• Journal of Korea Water Resources Association
• /
• v.46 no.8
• /
• pp.821-831
• /
• 2013

Recently image velocimetries, including particle image velocimetry (PIV) and surface image velocimetry (SIV), are often used to measure flow velocities in laboratories and rivers. The most difficult point in using image velocimetries may be how to determine the sizes of the interrogation areas and the measurement uncertainties. Especially, it is a little hard for unskilled users to use these instruments, since any standardized measuring techniques or measurement uncertainties are not well evaluated. Sometimes the user's skill and understanding on the instruments may make a wide gap between velocity measurement results. The present study aims to evaluate image velocimetry's uncertainties due to the changes in the sizes of interrogation areas and searching areas with the error analyses. For the purpose, we generated 12 series of artificial images with known velocity fields and various numbers and sizes of particles. The analysis results showed that the accuracy of velocity measurements of the image velocimetry was significantly affected by the change of the size of interrogation area. Generally speaking, the error was reduced as the size of interrogation areas became small. For the same sizes of interrogation areas, the larger particle sizes and the larger number of particles resulted smaller errors. Especially, the errors of the image velocimetries were more affected by the number of particles rather than the sizes of them. As the sizes of interrogation areas were increased, the differences between the maximum and the minimum errors seemed to be reduced. For the size of the interrogation area whose average errors were less than 5%, the differences between the maximum and the minimum errors seemed a little large. For the case, in other words, the uncertainty of the velocity measurements of the image velocimetry was large. In the viewpoint of the particle density, the size of the interrogation area was small for large particle density cases. For the cases of large number of particle and small particle density, however, the minimum size of interrogation area became smaller.

• Journal of Astronomy and Space Sciences
• /
• v.26 no.1
• /
• pp.89-98
• /
• 2009

In this study, the YLPODS (Yonsei Laser-ranging Precision Orbit Determination System) is developed for POD using SLR (Satellite Laser Ranging) NP (Normal Point) observations. The performance of YLPODS is tested using SLR NP observations of TOPEX/POSEIDON and CHAMP satellite. JPL's POE (Precision Orbit Ephemeris) is assumed to be true orbit, the measurement residual RMS (Root Mean Square) and the orbit accuracy (radial, along-track, cross-track) are investigated. The validation of POD using GPS (Global Positioning System) raw data is achieved by YLPODS performance and highly accurate SLR NP observations. YGPODS (Yonsei GPS-based Precision Orbit Determination System) is used for generating GPS based precise orbits for TOPEX/POSEIDON. The initial orbit for YLPODS is derived from the YGPODS results. To validate the YGPODS results the range residual of the first adjustment of YLPODS is investigated. The YLPODS results using SLR NP observations of TOPEX/POSEIDON and CHAMP satellite show that the range residual is less than 10 cm and the orbit accuracy is about 1 m level. The validation results of the YGPODS orbits using SLR NP observations of the TOPEX/POSEIDON satellite show that the range residual is less than 10 cm. This result predicts that the accuracy of this GPS based orbits is about 1m level and it is compared with JPL's POE. Thus this result presents that the YLPODS can be used for POD validation using SLR NP observations such as STSAT-2 and KOMPSAT-5.

• Korean Journal of Remote Sensing
• /
• v.35 no.2
• /
• pp.289-298
• /
• 2019

In this present study, the effects of Signal to Noise Ratio (SNR), Full Width Half Maximum (FWHM), Aerosol Optical Depth (AOD), $O_3$ Vertical Column Density ($O_3$ VCD), and Solar Zenith Angle (SZA) on the accuracy of sulfur dioxide Vertical Column Density ($SO_2$ VCD) retrieval have been quantified using the Differential Optical Absorption Spectroscopy (DOAS) method with the ground-based direct-sun synthetic radiances. The synthetic radiances produced based on the Beer-Lambert-Bouguer law without consideration of the diffuse effect. In the SNR condition of 650 (1300) with FWHM = 0.6 nm, AOD = 0.2, $O_3$ VCD = 300 DU, and $SZA=30^{\circ}$, the Absolute Percentage Difference (APD) between the true $SO_2$ VCD values and those retrieved ranges from 80% (28%) to 16% (5%) for the $SO_2$ VCD of $8.1{\times}10^{15}$ and $2.7{\times}10^{16}molecules\;cm^{-2}$, respectively. For an FWHM of 0.2 nm (1.0 nm) with the $SO_2$ VCD values equal to or greater than $2.7{\times}10^{16}molecules\;cm^{-2}$, the APD ranges from 6.4% (29%) to 6.2% (10%). Additionally, when FWHM, SZA, AOD, and $O_3$ VCD values increase, APDs tend to be large. On the other hand, SNR values increase, APDs are found to decrease. Eventually, it is revealed that the effects of FWHM and SZA on $SO_2$ VCD retrieval accuracy are larger than those of $O_3$ VCD and AOD. The SZA effects on the reduction of $SO_2$ VCD retrieval accuracy is found to be dominant over the that of FWHM for the condition of $SO_2$ VCD larger than $2.7{\times}10^{16}molecules\;cm^{-2}$.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.6-7
• /
• 2021

Vegetation development in rivers is one of the important issues not only in academic fields such as geomorphology, ecology, hydraulics, etc., but also in river management practices. The problem of river vegetation is directly connected to the harmony of conflicting values of flood management and ecosystem conservation. In Korea, since the 2000s, the issue of river vegetation and land formation has been continuously raised under various conditions, such as the regulating rivers downstream of the dams, the small eutrophicated tributary rivers, and the floodplain sites for the four major river projects. In this background, this study proposes a method for classifying the distribution of vegetation in rivers based on remote sensing data, and presents the results of applying this to the Naeseong Stream. The Naeseong Stream is a representative example of the river landscape that has changed due to vegetation development from 2014 to the latest. The remote sensing data used in the study are images of Sentinel 1 and 2 satellites, which is operated by the European Aerospace Administration (ESA), and provided by Google Earth Engine. For the ground truth, manually classified dataset on the surface of the Naeseong Stream in 2016 were used, where the area is divided into eight types including water, sand and herbaceous and woody vegetation. The classification method used a random forest classification technique, one of the machine learning algorithms. 1,000 samples were extracted from 10 pre-selected polygon regions, each half of them were used as training and verification data. The accuracy based on the verification data was found to be 82~85%. The model established through training was also applied to images from 2016 to 2020, and the process of changes in vegetation zones according to the year was presented. The technical limitations and improvement measures of this paper were considered. By providing quantitative information of the vegetation distribution, this technique is expected to be useful in practical management of vegetation such as thinning and rejuvenation of river vegetation as well as technical fields such as flood level calculation and flow-vegetation coupled modeling in rivers.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.25 no.2
• /
• pp.80-98
• /
• 2023

This study explores the effectiveness of various data preprocessing algorithms for improving subseasonal to seasonal (S2S) climate predictions from six climate forecast models and their Multi-Model Ensemble (MME) using a deep learning-based postprocessing model. A pipeline of data transformation algorithms was constructed to convert raw S2S prediction data into the training data processed with several statistical distribution. A dimensionality reduction algorithm for selecting features through rankings of correlation coefficients between the observed and the input data. The training model in the study was designed with TimeDistributed wrapper applied to all convolutional layers of U-Net: The TimeDistributed wrapper allows a U-Net convolutional layer to be directly applied to 5-dimensional time series data while maintaining the time axis of data, but every input should be at least 3D in U-Net. We found that Robust and Standard transformation algorithms are most suitable for improving S2S predictions. The dimensionality reduction based on feature selections did not significantly improve predictions of daily precipitation for six climate models and even worsened predictions of daily maximum and minimum temperatures. While deep learning-based postprocessing was also improved MME S2S precipitation predictions, it did not have a significant effect on temperature predictions, particularly for the lead time of weeks 1 and 2. Further research is needed to develop an optimal deep learning model for improving S2S temperature predictions by testing various models and parameters.

• The Korean Journal of Nuclear Medicine
• /
• v.31 no.1
• /
• pp.19-29
• /
• 1997

Recently, [I-123]IPT SPECT has been used for early diagnosis of Parkinson's patients(PP) by imaging dopamine transporters. The dynamic time activity curves in basal ganglia(BG) and occipital cortex(OCC) without blood samples were obtained for 2 hours. These data were then used to measure dopamine transporters by operationally defined ratio methods of (BG-OCC)/OCC at 2 hrs, binding potential $R_v=k_3/k_4$ using graphic method or $R_A$= (ABBG-ABOCC)/ABOCC for 2 hrs, where ABBG represents accumulated binding activity in basal ganglia(${\int}^{120min}_0$ BG(t)dt) and ABOCC represents accumulated binding activity in occipital cortex(${\int}^{120min}_0$ OCC(t)dt). The purpose of this study was to examine the IPT pharmacokinetics and investigate the usefulness of simplified methods of (BG-OCC)/OCC, $R_A$, and $R_v$ which are often assumed that these values reflect the true values of $k_3/k_4$. The rate constants $K_1,\;k_2\;k_3$ and $k_4$ to be used for simulations were derived using [I-123]IPT SPECT and aterialized blood data with a standard three compartmental model. The sensitivities and time activity curves in BG and OCC were computed by changing $K_l$ and $k_3$(only BG) for every 5min over 2 hours. The values (BG-OCC)/OCC, $R_A$, and $R_v$ were then computed from the time activity curves and the linear regression analysis was used to measure the accuracies of these methods. The late constants $K_l,\;k_2\;k_3\;k_4$ at BG and OCC were $1.26{\pm}5.41%,\;0.044{\pm}19.58%,\;0.031{\pm}24.36%,\;0.008{\pm}22.78%$ and $1.36{\pm}4.76%,\;0.170{\pm}6.89%,\;0.007{\pm}23.89%,\;0.007{\pm}45.09%$, respectively. The Sensitivities for ((${\Delta}S/S$)/(${\Delta}k_3/k_3$)) and ((${\Delta}S/S$)/(${\Delta}K_l/K_l$)) at 30min and 120min were measured as (0.19, 0.50) and (0.61, 0,23), respectively. The correlation coefficients and slopes of ((BG-OCC)/OCC, $R_A$, and $R_v$) with $k_3/k_4$ were (0.98, 1.00, 0.99) and (1.76, 0.47, 1.25), respectively. These simulation results indicate that a late [I-123]IPT SPECT image may represent the distribution of the dopamine transporters. Good correlations were shown between (3G-OCC)/OCC, $R_A$ or $R_v$ and true $k_3/k_4$, although the slopes between them were not unity. Pharmacokinetic computer simulations may be a very useful technique in studying dopamine transporter systems.