• Analytical Science and Technology
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• v.15 no.5
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• pp.433-438
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• 2002

Trace elements in two air filters (V-50, P-50) artificially loaded with urban dust provided from IAEA were determined non-destructively using instrumental neutron activation analysis. Standard reference material (Urban Particulate Matter, SRM 1648) of National Institute of Standard and Technology was used for analytical quality control. About 20 elements in both of loaded filter samples were determined. To evaluate inter-laboratory comparison and proficiency test, analytical data were statistically treated with the results which collected from 49 laboratories, 40 countries participated in this study using neutron activation analysis, particle induced X-ray emission, inductively coupled plasma mass spectroscopy, etc,. From the results of statistical treatment, Z-scores are within ${\pm}2$. Furthermore, accuracy and precision of obtained analytical values are passed according to the criteria of the proficiency test. Consequently, it was proved that analytical quality for air dust samples being performed has been controlled properly.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.1
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• pp.11-18
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• 2017

The performance of a modeling system consisting of WRF model v3.3 and CMAQ model v4.7.1 for forecasting $PM_{2.5}$ concentrations were evaluated during the period May 2012 through December 2014. Twenty-four hour averages of $PM_{2.5}$ and its major components obtained through filter sampling at the Bulgwang intensive measurement station were used for comparison. The mean predicted $PM_{2.5}$ concentration over the entire period was 68% of the mean measured value. Predicted concentrations for major components were underestimated except for $NO_3{^-}$. The model performance for $PM_{2.5}$ generally tended to degrade with increasing the concentration level. However, the mean fractional bias (MFB) for high concentration above the $80^{th}$ percentile fell within the criteria, the level of accuracy acceptable for standard model applications. Among three bias correction methods, the ratio adjustment was generally most effective in improving the performance. Albeit for limited test conditions, this analysis demonstrated that the effects of bias correction were larger when using the data with a larger bias of predicted values from measurement values.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.23 no.6
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• pp.501-510
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• 2020

Purpose: To develop a probability-based differential diagnosis for pediatric acute liver failure (PALF) based on age and socioeconomic status of the country of origin. Methods: Comprehensive literature search using PubMed, EMBASE, and SCOPUS databases was performed. Children 0-22 years of age who met PALF registry criteria were included. Articles included >10 children, and could not be a case report, review article, or editorial. No language filter was utilized, but an English abstract was required. Etiology of PALF, age of child, and country of origin was extracted from included articles. Results: 32 full text articles were reviewed in detail; 2,982 children were included. The top diagnosis of PALF in developed countries was acetaminophen toxicity (9.24%; 95% CredI 7.99-10.6), whereas in developing countries it was Hepatitis A (28.9%; 95% CredI 26.3-31.7). In developed countries, the leading diagnosis of PALF in children aged <1 year was metabolic disorder (17.2%; 95% CredI 10.3-25.5), whereas in developing countries it was unspecified infection (39.3%; CredI 27.6-51.8). In developed countries, the leading diagnosis in children aged >1 year was Non-A-B-C Hepatitis (8.18%; CredI 5.28-11.7), whereas in developing countries it was Hepatitis A (32.4%; CredI 28.6-36.3). Conclusion: The leading causes of PALF in children aged 0-22 years differ depending on the age and developmental status of their country of origin, suggesting that these factors must be considered in the evaluation of children with PALF.

• Analytical Science and Technology
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• v.28 no.1
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• pp.1-7
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• 2015

4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), a tobacco specific nitrosamine found only in tobacco products. The ability to monitor biomarker concentrations is very important in understanding environmental tobacco smoke (ETS). In this study, an efficient and sensitive method for the analysis of NNK in dust was developed and validated using liquid chromatography tandem mass spectrometry. Dust was collected with filter paper soaked in methanol. The standard solution and dust sample were diluted with 100 mM ammonium acetate and extracted using dichloromethane. Our calibration curves ranged from 25 to $10^4pg/mL$. Excellent linearity was obtained with correlation coefficient values between 0.9996 and 1.0000. The limit of detection (LOD) was 5 pg/mL ($S/N{\geq}3$) and the retention time was 10 min. The limit of quantification (LOQ) was 25 pg/mL, and the acceptance criteria was the rate of 98-103% (80-120% at levels up to $3{\times}LOQ$). The coefficient of variations (CV) was 2.8%. Accuracies determined from dust samples spiked with four different levels of NNK racurves ranged that from 25 to 104 pg/mL. Excellent linearity was obtained between 92.1% and 114%. The precision of the method was acceptable (5% of CV). The recovery rates of the whole analytical procedure at low, medium, and high levels were 105.7-116.5% for NNK. The carry-over effects during LC-MS/MS analysis were not observed for NNK. This manuscript summarizes the scientific evidence on the use of markers to measure ETS.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.8
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• pp.1890-1910
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• 2013

Routing overlay offers an ideal methodology to improve the end-to-end communication performance by deriving a backup path for any node pair. This paper focuses on a challenging issue of selecting a proper backup path to bypass the failures on the default path with high probability for any node pair. For existing backup path selection approaches, our trace-driven evaluation results demonstrate that the backup and default paths for any node pair overlap with high probability and hence usually fail simultaneously. Consequently, such approaches fail to derive a robust backup path that can take over in the presence of failure on the default path. In this paper, we propose a three-phase RBPS approach to identify a proper and robust backup path. It utilizes the traceroute probing approach to obtain the fine-grained topology information, and systematically employs the grid quorum system and the Bloom filter to reduce the resulting communication overhead. Two criteria, delay and fault-tolerant ability on average, of the backup path are proposed to evaluate the performance of our RBPS approach. Extensive trace-driven evaluations show that the fault-tolerant ability of the backup path can be improved by about 60%, while the delay gain ratio concentrated at 14% after replacing existing approaches with ours. Consequently, our approach can derive a more robust and available backup path for any node pair than existing approaches. This is more important than finding a backup path with the lowest delay compared to the default path for any node pair.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.6C
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• pp.462-468
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• 2012

In this paper, an adaptive digital automatic gain control(AGC) algorithm with two stages is proposed. AGC technique is crucial for mobile communication equipment because path loss in wireless channel and gain fluctuation in receiver front-end continuously change the received signal strength. Furthermore, adaptive criteria should be applied to the design of AGC algorithm in order to support many waveforms with one SDR communication device. With these reasons, a two-stage structure is proposed to satisfy both flexibility and adaptiveness. Compared with conventional method, it also requires shorter convergence time. Numerical results show that the gain value of variable gain amplifier(VGA) is converged within proper time and proposed scheme controls the input level of analog to digital converter(ADC) to be stable during long range of time.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.751-770
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• 2018

Web applications are indispensable in the software industry and continuously evolve either meeting a newer criteria and/or including new functionalities. However, despite assuring quality via testing, what hinders a straightforward development is the presence of defects. Several factors contribute to defects and are often minimized at high expense in terms of man-hours. Thus, detection of fault proneness in early phases of software development is important. Therefore, a fault prediction model for identifying fault-prone classes in a web application is highly desired. In this work, we compare 14 machine learning techniques to analyse the relationship between object oriented metrics and fault prediction in web applications. The study is carried out using various releases of Apache Click and Apache Rave datasets. En-route to the predictive analysis, the input basis set for each release is first optimized using filter based correlation feature selection (CFS) method. It is found that the LCOM3, WMC, NPM and DAM metrics are the most significant predictors. The statistical analysis of these metrics also finds good conformity with the CFS evaluation and affirms the role of these metrics in the defect prediction of web applications. The overall predictive ability of different fault prediction models is first ranked using Friedman technique and then statistically compared using Nemenyi post-hoc analysis. The results not only upholds the predictive capability of machine learning models for faulty classes using web applications, but also finds that ensemble algorithms are most appropriate for defect prediction in Apache datasets. Further, we also derive a consensus between the metrics selected by the CFS technique and the statistical analysis of the datasets.

• Journal of radiological science and technology
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• v.45 no.2
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• pp.145-150
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• 2022

The main purpose of this work was to restore the blurry chest CT images by applying a blind deconvolution algorithm. In general, image restoration is the procedure of improving the degraded image to get the true or original image. In this regard, we focused on a blind deblurring approach with chest CT imaging by using digital image processing in MATLAB, which the blind deconvolution technique performed without any whole knowledge or information as to the fundamental point spread function (PSF). For our approach, we acquired 30 chest CT images from the public source and applied three type's PSFs for finding the true image and the original PSF. The observed image might be convolved with an isotropic gaussian PSF or motion blurring PSF and the original image. The PSFs are assumed as a black box, hence restoring the image is called blind deconvolution. For the 30 iteration times, we analyzed diverse sizes of the PSF and tried to approximate the true PSF and the original image. For improving the ringing effect, we employed the weighted function by using the sobel filter. The results was compared with the three criteria including mean squared error (MSE), root mean squared error (RMSE) and peak signal-to-noise ratio (PSNR), which all values of the optimal-sized image outperformed those that the other reconstructed two-sized images. Therefore, we improved the blurring chest CT image by using the blind deconvolutin algorithm for optimal approach.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.20 no.4
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• pp.489-500
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• 2022

The decommissioning of nuclear facilities produces various types of radiologically contaminated waste. In addition, dismantlement activities, including cutting, packing, and clean-up at the facility site, result in secondary radioactive waste such as filters, resin, plastic, and clothing. Determining of the radionuclide content of this waste is an important step for the determination of a suitable management strategy including classification and disposal. In this work, we radiochemically characterized the radionuclide activities of filters used during the decommissioning of Korea Research Reactors (KRRs) 1 and 2. The results indicate that the filter samples contained mainly ³H (500-3,600 Bq·g^-1), ¹⁴C (7.5-29 Bq·g^-1), ⁵⁵Fe (1.1- 7.1 Bq·g^-1), ⁵⁹Ni (0.60-1.0 Bq·g^-1), ⁶⁰Co (0.74-70 Bq·g^-1), ⁶³Ni (0.60-94 Bq·g^-1), ⁹⁰Sr (0.25-5.0 Bq·g^-1), ¹³⁷Cs (0.64-8.7 Bq·g^-1), and ¹⁵²Eu (0.19-2.9) Bq·g^-1. In addition, the gross alpha radioactivity of the samples was measured to be between 0.32-1.1 Bq·g^-1. The radionuclide concentrations were below the concentration limit stated in the low- and intermediatelevel waste acceptance criteria of the Nuclear Safety and Security Commission, and used for the disposal of the KRRs waste drums to a repository site.

• Journal of the Korean earth science society
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• v.40 no.6
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• pp.549-560
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• 2019

The effects of the coal-fired power plant emissions, as the biggest point source of air pollutants, on spatiotemporal surface air pollution over the remote area are investigated in this study, based on a set of date selection and statistical technique to consider meteorological and geographical effects in the emission-concentration (source-receptor) relationship. We here proposed the sophisticated technique of data processing to separate and quantify the effects. The data technique comprises a set of data selection and statistical analysis procedure that include data selection criteria depending on meteorological conditions and statistical methods such as Kolmogorov-Zurbenko filter (K-Z filter) and empirical orthogonal function (EOF) analysis. The data selection procedure is important for filtering measurement data to consider the meteorological and geographical effects on the emission-concentration relationship. Together with meteorological data from the new high resolution ECMWF reanalysis 5 (ERA5) and the Korea Meteorological Administration automated surface observing system, air pollutant emission data from the telemonitoring system (TMS) of Dangjin and Taean power plants as well as spatio-temporal air pollutant concentrations from the air quality monitoring system are used for 4 years period of 2014-2017. Since all the data used in this study have the temporal resolution of 1 hour, the first EOF mode of spatio-temporal changes in air pollutant concentrations over the Seoul metropolitan area (SMA) due to power plant emission have been analyzed to explain over 97% of total variability under favorable meteorological conditions. It is concluded that SO₂, NO₂, and PM₁₀ concentrations over the SMA would be decreased by 0.468, 1.050 ppb, and 2.045 ㎍ m^-3 respectively if SO₂, NO₂, and TSP emissions from Dangjin power plant were reduced by 10%. In the same way, the 10% emission reduction in Taean power plant emissions would cause SO₂, NO₂, and PM₁₀ decreased by 0.284, 0.842 ppb, and 1.230 ㎍ m^-3 over the SMA respectively. Emissions from Dangjin power plant affect air pollution over the SMA in higher amount, but with lower R value, than those of Taean under the same meteorological condition.