• Journal of Life Science
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• v.20 no.1
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• pp.124-132
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• 2010

Among students of 'K' boarding high school, located in 'B' city, 32 students whose % body fat was 30% or above were divided into three groups - two exercise groups and one control group. They performed Combined Training - a mix of weight training (WT) and step box training (SBT) - for 65 min a day, 3 days a week, for 8 weeks in total. Group A performed WT 70-80%$RM{\times}3$ sets+SBT (RPE 11-13)${\times}1$ set, and group B performed WT 70-80%$RM{\times}1$ set+SBT (RPE 11-13)${\times}3$ sets to yield data on changes of body composition (Soft Lean Mass, SLM), %fat, WHR), HbA1c, and HOMA-IR. Paired t-test was used to process data within each group. Pre- and post experiment differences rates (%diff) were used to perform one-way ANOVA (Duncan test) for group comparisons. The conclusions derived are as follows. Regarding body composition, exercise groups showed an increase in SLM, but there was no such change in the control group. WHR decreased in group A, but increased in the control group. The % body fat decreased in both exercise groups, but increased in the control group. As for the group comparisons, SLM in group A showed a greater increase than in group B and the control group. WHR in groups A and B showed a greater decrease than the control group. The % body fat in groups A and B showed a greater decrease than the control group. The exercise groups showed a significant decrease in HOMA-IR, but the control group showed a significant increase in HOMA-IR. As for the group comparisons, groups A and B showed a greater decrease in HOMA-IR than the control group. The exercise groups showed a significant decrease in HbA1c, however, the control group showed no change in HbA1c. As for the group comparisons, group A showed a greater decrease in HbA1c than the control group. These results confirm that combined training is more effective in improving body composition and metabolic factors when it includes a high proportion of resistance training, rather than aerobic exercise. The results of the study suggest that it is advisable to set a high proportion of WT when deciding the intensity of combined training.

• Journal of the Korean Society of Hazard Mitigation
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• v.2 no.4 s.7
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• pp.123-129
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• 2002

In this study the results of Chungju reservoir inflow forecasting using 3 layered neural network model were analyzed in order to investigate the characteristics of neural network model for reservoir inflow forecasting. The proper neuron numbers of input and hidden layer were proposed after examining the variations of forecasted values according to neuron number and training epoch changes, and the probability of underestimation was judged by deliberating the variation characteristics of forecasting according to the differences between training and forecasting peak inflow magnitudes. In addition, necessary minimum training data size for precise forecasting was proposed. As a result, We confirmed the probability that excessive neuron number and training epoch cause over-fitting and judged that applying $8{\sim}10$ neurons, $1500{\sim}3000$ training epochs might be suitable in the case of Chungju reservoir inflow forecasting. When the peak inflow of training data set was larger than the forecasted one, it was confirmed that the forecasted values could be underestimated. And when the comparative short period training data was applied to neural networks, relatively inaccurate forecasting outputs were resulted and applying more than 600 training data was recommended for more precise forecasting in Chungju reservoir.

• Nuclear Engineering and Technology
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• v.41 no.9
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• pp.1181-1190
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• 2009

Knowing more about the Local Power Density (LPD) at the hottest part of a nuclear reactor core can provide more important information than knowledge of the LPD at any other position. The LPD at the hottest part needs to be estimated accurately in order to prevent the fuel rod from melting in a nuclear reactor. Support Vector Machines (SVMs) have successfully been applied in classification and regression problems. Therefore, in this paper, the power peaking factor, which is defined as the highest LPD to the average power density in a reactor core, was estimated by SVMs which use numerous measured signals of the reactor coolant system. The SVM models were developed by using a training data set and validated by an independent test data set. The SVM models' uncertainty was analyzed by using 100 sampled training data sets and verification data sets. The prediction intervals were very small, which means that the predicted values were very accurate. The predicted values were then applied to the first fuel cycle of the Yonggwang Nuclear Power Plant Unit 3. The root mean squared error was approximately 0.15%, which is accurate enough for use in LPD monitoring and for core protection that uses LPD estimation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.303-304
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• 2021

It is an important issue to detect automatically whether a mask is worn or not for corona prevention. It is known that mask wearing detection can be solved by learning the face data set. However, the search for whether a person is wearing a mask can be detected in a simpler way using OpenCV. In this paper, we describe that it is possible to easily detect whether a single person is wearing a mask or not with a general PC camera using OpenCV learning data results and simple OpenCV functions. Through experiments, the proposed method was shown to be effective.

• Journal of the Korea Computer Graphics Society
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• v.26 no.5
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• pp.15-24
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• 2020

We present a supervised learning method that estimates the simulation parameters required to simulate the fabric from the static drape shape of a given fabric sample. The static drape shape was inspired by Cusick's drape, which is used in the apparel industry to classify fabrics according to their mechanical properties. The input vector of the training model consists of the feature vector extracted from the static drape and the density value of a fabric specimen. The output vector consists of six simulation parameters that have a significant influence on deriving the corresponding drape result. To generate a plausible and unbiased training data set, we first collect simulation parameters for 400 knit fabrics and generate a Gaussian Mixed Model (GMM) generation model from them. Next, a large number of simulation parameters are randomly sampled from the GMM model, and cloth simulation is performed for each sampled simulation parameter to create a virtual static drape. The generated training data is fitted with a log-linear regression model. To evaluate our method, we check the accuracy of the training results with a test data set and compare the visual similarity of the simulated drapes.

• Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
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• v.12 no.6
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• pp.579-590
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• 2018

Anomaly detection on building energy consumption has been regarded as an effective tool to reduce energy saving on building operation and maintenance. However, it requires energy model and FDD expert for quantitative model approach or large amount of training data for qualitative/history data approach. Both method needs additional time and labors. This study propose a machine learning and data science approach to define faulty conditions on hourly building energy consumption with reducing data amount and input requirement. It suggests an application of Support Vector Data Description (SVDD) method on training normal condition of hourly building energy consumption incorporated with hourly outdoor air temperature and time integer in a week, 168 data points and identifying hourly abnormal condition in the next day. The result shows the developed model has a better performance when the ${\nu}$ (probability of error in the training set) is 0.05 and ${\gamma}$ (radius of hyper plane) 0.2. The model accuracy to identify anomaly operation ranges from 70% (10% increase anomaly) to 95% (20% decrease anomaly) for daily total (24 hours) and from 80% (10% decrease anomaly) to 10%(15% increase anomaly) for occupied hours, respectively.

• Journal of The Korean Society of Integrative Medicine
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• v.2 no.1
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• pp.91-100
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• 2014

Purpose : The purpose of this study was to know the influence of the difference in shoulder slope from balance training. Method : Training was divided that the 10 people were a Trampoline group, 10 people were a Togu group, 8 people were a Balance Board group and 9 people were a Control group. Method of the training was that the Trampoline group was carried out on the Trampoline. The training was carried out totally 2 times of 8 set per a week and had a break time during 10 seconds after carried out 30 seconds per one set. Togu group was carried out totally 2 times of 5 minutes per a week and had a break time during 30 seconds after carried out 2 minutes per one set. Balance Board group was carried out totally 2 times of 5 minutes per a week and had a break time 30 seconds after carried out 2 minutes per one set. Data was analyzed by repeated measure of one way ANOVA and repeated measure ANOVA. Result : The shoulder of the slope difference decreased significantly after balance training. The Trampoline group decreased from $3.13{\pm}1.01$ to $2.37{\pm}1.11$, the Togu group decreased from $3.78{\pm}0.85$ to $3.78{\pm}0.85$, the Balance Board group decreased from $1.78{\pm}0.82$ to $1.65{\pm}0.59$ and the Control group decreased from $1.77{\pm}1.16$ to $1.61{\pm}0.62$. Conclusion : The effectiveness improved in the order Togu group, Trampoline group, Balance Board group and Control group from the result of the balance training about difference of slope shoulder.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.575-588
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• 2012

Exploiting unlabeled text data with a relatively small labeled corpus has been an active and challenging research topic in text mining, due to the recent growth of the amount of biomedical literature. Biomedical named-entity recognition is an essential prerequisite task before effective text mining of biomedical literature can begin. This paper proposes an Active Co-Training (ACT) algorithm for biomedical named-entity recognition. ACT is a semi-supervised learning method in which two classifiers based on two different feature sets iteratively learn from informative examples that have been queried from the unlabeled data. We design a new classification problem to measure the informativeness of an example in unlabeled data. In this classification problem, the examples are classified based on a joint view of a feature set to be informative/non-informative to both classifiers. To form the training data for the classification problem, we adopt a query-by-committee method. Therefore, in the ACT, both classifiers are considered to be one committee, which is used on the labeled data to give the informativeness label to each example. The ACT method outperforms the traditional co-training algorithm in terms of f-measure as well as the number of training iterations performed to build a good classification model. The proposed method tends to efficiently exploit a large amount of unlabeled data by selecting a small number of examples having not only useful information but also a comprehensive pattern.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1075-1086
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• 2018

In this paper a cross-validation algorithm for training probabilistic neural networks (PNNs) is presented in order to be applied to automatic face identification. Actually, standard PNNs perform pretty well for small and medium sized databases but they suffer from serious problems when it comes to using them with large databases like those encountered in biometrics applications. To address this issue, we proposed in this work a new training algorithm for PNNs to reduce the hidden layer's size and avoid over-fitting at the same time. The proposed training algorithm generates networks with a smaller hidden layer which contains only representative examples in the training data set. Moreover, adding new classes or samples after training does not require retraining, which is one of the main characteristics of this solution. Results presented in this work show a great improvement both in the processing speed and generalization of the proposed classifier. This improvement is mainly caused by reducing significantly the size of the hidden layer.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.11
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• pp.927-933
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• 2020

This paper presents a fuel quantity measurement system (FQMS) for an aircraft supplementary fuel tank considering the change of aircraft attitude. The developed FQMS consists of fuel sensors, a signal process unit, an indicator and a software to estimate the fuel quantity from the sensor data. To replicate the change of the roll and pitch attitude on the ground, the test simulator is developed in this work. Using the test simulator, the sensor data at various fuel quantities, roll and pitch angles are automatically measured to build a training data set. The data-driven software to estimate the fuel quantity is then developed using a trilinear interpolation method with the training data set. The developed FQMS is verified by investigating the fuel estimation error of the test data set that we know the true values. Through the test, it is confirmed that the error of the developed FQMS system satisfies the criteria of TSO-C55 document.