Global warming and rapid climate change have long affected the characteristics of typhoons in the Northwest Pacific, which has induced increasing devastating disasters along the coastal regions of the Korean peninsula. Synthetic Aperature Radar (SAR), as one of the microwave sensors, makes it possible to produce high-resolution sea surface wind field around the typhoon under cloudy atmospheric conditions, which has been impossible to obtain the winds from satellite optical and infrared sensors. The Geophysical Model Functions (GMFs) for sea surface wind retrieval from SAR data requires the input of wind direction, which should be based on the accurate estimation of the center of the typhoon. This study estimated the typhoon centers using Sentinel-1A images to improve the problem of typhoon center detection method and to reflect it in retrieving the sea surface wind. The results were validated by comparing with the typhoon best track data provided by the Korea Meteorological Administration (KMA) and Japan Meteorological Agency (JMA), and also by using infrared images of Himawari-8 satellite. The initial center position of the typhoon was determined by using VH polarization, thereby reducing the possibility of error. The detected center showed a difference of 23.76 km on average with the best track data of the four typhoons provided by the KMA and JMA. Compared to the typhoon center estimated by Himawari-8 satellite, the results showed an average spatial variation of 11.80 km except one typhoon located near land with a large difference of 58.73 km. This result suggests that high-resolution SAR images can be used to estimate the center and retrieve sea surface wind around typhoons.
Marine seismic data have not only primary signals from subsurface but also ghost signals reflected from the sea surface. The ghost decreases temporal resolution of seismic data because it attenuates specific frequency components. For eliminating the ghost signals effectively, the exact ghost delaytimes and reflection coefficients are required. Because of undulation of the sea surface and vertical movements of airguns and streamers, the ghost delaytime varies spatially and randomly while acquiring seismic data. The reflection coefficient is a function of frequency, incidence angle of plane-wave and the sea state. In order to estimate the proper ghost delaytimes considering these characteristics, we compared the ghost delaytimes estimated with L-1 norm, L-2 norm and kurtosis of the deghosted trace and its autocorrelation on synthetic data. L-1 norm of autocorrelation showed a minimal error and the reflection coefficient was calculated using Kirchhoff approximation equation which can handle the effect of wave height. We applied the estimated ghost delaytimes and the calculated reflection coefficients to remove the source and receiver ghost effects. By removing ghost signals, we reconstructed the frequency components attenuated near the notch frequency and produced the migrated stack section with enhanced temporal resolution.
As there is no failure data for the entire lifecycle of a product, when analyzing reliability measures based on early failure data only, there may be a significant error between the estimated mean life and the real one, because it can be underestimated, or on the other hand, it can be overestimated when analyzing reliability measures based on a large amount of censored data with the failure data. To resolve the issue, this study proposes an optimal sampling estimation procedure that selects the proportion of censored data to estimate the optimal distribution with the idea that the estimated distribution could be approximated as closely as the real life distribution. This would work if we sampled the optimal proportion on the censored data, because failure data has real intrinsic distribution in any situation. We validate the proposed procedure using an actual example. If the proposed method is applied to the maintenance policy of TWC (Train to Wayside Communication) system, then we can establish the optimal maintenance policy. Thus, we expect that it will be effective for improvement of reliability and cost savings.
Global warming due to increased carbon dioxide is perceived as one of the factors threatening the future. Efforts are being made to reduce carbon dioxide emissions in each industry around the world. In particular, environmental loads and impacts during the life cycle of SOC structures and buildings have been quantitatively assessed through a quantitative method called Life Cycle Assessment (LCA). However, the construction sector has gone through difficulty in quantitative assessment for several reasons: 1) LCI DB is not fully established; 2) the life cycle is very long; 3) the building structures are unique. Therefore, it takes enormous effort and time to carry out LCA. Rather than estimating carbon emissions with accuracy, this study aims to present a simplified estimation model that allows owners or designers to easily estimate carbon dioxide emissions with little effort, given that rapid and rough decisions regarding environmental load reduction are to be made. This study performs the LCA using data from 25 road construction projects across the country, followed by multiple regression analyses to derive a simplified carbon estimation model (SLCA). The study also carries out a comparative analysis with values estimated by performing a typical LCA. The comparison analysis shows an error rate of less than 5% for 16 road projects.
This study presents a new method of estimating the size distribution of river bed gravel through image processing. The analysis was done in two steps; first the individual grain images were analyzed and then the grain particle segmentation of river-bed images were processed. In the first part of the analysis, the relationships (long axes, intermediate axes and projective areas) between grain features from images and those measured were compared. For this analysis, 240 gravel particles were collected at three river stations. All particles were measured with vernier calipers and weighed with scales. The measured data showed that river gravel had shape factors of 0.514~0.585. It was found that the weight of gravel had a stronger correlation with the projective areas than the long or intermediate axes. Using these results, we were able to establish an area-weight formula. In the second step, we calculated the projective areas of the river-bed gravels by detecting their edge lines using the ImageJ program. The projective areas of the gravels were converted to the grain-size distribution using the formula previously established. The proposed method was applied to 3 small- and medium- sized rivers in Korea. Comparisons of the analyzed size distributions with those measured showed that the proposed method could estimate the median diameter within a fair error range. However, the estimated distributions showed a slight deviation from the observed value, which is something that needs improvement in the future.
This study was conducted to estimate the deep percolation using numerical modeling and field observation data based on rainfall in Haean basin. Soil moisture sensors were installed to monitoring at 30, 60 and 90 cm depths in four sites (YHS1-4) and automatic weather station was installed to around YHS3. Soil moisture and meteorological data was observed from March 25, 2017 to March 25, 2018 and May 06, 2016 to May 06, 2018, respectively. Numerical analysis was performed from June to August, 2017 using the HYDRUS-1D. Average soil moisture contents were high to generally in YHS3 for 0.300 to $0.334m^3/m^3$ and lowest in YHS1 for 0.129 to $0.265m^3/m^3$ during the soil moisture monitoring period. The results of soil moisture flow modeling showed that field observations and modeling values were similar but the peak values were larger in the modeling result. Correlation analysis between observation and modeling data showed that r, $r^2$ and RMSE were 0.88, 0.77, and 0.0096, respectively. This show high correlation and low error rate. The total deep percolation was 744.2 mm during the period of modelling at 500 cm depth. This showed that 61.3% of the precipitation amount (1,214 mm) was recharged in 2017. Deep percolation amount was high in the study area. This study is expected to provide basic data for the estimation of groundwater recharge through unsaturated zone.
Dehnokhalaji, Morteza;Golbakhsh, Mohammad Reza;Siavashi, Babak;Talebian, Parham;Javidmehr, Sina;Bozorgmanesh, Mohammadreza
Asian Spine Journal
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v.12
no.6
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pp.1060-1068
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2018
Study Design: Retrospective study. Purpose: Lumbar intervertebral disc degeneration is an important cause of low back pain. Overview of Literature: Spinal fusion is often reported to have a good course for adolescent idiopathic scoliosis (AIS). However, many studies have reported that adjacent segment degeneration is accelerated after lumbar spinal fusion. Radiography is a simple method used to evaluate the orientation of the vertebral column. magnetic resonance imaging (MRI) is the method most often used to specifically evaluate intervertebral disc degeneration. The Pfirrmann classification is a well-known method used to evaluate degenerative lumbar disease. After spinal fusion, an increase in stress, excess mobility, increased intra-disc pressure, and posterior displacement of the axis of motion have been observed in the adjacent segments. Methods: we retrospectively secured and analyzed the data of 15 patients (four boys and 11 girls) with AIS who underwent a spinal fusion surgery. We studied the full-length view of the spine (anterior-posterior and lateral) from the X-ray and MRI obtained from all patients before surgery. Postoperatively, another full-length spine X-ray and lumbosacral MRI were obtained from all participants. Then, pelvic tilt, sacral slope, curve correction, and fused and free segments before and after surgery were calculated based on X-ray studies. MRI images were used to estimate the degree to which intervertebral discs were degenerated using Pfirrmann grading system. Pfirrmann grade before and after surgery were compared with Wilcoxon signed rank test. While analyzing the contribution of potential risk factors for the post-spinal fusion Pfirrmann grade of disc degeneration, we used generalized linear models with robust standard error estimates to account for intraclass correlation that may have been present between discs of the same patient. Results: The mean age of the participant was 14 years, and the mean curvature before and after surgery were 67.8 and 23.8, respectively (p<0.05). During the median follow-up of 5 years, the mean degree of the disc degeneration significantly increased in all patients after surgery (p<0.05) with a Pfirrmann grade of 1 and 2.8 in the L2-L3 before and after surgery, respectively. The corresponding figures at L3-L4, L4-L5, and L5-S1 levels were 1.28 and 2.43, 1.07 and 2.35, and 1 and 2.33, respectively. The lower was the number of free discs below the fusion level, the higher was the Pfirrmann grade of degeneration (p<0.001). Conversely, the higher was the number of the discs fused together, the higher was the Pfirrmann grade. Conclusions: we observed that the disc degeneration aggravated after spinal fusion for scoliosis. While the degree of degeneration as measured by Pfirrmann grade was directly correlated by the number of fused segments, it was negatively correlated with the number of discs that remained free below the lowermost level of the fusion.
Objective: The objective of this study was to determine the best approach for handling missing records of first to successful insemination (FS) in Japanese Black heifers. Methods: Of a total of 2,367 records of heifers born between 2003 and 2015 used, 206 (8.7%) of open heifers were missing. Four penalty methods based on the number of inseminations were set as follows: C1, FS average according to the number of inseminations; C2, constant number of days, 359; C3, maximum number of FS days to each insemination; and C4, average of FS at the last insemination and FS of C2. C5 was generated by adding a constant number (21 d) to the highest number of FS days in each contemporary group. The bootstrap method was used to compare among the 5 methods in terms of bias, mean squared error (MSE) and coefficient of correlation between estimated breeding value (EBV) of non-censored data and censored data. Three percentages (5%, 10%, and 15%) were investigated using the random censoring scheme. The univariate animal model was used to conduct genetic analysis. Results: Heritability of FS in non-censored data was $0.012{\pm}0.016$, slightly lower than the average estimate from the five penalty methods. C1, C2, and C3 showed lower standard errors of estimated heritability but demonstrated inconsistent results for different percentages of missing records. C4 showed moderate standard errors but more stable ones for all percentages of the missing records, whereas C5 showed the highest standard errors compared with noncensored data. The MSE in C4 heritability was $0.633{\times}10^{-4}$, $0.879{\times}10^{-4}$, $0.876{\times}10^{-4}$ and $0.866{\times}10^{-4}$ for 5%, 8.7%, 10%, and 15%, respectively, of the missing records. Thus, C4 showed the lowest and the most stable MSE of heritability; the coefficient of correlation for EBV was 0.88; 0.93 and 0.90 for heifer, sire and dam, respectively. Conclusion: C4 demonstrated the highest positive correlation with the non-censored data set and was consistent within different percentages of the missing records. We concluded that C4 was the best penalty method for missing records due to the stable value of estimated parameters and the highest coefficient of correlation.
This paper presents cell ID (cell identity) detection schemes using PSS/SSS (primary synchronization signal/secondary synchronization signal) for 5G NR (new radio) system and evaluates the detection performance. In this paper, we consider two cell ID detection schemes, i.e. two-stage detection and joint detection schemes. The two-stage detection scheme consists of two stages which estimate a channel gain between a transmitter and receiver and detect the PSS and SSS sequences. The joint detection scheme jointly detects the PSS and SSS sequences. In addition, this paper presents coherent and non-coherent combining schemes. The coherent scheme calculates the correlation value for the total length of the given PSS and SSS sequences, and the non-coherent combining scheme calculates the correlation within each group by dividing the total length of the sequence into several groups and then combines them non-coherently. For the detection schemes considered in this paper, the detection error rates of PSS, SSS and overall cell ID are evaluated and compared through computer simulations. The simulation results show that the joint detection scheme outperforms the two-stage detection scheme for both coherent and non-coherent combining schemes, but the two-stage detection scheme can greatly reduce the computational complexity compared to the joint detection scheme. In addition, the non-coherent combining detection scheme shows better performance under the additive white Gaussian noise (AWGN), fixed, and mobile environments.
Korean Journal of Agricultural and Forest Meteorology
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v.23
no.2
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pp.122-133
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2021
Cultivar parameter calibration can be affected by the reliability of the input data to a crop growth model. In South Korea, two sets of weather stations, which are included in the automated synoptic observing system (ASOS) or the automatic weather system (AWS), are available for preparation of the weather input data. The objectives of this study were to estimate the cultivar parameter using those sets of weather data and to compare the uncertainty of these parameters. The cultivar parameters of CERES-Rice model for Shindongjin cultivar was calibrated using the weather data measured at the weather stations included in either ASO S or AWS. The observation data of crop growth and management at the experiment farms were retrieved from the report of new cultivar development and research published by Rural Development Administration. The weather stations were chosen to be the nearest neighbor to the experiment farms where crop data were collected. The Generalized Likelihood Uncertainty Estimation (GLUE) method was used to calibrate the cultivar parameters for 100 times, which resulted in the distribution of parameter values. O n average, the errors of the heading date decreased by one day when the weather input data were obtained from the weather stations included in AWS compared with ASO S. In particular, reduction of the estimation error was observed even when the distance between the experiment farm and the ASOS stations was about 15 km. These results suggest that the use of the AWS stations would improve the reliability and applicability of the crop growth models for decision support as well as parameter calibration.
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