• Journal of Korean Medicine Rehabilitation
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• v.31 no.2
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• pp.81-97
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• 2021

Objectives This study was designed to identify the changes of autonomic nervous system (ANS) which was induced by acupuncture at the field of the auricular branch of the vagus nerve. Methods 30 healthy adults were selected and classified into two groups; experimental group, control group. After providing mental stress, acupuncture was applied at external ear in experimental group and no treatment executed in control group. The evaluation of ANS function was measured by heart rate variability (HRV). We statically analyzed the difference of HRV parameters which include mean heart rate (MHRT), standard deviation of all N-N intervals (SDNN), square root of the mean of the sum of the squares of differences between adjacent N-N intervals (RMSSD), total power (TP), low frequency power (LF), high frequency power (HF), LF/HF ratio. Results All subjects showed significant increase in SDNN, LF after stress stimulation (p<0.05). Immediately after intervention, MHRT was significantly decreased (p<0.001) and RMSSD, HF were significantly increased in experimental group (p<0.05). After the end of intervention, SDNN, HF, RMSSD, TP, LF were significantly increased in experimental group (p<0.01, p<0.05). And when comparing baseline HRV, SDNN, LF were significantly increased in control group (p<0.01) and SDNN, RMSSD, TP, LF were significantly increased in experimental group (p<0.05). In the subgroup analysis, normal balance of ANS group showed significant increase in TP, LF, SDNN, HF (p<0.01, p<0.05). Conclusions We suggested that acupuncture at external ear, region of the vagus nerve distribution could increase parasympathetic activity and cause changes and reregulation of the ANS.

• Kosin Medical Journal
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• v.33 no.3
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• pp.369-379
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• 2018

Objectives: Analysis of heart rate variability (HRV) has been used as a measure of cardiac autonomic function. According to the pNN50 statistic, the percentage of differences between successive normal RR intervals (RRI) that exceed 50 ms, has been known to reflect cardiac vagal modulation. Relatively little is known about the validity of pNN50 during general anesthesia (GA). Therefore, we evaluated the correlation of pNN50 with other variables such as HF, RMSSD, SD1 of HRV reflecting the vagal tone, and examined the validity of pNN50 in anesthetized patients. Methods: We assessed changes in RRI, pNN50, root mean square of successive differences of RRI (RMSSD), high frequency (HF) and standard deviation 1 (SD1) of $Poincar{\acute{e}}$ plots after GA using sevoflurane anesthesia. We also calculated the probability distributions for the family of pNNx statistics (x: 2-50 ms). Results: All HRV variables were significantly decreased during GA. HF power was not correlated with pNN50 during GA (r = 0.096, P = 0.392). Less than pNN47 was shown to have a correlation with other variables. Conclusions: These data suggest that pNN50 can not reflect the level of vagal tone during GA.

• Journal of Korean Society on Water Environment
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• v.35 no.5
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• pp.425-431
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• 2019

Recently, people have been attracting attention to the good quality of water resources as well as water welfare. to improve the quality of life. This study is a papers on the prediction of benthic macroinvertebrate index (BMI), which is a aquatic ecological health, using the machine learning based CFS (Correlation-based Feature Selection) method and the random forest model to compare the measured and predicted values of the BMI. The data collected from the Han River's branch for 10 years are extracted and utilized in 1312 data. Through the utilized data, Pearson correlation analysis showed a lack of correlation between single factor and BMI. The CFS method for multiple regression analysis was introduced. This study calculated 10 factors(water temperature, DO, electrical conductivity, turbidity, BOD, $NH_3-N$, T-N, $PO_4-P$, T-P, Average flow rate) that are considered to be related to the BMI. The random forest model was used based on the ten factors. In order to prove the validity of the model, $R^2$, %Difference, NSE (Nash-Sutcliffe Efficiency) and RMSE (Root Mean Square Error) were used. Each factor was 0.9438, -0.997, and 0,992, and accuracy rate was 71.6% level. As a result, These results can suggest the future direction of water resource management and Pre-review function for water ecological prediction.

• Korean Journal of Optics and Photonics
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• v.33 no.6
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• pp.324-330
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• 2022

We measured the Dill and Mack model parameters that determine the exposure and development characteristics of photoresists, respectively. First, photoresist samples were prepared while altering the exposure dose, and changes in transmittance were measured. Analyzing these results, the Dill model parameters A, B, and C were determined. In particular, the exact solution of the Dill model equation was used to determine the C parameter. In addition, changes in thickness were measured as a function of development time for different exposure doses, and the Mack model parameters R_min, R_max, a, and n were determined using the results. We also determined parameter values for the reduced Mack model that uses only three parameters, R_min, R_max, and n. The root mean square error between the model predictions and the measured values for the photoresist thickness was found to increase slightly compared to the case using the original Mack model with four parameters.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.161-167
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• 2021

This paper presents the method to quantitatively evaluate the uncertainty of the semi-empirical Bidirectional Reflectance Distribution Function (BRDF) model for Himawari-8/AHI. The uncertainty of BRDF modeling was affected by various issues such as assumption of model and number of observations, thus, it is difficult that evaluating the performance of BRDF modeling using simple uncertainty equations. Therefore, in this paper, Monte-Carlo method, which is most dependable method to analyze dynamic complex systems through iterative simulation, was used. The 1,000 input datasets for analyzing the uncertainty of BRDF modeling were generated using the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) Radiative Transfer Model (RTM) simulation with MODerate Resolution Imaging Spectroradiometer (MODIS) BRDF product. Then, we randomly selected data according to the number of observations from 4 to 35 in the input dataset and performed BRDF modeling using them. Finally, the uncertainty was calculated by comparing reproduced surface reflectance through the BRDF model and simulated surface reflectance using 6S RTM and expressed as bias and root-mean-square-error (RMSE). The bias was negative for all observations and channels, but was very small within 0.01. RMSE showed a tendency to decrease as the number of observations increased, and showed a stable value within 0.05 in all channels. In addition, our results show that when the viewing zenith angle is 40° or more, the RMSE tends to increase slightly. This information can be utilized in the uncertainty analysis of subsequently retrieved geophysical variables.

• Journal of Ocean Engineering and Technology
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• v.36 no.2
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• pp.101-107
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• 2022

Dean (1965) proposed the use of the root mean square error (RMSE) in the dynamic free surface boundary condition (DFSBC) and kinematic free-surface boundary condition (KFSBC) as an error evaluation criterion for wave theories. There are well known wave theories with RMSE more than 1%, such as Airy theory, Stokes theory, Dean's stream function theory, Fenton's theory, and trochodial theory for deep-water waves. However, none of them can be applied for deep-water breaking waves. The purpose of this study is to provide a closed-form solution for deep-water waves with RMSE less than 1% even for breaking waves. This study is based on a previous study (Shin, 2016), and all flow fields were simplified for deep-water waves. For a closed-form solution, all Fourier series coefficients and all related parameters are presented with Newton's polynomials, which were determined by curve fitting data (Shin, 2016). For verification, a wave in Miche's limit was calculated, and, the profiles, velocities, and the accelerations were compared with those of 5^th-order Stokes theory. The results give greater velocities and acceleration than 5^th-order Stokes theory, and the wavelength depends on the wave height. The results satisfy the Laplace equation, bottom boundary condition (BBC), and KFSBC, while Stokes theory satisfies only the Laplace equation and BBC. RMSE in DFSBC less than 7.25×10^-2% was obtained. The series order of the proposed method is three, but the series order of 5^th-order Stokes theory is five. Nevertheless, this study provides less RMSE than 5^th-order Stokes theory. As a result, the method is suitable for offshore structural design.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2107-2119
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• 2022

Accurate remaining useful life (RUL) prediction for critical components of nuclear power equipment is an important way to realize aging management of nuclear power equipment. The electric gate valve is one of the most safety-critical and widely distributed mechanical equipment in nuclear power installations. However, the electric gate valve's extended service in nuclear installations causes aging and degradation induced by crack propagation and leakages. Hence, it is necessary to develop a robust RUL prediction method to evaluate its operating state. Although the particle filter(PF) algorithm and its variants can deal with this nonlinear problem effectively, they suffer from severe particle degeneracy and depletion, which leads to its sub-optimal performance. In this study, we combined the whale algorithm with regularized particle filtering(RPF) to rationalize the particle distribution before resampling, so as to solve the problem of particle degradation, and for valve RUL prediction. The valve's crack propagation is studied using the RPF approach, which takes the Paris Law as a condition function. The crack growth is observed and updated using the root-mean-square (RMS) signal collected from the acoustic emission sensor. At the same time, the proposed method is compared with other optimization algorithms, such as particle swarm optimization algorithm, and verified by the realistic valve aging experimental data. The conclusion shows that the proposed method can effectively predict and analyze the typical valve degradation patterns.

• Geomechanics and Engineering
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• v.34 no.3
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• pp.267-284
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• 2023

Landslides are one of the most dangerous phenomena and natural disasters. Landslides cause many human and financial losses in most parts of the world, especially in mountainous areas. Due to the climatic conditions and topography, people in the northern and western regions of Iran live with the risk of landslides. One of the measures that can effectively reduce the possible risks of landslides and their crisis management is to identify potential areas prone to landslides through multi-criteria modeling approach. This research aims to model landslide potential area in the Oshvand watershed using a support vector machine algorithm. For this purpose, evidence maps of seven effective factors in the occurrence of landslides namely slope, slope direction, height, distance from the fault, the density of waterways, rainfall, and geology, were prepared. The maps were generated and weighted using the continuous fuzzification method and logistic functions, resulting values in zero and one range as weights. The weighted maps were then combined using the support vector machine algorithm. For the training and testing of the machine, 81 slippery ground points and 81 non-sliding points were used. Modeling procedure was done using four linear, polynomial, Gaussian, and sigmoid kernels. The efficiency of each model was compared using the area under the receiver operating characteristic curve; the root means square error, and the correlation coefficient . Finally, the landslide potential model that was obtained using Gaussian's kernel was selected as the best one for susceptibility of landslides in the Oshvand watershed.

• Geomechanics and Engineering
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• v.31 no.4
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• pp.339-352
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• 2022

Studying slope stability is an important branch of civil engineering. In this way, engineers have employed machine learning models, due to their high efficiency in complex calculations. This paper examines the robustness of various novel optimization schemes, namely equilibrium optimizer (EO), Harris hawks optimization (HHO), water cycle algorithm (WCA), biogeography-based optimization (BBO), dragonfly algorithm (DA), grey wolf optimization (GWO), and teaching learning-based optimization (TLBO) for enhancing the performance of adaptive neuro-fuzzy inference system (ANFIS) in slope stability prediction. The hybrid models estimate the factor of safety (F_S) of a cohesive soil-footing system. The role of these algorithms lies in finding the optimal parameters of the membership function in the fuzzy system. By examining the convergence proceeding of the proposed hybrids, the best population sizes are selected, and the corresponding results are compared to the typical ANFIS. Accuracy assessments via root mean square error, mean absolute error, mean absolute percentage error, and Pearson correlation coefficient showed that all models can reliably understand and reproduce the F_S behavior. Moreover, applying the WCA, EO, GWO, and TLBO resulted in reducing both learning and prediction error of the ANFIS. Also, an efficiency comparison demonstrated the WCA-ANFIS as the most accurate hybrid, while the GWO-ANFIS was the fastest promising model. Overall, the findings of this research professed the suitability of improved intelligent models for practical slope stability evaluations.

• Advances in concrete construction
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• v.16 no.3
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• pp.155-167
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• 2023

The penetrated chloride in concrete has different behavior with mix proportions and local exposure conditions, even in the same environments, so that it is very important to quantify surface chloride contents for durability design. As well known, the surface chloride content which is a key parameter like external loading in structural safety design increases with exposure period. In this study, concrete samples containing OPC (Ordinary Portland Cement), GGBFS (Ground Granulated Blast Furnace Slag), and FA (Fly Ash) had been exposed to submerged, tidal, and splash area for 5 years, then the surface chloride contents changing with exposure period were evaluated. The surface chloride contents were obtained from the chloride profile based on the Fick's 2nd Law, and the regression analysis for them was performed with exponential and square root function. After exposure period of 5 years in submerged and tidal area conditions, the surface chloride content of OPC concrete increased to 6.4 kg/m³ - 7.3 kg/m³, and the surface chloride content of GGBFS concrete was evaluated as 7.3 kg/m³ - 11.5 kg/m³. In the higher replacement ratio of GGBFS, the higher surface chloride contents were evaluated. The surface chloride content in FA concrete showed a range of 6.7 kg/m³ to 9.9 kg/m³, which was the intermediate level of OPC and GGBFS concrete. In the case of splash area, the surface chloride contents in all specimens were from 0.59 kg/m³ to 0.75 kg/m³, which was the lowest of all exposure conditions. Experimental constants available for durability design of chloride ingress were derived through regression analysis over exposure period. In the concrete with GGBFS replacement ratio of 50%, the increase rate of surface chloride contents decreased rapidly as the water to binder ratio increased.