• The Journal of Advanced Prosthodontics
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• 제14권2호
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• pp.88-95
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• 2022

PURPOSE. This study investigated the accuracy of full-arch intraoral scans obtained by various scan strategies with the segmental scan and merge methods. MATERIALS AND METHODS. Seventy intraoral scans (seven scans per group) were performed using 10 scan strategies that differed in the segmental scan (1, 2, or 3 segments) and the scanning motion (straight, zigzag, or combined). The three-dimensional (3D) geometric accuracy of scan images was evaluated by comparison with a reference image in an image analysis software program, in terms of the arch shape discrepancies. Measurement parameters were the intermolar distance, interpremolar distance, anteroposterior distance, and global surface deviation. One-way analysis of variance and Tukey honestly significance difference post hoc tests were carried out to compare differences among the scan strategy groups (α = .05). RESULTS. The linear discrepancy values of intraoral scans were not different among scan strategies performed with the single scan and segmental scan methods. In general, differences in the scan motion did not show different accuracies, except for the intermolar distance measured under the scan conditions of a 3-segmental scan and zigzag motion. The global surface deviations were not different among all scan strategies. CONCLUSION. The segmental scan and merge methods using two scan parts appear to be reliable as an alternative to the single scan method for full-arch intraoral scans. When three segmental scans are involved, the accuracy of complete arch scan can be negatively affected.

• 대한치과기공학회지
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• 제44권2호
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• pp.31-37
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• 2022

Purpose: The aim of this study was to evaluate the accuracy of provisional crowns manufactured using a milling machine and a digital light processing (DLP) printer. Methods: A full-contour crown was designed using computer-aided design software. Provisional crowns of this design were manufactured using a milling machine and using a DLP three-dimensional (3D) printer (N=20). The provisional crowns were digitized with an extraoral scanner, and 3D deviation analysis was applied to the scanned data to confirm their accuracy. An independent t-test was performed to detect the significant differences, and the Kolmogorov-Smirnov test was used for analysis (α=0.05). Results: No significant differences were found among the precision of marginal surface between the printed and milled crowns (p=0.181). The trueness of marginal and internal surfaces of the milled crowns were statistically higher than those of the printed crowns (p=0.024, p=0.001; respectively). Conclusion: The accuracy of provisional crowns manufactured using a milling machine and a 3D printer differed significantly except with regards to the precision of the internal surface. However, all the crowns were clinically acceptable, regardless of the manufacturing method used.

• Korean Journal of Radiology
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• 제22권7호
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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.

• 한국방재안전학회논문집
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• 제16권4호
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• pp.123-128
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• 2023

본 논문은 점검진단 보고서의 데이터에 기반한 교량의 내하율 추정 모델의 판별 정확도 분석 결과를 보여준다. 내하율 추정 모델은 2,161건의 점검진단 보고서 수집을 통해, 통계적 방법에 의해 도출하였다. 교량의 제원 및 유지관리 정보를 입력하면 해당 교량의 추정된 내하율을 확인할 수 있다. 추정 내하율 모델의 판별 정확도를 검증하기 위하여, 데이터 확보가 되어 있는 공용중 교량 164개소에 대해 추정 내하율과 점검진단 보고서 상의 내하율을 비교하였다. 교량 형식별로 차이가 있지만 추정 내하율 판별 정확도 80% 이상의 결과를 도출하였다.

• Clinical Nutrition Research
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• 제12권2호
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• pp.138-153
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• 2023

The prevalence of metabolic syndrome (MetS) and its cost are increasing due to lifestyle changes and aging. This study aimed to develop a deep neural network model for prediction and classification of MetS according to nutrient intake and other MetS-related factors. This study included 17,848 individuals aged 40-69 years from the Korea National Health and Nutrition Examination Survey (2013-2018). We set MetS (3-5 risk factors present) as the dependent variable and 52 MetS-related factors and nutrient intake variables as independent variables in a regression analysis. The analysis compared and analyzed model accuracy, precision and recall by conventional logistic regression, machine learning-based logistic regression and deep learning. The accuracy of train data was 81.2089, and the accuracy of test data was 81.1485 in a MetS classification and prediction model developed in this study. These accuracies were higher than those obtained by conventional logistic regression or machine learning-based logistic regression. Precision, recall, and F1-score also showed the high accuracy in the deep learning model. Blood alanine aminotransferase (β = 12.2035) level showed the highest regression coefficient followed by blood aspartate aminotransferase (β = 11.771) level, waist circumference (β = 10.8555), body mass index (β = 10.3842), and blood glycated hemoglobin (β = 10.1802) level. Fats (cholesterol [β = -2.0545] and saturated fatty acid [β = -2.0483]) showed high regression coefficients among nutrient intakes. The deep learning model for classification and prediction on MetS showed a higher accuracy than conventional logistic regression or machine learning-based logistic regression.

• 대한한의학회지
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• 제44권4호
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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.

• 분석과학
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• 제29권6호
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• pp.261-268
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• 2016

TXRF는 시료전처리 없이 분말상 시료의 정량분석이 가능하여 토양시료를 효율적으로 분석할 수 있다. 기존의 내부표준법을 이용한 분석법은 매질 효과 및 형광신호의 겹침으로 인한 간섭 효과로 인해 정확도가 떨어진다. 이를 개선하기 위해 외부표준법을 적용하여 용액화한 토양시료와 분말상 토양시료를 분석하였다. 용액화한 토양시료의 경우 개별표준물질로 만든 표준용액으로 작성한 검량선으로 분석하였는데, 내부 및 외부표준법 간 유의미한 차이가 없었다. 반면 분말상 토양시료로 부유용액을 만들어 검량선을 작성한 후 이를 이용하여 분말상 토양시료를 분석한 결과, 전반적으로 외부표준법을 적용한 결과가 내부표준법의 그것에 비해 정확도가 높았다. 두 가지 표준토양시료로 교차검증한 결과, Al, Fe, K, Ca, Ti, Ba, Mn, Sr, Rb, Cu 등 10개의 원소들에 대해 ${\pm}20%$ 내외의 상대오차가 측정되었다.

• 한국멀티미디어학회논문지
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• 제18권4호
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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.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2007년도 추계학술대회 논문집
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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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• 제54권3호
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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.