• Wind and Structures
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• 제36권6호
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• pp.423-434
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• 2023

Aerodynamic force coefficients are generally obtained from traditional wind tunnel tests or computational fluid dynamics (CFD). Unfortunately, the techniques mentioned above can sometimes be cumbersome because of the cost involved, such as the computational cost and the use of heavy equipment, to name only two examples. This study proposed to build a deep neural network model to predict the aerodynamic force coefficients based on data collected from CFD simulations to overcome these drawbacks. Therefore, a series of CFD simulations were conducted using different geometric parameters to obtain the aerodynamic force coefficients, validated with wind tunnel tests. The results obtained from CFD simulations were used to create a dataset to train a multilayer perceptron artificial neural network (ANN) model. The models were obtained using three optimization algorithms: scaled conjugate gradient (SCG), Bayesian regularization (BR), and Levenberg-Marquardt algorithms (LM). Furthermore, the performance of each neural network was verified using two performance metrics, including the mean square error and the R-squared coefficient of determination. Finally, the ANN model proved to be highly accurate in predicting the force coefficients of similar bridge sections, thus circumventing the computational burden associated with CFD simulation and the cost of traditional wind tunnel tests.

• 한국건설관리학회논문집
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• 제24권6호
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• pp.81-90
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• 2023

Climate change has resulted in increased frequency and intensity of heat waves, which poses a significant threat to the health and safety of construction workers, particularly those engaged in labor-intensive and heat-stress vulnerable working environments. To address this challenge, this study aimed to propose an interpretable machine learning approach for forecasting personal heat strain by considering the cumulative effect of heat exposure as a situational variable, which has not been taken into account in the existing approach. As a result, the proposed model, which incorporated the cumulative working time along with environmental and personal variables, was found to have superior forecast performance and explanatory power. Specifically, the proposed Multi-Layer Perceptron (MLP) model achieved a Mean Absolute Error (MAE) of 0.034 (℃) and an R-squared of 99.3% (0.933). Feature importance analysis revealed that the cumulative working time, as a situational variable, had the most significant impact on personal heat strain. These findings highlight the importance of systematic management of personal heat strain at construction sites by comprehensively considering the cumulative working time as a situational variable as well as environmental and personal variables. This study provided a valuable contribution to the construction industry by offering a reliable and accurate heat strain forecasting model, enhancing the health and safety of construction workers.

• Nuclear Engineering and Technology
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• 제56권1호
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• pp.48-55
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• 2024

The safety-related raw water system's strong operational condition supports the radiation defense and biological shield of nuclear plant containment structures. Gaps and failures in maintaining proper working condition of main equipment like pump were among the most common causes of unavailability of safety related raw water systems. We integrated the advanced data analytics tools to evaluate the maintenance records of water systems and gave special consideration to deficiencies related to pump. We utilized maintenance data over a three-and-a-half-year period to produce metrics like MTBF, MTTF, MTTR, and failure rate. The visual analytic platform using tableau identified the efficacy of maintenance & deficiency in the safety raw water systems. When the number of water quality violation was compared to the other O&M deficiencies, it was discovered that water quality violations account for roughly 15% of the system's deficiencies. The pumps were substantial contributors to the deficit. Pump availability was predicted and optimized with real time data using response surface method. The prediction model was significant with r-squared value of 0.98. This prediction model can be used to predict forth coming pump failures in nuclear plant.

• Bulletin of the Korean Chemical Society
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• 제33권3호
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• pp.975-979
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• 2012

Structure and Dynamics of dilute two-dimensional (2D) ring polymer solutions are investigated by using discontinuous molecular dynamics simulations. A ring polymer and solvent molecules are modeled as a tangent-hard disc chain and hard discs, respectively. Some of solvent molecules are confined inside the 2D ring polymer unlike in 2D linear polymer solutions or three-dimensional polymer solutions. The structure and the dynamics of the 2D ring polymers change significantly with the number ($N_{in}$) of such solvent molecules inside the 2D ring polymers. The mean-squared radius of gyration ($R^2$) increases with $N_{in}$ and scales as $R{\sim}N^{\nu}$ with the scaling exponent $\nu$ that depends on $N_{in}$. When $N_{in}$ is large enough, ${\nu}{\approx}1$, which is consistent with experiments. Meanwhile, for a small $N_{in}{\approx}0.66$ and the 2D ring polymers show unexpected structure. The diffusion coefficient (D) and the rotational relaxation time ($\tau_{rot}$) are also sensitive to $N_{in}$: D decreases and $\tau$ increases sharply with $N_{in}$. D of 2D ring polymers shows a strong size-dependency, i.e., D ~ ln(L), where L is the simulation cell dimension. But the rotational diffusion and its relaxation time ($\tau_{rot}$) are not-size dependent. More interestingly, the scaling behavior of $\tau_{rot}$ also changes with $N_{in}$; for a large $N_{in}$ $\tau_{rot}{\sim}N^{2.46}$ but for a small $N_{in}$ $\tau_{rot}{\sim}N^{1.43}$.

• 대한한의학회지
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• 제35권4호
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• pp.24-35
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• 2014

Objectives: Although the autonomic nervous system (ANS) is thought to play an important role in treatment of obesity, no study has been conducted to investigate acupuncture's effects on this aspect of the ANS. This study aimed to describe the effects of acupuncture in the ANS by means of heart rate variability (HRV) analysis. Methods: A total of 46 obese women aged from 21 to 54 with body mass index ranging from 25.1 to $39.3kg/m^2$ were recruited and randomized into both the real acupuncture group (n=23) and sham acupuncture group (n=23). A total of 3 instances of HRV analysis were conducted before, during, and after treatment. Statistically significant differences between time and groups were analyzed using repeated measure analysis of variance. Results: All parameters of time domain analysis and frequency domain analysis except for the square root of the mean squared differences of successive normal sinus intervals (RMSSD) and very low frequency (VLF) showed significant differences between times. The mean of all R-R intervals (mean RR) showed significant level of interaction between time and group. Between groups, time domain analysis of standard deviation of the normal-to-normal intervals (SDNN), RMSSD and frequency domain analysis of total power (TP) and high frequency (HF) showed significant differences. Conclusions: The real acupuncture group showed deactivation of parasympathetic function and relative increase of sympathetic activity in obese subjects. Further studies are necessary to uncover the mechanisms of acupuncture in obesity treatment.

• Environmental Engineering Research
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• 제21권4호
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• pp.333-340
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• 2016

An Artificial Neural Network including a Radial Basis Function (RBF) and a Time Delay Neural Network (TDNN) was used to predict total dissolved solid (TDS) in the river Zayanderud. Water quality parameters in the river for ten years, 2001-2010, were prepared from data monitored by the Isfahan Regional Water Authority. A factor analysis was applied to select the inputs of water quality parameters, which obtained total hardness, bicarbonate, chloride and calcium. Input data to the neural networks were pH, $Na^+$, $Mg^{2+}$, Carbonate ($CO{_3}^{-2}$), $HCO{_3}^{-1}$, $Cl^-$, $Ca^{2+}$ and Total hardness. For learning process 5-fold cross validation were applied. In the best situation, the TDNN contained 2 hidden layers of 15 neurons in each of the layers and the RBF had one hidden layer with 100 neurons. The Mean Squared Error and the Mean Bias Error for the TDNN during the training process were 0.0006 and 0.0603 and for the RBF neural network the mentioned errors were 0.0001 and 0.0006, respectively. In the RBF, the coefficient of determination ($R^2$) and the index of agreement (IA) between the observed data and predicted data were 0.997 and 0.999, respectively. In the TDNN, the $R^2$ and the IA between the actual and predicted data were 0.957 and 0.985, respectively. The results of sensitivity illustrated that $Ca^{2+}$ and $SO{_4}^{2-}$ parameters had the highest effect on the TDS prediction.

• 대한치과보철학회지
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• 제40권1호
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• pp.18-29
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• 2002

As the inaccuracy which was made in implant impression prevented passive fit, it needed to solder the sectioned framework at several locations to correct the inaccuracy. Many clinicians have suggested impression techniques which could make passive fit between implant and superstructure. The purpose of this research was to measure and compare the accuracy of three methods of taking impression with the strain amplifier. The experimental groups were classified as follows : Group I : splinted the two parts with $Futar^{(R)}D$ Occlusion. Group II : splinted the two parts with $DuraLay^{(R)}$. Group III ; didn't splint the two parts. The results were as follows ; 1. The values of strain in the vertical and horizontal surfaces were increased in the order of group I, group II and group III. 2. Group 1 showed higher accuracy of the duplicated casts in the vortical and horizontal sur faces than group II and group III (p<0.05). 3. There were no significant differences in the accuracy of the duplicated casts between group II and group III. From the above results, it is considered that the splinting method of impression copings could make an influence on the accuracy of the master casts. To improve the accuracy of the master casts, splinting the squared impression copings with the additional silicone occlusion materials is recommendable.

• 대한영양사협회학술지
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• 제13권2호
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• pp.157-168
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• 2007

The purposes of this study were to measure the resting metabolic rate(RMR) of 30-40 year old women and to compare it with values predicted using published equations. Body weight, height and body fat of subjects were measured. RMR was measured by two indirect calorimeter(method 1 and method 2). RMR was predicted using various equations. Average height, weight and body fat(%) of subjects were 158.6cm, 59.1kg and 30.9%, respectively. The RMR(1621.2$\pm$301.5 kcal/day) measured by portable indirect calorimeter(method 2) was significantly higher than RMR(1447.4$\pm$223.6 kcal/day) measured by typical indirect calorimeter(method 1). Comparison of measured RMR with predicted RMRs suggested that there was a least difference in RMR predicted by equation of Cunningham. According to RMSPEs(Root Mean Squared Prediction Errors), equations of Cunningham and body surface area were found to predict measured RMR(by method 1) most accurately (within 239.1kcal/day and 232.9kcal/day, respectively). The fat free mass and fat mass - adjusted correlation showed that measured RMR(by method 1) had negative relationships with muscle mass(r = -0.873) and fat free mass(r = -0.866). The equations of Cunningham and body surface area provide relatively accurate estimates of RMR when determining energy needs of 30-40 aged women. There are needs for development of RMR predicted equations that are derived from large samples of Korean.

• 전기전자학회논문지
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• 제19권3호
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• pp.282-287
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• 2015

본 연구에서는 연구에서는 Ce:GAGG 섬광체, 광섬유 그리고 광전자증배관을 이용하여 광섬유 검출기를 제작하였다. 섬광체의 단결정 크기는 MCNPX 코드를 이용하여 섬광체 깊이에 따른 감마선 계수효율을 전산모사하여 $3{\times}3{\times}20mm^3$로 설정하였다. 제작된 검출기는 표준 감마선원인 $^{137}Cs$과 $^{133}Ba$을 이용하여 세기의 따른 선형성 평가와 거리 변화에 따른 계수량 변화 측정을 하였다. 그 결과 추세선식 R-square 값이 0.99741로 매우 좋은 응답선형성을 보였고, 거리에 따른 검출 특성 또한 MCNPX값과 비교하였을 때 2% 이하로 좋은 검출특성을 보였다. 또한 단일선원과 혼합선원에서의 감마선 에너지 분광 결과 $^{137}Cs$은 662keV에서 그리고 $^{133}Ba$은 356keV에서 감마선 에너지 피크를 확인하였다. 광섬유 검출기를 사용한다면 작업자의 작업시간과 피폭을 줄여줄 것으로 보인다.

• 한국컴퓨터정보학회논문지
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• 제11권1호
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• pp.45-53
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• 2006

유한고장수를 가진 비동질적인 포아송 과정에 기초한 모형들에서 잔존 결함 1개당 고장 발생률은 일반적으로 상수, 혹은 단조증가 및 단조 감소 추세를 가지고 있다. 본 논문에서는 기존의 소프트웨어 신뢰성 모형인 Goel-Okumoto 모형과 Yamada-Ohba-Osaki 모형을 재조명하고 잔존 결함 1개당 고장 발생률이 증가추세를 가진 카이제곱 분포를 이용한 카이제곱 모형을 제안하였다. 고장 간격시간으로 구성된 자료를 이용한 모수추정 방법은 최우추정법과 일반적인 수치해석 방법인 이분법을 사용하여 모수 추정을 실시하고 효율적인 모형 선택은 편차자승합, AIC 통계량 및 콜모고로프 거리를 적용하여 모형들에 대한 효율성 입증방법을 설명하였다. 소프트웨어 고장 자료 분석에서는 카이제곱 모형에 대한 자유도를 형상모수의 척도로 간주하여 고장수가 비교적 큰 실측 자료(고장수가 86)인 Allen P.Nikora 와 Michael R.Lyu가 인용한 SYS2 자료을 통하여 분석하였다. 이 자료들에서 카이제곱 모형의 비교를 위하여 산술적 및 라플라스 검정, Kolmogorov검정 등을 이용하였다.