• Smart Structures and Systems
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• v.13 no.6
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• pp.901-925
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• 2014

The reliability of a Bridge management System depends on the quality of visual inspection and the reliable estimation of bridge condition rating. However, the current practices of visual inspection have been identified with several limitations, such as: they are time-consuming, provide incomplete information, and their reliance on inspectors' experience. To overcome such limitations, this paper presents an approach of automating the prediction of condition rating for bridges based on digital image analysis. The proposed methodology encompasses image acquisition, development of 3D visualization model, image processing, and condition rating model. Under this method, scaling defect in concrete bridge components is considered as a candidate defect and the guidelines in the Ontario Structure Inspection Manual (OSIM) have been adopted for developing and testing the proposed method. The automated algorithms for scaling depth prediction and mapping of condition ratings are based on training of back propagation neural networks. The result of developed models showed better prediction capability of condition rating over the existing methods such as, Naïve Bayes Classifiers and Bagged Decision Tree.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.150-150
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• 2018

The Numerical Weather Prediction (NWP) models provide information for weather forecasts. The highly nonlinear and complex interactions in the atmosphere are simplified in meteorological models through approximations and parameterization. Therefore, the simplifications may lead to biases and errors in model results. Although the models have improved over time, the biased outputs of these models are still a matter of concern in meteorological and hydrological studies. Thus, bias removal is an essential step prior to using outputs of atmospheric models. The main idea of statistical bias correction methods is to develop a statistical relationship between modeled and observed variables over the same historical period. The Model Output Statistics (MOS) would be desirable to better match the real time forecast data with observation records. Statistical post-processing methods relate model outputs to the observed values at the sites of interest. In this study three methods are used to remove the possible biases of the real-time outputs of the Weather Research and Forecast (WRF) model in Imjin basin (North and South Korea). The post-processing techniques include the Linear Regression (LR), Linear Scaling (LS) and Power Scaling (PS) methods. The MOS techniques used in this study include three main steps: preprocessing of the historical data in training set, development of the equations, and application of the equations for the validation set. The expected results show the accuracy improvement of the real-time forecast data before and after bias correction. The comparison of the different methods will clarify the best method for the purpose of the forecast skill enhancement in a real-time case study.

• Journal of dental hygiene science
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• v.18 no.3
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• pp.137-146
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• 2018

This systematic review aimed to investigate the effects of periodontal treatment on glycated hemoglobin A (HbA1c) levels in patients with type 2 diabetes who develop periodontal disease. The search of the MEDLINE, Embase, CINAHL, and Cochrane Library databases was completed on April 8, 2018. The study design was based on randomized clinical trials. Scaling and root planing was performed for the test group, whereas no periodontal treatment or simple oral training was performed for the control group. The main outcome variable was the change in HbA1c levels. We used the Review Manager statistical analysis software for the quantitative analysis of selected documents. Meta-analysis was performed using the inverse variance estimation method of the fixed-effect model to estimate the effects of periodontal treatment on HbA1c levels in patients with type 2 diabetes. A total of 1,011 documents were searched using search strategies, and 10 documents were included in the meta-analysis. The meta-analysis of the selected literature showed that periodontal treatment significantly reduced the HbA1c levels in patients with type 2 diabetes who develop periodontal disease (mean difference, -0.34; 95% confidence interval, -0.43 to -0.26; p<0.001). This study aimed to investigate the effects of periodontal treatment on HbA1c levels, which can be used as a basis for the increasing management of diabetic complications. To improve the quality of life and reduce the burden of medical expenses for patients with diabetes, periodontal disease management through nonsurgical periodontal treatment, such as scaling and root planing, is necessary.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.147-147
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• 2019

The Numerical Weather Prediction (NWP) models determine the future state of the weather by forcing current weather conditions into the atmospheric models. The NWP models approximate mathematically the physical dynamics by nonlinear differential equations; however these approximations include uncertainties. The errors of the NWP estimations can be related to the initial and boundary conditions and model parameterization. Development in the meteorological forecast models did not solve the issues related to the inevitable biases. In spite of the efforts to incorporate all sources of uncertainty into the forecast, and regardless of the methodologies applied to generate the forecast ensembles, they are still subject to errors and systematic biases. The statistical post-processing increases the accuracy of the forecast data by decreasing the errors. Error prediction of the NWP models which is updating the NWP model outputs or model output statistics is one of the ways to improve the model forecast. The regression methods (including linear, polynomial and scaling regression) are applied to the present study to improve the real time forecast skill. Such post-processing consists of two main steps. Firstly, regression is built between forecast and measurement, available during a certain training period, and secondly, the regression is applied to new forecasts. In this study, the WRF real-time forecast data, in comparison with the observed data, had systematic biases; the errors related to the NWP model forecasts were reflected in the underestimation of the meteorological data forecast by the WRF model. The promising results will indicate that the post-processing techniques applied in this study improved the meteorological forecast data provided by WRF model. A comparison between various bias correction methods will show the strength and weakness of the each methods.

• Smart Media Journal
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• v.9 no.3
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• pp.80-89
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• 2020

Out - of - distribution (OOD) samples are frequently encountered when deploying a classification model in plenty of real-world machine learning-based applications. Those samples are normally sampling far away from the training distribution, but many classifiers still assign them high reliability to belong to one of the training categories. In this study, we address the problem of removing OOD examples by estimating marginal density estimation using variational autoencoder (VAE). We also investigate other proper methods, such as temperature scaling, Gaussian discrimination analysis, and label smoothing. We use Chonnam National University (CNU) weeds dataset as the in - distribution dataset and CIFAR-10, CalTeach as the OOD datasets. Quantitative results show that the proposed framework can reject the OOD test samples with a suitable threshold.

• Journal of the Korean Physical Society
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• v.73 no.10
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• pp.1486-1494
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• 2018

Based on the semiclassical theory of chaotic scattering, Tanner et al. [J. Phys. B 40, F157 (2007)] proposed the fluctuation in the partial photoionization cross section of helium below the double-ionization threshold would show the same characteristics as in the total cross section, predicting that the Fourier spectrum of the fluctuation reveals peaks at the classical actions of closed triple collision orbits and the amplitude of the fluctuation decreases algebraically as the energy approaches the double-ionization threshold. In that paper, however, the predictions were not clearly confirmed due to the lack of experimental data with sufficient accuracy. So instead, we calculate the partial photoionization cross sections of collinear eZe helium for the energy range from the single-ionization threshold $I_{20}$ to $I_{32}$ in order to numerically confirm the predictions. Analysis of the fluctuation in the partial cross section shows that the predictions are indeed valid. Our findings mean that the fluctuation in the partial photoionization cross section can be described by classical triple collision orbits in the semiclassical limit. Thus it explains in a natural way the mirroring and mimicking structures observed in cross section signals for different ionization channels.

• Phonetics and Speech Sciences
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• v.15 no.3
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• pp.37-44
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• 2023

This study investigates perceptual confusions of English fricatives among 121 Korean elementary school English as a foreign language (EFL) learners with shorter periods of learning English. The objective is to examine how they perceive English fricative consonants and to provide educational guidelines. Two sets of English fricative identification tasks-voiceless fricatives and voiced fricatives-were administered to participants in a High Variability Phonetic Training (HVPT) setting. Their phonemic awareness of the fricatives was visualized in perceptual confusion maps via multidimensional scaling analysis. The findings are explored in terms of the impacts of Korean EFL learners' L1 linguistic aspects and a comparison with L1 learners. Learners' phonemic awareness patterns are then compared with their relative importance in speech intelligibility based on a functional load hierarchy. The results indicated that Korean elementary EFL learners recognized English fricatives in a manner largely akin to L1 learners, suggesting their ongoing acquisition progress. Additionally, the findings demonstrated that the young EFL learners possess sufficient phonemic awareness for most high functional load segments but encounter some difficulties with one high and one low functional pair. The findings of this study offer suggestions for diagnosing language learners' phonemic awareness abilities, thereby aiding in the development of practical guidelines for language instructional design and helping educators make informed decisions regarding teaching priority in L2 classes.

• Journal of dental hygiene science
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• v.6 no.2
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• pp.127-131
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• 2006

For study on the mouth health care of patients for practical training of dental hygiene department, this survey was conducted among patients for practical training of the junior class of dental hygiene department of S College from September 2, 2004 to May 29, 2005. Before scaling, the purposes of the study and contents of the questionnaire were explained and the questionnaires were distributed and the patients were requested to complete them personally and then they were collected. 249 copies except 11 copies of insincere answers were used for the analysis. The subject of study was selected by convenience sampling, nonprobability sampling. 1. As for the characteristics of the subject of study, for sex, male occupied 138 (55.4%) and female occupied 111(44.6%). For the habitation site, Chungcheongdo residents were 181 people (72.7%), Incheon Gyeonggi 55(22.1%) and Seoul 13(5.2%). For ages, 19-29 age people were 122(49.0%), 30- 39 age 25(10.0%), 40-49 age 45(18.1%), 50-59 age 42(16.9%) and 60-70 age 15(6.0%). For scaling experience, 144 people (57.8%) had it and 105 people (42.2%) did not have it. 2. As for the mouth health care, for brushing method, crossways brushing was 164 people (65.9%), rotation brushing was 63 people (25.3%) and longways brushing was 22 people (8.8%). For brushing times, 2 times was 134 people (53.8%), over 3 times was 99 people(39.8%), and 1 time was 16 people (6.4%). For brushing time, after a meal was 182 people (73.1%) and before a meal was 67 people (26.9%). As for the mouth aids, 40 people (16.1%) used them and 209 people (83.9%) answered not to use them. 3. As for brushing method according to sex, it was found that both male and female use crossways brushing most, and male uses crossways brushing and longways brushing more than female and female uses rotation brushing more. For brushing times, it showed that 2 times was the most as 59.4% for male and over 3 times was the most as 50.5% for female. The survey of brushing time found that 33.3% of male brush their teeth before a meal and 81.1% of female brush their teeth after a meal. 4. For brushing method according to the habitation site, it was found that Chungcheongdo, Incheon Gyeonggi and Seoul use crossways brushing most and longways brushing least. The mouth aids were proved to be used in Incheon Gyeonggi most and in Chungcheongdo least. For brushing times, 2 times was the most in all three locations. 5. As to brushing times according to ages, only 30-39 age did brushing over 3 times, and the rest ages did brushing 2 times most. The survey of the use of the mouth aids according to the experience of scaling found that as they have the experience of scaling, they use them.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.807-817
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• 2019

Airborne Gamma Ray Spectrometry (AGRS) with its important applications such as gathering radiation information of ground surface, geochemistry measuring of the abundance of Potassium, Thorium and Uranium in outer earth layer, environmental and nuclear site surveillance has a key role in the field of nuclear science and human life. The Broyden-Fletcher-Goldfarb-Shanno (BFGS), with its advanced numerical unconstrained nonlinear optimization in collaboration with Artificial Neural Networks (ANNs) provides a noteworthy opportunity for modern AGRS. In this study a new AGRS system empowered by ANN-BFGS has been proposed and evaluated on available empirical AGRS data. To that effect different architectures of adaptive ANN-BFGS were implemented for a sort of published experimental AGRS outputs. The selected approach among of various training methods, with its low iteration cost and nondiagonal scaling allocation is a new powerful algorithm for AGRS data due to its inherent stochastic properties. Experiments were performed by different architectures and trainings, the selected scheme achieved the smallest number of epochs, the minimum Mean Square Error (MSE) and the maximum performance in compare with different types of optimization strategies and algorithms. The proposed method is capable to be implemented on a cost effective and minimum electronic equipment to present its real-time process, which will let it to be used on board a light Unmanned Aerial Vehicle (UAV). The advanced adaptation properties and models of neural network, the training of stochastic process and its implementation on DSP outstands an affordable, reliable and low cost AGRS design. The main outcome of the study shows this method increases the quality of curvature information of AGRS data while cost of the algorithm is reduced in each iteration so the proposed ANN-BFGS is a trustworthy appropriate model for Gamma-ray data reconstruction and analysis based on advanced novel artificial intelligence systems.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.10 no.2
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• pp.1-17
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• 2000

We investigated dissatisfaction of elements of local exhaust system, needs for local exhaust system, and information networks for local exhaust system from June 1998 to September 1999 using the questionnaire structured. It contained questions concerning general characteristics of factory and local exhaust system, troubles and dissatisfaction of elements of local exhaust system, and information networks for local exhaust system. The collected data were analyzed by descriptive statistics analysis. Information networks for local exhaust system were analyzed by multidimensional scaling using path distance of network analysis and by graph analysis using Krackplot. Among complaints of local exhaust system, that of duct has show the highest percentage of complaint. In the information network for local exhaust system, Seoul is positioned in the center of network with mediating role.