• 대한방사선기술학회지:방사선기술과학
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• 제31권1호
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• pp.83-87
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• 2008

본 연구의 목적은 고체물등가팬텀을 사용하여 절대흡수선량을 측정할 때 물등가깊이에 비례되는 측정값을 보정하기 위한 보정인자를 구하는데 있다. 10MV X-선 빔에 대하여 백색폴리스티렌팬텀과 물팬텀에서 측정의 조건들은 선원 대 전리조 중심까지의 거리를 SAD 100 cm로 고정하였고, 조사면 크기(field size)는 각각 $10{\times}10\;cm^2$, $20{\times}20\;cm^2$를 사용하였으며, 깊이는 각각 2.3 cm, 5 cm, 10 cm, 15 cm를 사용한 것이다. 두 개의 팬텀에 대하여 분당 400 MU의 출력을 갖는 선형가속기로부터 100 MU의 전달로 각각의 조사면 크기와 깊이들에서 3번 측정으로 취득된 전리의 평균값을 측정값으로 얻었다. 이 실험으로부터 보정인자와 TPR에서 퍼센트 편차는 각각 0.97%, 0.53% 이하를 얻었다. 따라서, 고체물등가팬텀을 사용한 절대흡수선량 측정 시에는 보정인자와 TPR에서 퍼센트 편차를 사용하여 보정을 행하면 높은 정확도를 얻을 수 있다.

• 지능정보연구
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• 제15권2호
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• pp.15-31
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• 2009

차세대 전산환경은 사용자들이 필요한 전산자원을 네트워크를 통해 공급받는 그리드 컴퓨팅 기반의 개방형 전산환경으로 진화할 것으로 예상된다. 개방형 전산환경의 도입은 전산자원 활용의 효율성을 높이고 협업을 증진시키며 공급의 유연성과 비용 절감 등의 효과를 가져올 수 있다. 그러나 네트워크를 통해 공급되는 특성으로 인해 개방형 전산 자원의 성능에는 변동성이 수반된다. 전산자원의 성능에 변동성이 있는 경우, 주어진 예산과 시간만 고려하여 전산자원 서비스를 구성하는 단순 최적화 방법을 사용했을 때는 서비스의 최종 성능과 실행 시간 등을 규정한 서비스 수준계약(Service Level Agreement, SLA)을 만족시키지 못할 위험이 높다. 따라서 개방형 전산환경의 서비스 브로커는 전산자원 공급의 안정성을 높이기 위해서 서비스를 구성하는 개별 전산자원의 성능 변동성을 고려하여 위험을 최소화 하는전산자원포트폴리오를구성할것이요구된다. 본연구에서는평균절대편차(Mean-Absolute Deviation, MAD) 포트폴리오 최적화 기법을 이용하여 서비스 브로커의 공급 안정성을 향상시키는 방법을 제시하였다. 제시된 최적화 기법의 효과를 알아보기 위한 방법으로 가상의 개방형 전산환경을 모델링하고, 고객의 제약 조건과 개방형 전산환경의 변동성 정도에 따라 전산자원 공급 서비스의 성공률을 시뮬레이션 하였다. 시뮬레이션 결과로서, 첫째, 단순 최적화방법보다 변동성을 감안한 MAD 포트폴리오 최적화 기법을 이용했을 때 공급의 안정성이 뛰어난 것을 확인할 수 있었다. 둘째로는 특히 개별 전산자원의 변동성 예측의 정확성이 높아질수록 성능도 더욱 향상되는 결과를 가져왔다. 셋째, 측정된 변동성을 이용하여 개방형 전산자원의 가격을 할인하는 정책을 추진할 경우 서비스 공급 범위의 확대에도 효과가 클 것으로 예상되었다.

• 한국산림과학회지
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• 제108권3호
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• pp.357-363
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• 2019

본 연구는 Kozak 수간곡선모델을 이용하여 우리나라 상수리나무의 입목수간재적표를 개발하고자 수행하였다. 전국의 분포하고 있는 상수리나무를 총 2,700본의 표준목을 벌채하여 수간고별 직경과 수간석해 자료를 수집하여 생장특성을 조사하였다. 수간곡선식의 적합도 검정을 위하여 적합도지수(Fitness index), 편차(Bias), 평균절대편차(Mean Absolute Deviation)를 이용하여 모델의 적합성을 판단하였다. 추정된 모델의 적합도지수는 97%로 나타났고 편차는 0.017, 평균절대편차는 1.118로 높은 적합도를 보였다. 또한 현행재적표와 신규재적표의 재적간의 차이를 분석한 결과, 통계적으로 유의적인 재적차이를 보였다(p = 0.0008, <0.005). 이는 현실림을 반영한 신규재적표를 이용하는 것이 목재자원량의 평가시 손실을 줄이고 국가 및 지자체의 산림통계의 정확도를 향상시킬 것으로 판단된다. 본 연구의 주요한 결과인 추정된 수간곡선식을 이용한 입목수간재적표는 우리나라의 주요 활엽수종인 참나무류 중 상수리나무의 생장 정보 및 합리적 경영을 위한 경영제표가 될 것으로 판단된다.

• Acute and Critical Care
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• 제33권4호
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• pp.246-251
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• 2018

Background: Target temperature management (TTM) improves neurological outcomes for comatose survivors of out-of-hospital cardiac arrest. We compared the efficacy and safety of a gel pad cooling device (GP) and a water blanket (WB) during TTM. Methods: We performed a retrospective analysis in a single hospital, wherein we measured the time to target temperature ($<34^{\circ}C$) after initiation of cooling to evaluate the effectiveness of the cooling method. The temperature farthest from $33^{\circ}C$ was selected every hour during maintenance. Generalized estimation equation analysis was used to compare the absolute temperature differences from $33^{\circ}C$ during the maintenance period. If the selected temperature was not between $32^{\circ}C$ and $34^{\circ}C$, the hour was considered a deviation from the target. We compared the deviation rates during hypothermia maintenance to evaluate the safety of the different methods. Results: A GP was used for 23 patients among of 53 patients, and a WB was used for the remaining. There was no difference in baseline temperature at the start of cooling between the two patient groups (GP, $35.7^{\circ}C$ vs. WB, $35.6^{\circ}C$; P=0.741). The time to target temperature (134.2 minutes vs. 233.4 minutes, P=0.056) was shorter in the GP patient group. Deviation from maintenance temperature (2.0% vs. 23.7%, P<0.001) occurred significantly more frequently in the WB group. The mean absolute temperature difference from $33^{\circ}C$ during the maintenance period was $0.19^{\circ}C$ (95% confidence interval [CI], $0.17^{\circ}C$ to $0.21^{\circ}C$) in the GP group and $0.76^{\circ}C$ (95% CI, $0.71^{\circ}C$ to $0.80^{\circ}C$) in the WB group. GP significantly decreased this difference by $0.59^{\circ}C$ (95% CI, $0.44^{\circ}C$ to $0.75^{\circ}C$; P<0.001). Conclusions: The GP was superior to the WB for strict temperature control during TTM.

• Geomechanics and Engineering
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• 제28권4호
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• pp.397-404
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• 2022

Accurate prediction of the undrained shaft resistance is essential for robust design of bored piles in undrained condition. The undrained shaft resistance is calculated using the undrained adhesion factor multiplied by the undrained cohesion of the soil. However, the available correlations to predict the undrained adhesion factor have been developed using simple regression techniques and the accuracy of these correlations has not been thoroughly assessed in previous studies. The lack of the assessment of these correlations made it difficult for geotechnical engineers to select the most accurate correlation in routine designs. Furthermore, limited attempts have been made in previous studies to use advanced data mining techniques to develop simple and accurate correlation to predict the undrained adhesion factor. This research, therefore, has been conducted to fill these gaps in knowledge by developing novel and robust correlation to predict the undrained adhesion factor. The development of the new correlation has been conducted using the multi-objective evolutionary polynomial regression analysis. The new correlation outperformed the available empirical correlations, where the new correlation scored lower mean absolute error, mean square error, root mean square error and standard deviation of measured to predicted adhesion factor, and higher mean, a20-index and coefficient of correlation. The correlation also successfully showed the influence of the undrained cohesion and the effective stress on the adhesion factor. Hence, the new correlation enhances the design accuracy and can be used by practitioner geotechnical engineers to ensure optimized designs of bored piles in undrained conditions.

• Structural Engineering and Mechanics
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• 제81권5호
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• pp.565-574
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• 2022

The infrastructures such as offshore, bridges, power plant, oil and gas piping and aircraft operate in a harsh environment during their service life. Structural integrity of engineering components used in these industries is paramount for the reliability and economics of operation. Two regression models based on the concept of Gaussian process regression (GPR) and Minimax probability machine regression (MPMR) were developed to predict stress intensity factor range (𝚫K). Both GPR and MPMR are in the frame work of probability distribution. Models were developed by using the fatigue crack growth data in MATLAB by appropriately modifying the tools. Fatigue crack growth experiments were carried out on Eccentrically-loaded Single Edge notch Tension (ESE(T)) specimens made of API 5L X65 Grade steel in inert and corrosive environments (2.0% and 3.5% NaCl). The experiments were carried out under constant amplitude cyclic loading with a stress ratio of 0.1 and 5.0 Hz frequency (inert environment), 0.5 Hz frequency (corrosive environment). Crack growth rate (da/dN) and stress intensity factor range (𝚫K) values were evaluated at incremental values of loading cycle and crack length. About 70 to 75% of the data has been used for training and the remaining for validation of the models. It is observed that the predicted SIF range is in good agreement with the corresponding experimental observations. Further, the performance of the models was assessed with several statistical parameters, namely, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Efficiency (E), Root Mean Square Error to Observation's Standard Deviation Ratio (RSR), Normalized Mean Bias Error (NMBE), Performance Index (ρ) and Variance Account Factor (VAF).

• 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.

• Safety and Health at Work
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• 제4권1호
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• pp.27-36
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• 2013

Objectives: Mental health has been shown to be linked with certain underlying physiological mechanisms. The objective of this cross sectional study was to investigate the relationship between depressive symptoms and brachial artery reactivity (BAR) in an understudied population: police officers. Methods: Participants were 351 police officers who were clinically examined in the Buffalo Cardio-Metabolic Police Stress (BCOPS) study. BAR was performed using standard B-Mode ultrasound procedures. Depressive symptoms were measured using the Center for Epidemiological Studies Depression (CES-D) scale. Mean values of the difference between the baseline and maximum diameters of the brachial artery were determined across three categories of CES-D score using the analysis of variance and the analysis of covariance. p-values for linear trends were obtained from linear regression models. Results: The mean age (${\pm}$ standard deviation) of all officers was $40.9{\pm}7.2$ years. Women had a slightly higher mean CES-D score than men ($8.9{\pm}8.9$ vs. $7.4{\pm}6.4$) and a slightly higher percentage increase of BAR than men (6.90 vs. 5.26%). Smoking status significantly modified the associations between depressive symptoms and BAR. Among current smokers, mean absolute values of BAR significantly decreased as depressive symptoms increased after adjustment for age, gender, race/ethnicity, hypertension, and diabetes; the multivariate-adjusted p-values were 0.033 (absolute) and 0.040 (%). Associations between depressive symptoms and BAR were not statistically significant among former smokers or never smokers. Conclusion: Depressive symptoms were inversely associated with BAR among police officers who were current smokers and together may be considered a risk factor for cardiovascular disease among police officers. Further prospective research is warranted.

• 한국수자원학회논문집
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• 제55권11호
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• pp.903-911
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• 2022

물을 공급하기 위한 자원 중 하나인 지하수는 다양한 자연적 요인에 의해 수위의 변동이 발생한다. 최근, 인공신경망을 이용하여 지하수위의 변동을 예측하는 연구가 진행되었다. 기존에는 인공신경망 연산자 중 학습에 영향을 미치는 Optimizer로 경사하강법(Gradient Descent, GD) 기반 Optimizer를 사용하였다. GD 기반 Optimizer는 초기 상관관계 의존성과 해의 비교 및 저장 구조 부재의 단점이 존재한다. 본 연구는 GD 기반 Optimizer의 단점을 개선하기 위해 GD와 화음탐색법(Harmony Search, HS)를 결합한 새로운 Optimizer인 Gradient Descent combined with Harmony Search(GDHS)를 개발하였다. GDHS의 성능을 평가하기 위해 다층퍼셉트론(Multi Layer Perceptron, MLP)을 이용하여 이천율현 관측소의 지하수위를 학습 및 예측하였다. GD 및 GDHS를 사용한 MLP의 성능을 비교하기 위해 Mean Squared Error(MSE) 및 Mean Absolute Error(MAE)를 사용하였다. 학습결과를 비교하면, GDHS는 GD보다 MSE의 최대값, 최소값, 평균값 및 표준편차가 작았다. 예측결과를 비교하면, GDHS는 GD보다 모든 평가지표에서 오차가 작은 것으로 평가되었다.

• Animal Bioscience
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• 제35권5호
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• pp.648-658
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• 2022

Objective: The identification of nonlinear mixed models that describe the growth trajectory of New Zealand rabbits was performed based on weight records and carcass measures obtained using ultrasonography. Methods: Phenotypic records of body weight (BW) and loin eye area (LEA) were collected from 66 animals raised in a didactic-productive module of cuniculture located in the southern Piaui state, Brazil. The following nonlinear models were tested considering fixed parameters: Brody, Gompertz, Logistic, Richards, Meloun 1, modified Michaelis-Menten, Santana, and von Bertalanffy. The coefficient of determination (R²), mean squared error, percentage of convergence of each model (%C), mean absolute deviation of residuals, Akaike information criterion (AIC), and Bayesian information criterion (BIC) were used to determine the best model. The model that best described the growth trajectory for each trait was also used under the context of mixed models, considering two parameters that admit biological interpretation (A and k) with random effects. Results: The von Bertalanffy model was the best fitting model for BW according to the highest value of R² (0.98) and lowest values of AIC (6,675.30) and BIC (6,691.90). For LEA, the Logistic model was the most appropriate due to the results of R² (0.52), AIC (783.90), and BIC (798.40) obtained using this model. The absolute growth rates estimated using the von Bertalanffy and Logistic models for BW and LEA were 21.51g/d and 3.16 cm², respectively. The relative growth rates at the inflection point were 0.028 for BW (von Bertalanffy) and 0.014 for LEA (Logistic). Conclusion: The von Bertalanffy and Logistic models with random effect at the asymptotic weight are recommended for analysis of ponderal and carcass growth trajectories in New Zealand rabbits. The inclusion of random effects in the asymptotic weight and maturity rate improves the quality of fit in comparison to fixed models.