• Journal of Information Processing Systems
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• v.18 no.2
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• pp.197-207
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• 2022

The applications and user interfaces (UIs) of smart mobile devices are constantly diversifying. For example, deep learning can be an innovative solution to classify widgets in screen images for increasing convenience. To this end, the present research leverages captured images and the ReDraw dataset to write deep learning datasets for image classification purposes. First, as the validation for datasets using ResNet50 and EfficientNet, the experiments show that the dataset composed in this study is helpful for classification according to a widget's functionality. An implementation for widget detection and classification on RetinaNet and EfficientNet is then executed. Finally, the research suggests the Widg-C and Widg-D datasets-a deep learning dataset for identifying the widgets of smart devices-and implementing them for use with representative convolutional neural network models.

• International Journal of Advanced Culture Technology
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• v.12 no.2
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• pp.190-195
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• 2024

This study aimed to investigate types of emotional experiences that college students undergo, particularly those affecting learning motivation and academic performance. To this end, six college students residing in Seoul, South Korea participated in a series of 'focus-group interview (FGI)' sessions in which in-depths discussions took place. The researcher attempted to draw the participant students' opinions and ideas as they made interactions with each other. Three participants were placed in each of two groups, and each group had approximately 90-minutes-long sessions. The results showed that positive emotions, such as joy and enthusiasm, can increase learning motivation and academic achievement, while negative emotions such as anxiety and stress can hinder them. The findings also highlight that students actively employ coping strategies to manage negative emotions. Moreover, the study underscores students' desire for improved emotional support from instructors, indicating a gap between their expectations and the actual emotional care provided in educational settings. Relevant issues are discussed for future suggestions.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.91-102
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• 2022

With the evolution of wireless sensor network (WSN) technology, the routing policy has foremost importance in the Internet of Things (IoT). A systematic routing policy is one of the primary mechanics to make certain the precise and robust transmission of wireless sensor networks in an energy-efficient manner. In an IoT environment, WSN is utilized for controlling services concerning data like, data gathering, sensing and transmission. With the advantages of IoT potentialities, the traditional routing in a WSN are augmented with decision-making in an energy efficient manner to concur finer optimization. In this paper, we study how to combine IoT-based deep learning classifier with routing called, Kriging Regressive Deep Belief Neural Learning (KR-DBNL) to propose an efficient data packet routing to cope with scalability issues and therefore ensure robust data packet transmission. The KR-DBNL method includes four layers, namely input layer, two hidden layers and one output layer for performing data transmission between source and destination sensor node. Initially, the KR-DBNL method acquires the patient data from different location. Followed by which, the input layer transmits sensor nodes to first hidden layer where analysis of energy consumption, bandwidth consumption and light intensity are made using kriging regression function to perform classification. According to classified results, sensor nodes are classified into higher performance and lower performance sensor nodes. The higher performance sensor nodes are then transmitted to second hidden layer. Here high performance sensor nodes neighbouring sensor with higher signal strength and frequency are selected and sent to the output layer where the actual data packet transmission is performed. Experimental evaluation is carried out on factors such as energy consumption, packet delivery ratio, packet loss rate and end-to-end delay with respect to number of patient data packets and sensor nodes.

• International Journal of Contents
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• v.18 no.3
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• pp.58-65
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• 2022

This study proposed a soft skill integration activity for online EFL classrooms and investigated student responses. Toward this end, this study recruited 54 college students taking an English Presentation and Discussion class in South Korea. Participants were assigned into high and low-proficiency groups based on the Test of English for International Communication. This study employed questionnaire, class video recordings, and interview to obtain responses. Moreover, problem-based learning with challenge questions was applied to develop soft skills in online synchronous classes. Responses were examined in terms of whether a difference existed according to English proficiency. Major findings of this study were as follows. Regardless of proficiency levels, participants reported improvements in their IT and problem-solving skills and exhibited positive attitudes toward live online presentations via Zoom. However, this study observed significant differences in communication and teamwork skills, perceived learning, and confidence. Interviews with students with low English proficiency levels revealed that they were negatively affected by the lack of non-verbal cues, mechanical skills, and socialization time provided by online classes. Based on these results, pedagogical implications and directions for future studies are discussed.

• Journal of Digital Convergence
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• v.6 no.2
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• pp.145-155
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• 2008

The purpose of this research is to investigate the effects of the Cyber Home Learning Motivation Factors on its satisfaction and activation through surveying the actual conditions among the students present at a cyber home learning class. For this study, samples were collected around the end of a term from the students(300 in pilot test and 248 in main test) who were taking Cyber Home Lecture at high school level. Structural equation model by AMOS 5.0 was used to analyze the data. The result of our analysis is summarized as follows. First, the cyber home learning satisfaction has a positive effect on the cyber home learning activation. Second, the 4 factors of the cyber home learning motivation: relevancy, self-confidence and satisfaction has a positive effect on the cyber home learning satisfaction. But the factor 'attention' has no positive effect on the cyber home learning satisfaction. Therefore, the Good Cyber Home Learning Contents should provide the information quality which meets 3 conditions: relevancy, self-confidence and satisfaction.

• The Journal of Korean Academic Society of Nursing Education
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• v.18 no.1
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• pp.43-52
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• 2012

Purpose: The purpose of the study was to discover long-term effects of Problem-based learning (PBL) and Simulation Problem-based learning (S-PBL) on critical thinking, problem solving abilities, learning attitude, motivation, and learning satisfaction among nursing students at Cheju Halla College. These students were taking problem based learning and simulation as a problem based learning method with an integrated curriculum. Methods: This study used a pretest-posttest with repeated measure design. Data was collected using convenience sampling from the beginning of the 1st semester to the end of the 2nd year when the PBL and S-PBL were completed by those who were enrolled in the integrated nursing curriculum. One-hundred eighty-three surveys were collected and analyzed during the repeat data collection. Results: There we restatistically significant differences of critical thinking, problem solving abilities, learning attitude, motivation and satisfaction post PBL and S-PBL. Conclusion: This study contributes to our understanding of outcomes from the PBL and S-PBL approach. The students undertaking PBL and S-PBL demonstrated that they developed a more positive attitude about their educational experience. In addition, students' tendency to think critically and problem solve improved through the use of the PBL and S-PBL approach.

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.62-68
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• 2022

Evaluation as learning is important for the learner competency test, and the applicable method is studied. Assessment is the role of diagnosing the current learner's status and facilitating learning through appropriate feedback. The system is insufficient to enable process-oriented evaluation in small educational institute. Focusing on becoming familiar with the AI through experience can end up simply learning how to use the tools or just playing with them rather than achieving ultimate goals of AI education. In a previous study, the experience way of AI education with PLAY model was proposed, but the assessment stage is insufficient. In this paper, we propose ELAS (Experiential K-12 AI education Learning Assessment System) for small educational institute. In order to apply the Assessment factor in in this system, the AI-factor is selected by researching the goals of the current SW education and AI education. The proposed system consists of 4 modules as Assessment-factor agent, Self-assessment agent, Question-bank agent and Assessment -analysis agent. Self-assessment learning is a powerful mechanism for improving learning for students. ELAS is extended with the experiential way of AI education model of previous study, and the teacher designs the assessment through the ELAS system. ELAS enables teachers of small institutes to automate analysis and manage data accumulation following their learning purpose. With this, it is possible to adjust the learning difficulty in curriculum design to make better for your purpose.

• Archives of Plastic Surgery
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• v.47 no.3
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• pp.242-249
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• 2020

Background The Exoscope is a novel high-definition digital camera system. There is limited evidence signifying the use of exoscopic devices in microsurgery. This trial objectively assesses the effects of the use of the Exoscope as an alternative to the standard operating microscope (OM) on the performance of experts in a simulated microvascular anastomosis. Methods Modus V Exoscope and OM were used by expert microsurgeons to perform standardized tasks. Hand-motion analyzer measured the total pathlength (TP), total movements (TM), total time (TT), and quality of end-product anastomosis. A clinical margin of TT was performed to prove non-inferiority. An expert performed consecutive microvascular anastomoses to provide the exoscopic learning curve until reached plateau in TT. Results Ten micro sutures and 10 anastomoses were performed. Analysis demonstrated statistically significant differences in performing micro sutures for TP, TM, and TT. There was statistical significance in TM and TT, however, marginal non-significant difference in TP regarding microvascular anastomoses performance. The intimal suture line analysis demonstrated no statistically significant differences. Non-inferiority results based on clinical inferiority margin (Δ) of TT=10 minutes demonstrated an absolute difference of 0.07 minutes between OM and Exoscope cohorts. A 51%, 58%, and 46% improvement or reduction was achieved in TT, TM, TP, respectively, during the exoscopic microvascular anastomosis learning curve. Conclusions This study demonstrated that experts' Exoscope anastomoses appear non-inferior to the OM anastomoses. Exoscopic microvascular anastomosis was more time consuming but end-product (patency) in not clinically inferior. Experts' "warm-up" learning curve is steep but swift and may prove to reach clinical equality.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.2
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• pp.74-81
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• 2006

In this paper, we deal with the separation of data by concurrently determined, piecewise nonlinear discriminant functions. Toward the end, we develop a new $l_1$-distance norm error metric and cast the problem as a mixed 0-1 integer and linear programming (MILP) model. Given a finite number of discriminant functions as an input, the proposed model considers the synergy as well as the individual role of the functions involved and implements a simplest nonlinear decision surface that best separates the data on hand. Hence, exploiting powerful MILP solvers, the model efficiently analyzes any given data set for its piecewise nonlinear separability. The classification of four sets of artificial data demonstrates the aforementioned strength of the proposed model. Classification results on five machine learning benchmark databases prove that the data separation via the proposed MILP model is an effective supervised learning methodology that compares quite favorably to well-established learning methodologies.

• Journal of Power Electronics
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• v.10 no.6
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• pp.595-603
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• 2010

In this paper, teaching in a "switching converter (SC) design" course using problem-based learning (PBL) with dynamicbehavior- model simulation, given at Lunghwa University of Science and Technology (LHU), Taiwan, is proposed. The devised methodology encourages students to design and implement the SCs and regulate the controller's parameters in frequency domain by using 'sisitool' ('bode') in the MATLAB toolbox. The environment of PBL with converter characterization modeling and simulation reforms the learning outcome greatly and speeds up the teaching-learning process. To qualify and evaluate the learning achievements, a hands-on project cooperated with the continuous assessment approach is performed to modulate the teaching pace and learning direction in good time. Results from surveys conducted in the end of the course provided valuable opinions and suggestions for assessing and improving the learning effect of the proposed course successively. Positive feedbacks from the examinations, homework, questionnaires, and the answers to the lecturer's quizzes during class indicated that the presented pedagogy supplied more helpfulness to students in comparisons with conventional teaching paradigm, their learning accomplishments were better than expected as well.