• Atmosphere
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• v.31 no.5
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• pp.511-523
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• 2021

This study explores the optimal ensemble size to improve the prediction performance of the Korea Meteorological Administration's operational climate prediction system, global seasonal forecast system version 5 (GloSea5). The GloSea5 produces an ensemble of hindcast data using the stochastic kinetic energy backscattering version2 (SKEB2) and timelagged ensemble. An experiment to increase the hindcast ensemble from 3 to 14 members for four initial dates was performed and the improvement and effect of the prediction performance considering Root Mean Square Error (RMSE), Anomaly Correlation Coefficient (ACC), ensemble spread, and Ratio of Predictable Components (RPC) were evaluated. As the ensemble size increased, the RMSE and ACC prediction performance improved and more significantly in the high variability area. In spread and RPC analysis, the prediction accuracy of the system improved as the ensemble size increased. The closer the initial date, the better the predictive performance. Results show that increasing the ensemble to an appropriate number considering the combination of initial times is efficient.

• Physical Therapy Korea
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• v.29 no.1
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• pp.79-86
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• 2022

Background: Bird dog exercise (BDE) is one of the lumbar stabilization exercises that rehabilitate low back pain by co-contraction of the local and global muscles. Previous studies have reported the effect of various type of BDEs (for example, practicing the exercises on various surfaces and changing the limb movement) for muscle co-contraction. Objects: This study aimed to investigate the effect of knee joint flexion position of the raised lower limb on abdominal and back muscle activity during BDE in patients with chronic low back pain (CLBP). Methods: Thirteen males participated in this study (age: 32.54 ± 4.48 years, height: 177.38 ± 7.17 cm). Surface electromyographic (SEMG) data of the internal abdominal oblique (IO), external abdominal oblique (EO), lumbar multifidus (MF), and thoracic part of the iliocostalis lumborum (ICLT) were collected in two knee joint flexion positions (90° flexion versus 0° flexion) during BDE. The SEMG data were expressed as a percentage of root mean square mean values obtained in the maximal voluntary isometric contraction. Results: Greater muscle activity of the IO (p = 0.001), MF (p = 0.009), and ICLT (p = 0.021) of the raised lower limb side and the EO (p = 0.001) and MF (p = 0.009) of the contralateral side were demonstrated in the knee joint flexion position compared to the knee joint extension position. Greater local/global activity ratios of the abdominal muscle (i.e., IO and EO) of the raised lower limb (p = 0.002) and the back muscle (i.e., MF and ICLT) of the contralateral side (p = 0.028) were also noted in the knee joint flexion position. Conclusion: BDE with a knee joint flexion position might be recommended as an alternative lumbar stabilization exercise to enhance muscle activity in both the raised lower limb and the contralateral sides of the trunk for individuals with CLBP.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.1
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• pp.11-18
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• 2022

In order to improve the accuracy of particle tracking prediction techniques near the Korean Strait, this study compared and analyzed a particle tracking model based on a seawater flow numerical model and a machine learning based on a particle tracking model using field observation data. The data used in the study were the surface drifter buoy movement trajectory data observed in the Korea Strait, prediction data by machine learning (linear regression, decision tree) using the tide and wind data from three observation stations (Gageo Island, Geoje Island, Gyoboncho), and prediciton data by numerical models (ROMS, MOHID). The above three data were compared through three error evaluation methods (Correlation Coefficient (CC), Root Mean Square Errors (RMSE), and Normalized Cumulative Lagrangian Separation (NCLS)). As a final result, the decision tree model had the best prediction accuracy in CC and RMSE, and the MOHID model had the best prediction results in NCLS.

• The Journal of Advanced Prosthodontics
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• v.14 no.1
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• pp.45-55
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• 2022

PURPOSE. This in vitro study investigates the effect of different post-rinsing times and methods on the trueness and precision of denture base resin manufactured through stereolithography. MATERIALS AND METHODS. Ninety clear photopolymer resin specimens were fabricated and divided into nine groups (n = 10) based on rinsing times and methods. All specimens were rinsed with 99% isopropanol alcohol for 5, 10, and 15 min using three methods-automated, ultrasonic cleaning, and hand washing. The specimens were polymerized for 30 min at 40℃. For trueness, the scanned intaglio surface of each SLA denture base was superimposed on the original standard tessellation language (STL) file using best-fit alignment (n = 10). For precision, the scanned intaglio surface of the STL file in each specimen group was superimposed across each specimen (n = 45). The root mean square error (RMSE) was measured, and the data were analyzed statistically through one-way ANOVA and Tukey test (α < .05). RESULTS. The 10-min automated group exhibited the lowest RMSE. For trueness, this was significantly different from specimens in the 5-min hand-washed group (P < .05). For precision, this was significantly different from those of other groups (P < .05), except for the 15-min automated and 15-min ultrasonic groups. The color map results indicated that the 10-min automated method exhibited the most uniform distribution of the intaglio surface adaptation. CONCLUSION. The optimal postprocessing rinsing times and methods for achieving clear photopolymer resin were found to be the automated method with rinsing times of 10 and 15 min, and the ultrasonic method with a rinsing time of 15 min.

• Journal of Environmental Health Sciences
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• v.48 no.3
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• pp.151-158
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• 2022

Background: Chemical emissions in the environment have rapidly increased with the accelerated industrialization taking place in recent decades. Residents of industrial complexes are concerned about the health risks posed by chemical exposure. Objectives: This study was performed to suggest modeling methods that take into account multimedia and multi-pathways in human exposure and risk assessment. Methods: The concentration of benzene emitted at industrial complexes in Daesan, South Korea and the exposure of local residents was estimated using the Caltox model. The amount of human exposure based on inhalation rate was stochastically predicted for various activity stages such as resting, normal walking, and fast walking. Results: The coefficient of determination (R²) for the CalTOX model efficiency was 0.9676 and the root-mean-square error (RMSE) was 0.0035, indicating good agreement between predictions and measurements. However, the efficiency index (EI) appeared to be a negative value at -1094.4997. This can be explained as the atmospheric concentration being calculated only from the emissions from industrial facilities in the study area. In the human exposure assessment, the higher the inhalation rate percentile value, the higher the inhalation rate and lifetime average daily dose (LADD) at each activity step. Conclusions: Prediction using the Caltox model might be appropriate for comparing with actual measurements. The LADD of females was higher ratio with an increase in inhalation rate than those of males. This finding would imply that females may be more susceptible to benzene as their inhalation rate increases.

• Physical Therapy Korea
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• v.29 no.3
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• pp.187-193
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• 2022

Background: Neuromuscular electrical stimulation (NMES) is a physical modality used to activate skeletal muscles for strengthening. While voluntary muscle contraction (VMC) follows the progressive recruitment of motor units in order of size from small to large, NMES-induced muscle contraction occurs in a nonselective and synchronous pattern. Therefore, the outcome of muscle strengthening training using NMES-induced versus voluntary contraction might be different, which might affect balance performance. Objects: We examined how the NMES training affected balance and proprioception. Methods: Forty-four young adults were randomly assigned to NMES and VMC group. All participants performed one-leg standing on a force plate and sat on the Biodex (Biodex R Corp.) to measure balance and ankle proprioception, respectively. All measures were conducted before and after a training session. In NMES group, electric pads were placed on the tibialis anterior, gastrocnemius, and soleus muscles for 20 minutes. In VMC group, co-contraction of the three muscles was conducted. Outcome variables included mean distance, root mean square distance, total excursion, mean velocity, 95% confidence circle area acquired from the center of pressure data, and absolute error of dorsi/plantarflexion. Results: None of outcome variables were associated with group (p > 0.35). However, all but plantarflexion error was associated with time (p < 0.02), and the area and mean velocity were 37.0% and 18.6% lower in post than pre in NMES group, respectively, and 48.9% and 16.7% lower in post than pre in VMC group, respectively. Conclusion: Despite different physiology underlying the NMES-induced versus VMC, both training methods improved balance and ankle joint proprioception.

• Journal of the Korean Geosynthetics Society
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• v.21 no.2
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• pp.11-19
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• 2022

In this paper, we propose a GoogleNet transfer learning and CNN-LSTM combination method to improve the time-series prediction performance for crack detection using crack data captured inside the sewer pipes. LSTM can solve the long-term dependency problem of CNN, so spatial and temporal characteristics can be considered at the same time. The predictive performance of the proposed method is excellent in all test variables as a result of comparing the RMSE(Root Mean Square Error) for time series sections using the crack data inside the sewer pipe. In addition, as a result of examining the prediction performance at the time of data generation, the proposed method was verified that it is effective in predicting crack detection by comparing with the existing CNN-only model. If the proposed method and experimental results obtained through this study are utilized, it can be applied in various fields such as the environment and humanities where time series data occurs frequently as well as crack data of concrete structures.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1041-1041
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• 2001

Removal of variability in spectra data before the application of chemometric modeling will generally result in simpler (and presumably more robust) models. Particularly for sparsely sampled data, such as typically encountered in diode array instruments, the use of Savitzky-Golay (S-G) derivatives offers an effective method to remove effects of shifting baselines and sloping or curving apparent baselines often observed with scattering samples. The application of these convolution functions is equivalent to fitting a selected polynomial to a number of points in the spectrum, usually 5 to 25 points. The value of the polynomial evaluated at its mid-point, or its derivative, is taken as the (smoothed) spectrum or its derivative at the mid-point of the wavelength window. The process is continued for successive windows along the spectrum. The original paper, published in 1964 [1] presented these convolution functions as integers to be used as multipliers for the spectral values at equal intervals in the window, with a normalization integer to divide the sum of the products, to determine the result for each point. Steinier et al. [2] published corrections to errors in the original presentation [1], and a vector formulation for obtaining the coefficients. The actual selection of the degree of polynomial and number of points in the window determines whether closely situated bands and shoulders are resolved in the derivatives. Furthermore, the actual noise reduction in the derivatives may be estimated from the square root of the sums of the coefficients, divided by the NORM value. A simple technique to evaluate the actual convolution factors employed in the calculation by the software will be presented. It has been found that some software packages do not properly account for the sampling interval of the spectral data (Equation Ⅶ in [1]). While this is not a problem in the construction and implementation of chemometric models, it may be noticed in comparing models at differing spectral resolutions. Also, the effects on parameters of PLS models of choosing various polynomials and numbers of points in the window will be presented.

• Geomechanics and Engineering
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• v.31 no.6
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• pp.545-556
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• 2022

Evaluation and optimization of tunnel wall convergence (TWC) plays a vital role in preventing potential problems during tunnel construction and utilization stage. When convergence occurs at a high rate, it can lead to significant problems such as reducing the advance rate and safety, which in turn increases operating costs. In order to design an effective solution, it is important to accurately predict the degree of TWC; this can reduce the level of concern and have a positive effect on the design. With the development of soft computing methods, the use of deep learning algorithms and neural networks in tunnel construction has expanded in recent years. The current study aims to employ the long-short-term memory (LSTM) deep neural network predictor model to predict the TWC, based on 550 data points of observed parameters developed by collecting required data from different tunnelling projects. Among the data collected during the pre-construction and construction phases of the project, 80% is randomly used to train the model and the rest is used to test the model. Several loss functions including root mean square error (RMSE) and coefficient of determination (R²) were used to assess the performance and precision of the applied method. The results of the proposed models indicate an acceptable and reliable accuracy. In fact, the results show that the predicted values are in good agreement with the observed actual data. The proposed model can be considered for use in similar ground and tunneling conditions. It is important to note that this work has the potential to reduce the tunneling uncertainties significantly and make deep learning a valuable tool for planning tunnels.

• Geomechanics and Engineering
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• v.33 no.1
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• pp.1-9
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• 2023

The unconfined compression strength (UCS) of soils is commonly used either before or during the construction of geo-structures. In the pre-design stage, UCS as a mechanical property is obtained through a laboratory test that requires cumbersome procedures and high costs from in-situ sampling and sample preparation. As an alternative way, the empirical model established from limited testing cases is used to economically estimate the UCS. However, many parameters affecting the 1D soil compression response hinder employing the traditional statistical analysis. In this study, gene expression programming (GEP) is adopted to develop a prediction model of UCS with common affecting soil properties. A total of 79 undisturbed soil samples are collected, of which 54 samples are utilized for the generation of a predictive model and 25 samples are used to validate the proposed model. Experimental studies are conducted to measure the unconfined compression strength and basic soil index properties. A performance assessment of the prediction model is carried out using statistical checks including the correlation coefficient (R), the root mean square error (RMSE), the mean absolute error (MAE), the relatively squared error (RSE), and external criteria checks. The prediction model has achieved excellent accuracy with values of R, RMSE, MAE, and RSE of 0.98, 10.01, 7.94, and 0.03, respectively for the training data and 0.92, 19.82, 14.56, and 0.15, respectively for the testing data. From the sensitivity analysis and parametric study, the liquid limit and fine content are found to be the most sensitive parameters whereas the sand content is the least critical parameter.