• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.741-746
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• 2016

Albedo is one of the climate variables that modulate absorption of solar energy, and its retrieval is important process for climate change study. High spatial resolution and long-term consistent periods are important considerations in order to efficiently use the retrieved albedo data. This study retrieved surface broadband albedo based on Landsat 8 as high resolution which is consistent with Landsat 7. First of all, we analyzed consistency of Landsat 7 channel and Landsat 8 channel. As a result, correlation coefficient(R) on all channels is average 0.96. Based on this analysis, we used multiple linear regression model using Landsat 7 albedo, which is being used in many studies, and Landsat 8 reflectance channel data. The regression coefficients of each channel calculated by regression analysis were used to derive a formula for converting the Landsat 8 reflectance channel data to broadband albedo. After Landsat 8 albedo calculated using the derived formula is compared with Landsat 7 albedo data, we confirmed consistency of two satellite using Root Mean Square Error (RMSE), R-square ($R^2$) and bias. As a result, $R^2$ is 0.89 and RMSE is 0.003 between Landsat 7 albedo and Landsat 8 albedo.

• Journal of Digital Convergence
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• v.18 no.4
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• pp.243-250
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• 2020

Systematic farming can be planned and managed if long-term agricultural weather information of the plantation is available. Because the greatest risk factor for crop cultivation is the weather. In this study, a method for long-term predicting of agricultural weather using the GloSea5 and machine learning is presented for the cultivation of Chinese cabbage. The GloSea5 is a long-term weather forecast that is available up to 240 days. The deep neural networks and the spatial randomforest were considered as the method of machine learning. The longterm prediction performance of the deep neural networks was slightly better than the spatial randomforest in the sense of root mean squared error and mean absolute error. However, the spatial randomforest has the advantage of predicting temperatures with a global model, which reduces the computation time.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.531-536
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• 2003

In this paper, a camera modeling method that utilizes ephemeris data and imaging geometry is presented. The proposed method constructs a mathematical model only with parameters that are contained in the leader file and does not require any ground control points for model construction. Control points are only needed to eliminate geolocation error of the model that is originated from errors in the parameters that are used in model construction. With few (one or two) of control points, RMS error of less than pixel size can be obtained and control points are not necessarily uniformly distributed over the entire scene. This advantage is crucial in large project and will enable to reduce project cost dramatically.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2741-2761
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• 2017

With the purpose of copyright protection for digital video, a novel H.264/AVC watermarking algorithm based on JND model is proposed. Firstly, according to the characteristics of human visual system, a new and more accurate JND model is proposed to determine watermark embedding strength by considering the luminance masking, contrast masking and spatial frequency sensitivity function. Secondly, a new embedding strategy for H.264/AVC watermarking is proposed based on an analysis on the drift error of energy distribution. We argue that more robustness can be achieved if watermarks are embedded in middle and high components of $4{\times}4$ integer DCT since these components are more stable than dc and low components when drift error occurs. Finally, according to different characteristics of middle and high components, the watermarks are embedded using different algorithms, respectively. Experimental results demonstrate that the proposed watermarking algorithm not only meets the imperceptibility and robustness requirements, but also has a high embedding capacity.

• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.109-123
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• 2008

Many small area estimation methods have been suggested. Also for the comparison of the estimation methods, model diagnostic checking techniques have been studied. Almost all of the small area estimators were developed by minimizing MSE(Mean square error) and so the MSE is the well-known comparison criterion for superiority. In this paper we suggested a new small area estimator based on minimizing MSPE(Mean square percentage error) which is recently re-highlighted. Also we compared the new suggested estimator with the estimators explained in Shin et al. (2007) using MSE, MSPE and other diagnostic checking criteria.

• Journal of KIISE:Software and Applications
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• v.36 no.3
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• pp.217-225
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• 2009

In case of sketching a conceptual model in 3D space, it's not easy for designer to recognize the depth cue accurately and to draw a model correctly in short time. In this paper, multi-strokes based sketch is adopted not only to reduce the error of input point but to substantiate the shape o) the conceptual design effectively. The designer can see the drawing result immediately after stroking some curves. The shape can also be modified by stroking curves repeatedly and be confirmed the modified shape in real time. However, the multi-strokes based sketch needs to manage the great amount of input data. Therefore, the drawing space is divided into the limited spatial cubical grids and the movable infernal vertex in each spatial grid is implemented and used to define the surface by the multi-strokes. We implemented the spatial sketching system which allows the concept designer's intention to 3D model data efficiently.

• Journal of Korean Association for Spatial Structures
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• v.18 no.4
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• pp.69-80
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• 2018

In this study, an algorithm applying deep learning to the truss structures was proposed. Deep learning is a method of raising the accuracy of machine learning by creating a neural networks in a computer. Neural networks consist of input layers, hidden layers and output layers. Numerous studies have focused on the introduction of neural networks and performed under limited examples and conditions, but this study focused on two- and three-dimensional truss structures to prove the effectiveness of algorithms. and the training phase was divided into training model based on the dataset size and epochs. At these case, a specific data value was selected and the error rate was shown by comparing the actual data value with the predicted value, and the error rate decreases as the data set and the number of hidden layers increases. In consequence, it showed that it is possible to predict the result quickly and accurately without using a numerical analysis program when applying the deep learning technique to the field of structural analysis.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.2
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• pp.167-173
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• 2006

Ionosphere is the largest error source on propagation of GPS signals. Dual frequency (L1,L2) GPS receiver can be effectively able to eliminate the ionosphere error by using linear combination of two frequencies, but the single frequency receiver (L1) have to compute the ionosphere error. In this research, we developed the new ionospheric model with $1^{\circ}$ by $1^{\circ}$ spatial resolution based on the grid from using 9 GPS reference stations which have been operated by KASI (Korea Astronomy and Space Science Institute) and computed TEC (Total Electron Contents) over South Korea by epoch. This paper gives the positioning results of Klobuchar model with that of a newly developed KASI regional ionospheric model and shows the positioning precision of the KASI regional ionospheric model along with TEC variation of ionosphere.

• Atmosphere
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• v.26 no.1
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• pp.1-18
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• 2016

Eleven Tropical Cyclone (TC) intensity guidance models in the western North Pacific have been validated over 2008~2014 based on various analysis methods according to the lead time of forecast, year, month, intensity, rapid intensity change, track, and geographical area with an additional focus on TCs that influenced the Korean peninsula. From the evaluation using mean absolute error and correlation coefficients for maximum wind speed forecasts up to 72 h, we found that the Hurricane Weather Research and Forecasting model (HWRF) outperforms all others overall although the Global Forecast System (GFS), the Typhoon Ensemble Prediction System of Japan Meteorological Agency (TEPS), and the Korean version of Weather and Weather Research and Forecasting model (KWRF) also shows a good performance in some lead times of forecast. In particular, HWRF shows the highest performance in predicting the intensity of strong TCs above Category 3, which may be attributed to its highest spatial resolution (~3 km). The Navy Operational Global Prediction Model (NOGAPS) and GFS were the most improved model during 2008~2014. For initial intensity error, two Japanese models, Japan Meteorological Agency Global Spectral Model (JGSM) and TEPS, had the smallest error. In track forecast, the European Centre for Medium-Range Weather Forecasts (ECMWF) and recent GFS model outperformed others. The present results has significant implications for providing basic information for operational forecasters as well as developing ensemble or consensus prediction systems.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.149-153
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• 2006

Precipitation is the most important component and critical to the study of water and energy cycle. This study investigates the propagation of precipitation retrieval uncertainty in the simulation of hydrologic variables for varying spatial resolution on two different vegetation cover. We explore two remotely sensed rain retrievals (space-borne IR-only and radar rainfall) and three spatial grid resolutions. An offline Community Land Model (CLM) was forced with in situ meteorological data In turn, radar rainfall is replaced by the satellite rain estimates at coarser resolution $(0.25^{\circ},\;0.5^{\circ}\;and\;1^{\circ})$ to determine their probable impact on model predictions. Results show how uncertainty of precipitation measurement affects the spatial variability of model output in various modelling scales. The study provides some intuition on the uncertainty of hydrologic prediction via interaction between the land surface and near atmosphere fluxes in the modelling approach.