• Korean Journal of Remote Sensing
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• v.17 no.1
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• pp.57-69
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• 2001

This study propose a image processing system of unsupervised analysis. This system integrates low-level segmentation and high-level classification. The segmentation and classification are conducted respectively with and without spatial constraints on merging by a hierarchical clustering procedure. The clustering utilizes the local mutually closest neighbors and multi-window operation of a pyramid-like structure. The proposed system has been evaluated using simulated images and applied for the LANDSATETM+ image collected from Youngin-Nungpyung area on the Korean Peninsula.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.468-471
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• 2003

In this paper, experimental tests are performed to evaluate the efficiency of spatial clustering method using regular grid that is proposed in our recent research. In details, we estimate the execution time for finding clusters varying spatial objects on sample data sets with various distributions and perform experimental tests varying threshold value on a data set. We also compare the running time of cluster generating algorithm with that of cluster merging algorithm per each test.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.371-378
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• 2021

In this study, in order to analyze the spatial distribution of cold water occurred in the Southeast Sea of Korea, the K-means clustering method was used to analyze the ocean observatory buoy of Gori and Yangpo and GHTSST Level 4 from 2016 to 2018. The buoy data was used to identify the change in sea water temperature and the cold water occurrence at Gori and Yangpo in the Southeast Sea. As a result, the sea water temperature of Gori and Yangpo decreased equally at the cold water occurrence. Therefore, the reciprocal of the sea water temperature and the variance of SST were compared to see the changes of SST when the cold water occurs. When the reciprocal of the sea water temperature increases, the dispersion of SST also increases. Through this, it can be seen that there is a change in the water temperature distribution of SST in the sea when the cold water occurs. After that, K-means clustering was used to classify the cold water. After analyzing the optimal K value for clustering by using the Elbow method, it was possible to classify a region with cold water. Through this, it is estimated that the spatial distribution and diffusion range of the cold water, and it can be estimated and used in future studies to identify damage caused by the cold water and predict spatial spread.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.219-232
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• 2023

Mobility services need to change according to the regional characteristics of the target service area. Accordingly, analysis of mobility patterns and characteristics based on Origin-Destination (OD) data that reflect travel behaviors in the target service area is required. However, since conventional methods construct the OD data obtained from the administrative district-based zone system, it is hard to ensure spatial homogeneity. Hence, there are limitations in analyzing the inherent travel patterns of each mobility service, particularly for new mobility service like Demand Responsive Transit (DRT). Unlike the conventional approach, this study applies a data-driven clustering technique to conduct spatial analyses on OD travel patterns of regional mobility services based on reconstructed OD data derived from re-aggregation for original OD distributions. Based on the reconstructed OD data that contains information on the inherent feature vectors of the original OD data, the proposed method enables analysis of the spatial characteristics of regional mobility services, including public transit bus, taxi and DRT.

• The Korean Journal of Applied Statistics
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• v.27 no.4
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• pp.561-575
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• 2014

In this paper, we propose a penalized likelihood method to detect local spatial clusters associated with disease. The key computational algorithm is based on genlasso by Tibshirani and Taylor (2011). The proposed method has two main advantages over Kulldorff's method which is popoular to detect local spatial clusters. First, it is not needed to specify a proper cluster size a priori. Second, any type of covariate can be incorporated and, it is possible to find local spatial clusters adjusted for some demographic variables. We illustrate our proposed method using tuberculosis data from Seoul.

• IEMEK Journal of Embedded Systems and Applications
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• v.5 no.4
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• pp.206-216
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• 2010

Wireless sensor networks (WSNs) are used to collect various data in environment monitoring applications. A spatial clustering may reduce energy consumption of data collection by partitioning the WSN into a set of spatial clusters with similar sensing data. For each cluster, only a few sensor nodes (samplers) report their sensing data to a base station (BS). The BS may predict the missed data of non-samplers using the spatial correlations between sensor nodes. ASAP is a representative data collection algorithm using the spatial clustering. It periodically reconstructs the entire network into new clusters to accommodate to the change of spatial correlations, which results in high message overhead. In this paper, we propose a new data collection algorithm, name EPDC (Energy-efficient Periodic Data Collection). Unlike ASAP, EPDC identifies a specific cluster consisting of many dissimilar sensor nodes. Then it reconstructs only the cluster into subclusters each of which includes strongly correlated sensor nodes. EPDC also tries to reduce the message overhead by incorporating a judicious probabilistic model transfer method. We evaluate the performance of EPDC and ASAP using a simulation model. The experiment results show that the performance improvement of EPDC is up to 84% compared to ASAP.

• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.43-52
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• 2006

LRecently, with increasing interest in ubiquitous computing, knowledge discovery method is needed with consideration of the efficiency and the effectiveness of wide range and various forms of data. Spatial Characterization which extends former characterization method with consideration of spatial and non-spatial property enables to find various form of knowledge in spatial region. The previous spatial characterization methods have the problems as follows. Firstly, former study shows the problem that the result of searched knowledge is unable to perform the multiple spatial analysis. Secondly, it is unable to secure the useful knowledge search since it searches the limited spatial region which is allocated by the user. Thus, this study suggests spatial characterization which applies to density based clustering.

• Journal of the Korea Society of Computer and Information
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• v.21 no.9
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• pp.37-43
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• 2016

This paper introduces a method for video shot group detection needed for efficient management and summary of video. The proposed method detects shots based on low-level visual properties and performs temporal and spatial clustering based on visual similarity of neighboring shots. Shot groups created from temporal clustering are further clustered into small groups with respect to visual similarity. A set of representative shot frames are selected from each cluster of the smaller groups representing a scene. Shots excluded from temporal clustering are also clustered into groups from which representative shot frames are selected. A number of video clips are collected and applied to the method for accuracy of shot group detection. We achieved 91% of accuracy of the method for shot group detection. The number of representative shot frames is reduced to 1/3 of the total shot frames. The experiment also shows the inverse relationship between accuracy and compression rate.

• Journal of Information Processing Systems
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• v.18 no.2
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• pp.292-292
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• 2022

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2488-2511
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• 2015

Data redundancy has high impact on Wireless Sensor Network's (WSN) performance and reliability. Spatial and temporal similarity is an inherent property of sensory data. By reducing this spatio-temporal data redundancy, substantial amount of nodal energy and bandwidth can be conserved. Most of the data gathering approaches use either temporal correlation or spatial correlation to minimize data redundancy. In Collective Prediction exploiting Spatio Temporal correlation (CoPeST), we exploit both the spatial and temporal correlation between sensory data. In the proposed work, the spatial redundancy of sensor data is reduced by similarity based sub clustering, where closely correlated sensor nodes are represented by a single representative node. The temporal redundancy is reduced by model based prediction approach, where only a subset of sensor data is transmitted and the rest is predicted. The proposed work reduces substantial amount of energy expensive communication, while maintaining the data within user define error threshold. Being a distributed approach, the proposed work is highly scalable. The work achieves up to 65% data reduction in a periodical data gathering system with an error tolerance of 0.6℃ on collected data.