• Spatial Information Research
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• v.17 no.3
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• pp.251-259
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• 2009

As a result of the current trend towards promoting conservation of the ecosystem, there have been various studies conducted to determine ways to establish an ecological network. The development of analytical methods and an environmental database of GIS has made the creation of this network more efficient. This study focuses on the development of an urban spatial decision support system based on 'Landscape Ecology Theory'. The spatial decision support system suggested in this study consists of four stages. First, landscape patch for the core areas, which are major structures of the ecological network, was determined using the GIS overlay method. Second, a forest habitat was investigated to determine connectivity assessment. Using the gravity model, connectivity assessment at the habitat forest was conducted to select the needed connecting area. Third, the most suitable corridor routes for the eco-network were presented using the least-cost path analysis. Finally, a brief investigation was conducted to determine the conflict areas between the study result and landuse. The results of this study can be applied to urban green network planning. Moreover, the method developed in this study can be utilized to control urban sprawl, promote biodiversity.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.331-342
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• 1998

본 연구에서는 공간정보를 활용한 전산망 관리시스템을 개발하고자 한다. 이 시스템은 Spatial Data Manager, Network Manager, 3D Viewer 세 부분으로 구성되었다. Spatial Data Manger는 공간상의 Network 장비 및 각종 시설물의 위치와 속성정보를 보여준다. 속성정보 중에서 IP(Internet Protocol)정보는 Network Manager와 연결된다. Network Manager는 ICMP(Internet Control Message Protocol)를 사용하여 네트워크 상태를 파악한다. 3D Viewer는 사용자가 원하는 위치 및 방향에서 시설물을 볼 수 있도록 한다. Spatial Data Manager의 좌표는 3D Viewer의 Camera 좌표와 연동하여 사용자가 원하는 지역을 동시에 2차원과 3차원형태로 볼 수가 있다. 기존 전산망 관리시스템이 단지 수치적으로 네트워크의 상태에 관한 정보를 보여주던 것에 비하여, 개발된 시스템은 공간 위치를 같이 보여줌으로써 누전, 누수 등과 같이 다른 시설물이 전산망에 미칠 수 있는 영향과 전산망 케이블의 종류에 따라 다른 길이의 제약에 따른 영향 등을 분석할 수가 있다. 또한 다른 시설물의 도면 및 시설물에 관련된 기타 정보를 빠르게 검색할 수 있으므로 전산망관리 뿐만 아니라 통합적인 관리를 할 수 있다. 이를 통해 전산망 관리비용을 줄일 수 있을 것으로 기대된다.

• Journal of Korea Spatial Information System Society
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• v.9 no.2
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• pp.67-79
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• 2007

Recently, to support LBS(location-based services) and telematics applications efficiently, there have been many researches which consider the spatial network instead of Euclidean space. However, existing range query and k-nearest neighbor query algorithms show a linear decrease in performance as the value of radius and k is increased. In this paper, to increase the performance of query processing algorithm, we propose materialization-based range and k-nearest neighbor algorithms. In addition, we make the performance comparison to show the proposed algorithm achieves better retrieval performance than the existing algorithm.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.4
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• pp.186-198
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• 2016

HPAI(Highly Pathogenic Avian Influenza) is a contagious animal disease that spreads rapidly by diffusion after the first occurrence. The disease has brought tremendous social costs and economic losses. KAHIS (Korea Animal Health Information System) is the integrated system for supporting the task of preventing epidemics. They provide decision-support information, recording vehicle visiting times and facility location, etc., which is possible by enforcing registration of all livestock related facilities and vehicles. KAHIS has accumulated spatial and temporal information that enables effective tracing of potential disease trajectories and diffusion through vehicle movements. The contact network is created utilizing spatial and temporal information in KAHIS to inform facility connection via vehicle visitation. Based on the contact network, it is possible to infer spatial and temporal mechanism of disease spread and diffusion. The study objective is to empirically demonstrate how to utilize primary spatial and temporal information in KAHIS in the form of the contact network. Based on the contact network, facilities with the possibility of infection can be pinpointed within the potential spatial and temporal extent where the disease has spread and diffused. This aids the decision-making process in the task of preventing epidemics. By interpreting our demonstration results, policy implications were presented. Finally, some suggestions were made to comprehensively utilize the contact network to draw enhanced decision-support information.

• Korean Journal of Remote Sensing
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• v.20 no.1
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• pp.57-64
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• 2004

In GIS-based network analysis, topological measure of network structure can be considered as one of important factors in the urban transportation analysis. Related to this measure, it is known that the connectivity indices such as alpha index and gamma index, which mean degree of network connectivity and complexity on a graph or a circuit, provide fundamental information. On the other hand, shimbel index is one of GIS-based spatial metrics to characterize degree of network concentration. However, the approach using these quantitative indices has not been widely used in practical level yet. In this study, an application program, in complied as extension, running on ArcView- GIS is implemented and demonstrated case examples using basic layers such as road centerline and administrative boundary. In this approach, geo-spatial imagery can be effectively used to actual applications to determine the analysis zone, which is composed of networks to extract these indices. As the results of the implementation and the case examples, it is notified that alpha and gamma indices as well as shimbel index can be used as referential data or auxiliary information for urban planning and transportation planning.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10c
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• pp.83-87
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• 2006

Similarity search in moving object trajectories is an active area of research. In this paper, we introduce a new concept of measure that computes spatial distance (similarity) between two trajectories of moving objects on road networks. In addition, we propose an algorithm that generates a sequence of matching edge pairs for two trajectories that ate to be compared and computes spatial distance between them which is non Euclidian in nature. With an example, we explain how our algorithm works to show spatial similarity between trajectories of moving objects in spatial network.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1529-1541
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• 2006

Moving objects have been widely employed in traffic and logistic applications. Spatio-temporal aggregations mainly describe the moving object's behavior in the spatial data warehouse. The previous works usually express the object moving in some certain region, but ignore the object often moving along as the trajectory. Other researches focus on aggregation and comparison of trajectories. They divide the spatial region into units which records how many times the trajectories passed in the unit time. It not only makes the storage space quite ineffective, but also can not maintain spatial data property. In this paper, a spatio-temporal aggregation index structure for moving object trajectory in constrained network is proposed. An extended B-tree node contains the information of timestamp and the aggregation values of trajectories with two directions. The network is divided into segments and then the spatial index structure is constructed. There are the leaf node and the non leaf node. The leaf node contains the aggregation values of moving object's trajectory and the pointer to the extended B-tree. And the non leaf node contains the MBR(Minimum Bounding Rectangle), MSAV(Max Segment Aggregation Value) and its segment ID. The proposed technique overcomes previous problems efficiently and makes it practicable finding moving object trajectory in the time interval. It improves the shortcoming of R-tree, and makes some improvement to the spatio-temporal data in query processing and storage.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.314-322
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• 2021

Skeleton-based action recognition has attracted considerable attention in human action recognition. Recent methods for skeleton-based action recognition employ spatiotemporal graph convolutional networks (GCNs) and have remarkable performance. However, most of them have heavy computational complexity for robust action recognition. To solve this problem, we propose a shuffle graph convolutional network (SGCN) which is a lightweight graph convolutional network using pointwise group convolution rather than pointwise convolution to reduce computational cost. Our SGCN is composed of spatial and temporal GCN. The spatial shuffle GCN contains pointwise group convolution and part shuffle module which enhances local and global information between correlated joints. In addition, the temporal shuffle GCN contains depthwise convolution to maintain a large receptive field. Our model achieves comparable performance with lowest computational cost and exceeds the performance of baseline at 0.3% and 1.2% on NTU RGB+D and NTU RGB+D 120 datasets, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2053-2067
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• 2023

This paper proposes two video quality assessment methods based on deep neural network. (i)The first method uses the IQF-CNN (convolution neural network based on image quality features) to build image quality assessment method. The LIVE image database is used to test this method, the experiment show that it is effective. Therefore, this method is extended to the video quality assessment. At first every image frame of video is predicted, next the relationship between different image frames are analyzed by the hysteresis function and different window function to improve the accuracy of video quality assessment. (ii)The second method proposes a video quality assessment method based on convolution neural network (CNN) and gated circular unit network (GRU). First, the spatial features of video frames are extracted using CNN network, next the temporal features of the video frame using GRU network. Finally the extracted temporal and spatial features are analyzed by full connection layer of CNN network to obtain the video quality assessment score. All the above proposed methods are verified on the video databases, and compared with other methods.

• 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.