• 대한소아치과학회지
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• 제49권1호
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• pp.14-24
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• 2022

이 연구의 목적은 디지털 치열 모형에서 치아 폭경, 볼튼 비율, 수평 피개, 수직 피개를 포함한 교정적 측정의 타당성을 확인하는 것이다. 만 12 - 18세의 환자를 대상으로 세가지 형태의 치열 모형을 획득하였다. 기존의 석고 모형을 형성하였고, DOF freedom HD 모형 스캐너를 통해 디지털 모형으로 변환하였다. 그리고 CS3600 구강 스캐너로 디지털 모형을 형성하였다. 각 모형에서 측정 시행 후, 급내 상관 계수를 통해 계측의 신뢰성과 재현성을 확인하였으며, 대응 표본 t 검정을 사용하여 타당성을 평가하였다. 결과적으로 모든 군에서 급내 상관계수는 0.750을 초과하여 연구자 내 신뢰성과 연구자 간 재현성이 있음을 확인하였다. 모형 스캔한 군은 전체 및 전치 볼튼 비율, 수평 및 수직 피개에서 타당성을 보였다. 구강 스캔한 군은 전치 볼튼 비율, 수평 피개에서 타당성을 보였다. 구강 스캔한 디지털 모형을 이용한 계측은 인상 채득에 어려움이 있는 소아 청소년에게 고려할 수 있는 대안이다. 하지만 임상에서 이를 교정적 분석에 이용할 경우 오차를 고려한 적용이 필요하다.

• 대한가정학회:학술대회논문집
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• 대한가정학회 1997년도 Program & Abstracts * The 7th Korea-Japan Home Economics Joint Syposium
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• pp.105-105
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• 1997

• Biomolecules & Therapeutics
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• 제28권5호
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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.

• 한국정밀공학회지
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• 제16권2호통권95호
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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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• 제48권3호
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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.

• 한국추진공학회지
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• 제14권4호
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• pp.59-64
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• 2010

터보샤프트 엔진의 고공성능시험에서 주요 성능 인자인 축마력, 연료 유량, 비연료 소모율 및 공기유량에 대하여 측정의 수학적 모델을 제시하고 측정 불확도를 평가하였다. 터보제트 및 터보팬 엔진의 경우와 비교하여 차이점을 논의하였다. 시험 조건의 측정 불확도를 평가하였으며, 이를 보정된 성능 데이터 측정 불확도에 반영하는 방법을 제시하였다. 실제 터보샤프트 엔진 고공성능시험설비를 이용한 시험 사례에 대한 측정 불확도 평가 결과를 제시하였다. 주요 성능 인자의 측정 불확도는 시험 조건측정의 불확도를 반영하였을 경우 0.65~1.09%, 반영하지 않았을 경우 0.36~0.94%로 평가되었다.

• Computers and Concrete
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• 제21권5호
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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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• 제7권5호
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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.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2016년도 춘계학술대회
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• pp.178-180
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• 2016

군 무기체계에 사용되는 정밀측정장비는 수명주기 동안 측정신뢰성을 유지하기 위해 주기적으로 교정을 수행하고 있으며, 기관들은 장비 신뢰성을 향상시키면서 비용을 최소화하기 위한 증가하는 압력에 직면하고 있다. 이전 연구들은 샘플의 크기와 장비 특성을 고려하여 측정신뢰성 모델을 결정하도록 추천하고 있으나, 다양한 장비의 정비 데이터에 대해 단일 모델을 적용하는 것은 적합하지 않을 수 있다. 본 논문에서는 정비 데이터 기반 주요 측정신뢰성 모델들에서 계산된 교정주기를 통계적인 유의수준 검증을 통한 적합성 검정으로 추천하도록 제안하고자 한다. 실제 제안된 방법으로 다양한 종류의 장비에 대해 교정주기 분석방법을 적용한 결과, 교정주기 만료까지 신뢰도가 유지됨을 확인하였다.

• 한국분무공학회지
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• 제28권1호
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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.