• Proceedings of the Korean Geotechical Society Conference
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• 2010.09b
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• pp.76-80
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• 2010

This paper describes that estimation methods for the distribution of consolidation settlements to investigate the influence of distribution characteristics of compression index on the spatial distribution of consolidation settlements. When the variation of compression index is considerable, the spatial distribution of compression index is estimated using ordinary cokriging. The spatial distribution of consolidation settlements estimated by considering both the variation of compression index and void ratio (CASE-1) is different from the conventional mean value of all soil properties (CASE-2). The settlement of CASE-1 shows the larger variation at short distances rather than that of CASE-2. Whereas the spatial settlement distribution of CASE-1 is affected by the spatial distribution of compression index and the thickness of consolidation layer, the distribution of CASE-2 is significantly influenced by the distribution of the thickness of consolidation layer.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.5063-5073
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• 2016

In this paper, an efficient texture compression method is proposed for fast rendering, which exploits the spatial correlation among blocks through intra-picture block matching. Texture mapping is widely used to enhance the visual quality of results in real-time rendering applications. For fast texture mapping, it is necessary to identify an effective trade-off between compression efficiency and computational complexity. The conventional compression methods utilized for image processing (e.g., JPEG) provide high compression efficiency while resulting in high complexity. Thus, low complexity methods, such as ETC1, are often used in real-time rendering applications. Although these methods can achieve low complexity, the compression efficiency is still lower than that of JPEG. To solve this problem, we propose a texture compression method by reducing the spatial redundancy between blocks in order to achieve the better compression performance than ETC1 while maintaining complexity that is lower than that of JPEG. Experimental results show that the proposed method achieves better compression efficiency than ETC1, and the decoding time is significantly reduced compared to JPEG while similar to ETC1.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.7
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• pp.1253-1258
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• 2006

In the mobile vector map service environments, there are problems like as terminal resource shortage and transmission delay for the characteristics of large spatial data. For the normal mobile vector map services, some techniques are required to overcome the problems. Spatial data compression approach is one of the techniques to reduce the bandwidth and the waiting time at clients. However it also must be considered that the effect on total efficiency caused by the overhead of compression and restoration time. This thesis proposes two spatial data compression techniques. First approach is to get relative coordinates to first coordinate of each object. The other approach is to compute client coordinates before transmission. Through the implementation and experiments, proposed techniques are evaluated the compression effects and efficiency.

• Spatial Information Research
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• v.13 no.4 s.35
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• pp.355-364
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• 2005

Vector data sets (e.g. maps) are currently major sources of displaying, querying, and identifying locations of spatial features in a variety of applications. Especially in mobile environment, the needs for using spatial data is increasing, and the relative large size of vector maps need to be smaller. Recently, there have been several studies about vector map compression. There was clustering-based compression method with novel encoding/decoding scheme. However, precedent studies did not consider that spatial data have to be updated periodically. This paper explores the problem of existing clustering-based compression method. We propose an adaptive approximation method that is capable of handling data updates as well as reducing error levels. Experimental evaluation showed that when an updated event occurred the proposed adaptive approximation method showed enhanced positional accuracy compared with simple cluster based compression method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3641-3655
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• 2016

Wireless sensor networks (WSNs) provide a promising approach to monitor the physical environments, to prolong the network lifetime by exploiting the mutual correlation among sensor readings has become a research focus. In this paper, we design a hierarchical network framework which guarantees layered-compression. Meanwhile, a data sorting-based adaptive spatial compression scheme (DS-ASCS) is proposed to explore the spatial correlation among signals. The proposed scheme reduces the amount of data transmissions and alleviates the network congestion. It also obtains high compression performance by sorting original sensor readings and selectively discarding the small coefficients in transformed matrix. Moreover, the compression ratio of this scheme varies according to the correlation among signals and the value of adaptive threshold, so the proposed scheme is adaptive to various deploying environments. Finally, the simulation results show that the energy of sorted data is more concentrated than the unsorted data, and the proposed scheme achieves higher reconstruction precision and compression ratio as compared with other spatial compression schemes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.4
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• pp.950-954
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• 2004

According to the rapid advance of computer and communication techniques, the request of mobile internet services is highly increasing. However, the main obstacles for mobile vector map service environments, are large data volume and narrow wireless bandwidth. Among the many possible solutions, spatial data compression technique may contribute to reduce the load of bandwidth and client response time. This thesis proposes two methods for spatial data compression. The one is relative coordinates transformation method, and the other is client coordinates transformation method. And, this thesis also proposes the system architecture for experiments. The two compression methods could be evaluated the compression effect and the response time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.358-362
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• 2004

According to the rapid advance of computer and communication techniques, the request of mobile internet services is highly increasing. However, the main obstacles for mobile vector map service environments, are large data volume and narrow wireless bandwidth. Among the many possible solutions, spatial data compression technique may contribute to reduce the load of bandwidth and client response time. This thesis proposes two methods for spatial data compression. The one is relative coordinates transformation method, and the other is client coordinates transformation method. And, this thesis also proposes the system architecture for experiments. The two compression methods could be evaluated the compression effect and the response time.

• Journal of Korea Spatial Information System Society
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• v.7 no.3 s.15
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• pp.45-53
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• 2005

Nowadays, using the mobile communication devices, such as a mobile phone, PDA, telematics device, and so forth, are increasing. The large parts of the services with these mobile devices are the position tracking and the route planning. For offering these services, it is increasing the use of the spatial data on the mobile environment. Although the storage of mobile device expands more than before, it still lacks the necessary storage on the spatial data. In this paper, lossy compression technique on the spatial data is suggested, and then it is analyzed the compression ratio and the amount of loss data by the test. Suggested compression technique on the spatial data at this paper is applied to the real-data, and others methods, suggested at the previous studies, is applied to same data. According as the results from both are compared and analyzed, compression technique suggested at this study shows better performance when the compression result is demanded the high position accuracy.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.6
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• pp.49-54
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• 2008

Recently, with the development of wireless communications and GIS, interest about mobile computing is rising. In this case, the GIS needs to be placed in a constricted environment than the environment of server computing. For this reason large amounts of spatial data must be compressed into the mobile device. The compression technique of the spatial data is difficult and must be processed in the correct order because the size of the data is unpredictable. Therefore, in this paper, the cell-based compression technique of the spatial data for mobile GIS is presented. This paper includes the process of transformation of spatial data from a certain server to a mobile device by cell-based compression technique. In this paper, the cell-based compression technique of the spatial data has been proven practically efficient.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.3
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• pp.141-145
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• 2009

In this paper, we propose an efficient lossless compression algorithm using spatial and temporal information. The proposed method obtains higher lossless compression of images than other lossless compression techniques. It is divided into two parts, a motion adaptation based predictor part and a residual error coding part. The proposed nonlinear predictor can reduce prediction error by learning from its past prediction errors. The predictor decides the proper selection of the spatial and temporal prediction values according to each past prediction error. The reduced error is coded by existing context coding method. Experimental results show that the proposed algorithm has better performance than those of existing context modeling methods.