• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.127-142
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• 2016

Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.

• Journal of Korean Elementary Science Education
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• v.38 no.1
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• pp.43-54
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• 2019

In order to organize various places for science experience study, this study gathered and analyzed prior research on science experience study and various science experience perated in school. To that end, a total of 162 relevant prior studies of literature published from 2000 to 2016 were collected and 2,201 cases of science experience study conducted in 2015 were collected and analyzed. The place where the science experiential learning was done is divided into three areas of natural ecology, cultural history, facility experiential learning study, and the characteristics of participating subjects are examined. In terms of the number of articles published in the field of science-related experiential learning areas, 83 ecological experience study sites (51.2%), facilities institution experience study sites 56 (34.6%), and cultural history experience study books 23 (14.2%). Through this study, it was found out that research tendency to analyze science - related attitudes became prominent by setting study subjects using natural objects around and learning to play while playing and playing in nature. There was also an analysis by subjects of participation in science related experience learning centers. Cultural history experiential learning field was significantly lower than previous studies. In the lower grades, nature ecological experience learning was mainly performed. Combining the above findings, it can provide implications for the development of science-related experience activities. First, it is necessary to develop a technology-related experience learning center using local community resources. Second, it is necessary to expand the culture and history experience learning center related to science. Third, we need an education support center to support the expansion and operation of such a technology-related cultural history learning center.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.4126-4142
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• 2015

Finger vein recognition is a biometric technology using finger veins to authenticate a person, and due to its high degree of uniqueness, liveness, and safety, it is widely used. The traditional Symmetric Local Graph Structure (SLGS) method only considers the relationship between the image pixels as a dominating set, and uses the relevant theories to tap image features. In order to better extract finger vein features, taking into account location information and direction information between the pixels of the image, this paper presents a novel finger vein feature extraction method, Multi-Orientation Weighted Symmetric Local Graph Structure (MOW-SLGS), which assigns weight to each edge according to the positional relationship between the edge and the target pixel. In addition, we use the Extreme Learning Machine (ELM) classifier to train and classify the vein feature extracted by the MOW-SLGS method. Experiments show that the proposed method has better performance than traditional methods.

• Journal of Korea Entertainment Industry Association
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• v.15 no.5
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• pp.129-139
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• 2021

This study categorized the viewpoint of cooperation between schools and communities into marketability and publicness, and the viewpoint of school education and lifelong education. The perspective of school education consider to utilize local resources for the growth of students. The lifelong education perspective sees cooperation between schools communities to support the learning of residents. While the marketability perspective pursues individual choices and diversity of opportunities, the publicness perspective focuses on ensuring citizens' right to learn and evenly distributing learning opportunities. From the point of view of school education, it seeks to utilize local resources for the growth and development of students, and in the view point of lifelong education schools are understood to support the learning activities of residents. Cooperation between schools and communities could be presented by categorizing them into private organization-led, educational authorities-led, and provincial authorities-led depending on the subject of the promotion. Recently, local governments and educational governments, schools and communities are developing to a stage where they cooperate to realize the vision of a educational community. For the cooperation between schools and communities the local community, cooperation between local government and educational government and the harmony between publicness and marketability are emerging as tasks.

• Journal of the Korean Institute of Rural Architecture
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• v.13 no.1
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• pp.21-28
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• 2011

The main purpose of this study is to analyze construction of space through the investigation of the cases of school community library through Gangneung-si's life-learning project and the findings from the analysis could be summarized as below. Firstly, most space used for school community library has the size of two classes in school on average and locals use generally space for reference and learning at school community library. Secondly, the construction of space of school community library is categorized into one for book-returning, references, reading, group learning and information, and an audio-visual space is also used for group learning and reading. A space for book-returning has features based on the location of its entrance and a space for reading features stand-up and sitting-on space considering size and usability. And a space for group learning has the feature of space planning that makes it possible for local people to get library programs and seminars and a space for information shows its feature of space planning that uses the wall.

• 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.

• International Journal of Contents
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• v.13 no.3
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• pp.25-31
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• 2017

With tremendous advancement of information and communication technologies, mobile learning systems have been widely adopted in language learning contexts, and several frameworks have been developed for identifying and categorizing different factors of mobile-assisted language learning (MALL). However, pre-existing frameworks have limitations when evaluating the importance level of criteria. The purpose of this study is to develop a comprehensive hierarchical framework for identifying and categorizing success factors of MALL and prioritizing them according to the importance level. To do that, AHP method is used to quantitatively estimate weight values of MALL criteria. Results reveal that the priority of MALL criteria is ordered as follows: content, system, learner, language learning. Local weights of each criterion are also analyzed; for example, usefulness, accuracy, and authenticity are critical factors for improving MALL contents. Ease of use and mobility of MALL systems are also considered more critical than other systematic factors. In addition, availability of immediate feedback and self-directness has the highest weight values of importance. The findings of the study are discussed regarding hierarchical orders of MALL criteria and conclude that successful MALL implementation may be achieved if related elements are diversely measured and evaluated. Pedagogical implications and suggestions for further research are also presented.

• Journal of the Korea Computer Industry Society
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• v.6 no.5
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• pp.749-756
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• 2005

In the near future, we can access the information whenever we want, wherever we use because almost devices in ubiquitous environment are connected by either wired or wirelss networks. Especially, u-Learning which emphasizes on pedagogical property is enable to improve learning abilities. As researches of the previous u-Learning, there have been learning by mobile devices such as PDAs as well as the smart classroom which makes the remote students participate in the existing class. However, these researches have not satisfied pedagogical, cooperative and ubiquitous properties yet. Thus we suggest the framework for both local and mobile classroom, which can make the properties easy to satisfy.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.5
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• pp.171-177
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• 2018

Recently there are many image datasets which has variety of data class and point to extract general features. But in order to this variety data class and point, deep learning model trained this dataset has not good performance in heterogeneous data feature local area. In this paper, we propose the structure which use sub-category and openset object detection methods to train more robust model, named multi-branch tree using ASSL. By using this structure, we can have more robust object detection deep learning model in heterogeneous data feature environment.

• English Language & Literature Teaching
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• v.13 no.2
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• pp.25-45
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• 2007

In this article, I discuss an International Collaborative Writing Course on the Internet (ICWCI) that focused on the learning effectiveness Korean EFL students (KEFLSs) perceived to be necessary to exchange with international EFL students (IEFLSs). The course development was based on an internet-based instructional module, applying widely accepted EFL theories for modern foreign language instruction: collaborative learning, process writing, project-based learning, and integrated approaches. Data from online discussion forum, mid-of-semester and end-of-semester surveys, and final oral interviews are conducted and discussed. KEFLSs and IEFLSs were questioned about (a) changes in attitude towards computers assisted language learning (CALL); (b) effect of computer background on motivation; (c) perception of their acquired writing skills; and (d) attitude towards collaborative learning. The result of this study demonstrated that the majority of ICWCI participants said they enjoyed the course, gained fruitful confidence in English communication and computer skills, and felt that they made significant progress in writing skills. In spite of positive benefits created by the ICWCI, it was found that there were some issues that are crucial to run appropriate networked collaborative courses. This study demonstrates that participants' computer skills, basic language proficiency, and local time differences are important factors to be considered when incorporating the ICWCI as these may affect the quality of online instructional courses and students' motivation toward network based collaboration interaction.