• Journal of the korean academy of Pediatric Dentistry
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• v.49 no.1
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• pp.14-24
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• 2022

The aim of the present study is to evaluate the validity of orthodontic measurements including tooth width, Bolton ratio, overjet and overbite on the digital dental models. Dental models of the subjects aged 12 to 18 were obtained in 3 different forms, which were conventional stone model, digital model created with Freedom HD model scanner, and digital model produced with CS3600 intraoral scanner. After measurements were made on the models, reliability and reproducibility of the measurements were evaluated by using intraclass correlation coefficient, while validity was assessed with paired t-test. As a result, significant reliability and reproducibility were verified, with intraclass correlation coefficient exceeding 0.750 in all groups. Measurements of the model scanned group showed an adequate validity in overall and anterior Bolton ratio, overjet, and overbite. Intraoral scanned models showed an adequate validity in anterior Bolton ratio, and overjet. Measurement on intraoral scanned digital models can be considered as an alternative for young children who have difficulty in taking impression. Furthermore, careful considerations on measurement error should be made in clinical situations.

• Proceedings of KHEA Conference
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• 1997.07a
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• pp.105-105
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• 1997

• Biomolecules & Therapeutics
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• v.28 no.5
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• pp.414-422
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• 2020

Fangchinoline (FAN) is a bisbenzylisoquinoline alkaloid that is widely known for its anti-tumor properties. The goal of this study is to examine the effects of FAN on arthritis and the possible pathways it acts on. Human fibroblast-like synovial cells (FLS), carrageenan/kaolin arthritis rat model (C/K), and collagen-induced arthritis (CIA) mice model were used to establish the efficiency of FAN in arthritis. Human FLS cells were treated with FAN (1, 2.5, 5, 10 µM) 1 h before IL-1β (10 ng/mL) stimulation. Cell viability, reactive oxygen species measurement, and western blot analysis of inflammatory mediators and the MAPK and NF-κB pathways were performed. In the animal models, after induction of arthritis, the rodents were given 10 and 30 mg/kg of FAN orally 1 h before conducting behavioral experiments such as weight distribution ratio, knee thickness measurement, squeaking score, body weight measurement, paw volume measurement, and arthritis index measurement. Rodent knee joints were also analyzed histologically through H&E staining and safranin staining. FAN decreased the production of inflammatory cytokines and ROS in human FLS cells as well as the phosphorylation of the MAPK pathway and NF-κB pathway in human FLS cells. The behavioral parameters in the C/K rat model and CIA mouse model and inflammatory signs in the histological analysis were found to be ameliorated in FAN-treated groups. Cartilage degradation in CIA mice knee joints were shown to have been suppressed by FAN. These findings suggest that fangchinoline has the potential to be a therapeutic source for the treatment of rheumatoid arthritis.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.2 s.95
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• pp.201-210
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• 1999

One of the major limitations of productivity and quality in metal cutting is the machining accuracy of machine tools. The machining accuracy is affected by geometric and thermal errors of the machine tools. Therefore, a key requirement for improving te machining accuracy and product quality is to reduce the geometric and thermal errors of machine tools. This study models geometric error for error analysis and develops on-machine measurement system by which the volumetric erors are measured. The geometric error is modeled using form shaping function(FSF) which is defined as the mathematical relationship between form shaping motion of machine tool and machined surface. The constant terms included in the error model are found from the measurement results of on-machine measurement system. The developed on-machine measurement system consists of the spherical ball artifact (SBA), the touch probe unit with a star type stylus, the thermal data logger and the personal computer. Experiments, performed with the developed measurement system, show that the system provides a high measuring accuracy, with repeatability of ${\pm}2{\mu}m$ in X, Y and Z directions.

• Nuclear Engineering and Technology
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• v.48 no.3
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• pp.820-830
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• 2016

Pipe wall thinning by flow-accelerated corrosion and various types of erosion is a significant and costly damage phenomenon in secondary piping systems of nuclear power plants (NPPs). Most NPPs have management programs to ensure pipe integrity due to wall thinning that includes periodic measurements for pipe wall thicknesses using nondestructive evaluation techniques. Numerous measurements using ultrasonic tests (UTs; one of the nondestructive evaluation technologies) have been performed during scheduled outages in NPPs. Using the thickness measurement data, wall thinning rates of each component are determined conservatively according to several evaluation methods developed by the United States Electric Power Research Institute. However, little is known about the conservativeness or reliability of the evaluation methods because of a lack of understanding of the measurement error. In this study, quantitative models for UT thickness measurement deviations of nuclear pipes and fittings were developed as the first step for establishing an optimized thinning evaluation procedure considering measurement error. In order to understand the characteristics of UT thickness measurement errors of nuclear pipes and fittings, round robin test results, which were obtained by previous researchers under laboratory conditions, were analyzed. Then, based on a large dataset of actual plant data from four NPPs, a quantitative model for UT thickness measurement deviation is proposed for plant conditions.

• Journal of the Korean Society of Propulsion Engineers
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• v.14 no.4
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• pp.59-64
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• 2010

Measurement uncertainty assessment was performed for altitude performance test for a turboshaft engine. Mathematical models of measurement were suggested for major performance parameters such as shaft horse power, fuel flow, specific fuel consumption, and airflow. The procedure was compared with the test of turbojet or turbofan engines. Uncertainty involved with the test condition measurement was assessed. Influence of the test condition measurement uncertainty on the corrected performance data was discussed. Uncertainty assessment result was provided for a example test case using a real altitude test facility. For major performance parameters, measurement uncertainties were assessed as 0.65~1.09% including the test condition measurement uncertainty, 0.36~0.94% not including it.

• Computers and Concrete
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• v.21 no.5
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• pp.513-523
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• 2018

Time dependent strain due to creep is a significant factor in structural design. Multi-gene genetic programming (MGGP) and artificial neural network (ANN) are used to develop two models for prediction of creep compliance in concrete. The first model was developed by MGGP technique and the second model by hybridized MGGP-ANN. In the MGGP-ANN, the ANN is working in parallel with MGGP to predict errors in MGGP model. A total of 187 experimental data sets that contain 4242 data points are filtered from the NU-ITI database. These data are used in developing the MGGP and MGGP-ANN models. These models contain six input variables which are: average compressive strength at 28 days, relative humidity, volume to surface ratio, cement type, age at start of loading and age at the creep measurement. Practical equation based on MGGP was developed. A parametric study carried out with a group of hypothetical data generated among the range of data used to check the generalization ability of MGGP and MGGP-ANN models. To confirm validity of MGGP and MGGP-ANN models; two creep prediction code models (ACI209 and CEB), two empirical models (B3 and GL 2000) are used to compare their results with NU-ITI database.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.5
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• pp.155-166
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• 2020

This study investigates the importance of professional models in the promotion of the corporate brand attitude through differentiated marketing strategies in the saturated low-cost carrier (LCC) aviation market. The attributes of professional models affect brand attitude and brand loyalty. The study seeks to identify the factors affecting brand loyalty through the contribution of professional models. The empirical analysis is based on a questionnaire survey conducted online and off line over a seven-month period, from January to July 2019. Some 292 valid samples could be used. The study conducted a positive factor analysis using AMOS 18.0 and a reliability analysis using SPSS 18.0. Reliability of measurement tools was performed using Cronbach's alpha. The attributes of professional models relating to airline advertising include: reliability, attractiveness and expertise. These attributes are shown to have a significant impact on brand attitude and brand loyalty toward LCCs. The findings reveal that reliability and expertise have a significant influence on the brand attitude and the formation of brand loyalty. Professional models' attractiveness has no significant impact on brand attitudes and brand loyalty. The mediating effect of professional models' attributes on the relationship between brand attitude and brand loyalty also show a significant positive effect.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.178-180
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• 2016

TMDE(Test Measurement and Diagnostic Equipment) which is using in the military weapon system should perform the periodic calibration to maintain a measurement reliability during the life cycle, organizations are faced with increasing pressures to minimize costs while improving the reliability of test equipment. Previous studies suggest that reliability models are determined by considering simple size and characteristics of equipment, however an applying single Model may not be fit well maintenance data of many kinds of TMDEs. This paper presents that recommending an optimal calibration interval through the goodness of fit test with verifying statistical significance level among the several intervals which are computed with using major reliability models. According to the result of applying the actual proposed of calibration interval analysis method for various types of equipment, reliabilities are maintained for the end of calibration intervals.

• Journal of ILASS-Korea
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• v.28 no.1
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• pp.1-9
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• 2023

In the cylinder of gasoline direct injection engines, the spray targeting from injectors is of great significance for fuel consumption and pollutant emissions. The automotive industry is putting a lot of effort into improving injector targeting accuracy. To improve the targeting accuracy of injectors, it is necessary to develop models that can predict the spray targeting positions. When developing spray targeting models, the most used technique is computational fluid dynamics (CFD). Recently, due to the superiority of machine learning in prediction accuracy, the application of machine learning in this field is also receiving constant attention. The purpose of this study is to build a machine learning model that can accurately predict spray targeting based on the design parameters of injectors. To achieve this goal, this study firstly used laser sheet beam visualization equipment to obtain many spray cross-sectional images of injectors with different parameters at different injection pressures and measurement planes. The spray images were processed by MATLAB code to get the targeting coordinates of sprays. A total of four models were used for the prediction of spray targeting coordinates, namely ANN, LSTM, Conv1D and Conv1D & LSTM. Features fed into the machine learning model include injector design parameters, injection conditions, and measurement planes. Labels to be output from the model are spray targeting coordinates. In addition, the spray data of 7 injectors were used for model training, and the spray data of the remaining one injector were used for model performance verification. Finally, the prediction performance of the model was evaluated by R² and RMSE. It is found that the Conv1D&LSTM model has the highest accuracy in predicting the spray targeting coordinates, which can reach 98%. In addition, the prediction bias of the model becomes larger as the distance from the injector tip increases.