• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.1-8
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• 2021

With the rapid growth of intelligent devices and communication technologies, 5G network environment has become more heterogeneous and complex in terms of service management and orchestration. 5G architecture requires supportive technologies to handle the existing challenges for improving the Quality of Service (QoS) and the Quality of Experience (QoE) performances. Among many challenges, traffic steering is one of the key elements which requires critically developing an optimal solution for smart guidance, control, and reliable system. Mobile edge computing (MEC), software-defined networking (SDN), network functions virtualization (NFV), and deep learning (DL) play essential roles to complementary develop a flexible computation and extensible flow rules management in this potential aspect. In this proposed system, an accurate flow recommendation, a centralized control, and a reliable distributed connectivity based on the inspection of packet condition are provided. With the system deployment, the packet is classified separately and recommended to request from the optimal destination with matched preferences and conditions. To evaluate the proposed scheme outperformance, a network simulator software was used to conduct and capture the end-to-end QoS performance metrics. SDN flow rules installation was experimented to illustrate the post control function corresponding to DL-based output. The intelligent steering for network communication traffic is cooperatively configured in SDN controller and NFV-orchestrator to lead a variety of beneficial factors for improving massive real-time Internet of Things (IoT) performance.

• International conference on construction engineering and project management
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• 2017.10a
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• pp.80-88
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• 2017

Although hazard identification is one of the most important steps of safety management process, numerous hazards remain unidentified in the construction workplace due to the dynamic environment of the construction site and the lack of available resource for visual inspection. To this end, our previous study proposed the collective sensing approach for safety hazard identification and showed the feasibility of identifying hazards by capturing collective abnormalities in workers' walking patterns. However, workers generally performed different activities during the construction task in the workplace. Thereby, an additional process that can identify the worker's walking activity is necessary to utilize the proposed hazard identification approach in real world settings. In this context, this study investigated the feasibility of identifying walking activities during construction task using Wearable Inertial Measurement Units (WIMU) attached to the worker's ankle. This study simulated the indoor masonry work for data collection and investigated the classification performance with three different machine learning algorithms (i.e., Decision Tree, Neural Network, and Support Vector Machine). The analysis results showed the feasibility of identifying worker's activities including walking activity using an ankle-attached WIMU. Moreover, the finding of this study will help to enhance the performance of activity recognition and hazard identification in construction.

• International Journal of Contents
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• v.12 no.1
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• pp.25-43
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• 2016

An agent-centric event planning method is proposed for providing pedagogical experiences in an immersed environment. Two-level planning is required at in a macro-level (i.e., inter-event level) and an intra-event level to provide realistic experiences with the objective of learning declarative knowledge. The inter-event (horizontal) planning is based on search, while intra-event (vertical) planning is based on hierarchical decomposition. The horizontal search is dictated by several realistic types of association between events besides the conventional causality. The resulting schematic plan is further augmented by conditions associated with those agents cast into the roles of the events identified in the plan. Rather than following a main story plot, all the events potentially relevant to accomplishing an initial goal are derived in the final result of our planning. These derived events may progress concurrently or digress toward a new main goal replacing the current goal or event, and the plan could be merged or fragmented according to their respective lead agents' intentions and other conditions. The macro-level coherence across interconnected events is established via their common background world existing a priori. As the pivotal source of event concurrency and intricacy, agents are modeled to not only be autonomous but also independent, i.e., entities with their own beliefs and goals (and subsequent plans) in their respective parts of the world. Additional problems our method addresses for augmenting pedagogical experiences include casting of agents into roles based on their availability, subcontracting of subsidiary events, and failure of multi-agent event entailing fragmentation of a plan. The described planning method was demonstrated by monitoring implementation.

• Korean Journal of Ecology and Environment
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• v.43 no.1
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• pp.161-167
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• 2010

In this study, we implemented an experimental approach of ecological model development in order to emphasize the importance of input variable selection with respect to time-delayed arrangement between input and output variables. Time-series modeling requires relevant input variable selection for the prediction of a specific output variable (e.g. density of a species). Inadequate variable utility for input often causes increase of model construction time and low efficiency of developed model when applied to real world representation. Therefore, for future prediction, researchers have to decide number of time-delay (e.g. months, weeks or days; t-n) to predict a certain phenomenon at current time t. We prepared a total of 3,900 equation models produced by Time-Series Optimized Genetic Programming (TSOGP) algorithm, for the prediction of monthly averaged density of a potamic phytoplankton species Stephanodiscus hantzschii, considering future prediction from 0- (no future prediction) to 12-months ahead (interval by 1 month; 300 equations per each month-delay). From the investigation of model structure, input variable selectivity was obviously affected by the time-delay arrangement, and the model predictability was related with the type of input variables. From the results, we can conclude that, although Machine Learning (ML) algorithms which have popularly been used in Ecological Informatics (EI) provide high performance in future prediction of ecological entities, the efficiency of models would be lowered unless relevant input variables are selectively used.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.547-550
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• 2009

The object recognition mechanism of human being is not well understood yet. On research of animal experiment using an ape, however, neurons that respond to simple shape (e.g. circle, triangle, square and so on) were found. And Hypothesis has been set up as human being may recognize object as combination of such simple shapes. That mechanism is called Figure Alphabet Hypothesis, and those simple shapes are called Figure Alphabet. As one way to research object recognition algorithm, we focused attention to this Figure Alphabet Hypothesis. Getting idea from it, we proposed the feature extraction algorithm for object recognition. In this paper, we described recognition of binarized images of multifont alphabet characters by the recognition model which combined three-layered neural network in the feature extraction algorithm. First of all, we calculated the difference between the learning image data set and the template by the feature extraction algorithm. The computed finite difference is a feature quantity of the feature extraction algorithm. We had it input the feature quantity to the neural network model and learn by backpropagation (BP method). We had the recognition model recognize the unknown image data set and found the correct answer rate. To estimate the performance of the contriving recognition model, we had the unknown image data set recognized by a conventional neural network. As a result, the contriving recognition model showed a higher correct answer rate than a conventional neural network model. Therefore the validity of the contriving recognition model could be proved. We'll plan the research a recognition of natural image by the contriving recognition model in the future.

• Journal of the International Relations & Interdisciplinary Education
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• v.1 no.1
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• pp.46-60
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• 2021

The main purpose of this study was to examine the satisfaction of online classes of undergraduates who have experienced sudden online classes in college because of COVID-19, and to analyze the differences of the satisfaction of online classes depending on colleges. The participants were 1,380 college students. Descriptive statistics, one-way ANOVA, and Post-hoc Scheffé were performed. Undergraduates were highly satisfied with online classes in cultural subjects and major subjects, whereas they showed low satisfaction with appropriateness of the assignment. There were statistically significant differences in online class satisfaction between colleges. Students at the College of Education were highly satisfied with online classes satisfaction, while engineering students were less satisfied with online classes satisfaction. There were statistically significant differences in students engagements between colleges. Regardless of colleges, students displayed low satisfaction in e-class server and teaching and learning infrastructure. These findings suggest that we need to provide autonomous support and make an effective online learning environment such as server expansion and e-class improvement to enhance undergraduates' self-directed learning. We also should come up with effective online classes guidelines.

• Journal of the Korea Convergence Society
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• v.6 no.3
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• pp.103-109
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• 2015

TOLED(Transparent, Organic Light Emitting Diodes) based edutainment system has been studied to solve the actual feeling training and educational experience problem of e-learning. However, edutainment system using TOLED has a problem for the non-detection of multi marker array and rotate marker array, and it has problem for the dissonance phenomena caused by Illumination Environment between real world and virtual object. It also has a do not provide services through a variety of devices problem. Therefore, in this paper, we designed a system that provides a realistic actual feeling edutainment contents by recognizes the marker array rotation and a plurality of marker arrangement via an improved marker detection technique. And to unify the real space and virtual space of the lighting environment through a nested block layer.

• Communications of Mathematical Education
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• v.23 no.2
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• pp.231-253
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• 2009

This paper investigated the views of effective mathematics instruction on the part of teachers. The study was carried out a survey with 223 elementary school teachers in Korea. The questionnaire consisted of the following 4 main categories with a total of 48 factors: (a) the curriculum and content, (b) teaching and learning, (c) classroom environment and atmosphere, and (d) assessment. Some ideas teachers revealed about what would enable good mathematics teaching coincided with previous research. Specifically, teachers agreed with the idea of consideration of students' individual differences or focus on concepts. However, there were differences with regard to the use of technology and the importance of learning environment, which have been emphasized in mathematics education literature. Considering that the teacher plays a key role in implementing good instruction, this paper emphasizes us to attend to teachers' perspectives in order to initiate good teaching at the actual classroom.

• Journal of Digital Convergence
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• v.17 no.3
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• pp.227-234
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• 2019

The purpose of this study is to present a block type smart classroom model for comprehensive supply of educational contents, classroom environment and ICT technology in African countries where educational infrastructure is weak. It will provide a contextual solution that integrates learning management, power management, and classroom environment management systems, and will be a convergence model that can optimize economic and non-economic conditions for different African countries. It can be expected to enhance utilization as it is a differentiated model from existing classrooms with a single container, as well as independent research and development centered on services, content, and solutions. Through this integrated research process, we can overcome the spatial and functional limitations appearing in single container classrooms and build a flexible space for advanced e-learning technology. The depth and scope of the follow-up study can be carried by investigating the performance and models that are in line with the educational and infrastructure conditions of the various regions.

• Journal of Practical Engineering Education
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• v.11 no.1
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• pp.1-8
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• 2019

Virtual Training is an educational exercise in which the environment or the situation is virtually implemented for specific training and proceed like a real situation. In recent years, the virtual reality technology has developed rapidly, and the demand for experiencing situation that are not directly experienced in the real world is increasing more and more in virtual reality. Particularly, there is an increasing demand of contents for hands-on training and virtual training for equipment training that replaces high-risk and high-cost industry training. The virtual training contents have been developed and utilized for the purpose of technical training. However, it is known that virtual training is more effective when it is used as a supplementary training material or combined with e-learning contents rather than replacing one training course with virtual training contents because purpose and effect are different from general technical training course. In this study, we explored the development method for effective utilization of electrohydraulic servo control process, which is the virtual reality contents developed in 2017 in combination with e-learning contents. In addition, in order to establish a teaching and learning strategy, we actually develop and operate a case studies using virtual training contents. Surveys and case studies are conducted to investigate the effects of teaching and learning strategies applied in the classroom on students and their educational usefulness.