• Speech Sciences
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• v.14 no.3
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• pp.21-29
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• 2007

The necessity for improving the English listening ability of Korean learners has been emphasized since the ultimate goal of English education converted to CLT(Communicative Language Teaching) in Korea. Verbotonal Approach as an auditory-based strategy has been proved to be effective substantially in maximizing the listening skill of spoken foreign language. The purpose of this study is to find out an efficient way of improving listening ability for Korean middle school students by employing OFH(Optimal Frequency of Hearing) using Tonality Word Sentence Test, before & after using Listen II Verbotonal training unit based on VTS(Verbotonal System). The results of the listening tests showed that the listening ability of the subjects increased by 16.7% on the words and by 5.5% on the sentences after using Listen II, compared with before using Listen II and that the improvement rate of listening ability on the level of words is much higher than that on the level of sentences. From the results, we can come to a conclusion that training the listening skill with words in mid-tonality and low-tonality based on OFH might give a great positive effect in improving listening ability for Korean learners of English.

• Journal of Electrical Engineering and Technology
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• v.7 no.1
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• pp.109-114
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• 2012

In this paper, we utilize training strategy of hidden Markov model (HMM) to use in versatile issues such as classification of time-series sequential data such as electric transient disturbance problem in power system. For this, an automatic means of optimizing HMMs would be highly desirable, but it raises important issues: model interpretation and complexity control. With this in mind, we explore the possibility of using genetic algorithm (GA) and harmony search (HS) algorithm for optimizing the HMM. GA is flexible to allow incorporating other methods, such as Baum-Welch, within their cycle. Furthermore, operators that alter the structure of HMMs can be designed to simple structures. HS algorithm with parameter-setting free technique is proper for optimizing the parameters of HMM. HS algorithm is flexible so as to allow the elimination of requiring tedious parameter assigning efforts. In this paper, a sequential data analysis simulation is illustrated, and the optimized-HMMs are evaluated. The optimized HMM was capable of classifying a sequential data set for testing compared with the normal HMM.

• Journal of Korean Critical Care Nursing
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• v.10 no.2
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• pp.1-13
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• 2017

Purpose: This study investigated the effects that the use of a booklet for intensive care unit nurses had on radiation safety management education (knowledge about and behaviors in radiation safety management, and awareness of anxiety caused by radiation hazards). Methods: A randomized control group pretest-posttest design was used. A booklet about radiation safety management developed by the authors was used as educational material. Participants (N=42) were intensive care unit nurses of P hospital in B city. Training was provided to the experimental group (N=21). Knowledge about and behaviors in radiation safety management and awareness of anxiety caused by radiation hazards were measured by questionnaires before and after the intervention. Data was analyzed by an $X^2$-test, non-paired t-test, and paired t-test. Results: There was a significant difference between groups in knowledge of (t=-14.932, p<.001) and behaviors in (t=-8.297, p<.001) radiation safety management and awareness of anxiety caused by radiation hazards (t=9.378, p<.001). Conclusion: The levels of knowledge about and behaviors in radiation safety management and awareness of anxiety generated by radiation hazards of intensive care unit nurses increased after receiving one session of radiation safety management education using the booklet. Therefore, providing radiation safety management training is suggested as an effective strategy for improving radiation safety management.

• The Journal of Korean Academic Society of Nursing Education
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• v.23 no.1
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• pp.48-56
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• 2017

Purpose: This study was conducted to identify the relationships between the perceived and actual achievement on skill performance assessment and identify the relation to learning motivation factors. Methods: A total of 80 senior nursing students currently studying at a university participated in the study in 2015. Students completed a performance examination of 20 nursing skills at the end of their 7-week training period; their performance was rated using checklists. Students then completed a survey, which included questions about learning motivation and perceived achievement level. Data were analyzed by descriptive statistics, Pearson's correlation, and Kruskal-Wallis test. Results: There was a weak correlation between perceived and actual achievement. Intrinsic and extrinsic goal-orientation and self-efficacy in learning motivation factors was significantly correlated to perceived achievement. Perceived achievement and self-efficacy in middle quartile of actual achievement were higher than other upper- or lower-quartile group. Conclusion: The findings suggest that the motivation factors of learners should be taken into account to maximize academic achievement in nursing skills training. In addition, it should be considered a strategy to reduce the gap between perceived and actual achievement.

• The Journal of Korea Robotics Society
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• v.10 no.2
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• pp.61-69
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• 2015

In this paper, we propose and examine the feasibility of the robot-assisted behavioral intervention system so as to strengthen positive response of the children with autism spectrum disorder (ASD) for learning social skills. Based on well-known behavioral treatment protocols, the robot offers therapeutic training elements of eye contact and emotion reading respectively in child-robot interaction, and it subsequently accomplishes pre-allocated meaningful acts by estimating the level of children's reactivity from reliable recognition modules, as a coping strategy. Furthermore, for the purpose of labor saving and attracting children's interest, we implemented the robotic stimulation configuration with semi-autonomous actions capable of inducing intimacy and tension to children in instructional trials. From these configurations, by evaluating the ability of recognizing human activity as well as by showing improved reactivity for social training, we verified that the proposed system has some positive effects on social development, targeted for preschoolers who have a high functioning level.

• Journal of Korea Multimedia Society
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• v.20 no.5
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• pp.769-781
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• 2017

Deep learning networks like Convolutional Neural Networks (CNNs) show successful performances in many computer vision applications such as image classification, object detection, and so on. For implementation of deep learning networks in embedded system with limited processing power and memory, deep learning network may need to be simplified. However, simplified deep learning network cannot learn every possible scene. One realistic strategy for embedded deep learning network is to construct a simplified deep learning network model optimized for the scene images of the installation place. Then, automatic training will be necessitated for commercialization. In this paper, as an intermediate step toward automatic training under fisheye camera environments, we study more precise human localization in fisheye images, and propose an accurate human localization method, Automatic Ground-Truth Labelling Method (AGTLM). AGTLM first localizes candidate human object bounding boxes by utilizing GoogLeNet-LSTM approach, and after reassurance process by GoogLeNet-based CNN network, finally refines them more correctly and precisely(tightly) by applying saliency object detection technique. The performance improvement of the proposed human localization method, AGTLM with respect to accuracy and tightness is shown through several experiments.

• East Asian Journal of Business Economics (EAJBE)
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• v.9 no.4
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• pp.29-41
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• 2021

Purpose - The current study explores pro-environment human resource management attributes like an organization's leadership support, training, empowerment, and motivation practices to encourage employees to adopt an environmentally friendly lifestyle, leading to the success of the pro-environmental initiatives pursued by the organization. Research design, data, and methodology - The research subject is a considerable determinant that helps the research choose which qualitative textual analysis will suit that specific research. This study is suitable to conduct qualitative textual research because the justification for the qualitative content analysis used by a researcher is guided by the subject of the research, the available funds, the available time, and the research objectives. Result - Various solutions have been identified to ensure that all interventions taken by an organization, especially in educating and training their employees, are efficient, effective, and impactful. They revolve around the individual group, organizational, societal, and government policy approaches. Solutions will create a dedication to developing sustainability and ensuring that employees are positive when dealing with the surrounding. Conclusion - Consequently, combined efforts involving employees, society, organizations, and the government are necessary for formulating and implementing a practical course of action. This is to end the ongoing environmental degradation and foster positive behavioral change that involves activities and initiatives that will improve environmental performance for current and future generations.

• Structural Engineering and Mechanics
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• v.78 no.2
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• pp.209-218
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• 2021

For engineering, there are two major challenges in reliability analysis. First, to ensure the accuracy of simulation results, mechanical products are usually defined implicitly by complex numerical models that require time-consuming. Second, the mechanical products are fortunately designed with a large safety margin, which leads to a low failure probability. This paper proposes an efficient and high-precision adaptive active learning algorithm based on the Kriging surrogate model to deal with the problems with low failure probability and time-consuming numerical models. In order to solve the problem with multiple failure regions, the adaptive kernel-density estimation is introduced and improved. Meanwhile, a new criterion for selecting points based on the current Kriging model is proposed to improve the computational efficiency. The criterion for choosing the best sampling points considers not only the probability of misjudging the sign of the response value at a point by the Kriging model but also the distribution information at that point. In order to prevent the distance between the selected training points from too close, the correlation between training points is limited to avoid information redundancy and improve the computation efficiency of the algorithm. Finally, the efficiency and accuracy of the proposed method are verified compared with other algorithms through two academic examples and one engineering application.

• Structural Engineering and Mechanics
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• v.70 no.6
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• pp.671-681
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• 2019

This paper examines the applicability of artificial neural network (ANN) and multivariate adaptive regression splines (MARS) to predict the compressive strength of bacteria incorporated geopolymer concrete (GPC). The mix is composed of new bacterial strain, manufactured sand, ground granulated blast furnace slag, silica fume, metakaolin and fly ash. The concentration of sodium hydroxide (NaOH) is maintained at 8 Molar, sodium silicate ($Na_2SiO_3$) to NaOH weight ratio is 2.33 and the alkaline liquid to binder ratio of 0.35 and ambient curing temperature ($28^{\circ}C$) is maintained for all the mixtures. In ANN, back-propagation training technique was employed for updating the weights of each layer based on the error in the network output. Levenberg-Marquardt algorithm was used for feed-forward back-propagation. MARS model was developed by establishing a relationship between a set of predictors and dependent variables. MARS is based on a divide and conquers strategy partitioning the training data sets into separate regions; each gets its own regression line. Six models based on ANN and MARS were developed to predict the compressive strength of bacteria incorporated GPC for 1, 3, 7, 28, 56 and 90 days. About 70% of the total 84 data sets obtained from experiments were used for development of the models and remaining 30% data was utilized for testing. From the study, it is observed that the predicted values from the models are found to be in good agreement with the corresponding experimental values and the developed models are robust and reliable.

• Endocrinology and Metabolism
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• v.33 no.4
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• pp.435-444
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• 2018

The prevalence of chronic diseases including osteoporosis and sarcopenia increases as the population ages. Osteoporosis and sarcopenia are commonly associated with genetics, mechanical factors, and hormonal factors and primarily associated with aging. Many older populations, particularly those with frailty, are likely to have concurrent osteoporosis and sarcopenia, further increasing their risk of disease-related complications. Because bones and muscles are closely interconnected by anatomy, metabolic profile, and chemical components, a diagnosis should be considered for both sarcopenia and osteoporosis, which may be treated with optimal therapeutic interventions eliciting pleiotropic effects on both bones and muscles. Exercise training has been recommended as a promising therapeutic strategy to encounter the loss of bone and muscle mass due to osteosarcopenia. To stimulate the osteogenic effects for bone mass accretion, bone tissues must be exposed to mechanical load exceeding those experienced during daily living activities. Of the several exercise training programs, resistance exercise (RE) is known to be highly beneficial for the preservation of bone and muscle mass. This review summarizes the mechanisms of RE for the preservation of bone and muscle mass and supports the clinical evidences for the use of RE as a therapeutic option in osteosarcopenia.