• Journal of Korean Academy of Nursing Administration
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• v.8 no.4
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• pp.575-583
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• 2002

Purpose : The purpose of this study was examine the effects of Self-Development training on the Self-Identity for head nurses. Methods : The sample consisted of 24 head nurses in one university hospital in Seoul. The subjects were divided into two groups for the training. Self-Development training was developed by the researcher for during 4 sessions in two weeks that is, 2 hours a day/ 2 times a weeks / two weeks / each group. Self-Development training program consists of identification of self-development elements, self-identification I, self-identification II, and human relationship. Two-weeks before and 4-weeks after the training, subjects completed the questionnaires. Analysis was done by SPSS PC 10.0 for percentile, mean, standard deviation, paired t-test and correlation. Results : The results of this study showed that the Self-Identity had not significant differences before and after Self-Development Training. When compared 5 subscales, self-assertiveness is significant difference, and goal-directedness has the lowest score before and after training. 11(46%) of head nurses mean scores at the 4 weeks after training were slight higher. Conclusion: On the basis of the finding, the researcher makes the following conclusion. This study is one step towards understanding the impact of Self-Identity for the head nurses. It would be beneficial to replicate this study with larger, more diverse samples.

• Journal of Nutrition and Health
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• v.26 no.1
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• pp.47-55
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• 1993

To investigate the effects of exercise-training on serum lipids, fat distribution and several parameters of body fatness(percent body fat, skinfolds thickness, body circumference) were assessed in 24 healthy male subjects submitted to an 8-wk high intensive exercise-training. Blood sample was taken twice, per and post exercise-training, Exercise-training took place 5 days a week and daily energy intake and expenditure were observed. The results obtained are summarized as follows: 1) Through exercise-training body weight (changes : 1.1$\pm$1.1kg ; p=0.000) and percent body fat (changes : 2.4$\pm$1.3% ; p=0.000) decreased significantly . 2) Suprailiac was the most reduced site by exercise-training among eight site skinfolds. Central skinfolds were changed more by exercise-training than peripheral skinfolds with reduction of 1.7$\pm$1.7mm and 0.2$\pm$1.9mm. Central site circumferences were reduced more by exercise-training than peripheral site circumferences. 3) Total cholesterol, HDL-cholesterol and LDL-cholesterol were not significantly changed by exercise-training while only triglyceride decreased (changes : 20.7$\pm$44.8mg/dl ; p<0.05). Changes in body weight were grately related to changes in total cholesterol. Changes in deep abdominal adipose tissue were related to changes in triglyceride.

• Journal of International Academy of Physical Therapy Research
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• v.2 no.2
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• pp.288-293
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• 2011

The purpose of this study is to examine the effects of gait training using functional electrical stimulation on the improvement of hemiplegic patients' functions for balance and gait velocity. The subjects of the experiment were determined to be 10 each hemiplegic patients who had been diagnosed with stroke or brain damage six months or longer earlier assigned to an experimental group and a control group respectively. The subjects were evaluated before the experiment using Tetrax and 10M gait tests, received gait training five times a week for four weeks using functional electrical stimulation and were evaluated after the experiment in the same method as used in the evaluation before the experiment. In order to examine differences between the experimental group that received gait training using functional electrical stimulation and the control group that was treated by functional electrical stimulation and received gait training thereafter, differences between before and after the experiment were analyzed using paired sample t-tests and differences in changes after the experiment between the experimental group and the control group were analyzed using independent sample t-tests in order to compare the two groups with each other. Experimental results showed significant differences in weight bearing, balance and gait velocity between before and after the experiment in the experimental group(p<.05). In the control group, whereas weight bearing and gait velocity did not show any significant difference between before and after the experiment(p>.05), balance showed significant differences(p<.05). Weight bearing, balance and gait velocity change rates showed significant differences between the experimental group and the control group(p<.05). In conclusion, it was indicated that gait training using functional electrical stimulation is effective for enhancing stroke patients' weight bearing rates, balance abilities and gait velocity.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.1021-1031
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• 2020

This study aims to invest in empirical relationships in a model that becomes the process of Team-Performance due to participation in practical training through causality between Training Effectiveness variables, soft-skill competence, Employee-Creativity, and team performance. This study uses a quantitative approach. The analytical tool used is structural equation modeling (SEM) using AMOS version 23 software. Accidental sampling technique is used to collect the sample. As many as 202 respondents filled up a survey questionnaire with complete and valid answers. This study's results significantly contribute to fill the gap of inadequate empirical evidence that can answer critical questions about the missing link between training and employee-performance, more specifically between training-effectiveness and team-performance. The results showed that practical training would encourage employees' soft-skill competence, improve Employee-Creativity, and improve Team-Performance. Furthermore, training-effectiveness also plays a significant role in enhancing employee-creativity and helping in optimal team-performance. This study also found that the relationship between employee-creativity and team-performance did not show positive and significant results; therefore, empirically, it did not support the hypothesis built in this study. Practical training targeted towards increasing soft-skills and creativity is a fundamental reason which not only aims to contribute toward organizational performance but also provides personal feedback for self-development.

• The Journal of Korean Academic Society of Nursing Education
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• v.13 no.2
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• pp.184-191
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• 2007

Purpose : The purpose of this study was to examine the effect of a neurofeedback training on developing creativity in nursing students. Method : A nonequivalent control group pre-posttest design was used. A convenience sample of 36 students from the Department of Nursing, at D University were assigned to experimental(n=18) and control(n=18) groups. The intervention program was Neurofeedback training for 6 weeks, 3 times a week. Outcome variables were fluency, flexibility, originality, and creativity overall. Result : Neurofeedback training was effective in enhancing flexibility and creativity overall, but was not effective in enhancing fluency and originality. Conclusion : Neurofeedback training may be a useful intervention to promote creativity of nursing students.

• International Journal of Contents
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• v.14 no.4
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• pp.24-29
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• 2018

EEG-based brain-computer interfaces has focused on explicitly expressed intentions to assist physically impaired patients. For EEG-based-computer interfaces to function effectively, it should be able to understand users' implicit information. Since it is hard to gather EEG signals of human brains, we do not have enough training data which are essential for proper classification performance of implicit intention. In this paper, we improve the subject independent classification of implicit intention through the generation of additional training data. In the first stage, we perform the PCA (principal component analysis) of training data in a bid to remove redundant components in the components within the input data. After the dimension reduction by PCA, we train ICA (independent component analysis) network whose outputs are statistically independent. We can get additional training data by adding Gaussian noises to ICA outputs and projecting them to input data domain. Through simulations with EEG data provided by CNSL, KAIST, we improve the classification performance from 65.05% to 66.69% with Gamma components. The proposed sample generation method can be applied to any machine learning problem with fewer samples.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.6
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• pp.121-130
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• 2007

In this paper, we present the development of a new training system for equilibrium sense and postural control. This system consists of an unstable platform, a force plate, a computer, and training programs. The unstable platform provides 360 degrees of movement allowing for training in all directions. To evaluate the effects of the training system, we performed various experiments to train the ability of equilibrium sense and postural control of fifteen young healthy subjects. We measured the time a subject maintains his or her center of pressure on a target, the time a subject moves his or her center of pressure to a target, and the mean absolute deviation of the trace before and after the training. We analyzed these parameters obtained before and after the training using paried-sample T-test. The result shows that the subjects experienced distinctive enhancement in their ability of postural control through the training using our system.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.2
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• pp.54.2-54.2
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• 2016

We present a machine learning approach to estimating stellar atmospheric parameters, effective temperature (Teff), surface gravity (log g), and metallicity ([Fe/H]) for stars observed during the course of the Sloan Digital Sky Survey (SDSS). For training a neural network, we randomly sampled the SDSS data with stellar parameters available from SEGUE Stellar Parameter Pipeline (SSPP) to cover the parameter space as wide as possible. We selected stars that are not included in the training sample as validation sample to determine the accuracy and precision of each parameter. We also divided the training and validation samples into four groups that cover signal-to-noise ratio (S/N) of 10-20, 20-30, 30-50, and over 50 to assess the effect of S/N on the parameter estimation. We find from the comparison of the network-driven parameters with the SSPP ones the range of the uncertainties of 73~123 K in Teff, 0.18~0.42 dex in log g, and 0.12~0.25 dex in [Fe/H], respectively, depending on the S/N range adopted. We conclude that these precisions are high enough to study the chemical and kinematic properties of the Galactic disk and halo stars, and we will attempt to apply this technique to Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), which plans to obtain about 8 million stellar spectra, in order to estimate stellar parameters.

• Journal of Engineering Education Research
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• v.12 no.2
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• pp.89-95
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• 2009

This thesis investigated way of employment, education course of a training school of Programmable Logic Controller a Field. I inquired into a demand of an engineer and a necessary level of professional education. As a result, I have a purpose in what offer the information that cared for a beginning to work elevation and education training and boat development of a student more than. Faced a human power demand in an education demand and a field rehearsal student demand and analyzed it. The sample extraction used industrial classification, work of scale, Assignment sample extraction way (quota sampling). All data called at a silver phone and the investigated, The data parser analyzed the statistics that used Microsoft Excel.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1110-1127
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• 2022

To improve the training efficiency and generalization performance of a support vector machine (SVM) in a large-scale set, an optimal SVM learning method based on adaptive sparse sampling and the granularity shift factor is presented. The proposed method combines sampling optimization with learner optimization. First, an adaptive sparse sampling method based on the potential function density clustering is designed to adaptively obtain sparse sampling samples, which can achieve a reduction in the training sample set and effectively approximate the spatial structure distribution of the original sample set. A granularity shift factor method is then constructed to optimize the SVM decision hyperplane, which fully considers the neighborhood information of each granularity region in the sparse sampling set. Experiments on an artificial dataset and three benchmark datasets show that the proposed method can achieve a relatively higher training efficiency, as well as ensure a good generalization performance of the learner. Finally, the effectiveness of the proposed method is verified.