• The Journal of Korean Academic Society of Nursing Education
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• v.23 no.4
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• pp.409-418
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• 2017

Purpose: Ineffective communication between healthcare professionals leads to medical errors and puts patients at risk of harm. The aim of this study was to examine the effects of interdisciplinary communication training in simulated settings on self-confidence in communication, observed communication behavior, and technical skill performances of nursing students. Methods: A repeated measures design with one group was conducted. Data was collected from 92 nursing students through a self-administered questionnaire and an observed behavior checklist. Data analysis was performed using descriptive statistics, a paired t-test, the Wilcoxon signed rank test, the Friedmann test, a Repeated Measures ANOVA, and the Spearman correlation coefficient. Results: Self-confidence in communication, observed Identification-Situation-Background-Assessment-Recommendation-Read Back communication behavior, and technical skill performances of nursing students were significantly improved. In observed communication behavior, the performance of Assessment and Read Back communication significantly improved. However, communication of Background, Assessment, and Recommendation did not improve to a satisfactory level. Observed communication behavior was not correlated with the overall technical skill performance. Conclusion: These results indicate that interdisciplinary communication training in simulated settings was effective in improving nursing students' confidence and communication skills with physicians. Longitudinal studies with larger samples are recommended in order to verify the effects of interdisciplinary communication training on clinical outcomes as well as communication competence.

• Journal of the Korean Society of Costume
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• v.65 no.2
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• pp.125-143
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• 2015

This study is to propose an 'education-training-certificate of qualification' design of fashion accessories production, which can be applied to education in universities and individuals. It is based on the National Competency Standards (NCS), which was developed through the '2013 National Competency Standards Development Project' for the fashion accessories production. FGI (Focus Group Interviews), which is a research methodology, is carried out on target groups of educational experts and specialists in the field of fashion accessories production. Through this, five courses were suggested; first, 'fashion accessories design' course was proposed for the education and training of 'design development' and 'development of raw materials'. Secondly, 'fashion product production' course was proposed for the education and training of 'production of samples'. Thirdly, 'fashion product manufacture and planning' course was proposed for the competency element units: 'calculation of cost', 'determination of mass production model and price', 'planning of the main manufacturing process' and 'ordering of raw materials'. Lastly, 'mass production of fashion products' course and the 'field practice of fashion product manufacture' course were proposed for the competency element units: 'planning for mass production', 'preparation for mass production', 'mass production' and 'inspection of completed products'. In addition, a new certificate of 'technician of fashion accessory production' was proposed in order to test qualified skills for the fashion accessories production. The test is composed of a written examination of short-answer questions, technical drawing and production.

• Journal of Korean Physical Therapy Science
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• v.29 no.1
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• pp.47-54
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• 2022

Background: Plyometric exercise is an exercise exerting forceful power in a brief period using isotonic activation. It is effective to improve reaction of muscle, agility, endurance and athletics performance. Weight training is an exericse improving muscular strength, endurance and respirating ability applying diversely in frequency and load of exercise Plyometric exercise and Weight training is to facilitate the athletics performance though improving the function of lower limb muscle, there is a difference that Plyometic jump squats is the way to improve agility and Weight training is the way to improve muscular strength. Therefore, it is necessary to know how this difference effects on athletics performance as measuring ankle, ROM, and jumping ability. Design: Randomized controlled trial. Method: This study was conducted with the voluntary participation of 40 university students, who were randomly assigned to jump squat and calf raise groups (n=20 per group). For each subject, we measured the range of motion of the ankle joint before and after exercise, as well as a standing broad jump and vertical jump test performance. We compared the performance indices before and after exercises using paired t-tests, and between groups using independent-samples t-tests. Conclusions: Both jump squat and calf raise exercises improved ankle joint dorsiflexion and plantar flexion, as well as standing broad jump and vertical jump height performance. However, there were no significant differences before versus after exercise, or between exercise types. Although jump squats and calf raises have different purposes, it is thought that, in combination, these exercises improve performance more effectively than either alone, and that such a combined exercise program improves the quality of training in both the general public and athletes in various sports.

• The Plant Pathology Journal
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• v.38 no.4
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• pp.272-286
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• 2022

Black root rot (BRR) caused by Lasiodiplodia theobromae is an alarming disease of mulberry that causes tremendous economic losses to sericulture farmers in India and China. Successful control of this disease can be attained by screening germplasm and identifying resistant sources. Seventy four diseased root samples were collected from farmer's fields belonging to four major mulberry growing states of South India. Based on morpho-cultural and scanning electron microscopy studies, 57 fungal isolates were characterized and identified as L. theobromae. Phylogenetic analysis of concatenated internal transcribed spacer and β-tubulin sequences revealed variation of the representative 20 isolates of L. theobromae. Following the root dip method of inoculation, pathogenicity studies on susceptible mulberry genotypes (Victory-1 and Thailand male) recognized the virulent isolate MRR-142. Accordingly, MRR-142 isolate was used to evaluate resistance on a set of 45 diverse mulberry accessions. In the repeated experiments, the mulberry accession ME-0168 which is an Indonesian origin belonging to Morus latifolia was found to be highly resistant consistently against BRR. Eight accessions (G2, ME-0006, ME-0011, ME-0093, MI-0006, MI-0291, MI-0489, and MI-0501) were found to be resistant. These promising resistant resources may be exploited in mulberry breeding for developing BRR resistant varieties and to develop mapping populations which successively helps in the identification of molecular markers associated with BRR.

• Journal of Computing Science and Engineering
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• v.8 no.4
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• pp.207-214
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• 2014

In pedestrian detection applications, one of the most popular frameworks that has received extensive attention in recent years is widely known as a 'Hough forest' (HF). To improve the accuracy of detection, this paper proposes a novel split function to exploit the statistical information of the training set stored in each node during the construction of the forest. The proposed split function makes the trees in the forest more robust to noise and illumination changes. Moreover, the errors of each stage in the training forest are minimized using a global loss function to support trees to track harder training samples. After having the forest trained, the standard HF detector follows up to search for and localize instances in the image. Experimental results showed that the detection performance of the proposed framework was improved significantly with respect to the standard HF and alternating decision forest (ADF) in some public datasets.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.4106-4106
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• 2001

A method to discriminate human gallstones by nea. infrared(NIR) spectrometry using a soft independent modeling of class analogy (SIMCA) has been studied. The fifty NIR spectra of gallstones in the wavenumber range from 4500 to 10,000 cm$\^$-1/ were measured. The forty samples were classified to three classes, cholesterol stone, calcium bilirubinate stone and calcium carbonate stone according to the contents of major components in each gallstone. The training set which contained objects of the different known class was constructed using forty NIR spectra and the test set was made with ten different gallstone spectra. The number of important principal components(PCs) to describe the class was determined by cross validation in order to improve the decision criterion of the SIMCA for the training set. The score plots of the class training set whose objects belong to the other classes were inspected. The critical distance of each class was computed using both the Euclidean distance and the Mahalanobis distance at a proper level of significance(${\alpha}$). Two methods were compared with respect to classification and their robustness towards the number of PCs selected to describe different classes.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.3
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• pp.301-310
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• 2003

In this paper, we design and Implement the supervised classification systems for high resolution satellite images. The systems support various interfaces and statistical data of training samples so that we can select the m()st effective training data. In addition, the efficient extension of new classification algorithms and satellite image formats are applied easily through the modularized systems. The classifiers are considered the characteristics of spectral bands from the selected training data. They provide various supervised classification algorithms which include Parallelepiped, Minimum distance, Mahalanobis distance, Maximum likelihood and Fuzzy theory. We used IKONOS images for the input and verified the systems for the classification of high resolution satellite images.

• 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.

• East Asian Journal of Business Economics (EAJBE)
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• v.7 no.3
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• pp.75-81
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• 2019

Purpose - In this study, it investigates the relationship among brand asset, customer satisfaction, brand trust, and brand loyalty related to golf products. Research design and methodology - The study was conducted with 500 customers from five indoor and outdoor golf training centers located in Seoul, South Korea. The method of tabulation was developed using a nonprobability convenience sampling and the questionnaire was administered through self-administration. The survey was conducted on-site between July 2018 and August 2018 by four trained researchers, including the researchers. Five indoor golf training centers in Seoul were randomly selected, and a total of 500 samples were collected by radio at each training site. Of the 500 questionnaires collected, 449 were utilized once incomplete questionnaires were removed from the sample. Results - This study was as follows. First, brand asset was a significant predictor of customer satisfaction. Second, customer satisfaction was a significant predictor of brand asset. Third, customer satisfaction was a significant predictor of brand loyalty. Fourth, brand trust was a significant predictor of brand loyalty. Fifth, brand asset was a significant predictor of brand trust. Finally, brand trust was a significant predictor of brand loyalty. Conclusions - First, the results showed that brand assets had a significant impact on customer satisfaction. Second, customer satisfaction was shown to have a significant effect on brand trust. Third, customer satisfaction had a significant effect on brand loyalty.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.1
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• pp.49-59
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• 2019

Generative adversarial networks(GANs) have received great attention in the machine learning field for their capacity to model high-dimensional and complex data distribution implicitly and generate new data samples from the model distribution. This paper investigates the model training methodology, architecture, and various applications of generative adversarial networks. Experimental evaluation is also conducted for generating synthetic image dataset for defense using two types of GANs. The first one is for military image generation utilizing the deep convolutional generative adversarial networks(DCGAN). The other is for visible-to-infrared image translation utilizing the cycle-consistent generative adversarial networks(CycleGAN). Each model can yield a great diversity of high-fidelity synthetic images compared to training ones. This result opens up the possibility of using inexpensive synthetic images for training neural networks while avoiding the enormous expense of collecting large amounts of hand-annotated real dataset.