• Journal of Korean Neurosurgical Society
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• 제43권1호
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• pp.26-30
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• 2008

Objective : The authors developed a stereotactic device for irradiation of small animals with Leksell Gamma Knife Model C. Development and verification procedures were described in this article. Methods : The device was designed to satisfy three requirements. The mechanical accuracy in positioning was to be managed within 0.5 mm. The strength of the device and structure were to be compromised to provide enough strength to hold a small animal during irradiation and to interfere the gamma ray beam as little as possible. The device was to be used in combination with the Leksell G-$frame^{(R)}$ and $KOPF^{(R)}$ rat adaptor. The irradiation point was determined by separate imaging sequences such as plain X-ray images. Results : The absolute dose rate with the device in a Leksell Gamma Knife was 3.7% less than the value calculated from Leksell Gamma $Plan^{(R)}$. The dose distributions measured with $GAFCHROMIC^{(R)}$ MD-55 film corresponded to those of Leksell Gamma $Plan^{(R)}$ within acceptable range. The device was used in a series of rat experiments with a 4 mm helmet of Leksell Gamma Knife. Conclusion : A stereotactic device for irradiation of small animals with Leksell Gamma Knife Model C has been developed so that it fulfilled above requirements. Absorbed dose and dose distribution at the center of a Gamma Knife helmet are in acceptable ranges. The device provides enough accuracy for stereotactic irradiation with acceptable practicality.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2001년도 춘계학술대회 논문집
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• pp.135-139
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• 2001

This paper describes a string resonator that is used for the interrogation system of a Fiber Bragg Grating(FBG) strain sensor. The strain on the fiber piece is calculated from the measured frequency based on that the natural frequency of a string is a function of the applied absolute strain. Existing research considered a fiber as a string, but a fiber is not a string in the strict sense due to its bending stiffness, thus the fiber should be modeled as a beam accompanied with an axial force. In the vibration modeling, the relationship between the strain and the natural frequency is derived, and then the resonance condition is described in terms of both the phase and the mode shape for sustaining resonant motion. Several experiments verify the effectiveness of the proposed model of the fiber. The performance of the string resonator is analyzed by measuring the frequency change according to the applied strains in the dynamic range of 1100$\mu\varepsilon$ referred to the displacement from capacitance sensor. From the experimental results, the implemented string resonator provides the accuracy of $\pm$3$\mu\varepsilon$, the quasi-static resolution of ~0.1$\mu\varepsilon$(rms) which amount to be $\pm$0.17$\mu\textrm{m}$ and ~6nm respectively, in case of fiber length of 56mm. For a dynamic strain, it can provide the accuracy of ~3$\mu\varepsilon$ until the frequency comes to 8Hz. As a consequence, the string resonator proposed for FBG sensor provides the high accuracy and the high resolution in strain measurement, and also it is expecting to be used, for the application, to not only strain but also displacement measuring device.

• Structural Engineering and Mechanics
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• 제84권2호
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• pp.143-154
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• 2022

Corrosion of steel reinforcement is considered as the main cause of concrete structures deterioration, especially those under humid environmental conditions. Hence, fiber reinforced polymer (FRP) bars are being increasingly used as a replacement for conventional steel owing to their non-corrodible characteristics. However, predicting the shear strength of beams reinforced with FRP bars still challenging due to the lack of robust shear theory. Thus, this paper aims to develop an explicit data driven based model to predict the shear strength of FRP reinforced beams using multi-objective evolutionary polynomial regression analysis (MOGA-EPR) as data driven models learn the behavior from the input data without the need to employee a theory that aid the derivation, and thus they have an enhanced accuracy. This study also evaluates the accuracy of predictive models of shear strength of FRP reinforced concrete beams employed by different design codes by calculating and comparing the values of the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R²), and percentage of prediction within error range of ±20% (a20-index). Experimental database has been developed and employed in the model learning, validation, and accuracy examination. The statistical analysis illustrated the robustness of the developed model with MAE, RMSE, 𝜇, 𝜎, R², and a20-index of 14.6, 20.8, 1.05, 0.27, 0.85, and 0.61, respectively for training data and 10.4, 14.1, 0.98, 0.25, 0.94, and 0.60, respectively for validation data. Furthermore, the developed model achieved much better predictions than the standard predictive models as it scored lower MAE, RMSE, and 𝜎, and higher R² and a20-index. The new model can be used in future with confidence in optimized designs as its accuracy is higher than standard predictive models.

• 한국측량학회지
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• 제33권3호
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• pp.145-153
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• 2015

This study aims to accurately estimate population distribution more specifically than administrative unites using a RK (Regression-Kriging) model. The RK model is the areal interpolation technique that involves linear regression and the Kriging model. In order to estimate a population’s distribution using a sample region, four different models were used, namely; a regression model, RK model, OK (Ordinary Kriging) model and CK (Co-Kriging) model. The results were then compared with each other. Evaluation of the accuracy and validity of evaluation analysis results were the basis RMSE (Root Mean Square Error), MAE (Mean Absolute Error), G statistic and correlation coefficient (ρ). In the sample regions, every statistic value of the RK model showed better results than other models. The results of this comparative study will be useful to estimate a population distribution of the metropolitan areas with high population density

• 유통과학연구
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• 제12권2호
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• pp.59-71
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• 2014

Purpose - In recent years, many firms have attempted various approaches to cope with the continual increase of aviation transportation. The previous research into freight charge forecasting models has focused on regression analyses using a few influence factors to calculate the future price. However, these approaches have limitations that make them difficult to apply into practice: They cannot respond promptly to small price changes and their predictive power is relatively low. Therefore, the current study proposes a freight charge-forecasting model using time series data instead a regression approach. The main purposes of this study can thus be summarized as follows. First, a proper model for freight charge using the autoregressive integrated moving average (ARIMA) model, which is mainly used for time series forecast, is presented. Second, a modified ARIMA model for freight charge prediction and the standard process of determining freight charge based on the model is presented. Third, a straightforward freight charge prediction model for practitioners to apply and utilize is presented. Research design, data, and methodology - To develop a new freight charge model, this study proposes the ARIMAC(p,q) model, which applies time difference constantly to address the correlation coefficient (autocorrelation function and partial autocorrelation function) problem as it appears in the ARIMA(p,q) model and materialize an error-adjusted ARIMAC(p,q). Cargo Account Settlement Systems (CASS) data from the International Air Transport Association (IATA) are used to predict the air freight charge. In the modeling, freight charge data for 72 months (from January 2006 to December 2011) are used for the training set, and a prediction interval of 23 months (from January 2012 to November 2013) is used for the validation set. The freight charge from November 2012 to November 2013 is predicted for three routes - Los Angeles, Miami, and Vienna - and the accuracy of the prediction interval is analyzed using mean absolute percentage error (MAPE). Results - The result of the proposed model shows better accuracy of prediction because the MAPE of the error-adjusted ARIMAC model is 10% and the MAPE of ARIMAC is 11.2% for the L.A. route. For the Miami route, the proposed model also shows slightly better accuracy in that the MAPE of the error-adjusted ARIMAC model is 3.5%, while that of ARIMAC is 3.7%. However, for the Vienna route, the accuracy of ARIMAC is better because the MAPE of ARIMAC is 14.5% and the MAPE of the error-adjusted ARIMAC model is 15.7%. Conclusions - The accuracy of the error-adjusted ARIMAC model appears better when a route's freight charge variance is large, and the accuracy of ARIMA is better when the freight charge variance is small or has a trend of ascent or descent. From the results, it can be concluded that the ARIMAC model, which uses moving averages, has less predictive power for small price changes, while the error-adjusted ARIMAC model, which uses error correction, has the advantage of being able to respond to price changes quickly.

• International Journal of Fluid Machinery and Systems
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• 제3권2호
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• pp.113-121
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• 2010

LES(Large Eddy Simulation) with a cavitation model was performed to calculate an unsteady flow for a mixed flow pump with a closed type impeller. First, the comparison between the numerical and experimental results was done to evaluate a computational accuracy. Second, the torque acting on the blade was calculated by simulation to investigate how the cavitation caused the fluctuation of torque. The absolute pressure around the leading edge on the suction side of blade surface had positive impulsive peaks in both the numerical and experimental results. The simulation showed that those peaks were caused by the cavitaion which contracted and vanished around the leading edge. The absolute pressure was predicted by simulation with -10% error. The absolute pressure around the trailing edge on the suction side of blade surface had no impulsive peaks in both the numerical and experimental results, because the absolute pressure was 100 times higher than the saturated vapor pressure. The simulation results showed that the cavitation was generated around the throat, then contracted and finally vanished. The simulated pump had five throats and cavitation behaviors such as contraction and vanishing around five throats were different from each other. For instance, the cavitations around those five throats were not vanished at the same time. When the cavitation was contracted and finally vanished, the absolute pressure on the blade surface was increased. When the cavitation was contracted around the throat located on the pressure side of blade surface, the pressure became high on the pressure side of blade surface. It caused the 1.4 times higher impulsive peak in the torque than the averaged value. On the other hand, when the cavitation was contracted around the throat located on the suction side of blade surface, the pressure became high on the suction side of blade surface. It caused the 0.4 times lower impulsive peak in the torque than the averaged value. The cavitation around the throat caused the large fluctuation in torque acting on the blade.

• 한국터널지하공간학회 논문집
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• 제9권3호
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• pp.229-240
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• 2007

터널 및 지하공간 시공 중 일상계측에서의 계측관리 효율성 및 고급의 내공변위 분석을 위해 3차원 광파기, 선단앵커정착형 타겟부착장치, 절대변위 계측관리 프로그램으로 구성된 3차원 절대변위 계측시스템을 구축하였다. 본 연구를 통해 3차원 절대변위 계측에 필요한 광파기의 종류와 사양을 제시하였고, 기존 타겟부착장치의 문제점을 개선한 선단앵커정착형 타겟부착장치에 대한 성능시험을 수행하여 우수한 시준거리와 측정정밀도를 확인하였다. 또한 절대변위 계측관리 프로그램에 대한 현장성능시험을 수행하여 얻어진 경향선/영향선 등 다양한 분석방법을 활용하여 막장전방의 연약대 존재 예측이 가능하였고, TSP탐사결과와 비교하여 정확성을 확인하였다.

• 한국항만경제학회지
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• 제39권2호
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• pp.129-141
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• 2023

본 연구는 오차교정모형을 활용해 건화물선과 유조선 일간 해상운임의 동태적 특성과 예측 정확도를 분석한다. 공적분된 시계열 자료의 오차를 계산하기 위해 본 연구는 공통 확률적 추세 모형(Common Stochastic Trend Model, CSTM 모형)과 벡터오차교정모형(Vector Error Correction Model, VECM 모형)을 활용한다. 먼저, CSTM 모형의 오차를 사용한 오차교정모형이 VECM 모형의 경우보다 교정계수(adjustment speed coefficient)가 경제학적 이론에 더 부합하는 결과를 보인다. 나아가 조정결정계수(adjR²) 측면에서도 CSTM 모형의 경우가 VECM 모형에 비해 모형 적합도가 큰 것으로 나타난다. 둘째, 예측 정확도를 판단하는 지표인 평균 절대 오차와 평균 절대 척도 오차를 살펴보면, CSTM 모형의 오차를 이용한 모형이 VECM 모형의 오차를 이용한 모형보다 총 15가지 경우 중에 12가지 경우에서 예측 정확도가 높은 것을 확인할 수 있다. 미래 연구주제로서 1) 두 가지 오차를 모두 활용하는 분석 및 예측 과제, 2) 원자재 및 에너지 자원 시장의 데이터를 추가하는 과제, 3) 오차항의 부호에 따라 교정계수를 다르게 추정하는 과제 등을 제시한다.

• 대한원격탐사학회지
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• 제39권6_1호
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• pp.1273-1281
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• 2023

절대복사검보정은 위성 센서에서 얻은 전자기 신호의 물리량 변환을 위해 절대복사보정 계수를 결정하는 작업으로 위성 데이터의 정확도 개선 및 다른 위성 데이터와의 비교 및 통합을 위해 수행되어야 한다. 또한, 위성 센서는 시간에 따른 센서 노후화나 환경 조건의 영향을 받아 초기 설정된 보정 계수가 변화할 수 있으므로 주기적으로 이러한 변화를 모니터링 하는 것이 필수적이다. 이 연구에서는 차세대중형위성 1호(CAS500-1)의 다중 분광 채널에 대한 vicarious calibration을 수행하기 위해 필드 캠페인을 수행하였다. 구름이 없는 맑은 날의 조건 하에 총 두 차례의 유효한 현장 관측 자료를 얻었으며, MODTRAN 6 복사전달모델을 활용하여 대기 상단(top-of-atmosphere, TOA) radiance를 모의하였다. 모의된 TOA radiance와 CAS500-1의 digital number (DN)는 선형성은 보였지만, CAS500-1 영상의 넓은 시야각과 saturation 발생으로 향후 변환 계수의 보완이 필요한 것으로 보인다. 하지만, 본 연구는 CAS500-1의 절대복사보정에 대한 첫 시도를 하였으며, 향후 높은 신뢰성을 가진 계수 결정을 목표로 하는 연구들에 유용한 정보를 제공할 것으로 기대된다.

• Korean Journal of Radiology
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• 제22권12호
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• pp.2017-2025
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• 2021

Objective: To evaluate the accuracy and clinical efficacy of a hybrid Greulich-Pyle (GP) and modified Tanner-Whitehouse (TW) artificial intelligence (AI) model for bone age assessment. Materials and Methods: A deep learning-based model was trained on an open dataset of multiple ethnicities. A total of 102 hand radiographs (51 male and 51 female; mean age ± standard deviation = 10.95 ± 2.37 years) from a single institution were selected for external validation. Three human experts performed bone age assessments based on the GP atlas to develop a reference standard. Two study radiologists performed bone age assessments with and without AI model assistance in two separate sessions, for which the reading time was recorded. The performance of the AI software was assessed by comparing the mean absolute difference between the AI-calculated bone age and the reference standard. The reading time was compared between reading with and without AI using a paired t test. Furthermore, the reliability between the two study radiologists' bone age assessments was assessed using intraclass correlation coefficients (ICCs), and the results were compared between reading with and without AI. Results: The bone ages assessed by the experts and the AI model were not significantly different (11.39 ± 2.74 years and 11.35 ± 2.76 years, respectively, p = 0.31). The mean absolute difference was 0.39 years (95% confidence interval, 0.33-0.45 years) between the automated AI assessment and the reference standard. The mean reading time of the two study radiologists was reduced from 54.29 to 35.37 seconds with AI model assistance (p < 0.001). The ICC of the two study radiologists slightly increased with AI model assistance (from 0.945 to 0.990). Conclusion: The proposed AI model was accurate for assessing bone age. Furthermore, this model appeared to enhance the clinical efficacy by reducing the reading time and improving the inter-observer reliability.