• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.49-56
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• 2020

Detecting shadows in images and restoring or removing them was a very challenging task in computer vision. Traditional researches used color information, edges, and thresholds to detect shadows, but there were errors such as not considering the penumbra area of shadow or even detecting a black area that is not a shadow. Deep learning has been successful in various fields of computer vision, and research on applying deep learning has started in the field of shadow detection and removal. However, it was very difficult and time-consuming to collect data for network learning, and there were many limited conditions for shooting. In particular, it was more difficult to obtain shadow data from buildings and satellite images, which hindered the progress of the research. In this paper, we propose a method for generating shadow data from buildings and satellites using Unity3D. In the virtual Unity space, 3D objects existing in the real world were placed, and shadows were generated using lights effects to shoot. Through this, it is possible to get all three types of images (shadow-free, shadow image, shadow mask) necessary for shadow detection and removal when training deep learning networks. The method proposed in this paper contributes to helping the progress of the research by providing big data in the field of building or satellite shadow detection and removal research, which is difficult for learning deep learning networks due to the absence of data. And this can be a suboptimal method. We believe that we have contributed in that we can apply virtual data to test deep learning networks before applying real data.

• Journal of Advanced Marine Engineering and Technology
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• v.34 no.8
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• pp.1159-1164
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• 2010

The current maritime data communications mainly depend on radio and satellite which have restrictions on data rate and cost. That leads to needs of novel relatively-high-speed data communication systems at sea just like on land. This paper proposes a routing protocol (MWR) for newly designed model of ship-to-ship communication networks at sea. The MWR protocol finds out an optimal route by selecting an optimal network for each specific application from overlapped networks of available wireless media at sea.

• Journal of the Korean Geographical Society
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• v.46 no.3
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• pp.319-330
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• 2011

In this study, a soil moisture estimation model was proposed using the ground observation data of soil moisture, precipitation, surface temperature, MODIS NDVI and artificial neural networks. The model was calibrated and verified on the Yongdam dam watershed which has reliable ground soil moisture networks. The test statistics of calibration sites, Jucheon, Bugui, Sangjeon, showed that the correlation coefficients between observations and estimations are about 0.9353 and RMSE is about 1.4957%. Also that of the verification site, Cheoncheon2, showed that the correlation coefficient is about 0.8215 and RMSE is about 4.2077%. The soil moisture estimation model was applied to estimate the spatial distribution of soil moisture in the Yongdam dam watershed and results showed improved spatial soil moisture distribution since the model used satellite information of NDVI and artificial neural networks which can represent the nonlinear relationships between data well. The model should be useful to estimate wide range soil moisture information.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1998.10a
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• pp.28-33
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• 1998

GPS is a effective surveying instrument using satellite measurement system and can be applicable to digital map construction of irrigation canal networks. In this study, selected a main canal of a irrigation district and GPS surveyed. The obtained survey data was corrected with post-processed DGPS and imported to GIS for the digital map construction.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.837-840
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• 1999

It is difficult to analyze the impact of interference of NGSO/FSS systems on a GSO/FSS network because of time-varying nature of NGSO/FSS systems. In this paper, we present an efficient method to assess the impact of interference of NGSO/FSS satellite networks on the GSO/FSS carrier performances. The example analysis shows the impact on the GSO/FSS carrier performances in terms of elevation angles of earth stations in a GSO/FSS system.

• Magazine of the Korean Society of Agricultural Engineers
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• v.42
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• pp.9-16
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• 2000

GPS(Global Positioning System) surveying is an effective method using satellite measurement system and can be applied to construction of digital map of irrigation canal networks. In this study, GPS surveying method for irrigation structures was developed. A selected main canal of an irrigation district were surveyed by GPS. The obtained surveying results were corrected by post-processed DGPS (Differential Global Positioning System) and imported to GIS for the digital map construction.

• Journal of the Korean Association of Geographic Information Studies
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• v.4 no.3
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• pp.61-70
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• 2001

This paper presents a new method to extract semi-automatically roads from high spatial resolution satellite imagery. This method is based both on wavelet transform and on multiresolution analysis combined in the "$\grave{a}$ trous" algorithm. As an urban road network consists on different classes of streets, multiresolution processing allows to extract the streets class by class. The method was applied to a KVR-1000 image on a part of Busan Metropolitan City. The method was carried out for the road extraction of three different widths and it succeeded in extracting good fitted strips. The accuracy analysis for three types of streets was also performed. The overall accuracy in 4 pixels of width is 80.5%. The result suggests that this method can be used to update road networks in the studied urban network. In summary, the multiresolution approach based on the wavelet transform, used in this study, is regarded as one of effective methods to extract urban road network from high spatial resolution satellite images.

• Journal of Satellite, Information and Communications
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• v.8 no.1
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• pp.1-7
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• 2013

Currently satellite communication systems usually use the ACM(Adaptive Coding and Modulation) to extend the link availability and to increase the bandwidth efficiency. However, when ACM system is used for satellite communications, we should carefully consider TCP congestion control to avoid network congestions. Because MODCODs in ACM are changed to make a packet more robust according to satellite wireless link conditions, bandwidth of satellite forward link is also changed. Whereas TCP has a severe problem to control the congestion window for the changed bandwidth, then packet overflow can be experienced at MAC or PHY interface buffers. This is a reason that TCP in transport layer does not recognize a change of bandwidth capability form MAC or PHY layer. To overcome this problem, we propose the adaptive congestion control scheme of TCP for supporting ACM in Satellite PEP (Performance Enhancing Proxy) systems. Simulation results by using ns-2 show that our proposed scheme can be efficiently adapted to the changed bandwidth and TCP congestion window size, and can be useful to improve TCP performance.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.120-120
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• 2020

Spatial precipitation data is one of the essential components in modeling hydrological problems. The estimation of these data has achieved significant achievements own to the recent advances in remote sensing technology. However, there are still gaps between the satellite-derived rainfall data and observed data due to the significant dependence of rainfall on spatial and temporal characteristics. An effective approach based on the Convolutional Neural Network (CNN) model to correct the satellite-derived rainfall data is proposed in this study. The Mekong River basin, one of the largest river system in the world, was selected as a case study. The two gridded precipitation data sets with a spatial resolution of 0.25 degrees used in the CNN model are APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). In particular, PERSIANN-CDR data is exploited as satellite-based precipitation data and APHRODITE data is considered as observed rainfall data. In addition to developing a CNN model to correct the satellite-based rain data, another statistical method based on standard deviations for precipitation bias correction was also mentioned in this study. Estimated results indicate that the CNN model illustrates better performance both in spatial and temporal correlation when compared to the standard deviation method. The finding of this study indicated that the CNN model could produce reliable estimates for the gridded precipitation bias correction problem.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.9 no.1
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• pp.27-35
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• 1991

The total station which is able to measure angles and distances simultaneously, and G.P.S., 3-dimensional satellite surveying system will be employed as important equipments for establishing and re-adjusting large geodetic networks. The objective of this study is to suggest possibilities of application of combination adjustment by means of studying characteristics of networks adjusted according to detection gross errors included in angles and distances by least square method. It is expected that the results of this study were used for determination of national geodetic networks but also large or small construction site.