• Journal of Korean Society of Disaster and Security
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• v.16 no.4
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• pp.123-128
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• 2023

This paper presents the results of an analysis of the discrimination accuracy of a bridge load carrying capacity estimation model based on data from inspection reports. The load carrying rate estimation model was derived using statistical methods through the collection of 2,161 inspection reports. By entering the bridge specifications and maintenance information, you can check the estimated load carrying capacity of the bridge. In order to verify the discrimination accuracy of the estimated load carrying rate model, the estimated load carrying rate was compared with the load carrying rate in the inspection and diagnosis report for 164 public bridges for which data was available. Although there are differences depending on the bridge type, the results were obtained with an accuracy of over 80% in determining the estimated load carrying capacity.

• The Journal of Korean Medicine
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• v.44 no.4
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• pp.136-149
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• 2023

Objectives: Facial diagnosis is an important part of clinical diagnosis in traditional East Asian Medicine. In this paper, we proposed a model to quantitatively classify cold sensitivity using a fully automated facial image analysis system. Methods: We investigated cold sensitivity in 452 subjects. Cold sensitivity was determined using a questionnaire and the Cold Pattern Score (CPS) was used for analysis. Subjects with a CPS score below the first quartile (low CPS group) belonged to the cold non-sensitivity group, and subjects with a CPS score above the third quartile (high CPS group) belonged to the cold sensitivity group. After splitting the facial images into train/validation/test sets, the train and validation set were input into a convolutional neural network to learn the model, and then the classification accuracy was calculated for the test set. Results: The classification accuracy of the low CPS group and high CPS group using facial images in all subjects was 76.17%. The classification accuracy by sex was 69.91% for female and 62.86% for male. It is presumed that the deep learning model used facial color or facial shape to classify the low CPS group and the high CPS group, but it is difficult to specifically determine which feature was more important. Conclusions: The experimental results of this study showed that the low CPS group and the high CPS group can be classified with a modest level of accuracy using only facial images. There was a need to develop more advanced models to increase classification accuracy.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1203-1212
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• 2021

Objective: To investigate the diagnostic accuracy and complications of cone-beam CT-guided percutaneous transthoracic needle biopsy (PTNB) of juxtaphrenic lesions and identify the risk factors for diagnostic failure and complications. Materials and Methods: In total, 336 PTNB procedures for lung lesions (mean size ± standard deviation [SD], 4.3 ± 2.3 cm) abutting the diaphragm in 326 patients (189 male and 137 female; mean age ± SD, 65.2 ± 11.4 years) performed between January 2010 and December 2014 were included. The accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the PTNB procedures for the diagnosis of malignancy were measured based on the intention-to-diagnose principle. The risk factors for diagnostic failures and complications were evaluated using logistic regression analysis. Results: The accuracy, sensitivity, specificity, PPV, and NPV were 92.7% (293/316), 91.3% (219/240), 91.4% (74/81), 96.9% (219/226), and 77.9% (74/95), respectively. There were 23 diagnostic failures (7.3%), and lesion sizes ≤ 2 cm (p = 0.045) were the only significant risk factors for diagnostic failure. Complications occurred in 98 cases (29.2%), including 89 cases of pneumothorax (26.5%) and 7 cases of hemoptysis (2.1%). The multivariable analysis showed that old age (> 65 years) (p = 0.002), lesion size of ≤ 2 cm (p = 0.003), emphysema (p = 0.006), and distance from the pleura to the target lesion (> 2 cm) (p = 0.010) were significant risk factors for complications. Conclusion: The diagnostic accuracy of cone-beam CT-guided PTNB of juxtaphrenic lesions for malignancy was fairly high, and the target lesion size was the only significant predictor of diagnostic failure. Complications of cone-beam CT-guided PTNB of juxtaphrenic lesions occurred at a reasonable rate.

• Analytical Science and Technology
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• v.29 no.6
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• pp.261-268
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• 2016

TXRF is a powerful technique for the soil sample analysis due to its ability to conduct quantitative analysis of powder sample without complicated pre-treatment processes. The conventional internal standard method used for this technique suffers from relatively low accuracy because of varying matrix effects of soil. In order to improve the accuracy, external standard method was applied to analyze two types of soil samples; acid-dissolutionized soil solution and detergent-suspended soil powder. Individual ICP-AES/MS grade standards were mixed, diluted and measured to create standard curves, but applying these curves for analyzing the soil solution sample did not make any improvement in comparison with the internal standard method. On the other hand, standard curves were created with using standard soil powders for the analysis of soil powder samples, and we found that this method increased the accuracy significantly relative to the internal standard method. Especially, Al, Fe, K, Ca, Ti, Ba, Mn, Sr, Rb, Cu was measured with relatively high accuracy (relative error = ${\pm}20%$).

• Journal of Korea Multimedia Society
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• v.18 no.4
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• pp.524-532
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• 2015

All data created in BigData times is included potentially meaning and correlation in data. A variety of data during a day in all society sectors has become created and stored. Research areas in analysis and grasp meaning between data is proceeding briskly. Especially, accuracy of meaning prediction and data imbalance problem between data for analysis is part in course of something important in data analysis field. In this paper, we proposed data prediction model based on data weights and neural network using R for meaning analysis between data. Proposed data prediction model is composed of classification model and analysis model. Classification model is working as weights application of normal distribution and optimum independent variable selection of multiple regression analysis. Analysis model role is increased prediction accuracy of output variable through neural network. Performance evaluation result, we were confirmed superiority of prediction model so that performance of result prediction through primitive data was measured 87.475% by proposed data prediction model.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2007.10a
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• pp.271-274
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• 2007

Housing and insulation of electrical connectors are made of plastic resin by injection molding process. The metallic inner tube is easily deformed by high pressure during the injection process. In order to prevent deformation of the inner tube, it is desirable to simulate it by structural CAE analysis. However, it takes a long time to calculate the stress- of the part by commercially available injection molding CAE software with sufficient accuracy. In this study, structural analysis in conjunction with injection molding analysis is proposed to improve accuracy of the structural analysis. Pressure distribution on the inner tube is predicted by the injection molding CAE analysis, and then mapped onto the mesh of structural analysis by a mapping algorithm developed in this study. As a result reliable result is obtained in shorter time than the conventional method. The predicted deformation of the inner tube is compared with the actual part after experiment.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.1109-1114
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• 2022

The SMART (System-integrated Modular Advanced ReacTor) is an integral-type small modular reactor developed by KAERI (Korea Atomic Energy Research Institute). This paper discusses the feasibility and applicability of a 3D-based equivalent model using dynamic condensation method for seismic analysis of a SMART control rod drive mechanism. The equivalent model is utilized for complicated seismic analysis during the design of the SMART. While the 1D-based beam-mass equivalent model is widely used in the nuclear industry for its calculation efficiency, the 3D-based equivalent model is suggested for the seismic analysis of SMART to enhance the analysis accuracy of the 1D-based equivalent model while maintaining its analysis efficiency. To verify the suggested model, acceleration response spectra from seismic analysis based on the 3D-based equivalent model are compared to those from the 1D-based beam-mass equivalent model and experiments. The accuracy and efficiency of the dynamic condensation method are investigated by comparison to analysis results based on the conventional modeling methodology used for seismic analysis.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.1
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• pp.80-89
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• 2008

Mapping applications using satellite imagery have been possible to quantitative analysis since SPOT satellite with stereo image was launched. Especially, high resolution satellite imagery was efficiently used in the field of digital mapping for the areas which are difficult to produce large-scale maps by aerial photogrammetry or carry out ground control point surveying due to unaccessibility. This study extracted the geospatial information out of consideration for topographic characteristic from ortho imagery of the National Geospatial-intelligence Agency(NGA) in the United States of America and analyzed the accuracy of plane coordinate for ortho imagery. For this purpose, the accuracy according to topographic character by comparison between both extraction data from ortho imagery and the digital topographic maps of 1:5000 scale which were produced by Korea National Geographic Information Institute(NGI) was evaluated. It is expected that the results of this study will be fully used as basic information for ground control point acquisition or digital mapping in unaccessible area.

• Phonetics and Speech Sciences
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• v.10 no.1
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• pp.75-81
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• 2018

Diplophonia is generally defined as the perception of more than one fundamental frequency component in a voice. Its perceptual aspect has traditionally been used to evaluate diplophonia because the perceptions can be easily evaluated, but there are limitations in the validity of the reliability of the intra- and inter-raters, examination situation, and variation of voice sample. Therefore, the purpose of this study is to confirm the reliability and accuracy of auditory perceptual evaluation by comparing non-invasive indirect assessment methods (sound waveform and EGG analysis), and to identify their usefulness with diplophonia. A total of 28 diplophonic voices and 39 non-periodic voices were assessed. Three raters assessed the diplophonia by performing an auditory perception evaluation and identifying the quasi-periodic perturbations of the acoustic waveform and EGG. Among the three discrimination methods, intra- and inter-rater reliability, sensitivity, specificity, accuracy, positive likelihood ratio, and negative likelihood ratio were examined, and the McNemar test was performed to compare the discriminant agreement. The accuracy of the auditory perceptual evaluation (86.57%) was not significantly different from that of sound waveform acoustic (88.06%), but it was significantly different from that of EGG (83.33%). The reading time (6.02 s) for the auditory perceptual evaluation was significantly different from that for sound waveform analysis (30.15 s) and EGG analysis (16.41 s). In the discrimination of diplophonia, auditory perceptual evaluation has sufficient reliability and accuracy as compared to sound waveform and EGG. Since immediate feedback is possible, auditory perceptual evaluation is more convenient. Therefore, it can continue to be used as a tool to discriminate diplophonia in clinical practice.

• Structural Engineering and Mechanics
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• v.45 no.5
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• pp.595-611
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• 2013

This paper aims to compare three collocation point methods associated with the odd order stochastic response surface method (SRSM) in a systematical and quantitative way. The SRSM with the Hermite polynomial chaos is briefly introduced first. Then, three collocation point methods, namely the point method, the root method and the without origin method underlying the odd order SRSMs are highlighted. Three examples are presented to demonstrate the accuracy and efficiency of the three methods. The results indicate that the condition that the Hermite polynomial information matrix evaluated at the collocation points has a full rank should be satisfied to yield reliability results with a sufficient accuracy. The point method and the without origin method are much more efficient than the root method, especially for the reliability problems involving a large number of random variables or requiring complex finite element analysis. The without origin method can also produce sufficiently accurate reliability results in comparison with the point and root methods. Therefore, the origin often used as a collocation point is not absolutely necessary. The odd order SRSMs with the point method and the without origin method are recommended for the reliability analysis due to their computational accuracy and efficiency. The order of SRSM has a significant influence on the results associated with the three collocation point methods. For normal random variables, the SRSM with an order equaling or exceeding the order of a performance function can produce reliability results with a sufficient accuracy. The order of SRSM should significantly exceed the order of the performance function involving strongly non-normal random variables.