SVM classifier is verified, and analyzed its characteristics using the decision tree method.

• Journal of the Economic Geographical Society of Korea
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• v.11 no.3
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• pp.492-507
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• 2008

Many classical network analysis methods approach networks in aspatial perspectives. Measuring network reliability and finding critical nodes in particular, the analyses consider only network connection topology ignoring spatial components in the network such as node attributes and edge distances. Using local network autocorrelation measure, this study handles the problem. By quantifying similarity or clustering of individual objects' attributes in space, local autocorrelation measures can indicate significance of individual nodes in a network. As an application, this study analyzed internet backbone networks in the United States using both classical disjoint product method and Getis-Ord local G statistics. In the process, two variables (population size and reliability) were applied as node attributes. The results showed that local network autocorrelation measures could provide local clusters of critical nodes enabling more empirical and realistic analysis particularly when research interests were local network ranges or impacts.

• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.337-346
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• 2006

In order to analyze the behavior of moving objects, a measure for determining the similarity of trajectories needs to be defined. Although research has been conducted that retrieved similar trajectories of moving objects in Euclidean space, very little research has been conducted on moving objects in the space defined by road networks. In terms of real applications, most moving objects are located in road network space rather than in Euclidean space. In similarity measure between trajectories, however, previous methods were based on Euclidean distance and only considered spatial similarity. In this paper, we define similarity measure based on POI and TOI in road network space. With this definition, we present methods to retrieve similar trajectories using spatio-temporal similarity between trajectories. We show clustering results for similar trajectories. Experimental results show that similar trajectories searched by each method and consistency rate between each method for the searched trajectories.