• Journal of the Korean Geosynthetics Society
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• v.19 no.1
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• pp.11-23
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• 2020

In this paper development artificial neural networks (ANN) for preliminary design and prediction of urban tunnelling and deep excavation-induced ground settlement was presented. In order to form training and validation data sets for the ANN development, field design and measured data were collected for various tunnelling and deep-excavation sites. The field data were then used as a database for the ANN training. The developed ANN was validated against a testing set and the unused field data in terms of statistical parameters such as R², RMSE, and MAE. The practical use of ANN was demonstrated by applying the developed ANN to hypothetical conditions. It was shown that the developed ANN can be effectively used as a tool for preliminary excavation design and ground settlement prediction for urban excavation problems.

• Journal of Intelligence and Information Systems
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• v.14 no.3
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• pp.25-39
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• 2008

The objective of this study is to forecast the operational continuous ability using Artificial Neural Networks in battalion defensive operation for the commander decision making support. The forecasting of the combat result is one of the most complex issue in military science. However, it is difficult to formulate a mathematical model to evaluate the combat power of a battalion in defensive operation since there are so many parameters and high temporal and spatial variability among variables. So in this study, we used company combat power level data in Battalion Command in Battle Training as input data and used Feed-Forward Multilayer Perceptrons(MLP) and General Regression Neural Network (GRNN) to evaluate operational continuous ability. The results show 82.62%, 85.48% of forecasting ability in spite of non-linear interactions among variables. We think that GRNN is a suitable technique for real-time commander's decision making and evaluation of the commitment priority of troops in reserve.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1759-1769
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• 1997

The Error BackPropagation (EBP) algorithm for multi-layered neural networks is widely used in various areas such as associative memory, speech recognition, pattern recognition and robotics, etc. Nevertheless, many researchers have continuously published papers about improvements over the original EBP algorithm. The main reason for this research activity is that EBP is exceeding slow when the number of neurons and the size of training set is large. In this study, we developed new learning speed acceleration methods using variable learning rate, variable momentum rate and variable slope for the sigmoid function. During the learning process, these parameters should be adjusted continuously according to the total error of network, and it has been shown that these methods significantly reduced learning time over the original EBP. In order to show the efficiency of the proposed methods, first we have used binary data which are made by random number generator and showed the vast improvements in terms of epoch. Also, we have applied our methods to the binary-valued Monk's data, 4, 5, 6, 7-bit parity checker and real-valued Iris data which are famous benchmark training sets for machine learning.

• Smart Media Journal
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• v.5 no.3
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• pp.35-41
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• 2016

In this paper, we propose hand-gesture signal recognition based on EPS(Electronic Potential Sensor) using Deep learning model. Extracted signals which from Electronic field based sensor, EPS have much of the noise, so it must remove in pre-processing. After the noise are removed with filter using frequency feature, the signals are reconstructed with dimensional transformation to overcome limit which have just one-dimension feature with voltage value for using convolution operation. Then, the reconstructed signal data is finally classified and recognized using multiple learning layers model based on deep learning. Since the statistical model based on probability is sensitive to initial parameters, the result can change after training in modeling phase. Deep learning model can overcome this problem because of several layers in training phase. In experiment, we used two different deep learning structures, Convolutional neural networks and Recurrent Neural Network and compared with statistical model algorithm with four kinds of gestures. The recognition result of method using convolutional neural network is better than other algorithms in EPS gesture signal recognition.

• Membrane and Water Treatment
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• v.9 no.2
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• pp.115-121
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• 2018

Artificial neural network (ANN) simulation is used to predict the dynamic change of permeate flux during wheat starch industry wastewater microfiltration with and without static turbulence promoter. The experimental program spans range of a sedimentation times from 2 to 4 h, for feed flow rates 50 to 150 L/h, at transmembrane pressures covering the range of $1{\times}10^5$ to $3{\times}10^5Pa$. ANN predictions of the wastewater microfiltration are compared with experimental results obtained using two different set of microfiltration experiments, with and without static turbulence promoter. The effects of the training algorithm, neural network architectures on the ANN performance are discussed. For the most of the cases considered, the ANN proved to be an adequate interpolation tool, where an excellent prediction was obtained using automated Bayesian regularization as training algorithm. The optimal ANN architecture was determined as 4-10-1 with hyperbolic tangent sigmoid transfer function transfer function for hidden and output layers. The error distributions of data revealed that experimental results are in very good agreement with computed ones with only 2% data points had absolute relative error greater than 20% for the microfiltration without static turbulence promoter whereas for the microfiltration with static turbulence promoter it was 1%. The contribution of filtration time variable to flux values provided by ANNs was determined in an important level at the range of 52-66% due to increased membrane fouling by the time. In the case of microfiltration with static turbulence promoter, relative importance of transmembrane pressure and feed flow rate increased for about 30%.

• Journal of Convergence for Information Technology
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• v.9 no.10
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• pp.203-212
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• 2019

The purpose of this study was to identify the effects of stress factors of student pilots on mental health, and to reduce the safe and efficient misconduct education and psychological disharmony by identifying the psychological buffering role of stress coping style and social support. In order to achieve the research purpose, a research model and hypothesis were presented based on previous studies, and regression analysis and mediation effect verification were conducted through a questionnaire survey of 202 student pilots. As a result of the analysis, factors such as flight stress, values stress, professor stress, and friend relationship stress have been shown to affect emotional conditions or psychological well-being. Also we found that the parameters of disengagement coping, family/friend support and organization Support had a mediating effect on the factors between student pilot stress and psychological health. Therefore, student pilots need to manage problems and negative emotions that may cause from flight training, value distractions, professor and friendships' relationship and it is suggested that organization support for training and safety related to emotional support and delinquency of family and friends.

• Physical Therapy Rehabilitation Science
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• v.8 no.3
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• pp.134-140
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• 2019

Objective: The purpose of this study was to investigate the effect of exercise therapy and bedside ergometer exercise on muscle strength, function level, and quality of life of persons in intensive care. Design: Randomized Controlled Trial Methods: Sixteen patients in the ICU were randomly assigned to either the exercise group (n=8) or the bedside cycle ergometer group (n=8). Activities in the ICU exercise group (rolling, sitting at the edge of the bed, transfer from sitting to standing, standing balance training, ambulation) and bedside cycle ergometer group were performed 5 times a week for 30 minutes during the ICU admission period. Medical Research Council (MRC) and Functional Status Scale-Intensive Care Unit (FSS-ICU) parameters were assessed at the time of admission to the ICU, and reevaluation was assessed on the day of ICU discharge. The Short Form-36 (SF-36) was assessed at the time of discharge from the ICU. Results: MRC and FSS-ICU were significantly increased before and after intervention in both the experimental and control groups (p<0.05). There was a significant difference between MRC and FSS-ICU in the comparison of the changes before and after the intervention (p<0.05). SF-36 was compared between groups after intervention and there was a significant difference between the experimental and the control group (p<0.05). Conclusions: Muscle strength and functional levels improved after intervention in both the experimental and control groups. The ICU exercise group was more effective than the bedside cycle ergometer group to improve muscle strength, functional level, and quality of life performance of persons in the ICU.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.24 no.1
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• pp.54-64
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• 2021

Purpose: Nonalcoholic fatty liver disease (NAFLD) is the most frequent cause of chronic liver diseases in both adults and children with obesity. The aim of this study was to compare the changes in liver enzymes and metabolic profile in adolescents with fatty liver following selected school-based exercise (SBE) and high-intensity interval training (HIIT) interventions. Methods: In a semi-experimental study, 34 obese male adolescents with clinically defined NAFLD were divided into the HIIT (n=11, age=12.81±1.02 years, body mass index [BMI]=26.68±2.32 kg/㎡), selected SBE (n=11, age=13.39±0.95 years, BMI=26.47±1.74 kg/㎡), and control (n=12, age=13.14±1.49 years, BMI=26.45±2.21 kg/㎡) groups. The ultrasonography NAFLD grade, peak oxygen uptake (VO2peak), lipid profile, insulin resistance, and alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels of the participants were measured before and after the exercise interventions. Results: The BMI, waist-to-hip ratio, and body fat percentage of the participants decreased, and a significant increase in VO2peak was observed after the intervention; however, the HIIT group showed a significant improvement compared with the SBE group (p<0.01). Significant reductions were observed in the levels of insulin resistance, triglyceride, total cholesterol, ALT, and AST in both groups, although high-density lipoprotein levels decreased only in the HIIT group (p<0.01). Further, a significant reduction in low-density lipoprotein level was observed in the training groups (p<0.01), but this decrease was not significant compared with the control group (p>0.01). Conclusion: HIIT and SBE are equally effective in improving health parameters in obese children and adolescents.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.293-304
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• 2022

In this work, a novel reliability approach for combined high and low cycle fatigue (CCF) estimation is developed by combining active learning strategy with least squares support vector machines (LS-SVM) (named as ALS-SVM) surrogate model to address the multi-resources uncertainties, including working loads, material properties and model itself. Initially, a new active learner function combining LS-SVM approach with Monte Carlo simulation (MCS) is presented to improve computational efficiency with fewer calls to the performance function. To consider the uncertainty of surrogate model at candidate sample points, the learning function employs k-fold cross validation method and introduces the predicted variance to sequentially select sampling. Following that, low cycle fatigue (LCF) loads and high cycle fatigue (HCF) loads are firstly estimated based on the training samples extracted from finite element (FE) simulations, and their simulated responses together with the sample points of model parameters in Coffin-Manson formula are selected as the MC samples to establish ALS-SVM model. In this analysis, the MC samples are substituted to predict the CCF reliability of turbine blades by using the built ALS-SVM model. Through the comparison of the two approaches, it is indicated that the reliability model by linear cumulative damage rule provides a non-conservative result compared with that by the proposed one. In addition, the results demonstrate that ALS-SVM is an effective analysis method holding high computational efficiency with small training samples to gain accurate fatigue reliability.

• The Journal of the Acoustical Society of Korea
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• v.41 no.1
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• pp.30-37
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• 2022

This paper compares and evaluates model performance from two perspectives according to the learning target and network structure for training Deep Neural Network (DNN)-based speech enhancement models in the frequency domain. In this case, spectrum mapping and Time-Frequency (T-F) masking techniques were used as learning targets, and a real network and a complex network were used for the network structure. The performance of the speech enhancement model was evaluated through two objective evaluation metrics: Perceptual Evaluation of Speech Quality (PESQ) and Short-Time Objective Intelligibility (STOI) depending on the scale of the dataset. Test results show the appropriate size of the training data differs depending on the type of networks and the type of dataset. In addition, they show that, in some cases, using a real network may be a more realistic solution if the number of total parameters is considered because the real network shows relatively higher performance than the complex network depending on the size of the data and the learning target.