• The Korean Journal of Emergency Medical Services
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• v.9 no.2
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• pp.79-88
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• 2005

The purpose of this study was to develope the Complete Airway Obstruction Maneuver Training Program and identify its effects on learning motivation, knowledge and skill of choking management in the primary school students. The subjects for the experimental group of 38 students and the control group of 39 students, all of whom are primary school students in Gwangju, the Republic of Korea. A non-equivalent control group pretest-posttest design was used and data were collected from November to December, 2003. During about 2 weeks, 2 times for a week with 80 minutes at a time, the complete airway obstruction maneuver training program was conducted in the experimental group. Experimental data were analyzed through SPSS/win 11.0 PC+, and the tests examining general characteristics between the experimental group and the control group were conducted through $x^2$-test. Fisher's exact probability test and t-test, and identifying the effect of the complete airway obstruction Maneuver training program was analysed through t-test and ANCOVA. The results of the study were as follows: 1. After intervention on the complete airway obstruction maneuver training program, Learning motivation in the experimental group was significantly higher than that of the control group. 2. After intervention on the complete airway obstruction maneuver training program, knowledge of choking management the experimental group was significantly higher than that of the control group(F=223.637, p=.000). 3. After intervention on the complete airway obstruction maneuver training program, skill of choking management the experimental group was significantly higher than that of the control group(t=46.800, p=.014). These findings suggest that the complete airway obstruction maneuver training program can facilitate learning motivation, knowledge and skill of choking management in the primary school students. Therefore it is considered that the complete airway obstruction maneuver training program can be utilized as a effective way to implement the 7th national curriculum for creative extra-activities.

• Journal of Korean Society for Quality Management
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• v.38 no.1
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• pp.20-33
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• 2010

This study investigates the relationships of total quality management (TQM), organizational learning (OL) activities and business performance and examines the partial mediation effect of OL activities on business performance in Korean industrial manufacturing setting. Main target sample firms were all manufacturing companies listed in the Korea Composite Stock Price Index (KOSPI) and 206 firms participated. This study theoretically develops a conceptual model with 3 hypotheses regarding how TQM practices influence OL activities and how the OL activities partially mediate between the TQM practices and business performance. To examine these hypotheses, Structural Equation Modeling (SEM) was employed and an alternative model which includes a path between errors of leadership factor and OL construct was developed. The findings are TQM practices cannot directly influence business performance but indirectly impact business performance through OL activities. This study found that OL activities playa role as firms' critical competency to improve business performance.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.183-191
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• 2020

Today, taking photos using smartphones has become an essential element of modern people. According to these social changes, modern people need a larger storage capacity, and the number of unnecessary photos has increased. To support the storage, cloud-based photo storage services from various platforms have appeared, and many people are using the services. As the number of photos increases, it is difficult for users to find the photos they want, and it takes a lot of time to organize. In this paper, we propose a cloud-based photo management service that facilitates photo management by classifying photos and recommending unnecessary photos using deep learning. The service provides the function of tagging photos by identifying what the subject is, the function of checking for wrongly taken photos, and the function of recommending similar photos. By using the proposed service, users can easily manage photos and use storage capacity efficiently.

• Proceedings of the Safety Management and Science Conference
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• 2012.04a
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• pp.139-155
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• 2012

A fundamental concepts of business environment changes and the importance of stakeholder's value creation is changing in the business. This study ISO9004: 2009 quality management system of Category 5: Strategy and Policy, Category 10: improvement, innovation and learning (Note) SBK target was to develop a model that is the company's sustained success. Three concepts of the new revision of ISO9004" in response to environmental changes," "learning", "innovation" (Note) SBK applied to the project settings and talent establish long-term vision was to establish the process as the organization's learning content was TDR for the creation of exceptional and innovative programs were introduced. As a result, (Note) SBK three years of continuous business performance indicator has grown dramatically to more than 50% continued success is going to create business models. But 100 years to accomplish the vision, ISO9004 model needs to extends the entire category as a management system to achieve the optimization needed.

• The Journal of Industrial Distribution & Business
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• v.10 no.2
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• pp.25-37
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• 2019

Purpose - The goal of this study is to analyze the differences in education performances between students of the government's financial support program and those who do not receive support at a local university in Korea. Research design, data, and methodology - The questionnaire used was NASEL. NASEL is considered a highly suitable survey tool for professors, courses, and performances in Korean universities. The 290 students who participated and 44 students do not participate in the financial support program were surveyed for 10 days. The characteristics of students were investigated by frequency analysis and technical statistics. The analysis of student collective characteristics used independent t and f-tests,and one-way ANOVA with IBM SPSS Statistics 22.0 for statistical purposes. Results - The p-value of the group receiving financial support and the group without financial support in active-collaborative learning is 0.167. The p-value of the economically supported group and the non-supported group of the faculty-student interaction is 0.281. The confidence coefficient of the active-collaborative learning questionnaire is 0.861. The reliability coefficient of the questionnaire for the faculty-student interaction questionnaire is 0.871. Conclusions - There are no clear differences in active-collaborative learning and faculty-student interaction between participating and non-participating students in the economic program.

• Journal of Korean Society for Quality Management
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• v.47 no.4
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• pp.687-700
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• 2019

Purpose: The purpose of this study is to investigate the interaction effects between price determinants of artworks. We expand the methodology in art market by applying machine learning techniques to estimate the price of artworks and compare linear regression and machine learning in terms of prediction accuracy. Methods: Moderated regression analysis was performed to verify the interaction effects of artistic characteristics on price. The moderating effects were studied by confirming the significance level of the interaction terms of the derived regression equation. In order to derive price estimation model, we use multiple linear regression analysis, which is a parametric statistical technique, and k-nearest neighbor (kNN) regression, which is a nonparametric statistical technique in machine learning methods. Results: Mostly, the influences of the price determinants of art are different according to the auction types and the artist 's reputation. However, the auction type did not control the influence of the genre of the work on the price. As a result of the analysis, the kNN regression was superior to the linear regression analysis based on the prediction accuracy. Conclusion: It provides a theoretical basis for the complexity that exists between pricing determinant factors of artworks. In addition, the nonparametric models and machine learning techniques as well as existing parameter models are implemented to estimate the artworks' price.

• Journal of Information Management
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• v.38 no.3
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• pp.137-160
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• 2007

The purpose of this study is to examine the meaning and status of the current service of academic libraries in the aspect of its supporting roles for academic courses. The research methods include an examination of model cases from the U.S.A. and Hong Kong and also an electronic questionnaire survey of 32 academic libraries in Korea(67% response rate). With the result of the research analysis, this study aimed to provide optimal administrative plans in e-learning environment.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.459-471
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• 2022

Purpose: The purpose of this study was to increase prediction accuracy for an anomaly interval identified using an artificial intelligence-based time series anomaly detection technique by establishing a pre-processing process. Methods: Significant variables were extracted by applying feature selection techniques, and anomalies were derived using the TadGAN time series anomaly detection algorithm. After applying machine learning and deep learning methodologies using normal section data (excluding anomaly sections), the explanatory power of the anomaly sections was demonstrated through performance comparison. Results: The results of the machine learning methodology, the performance was the best when SHAP and TadGAN were applied, and the results in the deep learning, the performance was excellent when Chi-square Test and TadGAN were applied. Comparing each performance with the papers applied with a Conventional methodology using the same data, it can be seen that the performance of the MLR was significantly improved to 15%, Random Forest to 24%, XGBoost to 30%, Lasso Regression to 73%, LSTM to 17% and GRU to 19%. Conclusion: Based on the proposed process, when detecting unsupervised learning anomalies of data that are not actually labeled in various fields such as cyber security, financial sector, behavior pattern field, SNS. It is expected to prove the accuracy and explanation of the anomaly detection section and improve the performance of the model.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.13-25
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• 2023

This study analyzed the impact of expectations congruence, usefulness, ease of use, and interaction on the satisfaction of learners who participated in distance learning. For this purpose, a survey was conducted targeting students taking remote classes operated by universities after COVID-19. The survey was conducted online using learning management system notices and mobile notifications at the end of remote classes. The research results are as follows. First, expectation confirmation was found to have a significant effect on satisfaction. Second, usability was found to have no significant effect on satisfaction. Third, ease of use was found to have a significant effect on satisfaction. Fourth, interaction was found to have the greatest impact on satisfaction. This study analyzed the impact on satisfaction with university distance learning after COVID-19. Based on the research results, it is necessary to specify teaching strategies such as supporting a distance learning environment optimized for learners and strengthening interaction.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.87-106
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• 2024

Vehicle-to-Home (V2H) and Home Centralized Photovoltaic (HCPV) systems can address various energy storage issues and enhance demand response programs. Renewable energy, such as solar energy and wind turbines, address the energy gap. However, no energy management system is currently available to regulate the uncertainty of renewable energy sources, electric vehicles, and appliance consumption within a smart microgrid. Therefore, this study investigated the impact of solar photovoltaic (PV) panels, electric vehicles, and Micro-Grid (MG) storage on maximum solar radiation hours. Several Deep Learning (DL) algorithms were applied to account for the uncertainty. Moreover, a Reinforcement Learning HCPV (RL-HCPV) algorithm was created for efficient real-time energy scheduling decisions. The proposed algorithm managed the energy demand between PV solar energy generation and vehicle energy storage. RL-HCPV was modeled according to several constraints to meet household electricity demands in sunny and cloudy weather. Simulations demonstrated how the proposed RL-HCPV system could efficiently handle the demand response and how V2H can help to smooth the appliance load profile and reduce power consumption costs with sustainable power generation. The results demonstrated the advantages of utilizing RL and V2H as potential storage technology for smart buildings.