• Journal of the Korean Society for Aviation and Aeronautics
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• 제16권2호
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• pp.12-20
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• 2008

Japanese MSAS (Multi-functional Satellite Augmentation System) satellites have been transmitting GPS satellite orbit and ionosphere correction information since 2005. MSAS coverage includes Far East Asia, and it can improve the accuracy and integrity of GPS position solutions in Korea. This research analyzed the ionosphere correction information from the MSAS ionosphere correction data. The ionosphere delay data observed by a dual frequency receiver is compared with the MSAS ionosphere correction data. The variation of MSAS GIVE values are analyzed in connection with the equatorial anomaly and ionosphere scintillation.

• Journal of Korean Institute of Industrial Engineers
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• 제37권4호
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• pp.400-407
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• 2011

Modeling and analysis of high-dimensional spectral data provide an opportunity to uncover inherent patterns in various information-rich data. Orthogonal signal correction (OSC) a preprocessing technique has been widely used to remove unwanted variations of spectral data that do not contribute to prediction or classification. In the present study we propose a novel OSC algorithm called adjusted direct OSC to improve visualization and the ability of classification. Experimental results with real mass spectral data from condom lubricants demonstrate the effectiveness of the proposed approach.

• Journal of IKEEE
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• 제19권2호
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• pp.168-175
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• 2015

In this paper, we propose FPGA design technique for rotating LED display device which is capable of displaying videos with the use of the afterimage effect. The proposed technique is made up of image data correction process based on inverse gamma correction and error diffusion, block interleaving process, and data serial output process. The data correction process based on inverse gamma correction and error diffusion is an image data correction step in which image data received are corrected by inverse gamma correction process to convert the data into linear brightness characteristics, and by error diffusion process to reduce the brightness reduction phenomenon in low-gray-level which is caused by inverse gamma correction. In the block interleaving process, the data of the frames entered transversely are first saved in accordance with entrance order, and then only the longitudinal image data are read. The data serial output process is applied to convert the parallel data in a rotating location into serial data and send them to LED Driver IC, in order to send data which will be displayed on high-speedy rotating LED Bar. To evaluate the accuracy of the proposed FPGA design technique, this paper used XC6SLX45-FG484, a Spartan 6 family of Xilinx, as FPGA, and ISE 14.5 as a design tool. According to the evaluation analysis, it was found that goal values were consistent with simulation values in terms of accurate operation of inverse gamma and error diffusion correction, block interleaving operation, and serialized operation of image data.

• Korean Journal of Remote Sensing
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• 제38권1호
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• pp.83-101
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• 2022

Accurate atmospheric correction is essential for the analysis of land surface and environmental monitoring. Aerosol optical depth (AOD) information is particularly important in atmospheric correction because the radiation attenuation by Mie scattering makes the differences between the radiation calculated at the satellite sensor and the radiation measured at the land surface. Thus, it is necessary to use high-quality AOD data for an appropriate atmospheric correction of high-resolution satellite images. In this study, we examined the Second Simulation of a Satellite Signal in the Solar Spectrum (6S)-based atmospheric correction results for the Sentinel-2 images in South Korea using raster AOD (MODIS) and single-point AOD (AERONET). The 6S result was overall agreed with the Sentinel-2 level 2 data. Moreover, using raster AOD showed better performance than using single-point AOD. The atmospheric correction using the single-point AOD yielded some inappropriate values for forest and water pixels, where as the atmospheric correction using raster AOD produced stable and natural patterns in accordance with the land cover map. Also, the Sentinel-2 normalized difference vegetation index (NDVI) after the 6S correction had similar patterns to the up scaled drone NDVI, although Sentinel-2 NDVI had relatively low values. Also, the spatial distribution of both images seemed very similar for growing and harvest seasons. Future work will be necessary to make efforts for the gap-filling of AOD data and an accurate bi-directional reflectance distribution function (BRDF) model for high-resolution atmospheric correction. These methods can help improve the land surface monitoring using the future Compact Advanced Satellite 500 in South Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 한국수자원학회 2020년도 학술발표회
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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 Korea Convergence Society
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• 제9권3호
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• pp.15-21
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• 2018

The ocean drifter is a device for observing the ocean weather by floating off the sea surface. The data observed through the drifter is utilized in the ocean weather prediction and oil spill. Observed data may contain incorrect or missing data at the time of observation, and accuracy may be lowered when we use the data. In this paper, we propose a data correction model using recurrent neural networks. We corrected data collected from 7 drifters in 2015 and 8 drifters in 2016, and conducted experiments of drifter moving prediction to reflect the correction results. Experimental results showed that observed data are corrected by 13.9% and improved the performance of the prediction model by 1.4%.

• Journal of Positioning, Navigation, and Timing
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• 제12권2호
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• pp.141-148
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• 2023

In this paper, impacts of tropospheric delay gradient correction on PPP positioning performance were analyzed. A correction for tropospheric delay error due to the gradient was created and applied using external data, and reference station data were collected on a sunny day and a rainy day to analyze the GPS only dual-frequency PPP positioning results. As a result, on the sunny day, the convergence time was about 35 minutes and the final 3D position error was 10 cm, regardless of whether the correction for the tropospheric delay error by the gradient was applied. On the other hand, on the rainy day, the 3D position error converges only when the correction was applied, and the convergence time was about 34 minutes. Furthermore, the final 3D position error was improved from 30 cm to 10 cm. In addition, the analysis of the PPP by reference station location on the rainy day showed that the PPP positioning performance was improved when the correction was applied to a user located in an area where the weather changes.

• Journal of the Korean Association of Geographic Information Studies
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• 제9권2호
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• pp.159-172
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• 2006

In this study, the series of Landsat TM/ETM+ images was acquired to extract land surface temperature for wide-area and executed geometric correction and radiometric correction. And the land surface temperature was extracted using NASA Model, and achieved the first correction by performing land coverage category for study area and applied characteristic emission rate. Land surface temperature which was acquired by the first correction was analyzed in correlation with Meteorological Administration's temperature data by regression analysis, and established correction formula. And I wished to improve accuracy of land surface temperature extraction using satellite image by second correcting deviations between two data using establishing correction formula. As a result, land surface temperature acquired by 1st and 2st correction could be corrected in mean deviation of about ${\pm}3.0^{\circ}C$ with Meteorological Administration data. Also, I could acquire land surface temperature about study area by higher accuracy by applying to other Landsat images for re-verification of study results.

• Journal of The Korean Association of Information Education
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• 제15권1호
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• pp.147-154
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• 2011

In this Paper, we suggest the effective pre-correction algorithm on sensor values and the classification algorithm for gesture recognition that use values for each axis of the accelerometer to send data(a number or specific input data) to device. we know that creation of reliable preprocessed data in experimental results through the error rate of X-Axis and Y-Axis for pre-correction and post-correction. we can show high recognition rate through recognizer using the normalization and classification algorithm for the preprocessed data.

• New & Renewable Energy
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• 제4권4호
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• pp.23-29
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• 2008

Wind farm development project contains high business risks because that a wind farm, which is to be operating for 20 years, has to be designed and assessed only relying on a year or little more in-situ wind data. Accordingly, long-term correction of short-term measurement data is one of most important process in wind resource assessment for project feasibility investigation. This paper shows comparison of general Measure-Correlate-Prediction models and neural network, and presents new method using neural network for increasing prediction accuracy by accommodating multiple reference data. The proposed method would be interim step to complete long-term correction methodology for Korea, complicated Monsoon country where seasonal and diurnal variation of local meteorology is very wide.