• Journal of Radiation Protection and Research
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• v.47 no.2
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• pp.77-85
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• 2022

Background: After the Fukushima Daiichi Nuclear Power Station (FDNPS) accident, a model was developed to estimate the external exposure doses for residents who were expected to return to their homes after evacuation orders were lifted. However, the model's accuracy and uncertainties in parameters used to estimate external doses have not been evaluated. Materials and Methods: The model estimates effective doses based on the integrated ambient dose equivalent (H^*(10)) and life patterns, considering a dose reduction factor to estimate the indoor H^*(10) and a conversion factor from H^*(10) to the effective dose. Because personal dose equivalent (H_p(10)) has been reported to agree well with the effective dose after the FDNPS accident, this study validates the model's accuracy by comparing the estimated effective doses with H_p(10). The H_p(10) and life pattern data were collected for 36 adult participants who lived or worked near the FDNPS in 2019. Results and Discussion: The estimated effective doses correlated significantly with H_p(10); however, the estimated effective doses were lower than H_p(10) for indoor sites. A comparison with the measured indoor H^*(10) showed that the estimated indoor H^*(10) was not underestimated. However, the H_p(10) to H^*(10) ratio indoors, which corresponds to the practical conversion factor from H^*(10) to the effective dose, was significantly larger than the same ratio outdoors, meaning that the conversion factor of 0.6 is not appropriate for indoors due to the changes in irradiation geometry and gamma spectra. This could have led to a lower effective dose than H_p(10). Conclusion: The estimated effective doses correlated significantly with H_p(10), demonstrating the model's applicability for effective dose estimation. However, the lower value of the effective dose indoors could be because the conversion factor did not reflect the actual environment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2406-2419
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• 2010

This study aims to elevate the usefulness of the current annual Accreditation of Hospitals. To achieve this purpose, A modeling of an efficiency analysis based on DEA and AHP to the Accreditation of Hospitals Data from 2004 to 2008. By applying to AHP and DEA_AR to the scores derived from the various domains in data, An adequate prediction model about conversion factor in fee contract is made. By summarizing information derived from DEA, factor analysis and Generalized Linear Model, The linear functions combining conversion factor and efficiency index is successfully established. The factor analysis with AHP was used to merge diverse scores from the domains of evaluation. Not only the input and output initially introduced, AHP scores, dummy variables of hospital classification, geographical location are effective variables to forecast a conversion factor. If a predicted conversion factors from efficiency is used, It will be a great contributions to the annul doctor's fee contract.

• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.13-15
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• 2014

At frequencies above 10 GHz, rain is a dominant propagation phenomenon on satellite link attenuation. The prediction of rain attenuation is based on the point rainfall rate for 0.01 % of an average year with one minute integration time. Most of available rain data have been measured with 60 minutes integration time, and many researchers have been studying on converting the rainfall rate data from various integration times to one minute integration time. This paper proposes a new Multiple Regression model for the conversion, and the proposed schemes show better performance than the existing schemes.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.473-482
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• 2021

Although several Hangul generation models using deep learning have been introduced, they require a lot of data, have a complex structure, requires considerable time and resources, and often fail in style conversion. This paper proposes a model CKFont using the components of the initial, middle, and final components of Hangul as a way to compensate for these problems. The CKFont model is an end-to-end Hangul generation model based on GAN, and it can generate all Hangul in various styles with 28 characters and components of first, middle, and final components of Hangul characters. By acquiring local style information from components, the information is more accurate than global information acquisition, and the result of style conversion improves as it can reduce information loss. This is a model that uses the minimum number of characters among known models, and it is an efficient model that reduces style conversion failures, has a concise structure, and saves time and resources. The concept using components can be used for various image transformations and compositing as well as transformations of other languages.

• Transactions of the Korean Society of Automotive Engineers
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• v.16 no.5
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• pp.68-76
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• 2008

Diesel $NO_x$ reduction by $NH_3$-SCR in conjunction with the effective oxidation precatalyst was analytically investigated. Physicochemical processes in regard to $NH_3$-SCR $NO_x$ reduction and catalytic NO-$NO_2$ conversion are formulated with detailed descriptions on the commanding reactions. A unified model is correctly validated with experimental data in terms of extents of $NO_x$ reduction by SCR and NO-$NO_2$ conversion by DOC. The present deterministic model based on the rate expressions of Langmuir-Hinshelwood reaction scheme finds a conversion extent directly. A series of numerical experiments concomitant with parametric analysis of the $NO_x$ reduction was conducted. $NO_x$ reduction is promoted in proportion to DOC volume ar lower temperatures and an opposite holds at lower space velocity and intermediate temperatures. $NO_x$ conversion is weakly correlated to the space velocity and the DOC volume at higher exhaust temperature. In DOC-SCR system, the $NO_x$ reduction efficiency depends on the $NH_3/NO_x$ ratio.

• Journal of Korean Society of Rural Planning
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• v.26 no.3
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• pp.25-38
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• 2020

The purpose of this study is constructing a regional-level crop acreage choice model incorporating the impacts of producer risk aversion, and applying the constructed model to the Korean policy that promotes rice paddy conversion into non-rice crop fields. The study adopts the approach of Paris (2018) which estimates the absolute risk aversion coefficient inside of a positive mathematical programming model. A panel data set of 143 cities/counties is used for the empirical study where agricultural land in each region is allocated to 8 crops. Our estimated absolute risk aversion coefficients are smaller than those of Paris (2018), but are a little bit larger than those of the existing Korea studies based on survey or econometric methods. We found that there are close relationships among the estimated risk aversion, regional characteristics, and farming patterns. We also found that incorporating the estimated risk attitudes results in substantial differences in the impacts of the rice paddy conversion policy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.294-296
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• 2022

Atherosclerosis is a chronic vascular inflammatory disease in which plaque builds up in the arteries and impairs blood flow. This can lead to heart disease and stroke. Since most people do not have any symptoms until the artery is severely narrowed, early detection of atherosclerosis is critical. In this paper, in order to effectively detect atherosclerotic lesions in tube-shaped blood vessels, polar conversion is applied to MRI images based on the vessel center. We then propose a SE-LSTMNet model using continuous signal information for each angle of a polar coordinate image. The trained model showed classification performance of 0.9194 accuracy, 0.9370 sensitivity, 0.8796 specificity, 0.8700 F1 score, and 0.9719 AUC on the validation data.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.45-48
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• 1999

Model predictive control algorithm requires a relevant model of the system to be controlled. Unfortunately, the first principle model describing a polymerization reaction system has a large number of parameters to be estimated. Thus there is a need for the identification and control of a polymerization reactor system by using available input-output data. In this work, the polynomial auto-regressive moving average (ARMA) models are employed as the input-output model and combined into the nonlinear model predictive control algorithm based on the successive linearization method. Simulations are conducted to identify the continuous styrene polymerization reactor system. The input variables are the jacket inlet temperature and the feed flow rate whereas the output variables are the monomer conversion and the weight-average molecular weight. The polynomial ARMA models obtained by the system identification are used to control the monomer conversion and the weight-average molecular weight in a continuous styrene polymerization reactor It is demonstrated that the nonlinear model predictive controller based on the polynomial ARMA model tracks the step changes in the setpoint satisfactorily. In conclusion, the polynomial ARMA model is proven effective in controlling the continuous styrene polymerization reactor.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.3
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• pp.597-608
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• 1997

Life cycle product data is very important yet difficult to handle for manufacturing companies. Shoring and exchanging product data over world-wide-web is a part of key technology to implement PDM or CALS. STEP is widely accepted as a standard to represent the life-cycle product model data. Described in this paper is a web browser plug-in that can graphically display and explore product data represented by STEP over internet. By the use of the plug-in (named "npSTEP"), a product model data stored in STEP format on a web server can be displayed on a commonly used web client (browser), such as Netscape navigator, without any format conversion process. Furthermore one can explore the components or attributes of the product model data in hierarchical manner.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.1
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• pp.11-16
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• 2020

3D printing is one of the emerging technologies and getting a lot of attention. To do 3D printing, 3D model is first generated, and then converted to G-code which is 3D printer's operations. Facet, which is a small triangle, represents a small surface of 3D model. Depending on the height or precision of the 3D model, the number of facets becomes very large and so the conversion time from 3D model to G-code takes longer. Apach Hadoop is a software framework to support distributed processing for large data set and its application range gets widening. In this paper, Hadoop is used to do the conversion works time-efficient way. 2-phase distributed algorithm is developed first. In the algorithm, all facets are sorted according to its lowest Z-value, divided into N parts, and converted on several nodes independently. The algorithm is implemented in four steps; preprocessing - Map - Shuffling - Reduce of Hadoop. Finally, to show the performance evaluation, Hadoop systems are set up and converts testing 3D model while changing the height or precision.