• Journal of Korea Multimedia Society
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• v.23 no.9
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• pp.1139-1146
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• 2020

Recently, as e-learning technology advances, interaction and data exchange between lecturers and learners have become very important. In addition, accuracy of data delivery and efficiency of system implementation should be ensured. Considering these aspects, SignalR is the most suitable communication method for constructing an audience response system in e-learning. Existing audience response systems require separate wireless devices and have problems with system compatibility. SignalR, on the other hand, is capable of operating in all environments including PC programs, web, Android, and iOS, and has an advantage of being easy to develop applications. As such, SignalR is widely used in chatting functions for small scale, real-time communication system, and it has never been used to implement an audience response system. Thus, for the first time in this paper, an audience response system using SignalR was proposed and an experiment was conducted on whether it was applicable at the e-learning education field. Therefore, from the results fo an experiment, a variety of e-learning environments can be built through the audience response system using SignalR proposed in this paper.

• The Journal of Korean Academic Society of Nursing Education
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• v.16 no.1
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• pp.33-42
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• 2010

Purpose: The purpose of this study was to ascertain the degree of students' educational satisfaction according to their Problem-based learning strategy. Method: The subjects were 277 nursing students in C College. A questionnaire modified by researchers was used and analyzed by the SPSS WIN 12.0 program. Result: This study showed that there's a positive relationship between the level of students' educational satisfaction and their learning strategies, including collaborative, self-directed, self-expression and time management strategies. Those who were in the second year and those who have considered temporary absence from school and/or change of academic courses used the least learning strategies and showed the lowest level of educational satisfaction. The top three learning strategies influencing educational satisfaction were time management, collaborative strategies and self-directed strategies respectively. Self-expression strategy was not statistically significant as an influencing factor on educational satisfaction. Conclusion: The more learning strategies that are used, the higher the level of educational satisfaction as a whole. Further studies on how to increase student's educational satisfaction and a way to advance in learning strategies are recommended.

• Journal of Internet Computing and Services
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• v.9 no.6
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• pp.127-139
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• 2008

The value of up-to-date Mobile PC such as UMPC (Ultra Mobile Personal Computer) is recognized greatly in learning environment that busywork such as characteristic of transfer easy and real time communication possibility etc. and conversation with a colleague student, free sending of studying data and public ownership etc. is required. Wish to recognize whether is acting relevant role in u - unfold learning that inflect UMPC in integration research model, and UMPC is u searching for relevant element at studying activity unfolding process u - integration Inquiry-Based Learning that present in Korean education & research information service (KERIS) at fifth-year student science time In primary school in this research. This research result could take charge role of UMPCs' studying-activity though there is persistent feedback with teacher among studying-activity although UMPC's role is utilized on constituent that can be related with studying-activity in learning process.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.313-318
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• 2021

The advent of the Deep Learning has applied to many industrial and general applications having an impact on our lives these days. Certain type of machine learning model is needed to be designed for a specific problem of field. Recently, there are many instances to solve the various COVID-19 related problems using deep learning model. Therefore, in this paper, a deep learning model for predicting number of outpatients of a hospital in advance is suggested. The suggested deep learning model is designed by using the Keras in Jupyter Notebook. The prediction result is being analyzed with the real data in graph, as well as the loss rate with some validation data to verify either for the underfitting or the overfitting.

• The Journal of Korean Physical Therapy
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• v.31 no.4
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• pp.212-215
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• 2019

Purpose: This study examined whether there is a difference in motor learning through short-term repetitive movement practice in stroke survivors with a unilateral brain injury compared to normal elderly participants. Methods: Twenty-six subjects who were divided into a stroke group (n=13) or sex-aged matched normal elder group (n=13) participated in this study. To evaluate the effects of motor learning, the participants conducted a tracking task for visuomotor coordination. The accuracy index was calculated for each trial. Both groups received repetitive tracking task training of metacarpophalangeal joint for 50 trials. The stroke group performed a tracking task in the upper extremity insi-lesional to the damaged hemisphere, and the normal elder group performed the upper extremity matched for the same side. Results: Two-way repetitive ANOVA revealed a significant difference in the interactions ($time{\times}group$) and time effects. These results indicated that the motor skill improved in both the stroke and normal elder group with a tracking task. On the other hand, the stroke group showed lesser motor learning skill than the normal elder group, in comparison with the amount of motor learning improvement. Conclusion: These results provide novel evidence that stroke survivors with unilateral brain damage might have difficulty in performing ipsilateral movement as well as in motor learning with the ipsilateral upper limb, compared to normal elderly participants.

• Journal of Korean Physical Therapy Science
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• v.28 no.3
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• pp.1-10
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• 2021

Background: The purpose of this study is to investigate the effect of the online team project based learning on problem-solving, cooperative self-efficacy, and cooperative self-regulation of college students. Design: Single group pre-post design. Methods: The online team project based learning was conducted for a total of 92 college students for 8 weeks. A survey was conducted on problem-solving ability, cooperative self-efficacy, and cooperative self-regulation. In the online team project-based class, two projects were performed. It consists of video lectures and real-time video conferencing. Through the real-time video conference, the project was carried out based on discussion among learners and feedback was provided. Results: There was a significant difference in the change in problem-solving ability compared to before learning (p<0.05). As a result of the evaluation of cooperative self-efficacy, there was a significant difference (p<0.05). There was a significant differences in cooperative self-regulation compared to before learning (p<0.05). Conclusion: The online team project-based learning are effective in improving learners' problem-solving ability, cooperative self-efficacy, and cooperative self-regulation.

• Computers and Concrete
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• v.32 no.3
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• pp.263-286
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• 2023

Experimenting with concrete to determine its compressive and tensile strengths is a laborious and time-consuming operation that requires a lot of attention to detail. Researchers from all around the world have spent the better part of the last several decades attempting to use machine learning algorithms to make accurate predictions about the technical qualities of various kinds of concrete. The research that is currently available on estimating the strength of concrete draws attention to the applicability and precision of the various machine learning techniques. This article provides a summary of the research that has previously been conducted on estimating the strength of concrete by making use of a variety of different machine learning methods. In this work, a classification of the existing body of research literature is presented, with the classification being based on the machine learning technique used by the researchers. The present review work will open the horizon for the researchers working on the machine learning based prediction of the compressive strength of concrete by providing the recommendations and benefits and drawbacks associated with each model as determining the compressive strength of concrete practically is a laborious and time-consuming task.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.29-30
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• 2022

The unit-water content has a very significant effect on the durability of the construction structure and the quality of concrete. Although there are various methods for measuring the unit-water content, there are problems of time required for measurement, precision, and reproducibility. Recently, there is an FDR sensor capable of measuring moisture content in real time through an apparent dielectric constant change of electromagnetic waves. In addition, various artificial intelligence techniques that can non-linearly supplement the accuracy of FDR sensors are being studied. In this study, the accuracy of unit-water content measurement was compared and evaluated using machine learning and deep learning techniques after normalizing the data secured in concrete using frequency domain reflectometry (FDR) sensors used to measure soil moisture at home and abroad. The result of comparing the accuracy of machine learning and deep learning is judged to be excellent in the accuracy of deep learning, which can well express the nonlinear relationship between FDR sensor data and concrete unit-water content.

• International Journal of Advanced Culture Technology
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• v.12 no.1
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• pp.242-248
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• 2024

Contemporary societal and environmental transformations coincide with the emergence of novel mental health challenges. anxiety disorder, a chronic and highly debilitating illness, presents with diverse clinical manifestations. Epidemiological investigations indicate a global prevalence of 5%, with an additional 10% exhibiting subclinical symptoms. Notably, 9% of adolescents demonstrate clinical features. Untreated, anxiety disorder exerts profound detrimental effects on individuals, families, and the broader community. Therefore, it is very meaningful to predict anxiety disorder through machine learning algorithm analysis model. The main research content of this paper is the analysis of the prediction model of anxiety disorder by machine learning algorithms. The research purpose of machine learning algorithms is to use computers to simulate human learning activities. It is a method to locate existing knowledge, acquire new knowledge, continuously improve performance, and achieve self-improvement by learning computers. This article analyzes the relevant theories and characteristics of machine learning algorithms and integrates them into anxiety disorder prediction analysis. The final results of the study show that the AUC of the artificial neural network model is the largest, reaching 0.8255, indicating that it is better than the other two models in prediction accuracy. In terms of running time, the time of the three models is less than 1 second, which is within the acceptable range.

• Journal of KIBIM
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• v.6 no.4
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• pp.35-41
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• 2016

Learning effect is an observation that the more times a task is performed, the less time is required to produce the same amount of outcomes. The construction industry heavily relies on repeated tasks where the learning effect is an important measure to be used. However, most construction durations are calculated and applied in real projects without considering the learning effects in each of the repeated activities. This paper applied the learning effect to the repeated activities in a small sized apartment construction project. The result showed that there was about 10 percent of difference in duration (one approach of the total duration with learning effects in 41 days while the other without learning effect in 36.5 days). To make the comparison between the two approaches, a large number of BIM based computer simulations were generated and useful patterns were recognized using machine learning algorithm named Decision Tree (See5). Machine learning is a data-driven approach for pattern recognition based on observational evidence.