• Journal of the Economic Geographical Society of Korea
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• v.1 no.1
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• pp.137-149
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• 1998

It is likely that the spatial structure of the intraurban accessibility as well as the accessibility value of each of the nodes in the subway network is affected by the addition of new linkages. The changes in the accessibility at individual nodes also affect the accessibility in the surrounding areas at some distances away from the nodes. Graph-theoretic algorithms have been developed as a proper measurement scheme for the nodal accessibility in tracked transport networks such as subway networks. However, the graph-theoretic measurements have limitations to estimate the spatial diffusion effect on the surrounding areas. This study proposes a new model for the spatial diffusion effect estimation of nodal accessibility increment in the subway network toward the surrounding areas. Since the distance decay trend of subway station use reflect the spatial diffusion effect of the accessibility of subway station toward the surrounding area. The model is deduced from the subway station use density function which is formulated by the questionnaire survey data.

• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.15-22
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• 2020

In this paper, we proposed a deep learning based super-resolution method that combines Channel Attention and Spatial Attention feature enhancement methods. It is important to restore high-frequency components, such as texture and features, that have large changes in surrounding pixels during super-resolution processing. We proposed a super-resolution method using feature enhancement that combines Channel Attention and Spatial Attention. The existing CNN (Convolutional Neural Network) based super-resolution method has difficulty in deep network learning and lacks emphasis on high frequency components, resulting in blurry contours and distortion. In order to solve the problem, we used an emphasis block that combines Channel Attention and Spatial Attention to which Skip Connection was applied, and a Residual Block. The emphasized feature map extracted by the method was extended through Sub-pixel Convolution to obtain the super resolution. As a result, about PSNR improved by 5%, SSIM improved by 3% compared with the conventional SRCNN, and by comparison with VDSR, about PSNR improved by 2% and SSIM improved by 1%.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1491-1495
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• 2023

This study utilized automated machine learning (AutoML) to calculate Arctic ice surface temperature (IST). AutoML-derived IST exhibited a strong correlation coefficient (R) of 0.97 and a root mean squared error (RMSE) of 2.51K. Comparative analysis with deep neural network (DNN) models revealed that AutoML IST demonstrated good accuracy, particularly when compared to Moderate Resolution Imaging Spectroradiometer (MODIS) IST and ice mass balance (IMB) buoy IST. These findings underscore the effectiveness of AutoML in enhancing IST estimation accuracy under challenging polar conditions.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.139-144
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• 2017

As technologies related to sensor network are currently emerging and the use of GeoSensor is increasing along with the development of IoT technology, spatial query processing systems to efficiently process spatial sensor data are being actively studied. However, existing spatial query processing systems do not support a spatial-temporal data type and a spatial-temporal operator for processing spatial-temporal sensor data. Therefore, they are inadequate for processing spatial-temporal sensor data like GeoSensor. Accordingly, this paper developed a spatial-temporal query processing system, for efficient spatial-temporal query processing of spatial-temporal sensor data in a sensor network. Lastly, this paper verified the utility of System through a scenario, and proved that this system's performance is better than existing systems through performance assessment of performance time and memory usage.

• Korean System Dynamics Review
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• v.12 no.2
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• pp.69-94
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• 2011

Daejeon, encompassing Daedeok Science Town and Daedeok Innopolis, possesses the advantage of portraying relatively higher regional innovation capacity and facilitating network formation among regional professional research organizations. Applying the Social Network Analysis(SNA) techniques, this paper focuses on divulging structural and spatial characteristics of the Daejeon Information and Communication(ICT) industry network, analyzing co-research projects implemented by the Daejeon-based universities. For the analytical tool, it depends on NETMINER 3.0. Furthermore, based on the Systems Thinking approaches, this study suggests a couple of policy implications. Judging from the Korea Standard Industrial Classification principles, the existing ICT industry is subdivided into 11 sub-industries. The highest degree centralization value comes from the Mobile Communication sub-industry(188.668%), indicating that Mobile Communication sub-industry exerts the most significant impact on the regional innovation networking in Daejeon. Among various stakeholders, Korea Advanced Institute of Science and Technology(KAIST) records the top ranking in most categories, conspicuously leading the institute-industry linkage. In terms of the ICT spatial distribution, the intra-regional cooperation examples present the strongest linkage values, followed by Daejeon-the Capital Region ones. Finally, as well shown in a series of causal loop analyses, this study recommends that Daejeon should put top policy priority in strengthening the internal ICT network within Daejeon proper. Here, Daejeon should keep in mind the fact that there exist reinforcing loops between Daejeon's attractiveness and the entering of new ICT firms.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.961-973
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• 2020

In this manuscript, a new pansharpening model based on Convolutional Neural Network (CNN) was developed. Dilated convolution, which is one of the representative convolution technologies in CNN, was applied to the model by making it deep and complex to improve the performance of the deep learning architecture. Based on the dilated convolution, the residual network is used to enhance the efficiency of training process. In addition, we consider the spatial correlation coefficient in the loss function with traditional L1 norm. We experimented with Dilated Residual Networks (DRNet), which is applied to the structure using only a panchromatic (PAN) image and using both a PAN and multispectral (MS) image. In the experiments using KOMPSAT-3A, DRNet using both a PAN and MS image tended to overfit the spectral characteristics, and DRNet using only a PAN image showed a spatial resolution improvement over existing CNN-based models.

• Journal of Advanced Navigation Technology
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• v.18 no.6
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• pp.546-554
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• 2014

For improvement of network RTK performance in survey field, Spatial Information Research Institute (SIRI), LX Korea Cadastral Survey Corporation installed 30 GNSS permanent stations in Korea Peninsula, and has been running the MAC-based network RTK service as a test version. In this paper, we introduce the LX GNSS network and analyze the positioning accuracy of the LX MAC RTK service. For field test of the LX MAC RTK service, we installed temporally fixed anchor points and observed simultaneously with VRS of National Geographic Information Institute. As a result, the horizontal position differences and initial times of LX MAC with respect to NGII VRS are $1{\sim}2{\pm}1cm$ and <10 seconds, respectively.

• Journal of Korea Spatial Information System Society
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• v.1 no.2 s.2
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• pp.137-149
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• 1999

Spatial database systems in client-server environment have network overload due to the large amount of spatial data transmission. Users use the window query that loads partial region of a whole map for quick response time in the environment. A series of window query such as screen movement, enlargement or shrinkage requires data in similar region and this increases network overload by re-transmitting the same data in intersected region with the earlier transmitted region. Removing the transmitted data from query results can solve this problem. In this paper, we design and implement a spatial object manager in order to remove the intersected region occurred by a series of window query. The spatial object manager manages the object identifiers of transmitted objects and removes transmitted objects from spatial objects of the query result by using the removal technique of the intersected region for the transmission and comparison. We utilize GEOMania Millennium server, an open client-server spatial database system, as spatial object manager in this paper. The result of the performance evaluation shows that the spatial object manager removes the transmission of the data redundancy, reduces network overload and improves the overall system performance.

• ETRI Journal
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• v.30 no.6
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• pp.853-855
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• 2008

This paper presents an energy-efficient spatial join algorithm for multiple sensor networks employing a spatial semijoin strategy. For optimization of the algorithm, we propose a GR-tree index and a grid-ID-based spatial approximation method, which are unique to sensor networks. The GR-tree is a distributed spatial index over the sensor nodes, which efficiently prunes away the nodes that will not participate in a spatial join result. The grid-ID-based approximation provides great reduction in communication cost by approximating many spatial objects in simpler forms. Our experiments demonstrate that the algorithm outperforms existing methods in reducing energy consumption at the nodes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.10
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• pp.2548-2566
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• 2012

This paper derives two main end-to-end performance metrics, namely the spatial capacity density and the average end-to-end delay of the multi-hop wireless ad hoc networks with multi-antenna communications. Based on the closed-form expressions of these performance metrics, three hopping strategies, i.e., the closest neighbor, the furthest neighbor and the randomly selected neighbor hopping strategies have been investigated. This formulation provides insights into the relations among node density, diversity gains, number of hops and some other network design parameters which jointly determine network performances, and a method of choosing the best hopping strategy which can be formulated from a network design perspective.