• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.4
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• pp.69-76
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• 2018

The purpose of this study is to propose and verify a spatial network construction method that integrated roads and subway lines to improve the predictability of the urban spatial analysis model. The existing axial map for urban spatial analysis did not reflect the subway line that serves as an important moving space in modern cities. To improve this axial map, proposed a Ground-Underground Spatial Network by integrating the underground spatial network with the axial map. As a result of the integration analysis, the Ground-Underground Spatial Network(GUSN) were similar to the movement frequency. Correlation of GUSN was 0.723, which showed higher explanatory power than correlation coefficient of 0.575 in axial map. The result of this study is expected to be a theoretical basis for constructing spatial network in urban space analysis with subway.

• Korean Journal of Computational Design and Engineering
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• v.17 no.1
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• pp.7-16
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• 2012

One distinguishing feature of BIM(Building Information Modeling) is the objectification of spatial elements independently, which makes it easy to represent spatial network. From this perspective, this study aimed to develop the spatial network analysis tool based on open BIM technologies. From the literature review, an object model of spatial network with nodes and links and a process model from construction to visualization were established. A prototype system implementing the proposed models, named SNAT(Spatial Network Analysis Tool), was developed in Java platform with using its open source packages. SNAT can create a spatial network from IFC-BIM model, calculate the indices of spatial network analysis, and visualize it with the representing types(map, graph, matrix and table).

• Journal of Korea Spatial Information System Society
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• v.10 no.4
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• pp.59-66
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• 2008

GeoSensor network means sensor network infra and related software of specific form monitoring a variety of circumstances over geospatial. And these GeoSensor network is implemented by mixing data stream with spatial attribute, spatial relation. But, until a recent date sensor network system has been concentrated on a store and search method of sensor data stream except for a spatial information. In this paper, we propose a definition of spatial data stream and its join strategy model at GeoSensor network, which combine data stream with spatial data. Spatial data stream s defining in this paper are dynamic spatial data stream of a moving object type and static spatial data stream of a fixed type. Dynamic spatial data stream is data stream transmitted by moving sensor as GPS, while static spatial data stream is generated by joining a data stream of general sensor and a relation with location values of these sensors. This paper propose joins of dynamic spatial data stream and static spatial data stream, and cost models estimating join cost. Finally, we show verification of proposed cost models and performance by join strategy.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.10
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• pp.130-138
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• 1996

In this paper, we propose a neural network which detect edges of different orentation and spatial frequency in arbitrary image data. We constructed the proposed neural network iwth two different types neural network. A diffusion network performs the gaussian operation efficiently by the diffusion process. And the spatial difference network has specially designed connections suitble to detect the contours of a specific oriention. Simulation results showed that the proposed neural network can extract the edges of selected orientation efficiently by applying the neural network to a test pattern and the real image.

• Journal of The Korean Society of Agricultural Engineers
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• v.53 no.1
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• pp.1-7
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• 2011

The nature of a network implies movement among vertices, and can be regarded as flows. Based on the flow concept which network follows the hydraulic fluid principle, we develop a spatial network model using Bernoulli equation. Then we explore the organization of spatial network and growth by the velocity of network flows. Results show that flow velocity determines network connections or influence of a vertex up to a point, and that the overall network structure is the result of pull force (pressure) and flow velocity. We demonstrate how one vertex can monopolize connections within a network.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.844-849
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• 2002

A provision of spatial information is expected to make a market explosion in various fields. A distribution of spatial data on wireless mobile environments indicates a huge expansion of mobile technology as well as a spread of geospatial applications. For high quality spatial information, 4S technology project integrating 4 kinds of spatial systems is currently being executed with the goal of nationwide integration of spatial data and spatial information systems. In terms of network environments, a mobile ad hoc network where mobile terminals communicate with each other without any infrastructures has been standardized for the next generation mobile wireless network. With respect to the future technologies for spatial information, it is necessary to design 4S applications on mobile ad hoc networks. This paper addresses the issue, which is proposing design points for distributing 4S spatial data on mobile ad hoc networks and ad hoc styled 4S applications.

• Korean Journal of Remote Sensing
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• v.19 no.1
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• pp.81-89
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• 2003

A provision of spatial information is expected to make a market explosion in various fields. A distribution of spatial data on wireless mobile environments indicates a huge expansion of mobile technology as well as a spread of geospatial applications. For high-qualified spatial information, the 4S technology Project that is integrating four kinds of spatial systems is currently being executed with the goal of nationwide integration of spatial data and spatial information systems. In terms of network environments, a mobile ad hoc network where mobile terminals communicate with each other without any infrastructures has been standardized for the next generation mobile wireless network. With respect to the future technologies for spatial information, it is necessary to design 4S applications on mobile ad hoc networks. This paper addresses the issue, which is proposing design concepts for distributing 4S spatial data on mobile ad hoc networks and for ad hoc styled 4S applications.

• Spatial Information Research
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• v.19 no.5
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• pp.63-74
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• 2011

Recently, there has been much interest in the spatial query which energy-efficiently acquires sensor readings from sensor nodes inside specified geographical area of interests. The centralized approach which performs the spatial query at a server after acquiring all sensor readings, though simple, it incurs high wireless transmission cost in accessing all sensor nodes. In order to remove the high wireless transmission cost, various in-network spatial indexing schemes have been proposed. They have focused on reducing the transmission cost by performing distributed spatial filtering on sensor nodes. However, these in-network spatial indexing schemes have a problem which cannot optimize both the spatial filtering and the wireless routing among sensor nodes, because these schemes have been developed by simply applying the existing spatial indexing schemes into the in-network environment. Therefore, we propose a new distributed spatial indexing scheme of the GR-tree. The GR-tree which form s a MBR-based tree structure, can reduce the wireless transmission cost by optimizing both the efficient spatial filtering and the wireless routing. Finally, we compare the existing spatial indexing scheme through extensive experiments and clarify our approach's distinguished features.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2804-2823
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• 2013

Wireless sensor network (WSN) is a promising technology for monitoring physical phenomena at fine-grained spatial and temporal resolution. However, the typical approach of sending each sensed measurement out of the network for detailed spatial analysis of transient physical phenomena may not be an efficient or scalable solution. This paper focuses on in-network physical phenomena detection schemes, particularly the distributed computation of the boundary of physical phenomena (i.e. event), to support energy efficient spatial analysis in wireless sensor networks. In-network processing approach reduces the amount of network traffic and thus achieves network scalability and lifetime longevity. This study investigates the recent advances in distributed event detection based on in-network processing and includes a concise comparison of various existing schemes. These boundary detection schemes identify not only those sensor nodes that lie on the boundary of the physical phenomena but also the interior nodes. This constitutes an event geometry which is a basic building block of many spatial queries. In this paper, we introduce the challenges and opportunities for research in the field of in-network distributed event geometry boundary detection as well as illustrate the current status of research in this field. We also present new areas where the event geometry boundary detection can be of significant importance.

• Journal of Korean Society of Rural Planning
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• v.23 no.2
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• pp.43-54
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• 2017

When an age of low growth and population decline, population migration plays an important role in spatial structure of region. There have been many researches on migration and regional spatial structure. The purpose of this study is to examine the changes of Gwangju and Jeonnam region's spatial structure and central area using social network analysis methods. For analysis it was used that population and migration data and passenger OD(Origin and Destination) travel data released by Statistics Korea and Korea Transport Database(KTDB). Using Gephi 0.8.2, migration and passenger OD networks were visualized, and this describe network flow and density. The results of the network centrality analysis show that the most populated village is not always network center though population mass is an important factor of central places. The average eigenvector centrality of 2010 migration is the lowest during 2005-2015, and it means few regions have high centralities. When comparing migration and travel networks, travel data is more effective than migration data in determining the central location considering spatial functions.