• Atmosphere
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• v.29 no.5
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• pp.551-566
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• 2019

Quantitative understanding of a random error that is associated with Lagrangian particle dispersion modeling is a prerequisite for backward-in-time mode simulations. This study aims to quantify the random error of the WRF-FLEXPART model and suggest an optimum number of the Lagrangian particles for backward-in-time simulations over the Seoul metropolitan area. A series of backward-in-time simulations of the WRF-FLEXPART model has conducted at two receptor points by changing the number of Lagrangian particles and the relative error, as a quantitative indicator of random error, is analyzed to determine the optimum number of the release particles. The results show that in the Seoul metropolitan area a 1-day Lagrangian transport contributes 80~90% in residence time and ~100% in atmospheric enhancement of carbon monoxide. The relative errors in both the residence time and the atmospheric concentration enhancement are larger when the particles release in the daytime than in the nighttime, and in the inland area than in the coastal area. The sensitivity simulations reveal that the relative errors decrease with increasing the number of Lagrangian particles. The use of small number of Lagrangian particles caused significant random errors, which is attributed to the random number sampling process. For the particle number of 6000, the relative error in the atmospheric concentration enhancement is estimated as -6% ± 10% with reduction of computational time to 21% ± 7% on average. This study emphasizes the importance of quantitative analyses of the random errors in interpreting backward-in-time simulations of the WRF-FLEXPART model and in determining the number of Lagrangian particles as well.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1330-1337
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• 2017

In off-line phase of the preliminary Cut-off indoor positioning scheme, which is one of the indoor positioning scheme using the fingerprint, relative ranks of peak RSSIs received from beacons at each reference point are stored in the fingerprint map. In some reference points, signals of multiple beacons may be received. In this case, the relative ranks may be different when constructing fingerprint and when receiving signals in real-time. To solve this problem, we propose a method to utilize only up to five beacons with high ranking when constructing a fingerprint and when receiving signals in real-time and comparing them with stored information of a fingerprint. Experiments were conducted on the estimation probabilities and the average error when using this method. Those are compared with the previous methods. Experimental results show that the estimation probabilities and the average error are improved by removing only the remaining five beacons at each reference point of the fingerprint.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.40 no.4
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• pp.13-20
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• 2003

The model of the transistor saturation current of the BJT for integrated circuits based upon the semiconductor physics is proposed. The method for calculating the doping profile in the base region using process conditions is presented and the method for calculating the base Gummel number of lateral PNP BJT and vertical NPN BJT is proposed. The transistor saturation currents of NPN BJT using 20V and 30V process conditions obtained from the proposed method show an average relative error of 6.7% compared with the measured data and the transistor saturation currents of PNP BJT show an average relative error of 6.0% compared with the measured data.

• Journal of Korean Society of Rural Planning
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• v.12 no.4 s.33
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• pp.23-32
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• 2006

Recently, Owing to booming of leisure activities and national enforcement of 5-day workweek system, Korean government has been promoting rural tourism policy of which operating project's title is Green Rural Experience Village, Rural Traditional Theme Village, etc. In this study, ken investigation result on Green Rural Experience Village sites, an estimation model of returns by green-tourism activities was developed. The model was constructed through factor analysis and regression analysis method. Regression model developed can estimate green-tourism revenue by investment budget, homepage preengagement sales, homepage visitors, capacity of eating and drinking facilities, capacity of lodging facilities. The model developed was applied in sample villages. With these results, estimation revenue was recorded average 138.3% of survey revenue, and statistical significance was good(correlation coefficient $R^2$ = 0.8255, level of significance : 0.000), and the range of relative error was recorded largely from -7.1% to 158.6%, and average relative error was 38.3% and good. And, the model developed in this study have the critical point in aspects of insufficient data, but the results will be used in green-tourism policies and projects, and revenue estimation about each village in the present and future is limited, but in province or the whole country the application is good.

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1439-1448
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• 2022

Background: We investigated the feasibility of in vitro radiosensitivity prediction with gene expression using deep learning. Methods: A microarray gene expression of the National Cancer Institute-60 (NCI-60) panel was acquired from the Gene Expression Omnibus. The clonogenic surviving fractions at an absorbed dose of 2 Gy (SF2) from previous publications were used to measure in vitro radiosensitivity. The radiosensitivity prediction model was based on the convolutional neural network. The 6-fold cross-validation (CV) was applied to train and validate the model. Then, the leave-one-out cross-validation (LOOCV) was applied by using the large-errored samples as a validation set, to determine whether the error was from the high bias of the folded CV. The criteria for correct prediction were defined as an absolute error<0.01 or a relative error<10%. Results: Of the 174 triplicated samples of NCI-60, 171 samples were correctly predicted with the folded CV. Through an additional LOOCV, one more sample was correctly predicted, representing a prediction accuracy of 98.85% (172 out of 174 samples). The average relative error and absolute errors of 172 correctly predicted samples were 1.351±1.875% and 0.00596±0.00638, respectively. Conclusion: We demonstrated the feasibility of a deep learning-based in vitro radiosensitivity prediction using gene expression.

• Particle and aerosol research
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• v.19 no.1
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• pp.13-20
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• 2023

The Asian dust observation network operates β-ray attenuation samplers to measure PM₁₀ concentrations. In addition, equivalence evaluation and accuracy inspection(Precision Tests) are conducted every year for the reliability of data. β-ray attenuation samplers(16 units) were comparatively observed from May to June 2020 and from July to December 2021. During the observation period, the average daily temperature was the lowest at 6.4℃ in December and the highest at 27.3℃ in August. The average daily humidity ranged from 60% to 100%, but the average daily humidity was over 75% from July to September. The minimum value of the PM₁₀ Gravimetric method was 5.0 ㎍/m³, the maximum value was 53.4 ㎍/m³, and the average value was 17.8 ㎍/m³. The equivalence evaluation results of the PM₁₀ Gravimetric method and β-ray attenuation samplers satisfied the criteria (slope: 1±0.1, intercept: 0±0.5). A relative error analysis between the PM₁₀ Gravimetric method and β-ray attenuation samplers equipment showed that the relative error increased when the concentration was low and the temperature and humidity were high. In addition, in the β-ray attenuation samplers 5-minute interval observation data in May 2020, a relatively large Standard devication was shown as an average maximum ±23.4 ㎍/m³ and a minimum ±15.2 ㎍/m³. At standard deviations of 10% and 90%, equipment with high variability (deviation) was measured at 6 ㎍/m³and 61 ㎍/m³, and equipment with low variability was measured at 12 ㎍/m³ and 47 ㎍/m³. It was confirmed that concentration differences occurred due to differences in variability for each equipment.

• Magazine of the Korean Society of Agricultural Engineers
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• v.32 no.4
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• pp.27-43
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• 1990

This study aimed at developing a generalized model on the estimation of the long - term run - off volume for practical purpose. During the research period of last 3 years( 1986-1988), 3 types of estimation model on the long - term run - off volume(Effective rainfall model, unit hydrograph model and barne's model for dry season) had been developed by the author. In this study, through regressional analysis between determinant factors (bi of effective rainfall model, ai of unit hydrograph model and Wi of barne's model) and catchment characteris- tics(catchment area, distance round the catchment area, massing degree coefficient, river - exte- nsion, river - slope, river - density, infiltration of Watershed) of 11 test case areas by multiple regressional method, a new methodology on the derivation of determinant factors from catchment characteristics in the watershed areas having no hydrological station was developed. Therefore, in the resulting step, estimation equations on run - off volume for practical purpose of which input facor is only rainfall were developed. In the next stage, the derived equations were applied on the Kang - and Namgye - river catchment areas for checking of their goodness. The test results were as follows ; 1. In Kang - river area, average relative estimation errors of 72 hydrographs and of continuous daily run - off volume for 245 days( 1/5/1982 - 31/12) were calculated as 6.09%, 9.58% respectively. 2. In Namgye - river area, average relative estimation errors of 65 hydrographs and of conti- nuous daily run - off volume for 2fl days(5/4/1980-31/12) were 5.68%, 10.5% respectively. In both cases, relative estimation error was averaged as 7.96%, and so, the methodology in this study might be hetter organized than Kaziyama's formula when comparing with the relative error of the latter, 24~54%. However, two case studies cannot be the base materials enough for the full generalization of the model. So, in the future studies, many test case studies of this model should he carries out in the various catchment areas for making its generalization.

• Communications for Statistical Applications and Methods
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• v.28 no.4
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• pp.351-368
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• 2021

For a probabilistic model with positively skewed data, a lognormal distribution is one of the key distributions that play a critical role. Several lognormal models can be found in various areas, such as medical science, engineering, and finance. In this paper, we propose a new estimator for a lognormal mean and depict the performance of the proposed estimator in terms of the relative mean squared error (RMSE) compared with Shen's estimator (Shen et al., 2006), which is considered the best estimator among the existing methods. The proposed estimator includes a tuning parameter. By finding the optimal value of the tuning parameter, we can improve the average performance of the proposed estimator over the typical range of σ². The bias reduction of the proposed estimator tends to exceed the increased variance, and it results in a smaller RMSE than Shen's estimator. A numerical study reveals that the proposed estimator has performance comparable with Shen's estimator when σ² is small and exhibits a meaningful decrease in the RMSE under moderate and large σ² values.

• Journal of Korea Water Resources Association
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• v.50 no.9
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• pp.597-607
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• 2017

In this study, the applicability of the surface image velocimeter using the far-infrared camera was examined in order to solve the application problem of the measurement in night time, which has been pointed out in previous studies as the limit of the surface image velocimeter. For this purpose, the accuracy evaluation of measurement of the far-infrared camera was conducted for two conditions. Accuracy was evaluated by calculating the relative error of the results of the measurements of surface image velocimeter using the normal video camera during the daytime that was already verified. As a result, the relative error of the surface velocimeter using the far infrared camera was 4.3% at maximum, the average error was about 1%, and the error of the fog condition was maximum 5.2% with an average of 2%. In conclusion, it is possible to measure with high accuracy when using far-infrared camera in a invisible environments where the water flow can not be visualized with a general camera.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.12
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• pp.1527-1536
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• 1999

In this paper, a triply-encoded Hadamard transform imaging spectrometer is proposed by applying the grill spectrometer to the Hadamard transform imaging spectrometer. The proposed system encodes the input radiation triply ; once through the input image mask and twice through the two masks in the grill spectrometer. We use an electro-optical mask in the grill spectrometer which is controlled by a left-cyclic simplex matrix. Then we modeled the system using $D^{-1}$ method. In this paper, the average mean square error associated with a recovered estimate is considered for performance evaluation. The relative performance is compared with those of the other conventional systems.