• Nuclear Engineering and Technology
• /
• v.52 no.11
• /
• pp.2452-2459
• /
• 2020

Neutron spectrum is essential to the safe operation of reactors. Traditional online neutron spectrum measurement methods still have room to improve accuracy for the application cases of wide energy range. From the application of artificial neural network (ANN) algorithm in spectrum unfolding, its accuracy is difficult to be improved for lacking of enough effective training data. In this paper, an adaptive deviation-resistant neutron spectrum unfolding method based on transfer learning was developed. The model of ANN was trained with thousands of neutron spectra generated with Monte Carlo transport calculation to construct a coarse-grained unfolded spectrum. In order to improve the accuracy of the unfolded spectrum, results of the previous ANN model combined with some specific eigenvalues of the current system were put into the dataset for training the deeper ANN model, and fine-grained unfolded spectrum could be achieved through the deeper ANN model. The method could realize accurate spectrum unfolding while maintaining universality, combined with detectors covering wide energy range, it could improve the accuracy of spectrum measurement methods for wide energy range. This method was verified with a fast neutron reactor BN-600. The mean square error (MSE), average relative deviation (ARD) and spectrum quality (Qs) were selected to evaluate the final results and they all demonstrated that the developed method was much more precise than traditional spectrum unfolding methods.

• Journal of the Korea Concrete Institute
• /
• v.23 no.1
• /
• pp.77-86
• /
• 2011

The concrete creep deformation of a hybrid composite section can cause additional deformation of the composite section and the stress relaxation of pre-compressive stress on the concrete section due to partial restraint of the deformation. In this study, the stress relaxation coefficient method (SRCM) is derived for simple analysis of complicate hybrid or composite sections for engineering purpose. Also, an equation of the stress relaxation coefficient (SRC) required for the SRCM is proposed. The SRCM is derived with the parameters of a creep coefficient, section and loading properties using the same method as the constant-creep step-by-step method (CC-SSM). The errors of the SRCM is improved by using the proposed SRC equation than the average SRC's which were estimated from the CC-SSM. The root mean square error (RMSE) of the SRCM with the proposed SRC equation for concrete with creep coefficient less than 3 was less than 1.2% to the creep deformation at the free condition and was 3.3% for the 99% reliability. The proposed SRC equation reflects the internal restraint of composite sections, and the effective modulus of elasticity computed with the proposed SRC can be used effectively to estimate the rigidity of a composite section in a numerical analysis which can be applied in analysis of the external restrain effect of boundary conditions.

• Investigative Magnetic Resonance Imaging
• /
• v.13 no.1
• /
• pp.31-39
• /
• 2009

Purpose : This study proposes the keyhole method in order to improve the time resolution of the proton resonance frequency(PRF) MR temperature monitoring technique. The values of Root Mean Square (RMS) error of measured temperature value and Signal-to-Noise Ratio(SNR) obtained from the keyhole and full phase encoded temperature images were compared. Materials and Methods : The PRF method combined with GRE sequence was used to get MR temperature images using a clinical 1.5T MR scanner. It was conducted on the tissue-mimic 2% agarose gel phantom and swine's hock tissue. A MR compatible coaxial slot antenna driven by microwave power generator at 2.45GHz was used to heat the object in the magnetic bore for 5 minutes followed by a sequential acquisition of MR raw data during 10 minutes of cooling period. The acquired raw data were transferred to PC after then the keyhole images were reconstructed by taking the central part of K-space data with 128, 64, 32 and 16 phase encoding lines while the remaining peripheral parts were taken from the 1st reference raw data. The RMS errors were compared with the 256 full encoded self-reference temperature image while the SNR values were compared with the zero filling images. Results : As phase encoding number at the center part on the keyhole temperature images decreased to 128, 64, 32 and 16, the RMS errors of the measured temperature increased to 0.538, 0.712, 0.768 and 0.845$^{\circ}C$, meanwhile SNR values were maintained as the phase encoding number of keyhole part is reduced. Conclusion : This study shows that the keyhole technique is successfully applied to temperature monitoring procedure to increases the temporal resolution by standardizing the matrix size, thus maintained the SNR values. In future, it is expected to implement the MR real time thermal imaging using keyhole method which is able to reduce the scan time with minimal thermal variations.

• Spatial Information Research
• /
• v.22 no.1
• /
• pp.55-63
• /
• 2014

Near surface air temperature data which are one of the essential factors in hydrology, meteorology and climatology, have drawn a substantial amount of attention from various academic domains and societies. Meteorological observations, however, have high spatio-temporal constraints with the limits in the number and distribution over the earth surface. To overcome such limits, many studies have sought to estimate the near surface air temperature from satellite image data at a regional or continental scale with simple regression methods. Alternatively, we applied various Kriging methods such as ordinary Kriging, universal Kriging, Cokriging, Regression Kriging in search of an optimal estimation method based on near surface air temperature data observed from automatic weather stations (AWS) in South Korea throughout 2010 (365 days) and MODIS land surface temperature (LST) data (MOD11A1, 365 images). Due to high spatial heterogeneity, auxiliary data have been also analyzed such as land cover, DEM (digital elevation model) to consider factors that can affect near surface air temperature. Prior to the main estimation, we calculated root mean square error (RMSE) of temperature differences from the 365-days LST and AWS data by season and landcover. The results show that the coefficient of variation (CV) of RMSE by season is 0.86, but the equivalent value of CV by landcover is 0.00746. Seasonal differences between LST and AWS data were greater than that those by landcover. Seasonal RMSE was the lowest in winter (3.72). The results from a linear regression analysis for examining the relationship among AWS, LST, and auxiliary data show that the coefficient of determination was the highest in winter (0.818) but the lowest in summer (0.078), thereby indicating a significant level of seasonal variation. Based on these results, we utilized a variety of Kriging techniques to estimate the surface temperature. The results of cross-validation in each Kriging model show that the measure of model accuracy was 1.71, 1.71, 1.848, and 1.630 for universal Kriging, ordinary Kriging, cokriging, and regression Kriging, respectively. The estimates from regression Kriging thus proved to be the most accurate among the Kriging methods compared.

• Korean Journal of Remote Sensing
• /
• v.37 no.2
• /
• pp.221-232
• /
• 2021

This study analyzed co-registration results according to the geometric processing level of reference image, which are Level 1R and Level 1G provided from KOMPSAT-3 and KOMPSAT-3A images. We performed co-registration using each Level 1R and Level 1G image as a reference image, and Level 1R image as a sensed image. For constructing the experimental dataset, seven Level 1R and 1G images of KOMPSAT-3 and KOMPSAT-3A acquired from Daejeon, South Korea, were used. To coarsely align the geometric position of the two images, SURF (Speeded-Up Robust Feature) and PC (Phase Correlation) methods were combined and then repeatedly applied to the overlapping region of the images. Then, we extracted tie-points using the SURF method from coarsely aligned images and performed fine co-registration through affine transformation and piecewise Linear transformation, respectively, constructed with the tie-points. As a result of the experiment, when Level 1G image was used as a reference image, a relatively large number of tie-points were extracted than Level 1R image. Also, in the case where the reference image is Level 1G image, the root mean square error of co-registration was 5 pixels less than the case of Level 1R image on average. We have shown from the experimental results that the co-registration performance can be affected by the geometric processing level related to the initial geometric relationship between the two images. Moreover, we confirmed that the better geometric quality of the reference image achieved the more stable co-registration performance.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.24 no.3
• /
• pp.164-178
• /
• 2022

Uncertainties in weather forecasts would affect the reliability of yield prediction using crop models. The objective of this study was to compare uncertainty in crop yield prediction caused by the use of the weather forecast data. Daily weather data were produced at 10 km spatial resolution using W eather Research and Forecasting (W RF) model. The nearest neighbor method was used to downscale these data at the resolution of 5 km (W RF5K). Parameter-elevation Regressions on Independent Slopes Model (PRISM) was also applied to the WRF data to produce the weather data at the same resolution. W RF5K and PRISM data were used as inputs to the CROPGRO-SOYBEAN model to predict crop yield. The uncertainties of the gridded data were analyzed using cumulative growing degree days (CGDD) and cumulative solar radiation (CSRAD) during the soybean growing seasons for the crop of interest. The degree of agreement (DOA) statistics including structural similarity index were determined for the crop model outputs. Our results indicated that the DOA statistics for CGDD were correlated with that for the maturity dates predicted using WRF5K and PRISM data. Yield forecasts had small values of the DOA statistics when large spatial disagreement occured between maturity dates predicted using WRF5K and PRISM. These results suggest that the spatial uncertainties in temperature data would affect the reliability of the phenology and, as a result, yield predictions at a greater degree than those in solar radiation data. This merits further studies to assess the uncertainties of crop yield forecasts using a wide range of crop calendars.

• Korean Journal of Remote Sensing
• /
• v.40 no.4
• /
• pp.387-396
• /
• 2024

Most very high-resolution (VHR) satellite images provide rational polynomial coefficients (RPC) data to facilitate the transformation between ground coordinates and image coordinates. However, initial RPC often contains geometric errors, necessitating correction through matching with ground control points (GCPs). A GCP chip is a small image patch extracted from an orthorectified image together with height information of the center point, which can be directly used for geometric correction. Many studies have focused on area-based matching methods to accurately align GCP chips with VHR satellite images. In cases with seasonal differences or changed areas, edge-based algorithms are often used for matching due to the difficulty of relying solely on pixel values. However, traditional edge extraction algorithms,such as canny edge detectors, require appropriate threshold settings tailored to the spectral characteristics of satellite images. Therefore, this study utilizes deep learning-based edge information that is insensitive to the regional characteristics of satellite images for matching. Specifically,we use a pretrained pixel difference network (PiDiNet) to generate the edge maps for both satellite images and GCP chips. These edge maps are then used as input for normalized cross-correlation (NCC) and relative edge cross-correlation (RECC) to identify the peak points with the highest correlation between the two edge maps. To remove mismatched pairs and thus obtain the bias-compensated RPC, we iteratively apply the data snooping. Finally, we compare the results qualitatively and quantitatively with those obtained from traditional NCC and RECC methods. The PiDiNet network approach achieved high matching accuracy with root mean square error (RMSE) values ranging from 0.3 to 0.9 pixels. However, the PiDiNet-generated edges were thicker compared to those from the canny method, leading to slightly lower registration accuracy in some images. Nevertheless, PiDiNet consistently produced characteristic edge information, allowing for successful matching even in challenging regions. This study demonstrates that improving the robustness of edge-based registration methods can facilitate effective registration across diverse regions.

• Journal of the Korean Society of Safety
• /
• v.35 no.2
• /
• pp.28-33
• /
• 2020

Recently, the overseas construction market has been actively promoted for about 10 years, and overseas dam construction has been continuously performed. For the economic and safe construction of the dam, it is important to prepare the main dam construction plan considering the design frequency of the diversion tunnel and the cofferdam. In this respect, the prediction of river level during the rainy season is significant. Since most of the overseas dam construction sites are located in areas with poor infrastructure, the most efficient and economic method to predict the water level in dam construction is to use the upstream water level. In this study, a linear regression model, which is one of the simplest statistical methods, was proposed and examined to predict the downstream level from the upstream level. The Pyeongchang River basin, which has the characteristics of the upper stream (mountain stream), was selected as the target site and the observed water level in Pyeongchang and Panwoon gaging station were used. A regression equation was developed using the water level data set from August 22th to 27th, 2017, and its applicability was tested using the water level data set from August 28th to September 1st, 2018. The dependent variable was selected as the "level difference between two stations," and the independent variable was selected as "the level of water level in Pyeongchang station two hours ago" and the "water level change rate in Pyeongchang station (m/hr)". In addition, the accuracy of the developed equation was checked by using the regression statistics of Root Mean Square Error (RMSE), Adjusted Coefficient of Determination (ACD), and Nach Sutcliffe efficiency Coefficient (NSEC). As a result, the statistical value of the linear regression model was very high, so the downstream water level prediction using the upstream water level was examined in a highly reliable way. In addition, the results of the application of the water level change rate (m/hr) to the regression equation show that although the increase of the statistical value is not large, it is effective to reduce the water level error in the rapid level rise section. Accordingly, this is a significant advantage in estimating the evacuation water level during main dam construction to secure safety in construction site.

• Korean Journal of Applied Biomechanics
• /
• v.27 no.2
• /
• pp.117-124
• /
• 2017

Objective: The purpose of this study was to examine the effect of changes in degrees of freedom of the fingers (i.e., the number of the fingers involved in tasks) on the task performance during force production and releasing task. Method: Eight right-handed young men (age: $29.63{\pm}3.02yr$, height: $1.73{\pm}0.04m$, weight: $70.25{\pm}9.05kg$) participated in this study. The subjects were required to press the transducers with three combinations of fingers, including the index-middle (IM), index-middle-ring (IMR), and index-middle-ring-little (IMRL). During the trials, they were instructed to maintain a steady-state level of both normal and tangential forces within the first 5 sec. After the first 5 sec, the subjects were instructed to release the fingers on the transducers as quickly as possible at a self-selected manner within the next 5 sec, resulting in zero force at the end. Customized MATLAB codes (MathWorks Inc., Natick, MA, USA) were written for data analysis. The following variables were quantified: 1) finger force sharing pattern, 2) root mean square error (RMSE) of force to the target force in three axes at the aiming phase, 3) the time duration of the release phase (release time), and 4) the accuracy and precision indexes of the virtual firing position. Results: The RMSE was decreased with the number of fingers increased in both normal and tangential forces at the steady-state phase. The precision index was smaller (more precise) in the IMR condition than in the IM condition, while no significant difference in the accuracy index was observed between the conditions. In addition, no significant difference in release time was found between the conditions. Conclusion: The study provides evidence that the increased number of fingers resulted in better error compensation at the aiming phase and performed a more constant shooting (i.e., smaller precision index). However, the increased number of fingers did not affect the release time, which may influence the consistency of terminal performance. Thus, the number of fingers led to positive results for the current task.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.25 no.7
• /
• pp.747-756
• /
• 2014

In this paper, we propose a path loss model with the multiple antennas using diversity effect. Currently wireless communication systems use the multiple antennas in order to improve the channel capacity or diversity gain. However, until recently, many researches on path loss model only consider geographical environment between the transmitter and the receiver. There is no study about path loss model considering diversity effect. Nowaday wireless communication use the multiple antennas and we in common find examples using diversity scheme that is method in order to enhance a channel capacity. Moreover we anticipate that it work harder in future researches. But in this communication system, path loss model isn't established that predict strength of received signal. So, in order to predict strength of received signal, we take changing SNR by diversity gain. When exceeding the number of antennas of receiver are 7 in proposed model, diversity effect is saturated. Therefore we consider the number of antenna of receiver until 10. We find RMSE between proposed model and value of calculation is 1. We calculate the diversity gain by conventional BER curve. Proposed model can predict loss of received signal in system using multiple antennas.