• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.713-723
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• 2013

An objective Bayesian estimation procedure of the two-parameter Pareto distribution is presented under the reference prior and the noninformative prior. Bayesian estimators are obtained by Gibbs sampling. The steps to generate parameters in the Gibbs sampler are from the shape parameter of the gamma distribution and then the scale parameter by the adaptive rejection sampling algorism. A numerical study shows that the proposed objective Bayesian estimation outperforms other estimations in simulated bias and mean squared error.

• Food Science and Biotechnology
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• v.18 no.5
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• pp.1118-1123
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• 2009

Event-specific real-time polymerase chain reaction (PCR) detection method for genetically modified (GM) maize MIR604 was developed based on integration junction sequences between the host plant genome and the integrated transgene. In this study, 2 primer pairs and probes were designed for specific amplification of 100 and 111 bp DNA fragments from the zSSIIb gene (the maize endogenous reference gene) and MIR604. The quantitative method was validated using 3 certified reference materials (CRMs) with levels of 0.1, 1, and 10% MIR604. The method was also assayed with 14 different plants and other GM maize. No amplification signal was observed in real-time PCR assays with any of the species tested other than MIR604 maize. As a result, the bias from the true value and the relative deviation for MIR604 was within the range from 0 to 9%. Precision, expressed as relative standard deviation (RSD), varied from 2.7 to 10% for MIR604. Limits of detections (LODs) of qualitative and quantitative methods were all 0.1%. These results indicated that the event-specific quantitative PCR detection system for MIR604 is accurate and useful.

• Journal of Sasang Constitutional Medicine
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• v.24 no.3
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• pp.1-16
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• 2012

Objectives For the methodological review on the accuracy study of questionnaire for Sasang constitution diagnosis, we searched the various diagnostic accuracy study of the questionnaires for Sasang constitution. Methods We searched MEDLINE, the Cochrane Library, KISS, and DBPIA. Additionally, We hand-searched the main oriental medical journals. All articles were independently reviewed and selected by two evaluators. And selected articles were assessed by "Quality Assessment of Diagnostic Accuracy Studies Tool"(QUADAS Tool) for the methodological review. Results The twenty eight studies initially identified studies were included in the methodological review. The part of "Acceptable reference standard", "Uninterpretable results reported" and "Withdrawals explained" was very weak in the risk of bias. The part of "Representative spectrum", "Acceptable delay between tests", "Incorporation avoided", "Reference standard results blinded", "Index test results blinded" was unclear in the description. Conclusions For the further study on the accuracy study of Sasang constitution diagnosis, we have to improve the aforementioned errors. Additionally, the checklist for the description of study might be needed.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.1
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• pp.89-99
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• 2018

This study analyzed the change of the dam inflow and evapotranspiration in the Soyanggang dam basin using the results of 26 CMIP5 GCMs based on AR5 RCP 4.5 and RCP 8.5 scenarios. The SWAT model was used to simulate the dam inflow and evapotranspiration in the target watershed. The simulation was performed during 2010~2016 as the reference year and during 2010~2099 as the analysis period. Bias correction of input data such as precipitation and air temperature were conducted for the reference period of 2006~2016. Results were analyzed for 3 different periods, 2025s (2010~2040), 2055s (2041~2070), and 2085s (2071~2099). It demonstrated that the change of dam inflow gradually increases 9.5~15.9 % for RCP 4.5 and 13.3~29.8 % for RCP 8.5. The change of evapotranspiration gradually increases 1.6~8.6 % for RCP 4.5 and 1.5~8.5 % for RCP8.5.

• Mass Spectrometry Letters
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• v.5 no.2
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• pp.57-61
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• 2014

A gravimetric standard addition method combined with internal standard calibration has been successfully developed for the accurate analysis of total arsenic in a laver candidate reference material. A model equation for the gravimetric standard addition approach using an internal standard was derived to determine arsenic content in samples. Handlings of samples, As standard and internal standard were carried out gravimetrically to avoid larger uncertainty and variability involved in the volumetric preparation. Germanium was selected as the internal standard because of its close mass to the arsenic to minimize mass-dependent bias in mass spectrometer. The ion signal ratios of $^{75}As^+$ to $^{72}Ge^+$ (or $^{73}Ge^+$) were measured in high resolution mode ($R{\geq}10,000$) to separate potential isobaric interferences by high resolution ICP/MS. For method validation, the developed method was applied to the analysis of arsenic content in the NMIJ 7402-a codfish certified reference material (CRM) and the result was $37.07mg{\cdot}kg^{-1}{\pm}0.45mg{\cdot}kg^{-1}$ which is in good agreement with the certified value, $36.7mg{\cdot}kg^{-1}{\pm}1.8mg{\cdot}kg^{-1}$. Finally, the certified value of the total arsenic in the candidate laver CRM was determined to be $47.15mg{\cdot}kg^{-1}{\pm}1.32mg{\cdot}kg^{-1}$ (k = 2.8 for 95% confidence level) which is an excellent result for arsenic measurement with only 2.8 % of relative expanded uncertainty.

• Journal of Advanced Navigation Technology
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• v.15 no.5
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• pp.705-713
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• 2011

In this paper, we introduce two techniques for resolving integer ambiguities between reference stations, which is one of the most important processes in Network RTK correction generation process. Each techniques uses Hatch filter and combination of L1/L2 measurements and we used simulation data and real data to evaluate performance of the techniques. For evaluating performance of each technique, we compared corrections generated from user site and Network RTK. As a result, Network RTK with the technique which uses Hatch filter improves user performance much more than single baseline RTK does. Residual of user is smaller than a half size of wavelength so it does not affect user integer ambiguity resolution, however, it contains significant bias error. On the other hand, when we used the technique which uses combination of L1/L2 measurements, residual error of user is largely reduced compared to the technique using Hatch filter.

• Nutrition Research and Practice
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• v.11 no.1
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• pp.51-56
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• 2017

BACKGROUND/OBJECTIVES: The purpose of this study was to assess the accuracy of a dietary reference intake (DRI) predictive equation for estimated energy requirements (EER) in female college tennis athletes and non-athlete students using doubly labeled water (DLW) as a reference method. MATERIALS/METHODS: Fifteen female college students, including eight tennis athletes and seven non-athlete subjects (aged between 19 to 24 years), were involved in the study. Subjects' total energy expenditure (TEE) was measured by the DLW method, and EER were calculated using the DRI predictive equation. The accuracy of this equation was assessed by comparing the EER calculated using the DRI predictive equation ($EER_{DRI}$) and TEE measured by the DLW method ($TEE_{DLW}$) based on calculation of percentage difference mean and percentage of accurate prediction. The agreement between the two methods was assessed by the Bland-Altman method. RESULTS: The percentage difference mean between the methods was -1.1% in athletes and 1.8% in non-athlete subjects, whereas the percentage of accurate prediction was 37.5% and 85.7%, respectively. In the case of athletic subjects, the DRI predictive equation showed a clear bias negatively proportional to the subjects' TEE. CONCLUSIONS: The results from this study suggest that the DRI predictive equation could be used to obtain EER in non-athlete female college students at a group level. However, this equation would be difficult to use in the case of athletes at the group and individual levels. The development of a new and more appropriate equation for the prediction of energy expenditure in athletes is proposed.

• Nutrition Research and Practice
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• v.16 no.sup1
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• pp.70-88
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• 2022

Sodium is a physiologically essential nutrient, but excessive intake is linked to the increased risk of various chronic diseases, particularly cardiovascular. It is, therefore, necessary to accomplish an evidence-based approach and establish the Korean Dietary Reference Intakes (KDRIs) index, to identify both the nutritional adequacy and health effects of sodium. This review presents the rationale for and the process of revising the KDRIs for sodium and, more importantly, establishing the sodium Chronic Disease Risk Reduction Intake (CDRR) level, which is a new specific set of values for chronic disease risk reduction. To establish the 2020 KDRIs for dietary sodium, the committee conducted a systematic literature review of the intake-response relationships between the selected indicators for sodium levels and human chronic diseases. In this review, 43 studies published from January 2014 to December 2018, using databases of PubMed and Web of Science, were finally included for evaluating the risk of bias and strength of evidence (SoE). We determined that SoE of the relationship between dietary sodium and cardiovascular diseases, cerebrovascular disease, and hypertension, was moderate to strong. However, due to insufficient scientific evidence, we were unable to establish the estimated average requirement and the recommended nutrient intake for dietary sodium. Therefore, the adequate intake of sodium for adults was established to be 1,500 mg/day, whereas the CDRR for dietary sodium was established at 2,300 mg/day for adults. Intake goal for dietary sodium established in the 2015 KDRIs instead of the tolerable upper intake level was not presented in the 2020 KDRIs. For the next revision of the KDRIs, there is a requirement to pursue further studies on nutritional adequacy and toxicity of dietary sodium, and their associations with chronic disease endpoint in the Korean population.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.31-36
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• 2024

Recently, mobile apps that utilize AI technologies are increasing. In the personal mobile environment, performance degradation may occur during the training phase of large AI workload due to limitations in memory capacity. In this paper, we extract memory reference traces of AI workloads and analyze their characteristics. From this analysis, we observe that AI workloads can cause frequent storage access due to weak temporal locality and irregular popularity bias during memory write operations, which can degrade the performance of mobile devices. Based on this observation, we discuss ways to efficiently manage memory write operations of AI workloads using persistent memory-based swap devices. Through simulation experiments, we show that the system architecture proposed in this paper can improve the I/O time of mobile systems by more than 80%.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.23 no.4
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• pp.153-178
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• 2018

Most operational uses of wind speed data require measurements at, or estimates generated for, the reference height of 10 m above mean sea level (AMSL). On the Ieodo Ocean Research Station (IORS), wind speed is measured by instruments installed on the lighthouse tower of the roof deck at 42.3 m AMSL. This preliminary study indicates how these data can best be converted into synthetic 10 m wind speed data for operational uses via the Korea Hydrographic and Oceanographic Agency (KHOA) website. We tested three well-known conventional empirical neutral wind profile formulas (a power law (PL); a drag coefficient based logarithmic law (DCLL); and a roughness height based logarithmic law (RHLL)), and compared their results to those generated using a well-known, highly tested and validated logarithmic model (LMS) with a stability function (${\psi}_{\nu}$), to assess the potential use of each method for accurately synthesizing reference level wind speeds. From these experiments, we conclude that the reliable LMS technique and the RHLL technique are both useful for generating reference wind speed data from IORS observations, since these methods produced very similar results: comparisons between the RHLL and the LMS results showed relatively small bias values ($-0.001m\;s^{-1}$) and Root Mean Square Deviations (RMSD, $0.122m\;s^{-1}$). We also compared the synthetic wind speed data generated using each of the four neutral wind profile formulas under examination with Advanced SCATterometer (ASCAT) data. Comparisons revealed that the 'LMS without ${\psi}_{\nu}^{\prime}$ produced the best results, with only $0.191m\;s^{-1}$ of bias and $1.111m\;s^{-1}$ of RMSD. As well as comparing these four different approaches, we also explored potential refinements that could be applied within or through each approach. Firstly, we tested the effect of tidal variations in sea level height on wind speed calculations, through comparison of results generated with and without the adjustment of sea level heights for tidal effects. Tidal adjustment of the sea levels used in reference wind speed calculations resulted in remarkably small bias (<$0.0001m\;s^{-1}$) and RMSD (<$0.012m\;s^{-1}$) values when compared to calculations performed without adjustment, indicating that this tidal effect can be ignored for the purposes of IORS reference wind speed estimates. We also estimated surface roughness heights ($z_0$) based on RHLL and LMS calculations in order to explore the best parameterization of this factor, with results leading to our recommendation of a new $z_0$ parameterization derived from observed wind speed data. Lastly, we suggest the necessity of including a suitable, experimentally derived, surface drag coefficient and $z_0$ formulas within conventional wind profile formulas for situations characterized by strong wind (${\geq}33m\;s^{-1}$) conditions, since without this inclusion the wind adjustment approaches used in this study are only optimal for wind speeds ${\leq}25m\;s^{-1}$.