• Journal of the Korea Society of Computer and Information
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• v.25 no.8
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• pp.111-117
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• 2020

Recently, with the high interest of machine learning, the need for educational controllers to interface with physical devices has increased. However, existing controllers are limited in terms of high cost and area of utilization for educational purposes. In this paper, motion control controllers using brain waves are proposed for the purpose of students' machine learning applications. The brain motion that occurs when imagining a specific action is measured and sampled, then the sample values were learned through Tensor Flow and the motion was recognized in contents such as games. Movement variation for motion recognition consists of directionality and jump motion. The identification of the recognition behavior is sent to a game produced by an Unreal Engine to operate the character in the game. In addition to brain waves, the implemented controller can be used in various fields depending on the input signal and can be used for educational purposes such as machine learning applications.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1615-1622
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• 2022

In order to overcome the limitations of the instructor-centered lecture-style teaching method, recently, flipped learning, a learner-centered teaching method, has been widely introduced. However, despite the many advantages of flipped learning, there is a problem that students cannot solve questions that arise during prior learning in real time. Therefore, in order to solve this problem, we developed DBbot, an assistant chatbot for database design course managed in the flipped learning method. The DBBot is composed of a chatbot app for learners and a chatbot management app for instructors. Also, it's implemented so that questions that instructors can anticipate in advance, such as questions related to class operation and every semester repeated questions related to learning content, can be answered using Google's DialogFlow. It's implemented so that questions that the instructor cannot predict in advance, such as questions related to team projects, can be answered using the question/answer DB and the BM25 algorithm, which is a similarity comparison algorithm.

• The Journal of Korean Academic Society of Nursing Education
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• v.30 no.3
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• pp.290-300
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• 2024

Purpose: Nursing clinical practice education is transforming with the advent of mobile education and the unique experiences it offers in caring for virtual patients. For this innovative approach, this study aims to evaluate the efficacy of mobile-based virtual women's breast cancer nursing simulation training content on nursing students' confidence, satisfaction, and learning flow. It also examines the nursing students' virtual patient care experiences. Methods: A mixed methods approach using a convergent design was employed to examine students' cancer care confidence and satisfaction, learning flow, and learning experiences. Quantitative data through online questionnaires and qualitative data through focus group interviews were collected, merged, and analyzed. Results: This study developed a virtual nursing training module aimed at caring for women with breast cancer, a novel approach to facilitate mobile-based simulation training for nursing students. Data were analyzed using descriptive analysis, a chi-squared test, Fisher's exact test, t-test for participant homogeneity (experimental: 20, control: 20), independent t-test, and paired t-test. Satisfaction (t=3.53, p=.001) and confidence (t=4.07, p=.001), as well as flow (t=3.78, p=.001), significantly improved in the experimental group compared to the control group. Two core themes and five sub-themes were derived from the experimental group's experiences acquired by caring for women with breast cancer virtually, including that the students "Virtually cared for breast cancer patients, learning as if real." Conclusion: The mobile-based virtual nursing simulation training content allowed nursing students to upgrade their comprehensive nursing care skills by experiencing a fun and practical environment made possible by a new learning method.

• Journal of the Korea Society of Computer and Information
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• v.29 no.10
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• pp.129-135
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• 2024

In this paper, we design and implement a worker musculoskeletal assessment platform. The three core components of this platform are the Mobile App, the Modeling Server, and the Web Platform. The Mobile App is an Android application developed in Kotlin, targeting Android platform 12 (S) and Android API Level 31 devices. The app utilizes the camera to capture various worker motion data and transmits it to the Modeling Server. The Modeling Server is implemented using Node.js. This server converts the worker's motion data-such as points, skeleton, and x, y, z coordinate data, measured by the mobile app-into multidimensional arrays. It then applies machine learning frameworks like TensorFlow and Keras to predict the worker's posture. The worker posture learning model is built using Teachable Machine. The Web Platform is developed using React and visualizes the worker's movements as 3D animations along a timeline. The machine learning-based worker musculoskeletal assessment platform developed in this paper aims to contribute to minimizing musculoskeletal disorders in workers at industrial sites.

• Korean Management Science Review
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• v.31 no.3
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• pp.77-94
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• 2014

This study empirically investigated the relationships among inter-organizational cost management (IOCM), cooperation with suppliers, information exchange between partners, inter-organizational learning, control integration, and the supply-chain performance of a firm. The results showed that the adoption of IOCM positively affects the collaboration between buyers and suppliers, which also leads to the increased information flow between them. According to the results of this study, it was found that inter-organizational information flow causes inter-organizational learning, and this learning contributes to the improved supply-chain performance. In this study, the positive effects of the cooperation with suppliers through IOCM on the control integration in supply-chains were not empirically confirmed. However, the impact of IOCM on control integration was significant and positive. Finally, the fact that the enhanced control integration can improve the supply-chain performance of a firm was empirically demonstrated.

• Computers and Concrete
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• v.16 no.5
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• pp.741-757
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• 2015

In the last decade, several modeling approaches have been proposed and applied to estimate the high-performance concrete (HPC) slump flow. While HPC is a highly complex material, modeling its behavior is a very difficult issue. Thus, the selection and application of proper modeling methods remain therefore a crucial task. Like many other applications, HPC slump flow prediction suffers from noise which negatively affects the prediction accuracy and increases the variance. In the recent years, ensemble learning methods have introduced to optimize the prediction accuracy and reduce the prediction error. This study investigates the potential usage of bagging (Bag), which is among the most popular ensemble learning methods, in building ensemble models. Four well-known artificial intelligence models (i.e., classification and regression trees CART, support vector machines SVM, multilayer perceptron MLP and radial basis function neural networks RBF) are deployed as base learner. As a result of this study, bagging ensemble models (i.e., Bag-SVM, Bag-RT, Bag-MLP and Bag-RBF) are found superior to their base learners (i.e., SVM, CART, MLP and RBF) and bagging could noticeable optimize prediction accuracy and reduce the prediction error of proposed predictive models.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.441-444
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• 2016

Peace on the Korean Peninsula is threatened by physical aggressions and cyber terrors such as nuclear tests, missile launchings, senior government officials' smart phone hackings and DDos attacks to banking systems. Cyber attacks such as vulnerability for the hackings, malware distributions are generally defended by passive defense through the detecting signs of first invasion and attack, data analysis, adding library and updating vaccine programs. In this paper the concept of security program based on Google TensorFlow machine learning ability to perform adding libraries and solving security vulnerabilities by itself is researched and proposed.

• Journal of Korean Dental Science
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• v.16 no.1
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• pp.47-62
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• 2023

Purpose: We aimed to investigate the objective cutoff values of unstimulated flow rates (UFR) and stimulated salivary flow rates (SFR) in patients with xerostomia and to present an optimal machine learning model with a classification and regression tree (CART) for all ages. Materials and Methods: A total of 829 patients with oral diseases were enrolled (591 females; mean age, 59.29±16.40 years; 8~95 years old), 199 patients with xerostomia and 630 patients without xerostomia. Salivary and clinical characteristics were collected and analyzed. Result: Patients with xerostomia had significantly lower levels of UFR (0.29±0.22 vs. 0.41±0.24 ml/min) and SFR (1.12±0.55 vs. 1.39±0.94 ml/min) (P<0.001), respectively, compared to those with non-xerostomia. The presence of xerostomia had a significantly negative correlation with UFR (r=-0.603, P=0.002) and SFR (r=-0.301, P=0.017). In the diagnosis of xerostomia based on the CART algorithm, the presence of stomatitis, candidiasis, halitosis, psychiatric disorder, and hyperlipidemia were significant predictors for xerostomia, and the cutoff ranges for xerostomia for UFR and SFR were 0.03~0.18 ml/min and 0.85~1.6 ml/min, respectively. Conclusion: Xerostomia was correlated with decreases in UFR and SFR, and their cutoff values varied depending on the patient's underlying oral and systemic conditions.

• Journal of Technologic Dentistry
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• v.42 no.3
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• pp.248-253
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• 2020

Purpose: This study aimed to verify whether blended learning is worth alternating with traditional face-to-face learning for some dental technology students in practice teaching. Methods: A total of 68 students were included in this study. They were divided into two groups to compare blended learning and traditional face-to-face learning. The experiment had been carried out over 15 weeks. The following tests were performed: test of instructional quality, test of learning satisfaction, test of perceived usefulness, and test of learning flow. The IBM SPSS software was used to analyze the data. Results: The learning satisfaction and the perceived useful of blended learning by students appeared to be higher than that of traditional face-to-face learning. However, there was no significant difference in the variables of traditional face-to-face learning and those of blended learning (p<0.05). Conclusion: Blended learning is an alternative to traditional face-to-face learning for some dental technology students in practice teaching.

• Journal of Digital Convergence
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• v.9 no.4
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• pp.281-289
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• 2011

This paper provides the emerging flow of new communication technology using ubiquitous learning (u-Learning). In the intelligent Ubiquitous environment, humans and devices with computing abilities become interoperable. u-Learning will lead students to open their minds to the world and motivate self-learning, which may lead them to learn and communicate more efficiently, and save time, cost and energy. Through case research, regarding education, learning attitude, custom and, personal relations, one must solve the fundamental issues of misuse and outflow problems regarding personal information that will be widely collected in detail than the present condition, and in order for this not to happen, further support of the law and system, plus ethical perspectives must be considered in order to progress.