• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.479-493
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• 2019

The purpose of this study was to present a method of literacy education that would generate interest and enhance the effectiveness of literacy education from elderly literacy learners. For that end, the researcher interviewed and did participation observation with a total of 11 middle and old aged literacy teachers, operators, lifelong education teachers, and literacy students in North Chungcheong Province. According to the research, elderly literacy learners have a tendency to forget easily what they have learned and to learn properly through dictation, and have high level of competitive spirit, to make studying the top priority in their daily lives. Many playful activities for knowing meaning of writing, learning connected to real life, and dictating are effective in improving their memory and cognition. In addition, using familiar materials in everyday life, conducting role plays with comedies and poems in textbooks, utilizing large-picture fairytales, team-based games and activities, learning songs and instruments to play easily, performances and presentations on the stage, and field experiences in educational and cultural facilities can increase their interest and effectiveness in literacy. Several programmes such as presentations and joint events for sharing results, materials and materials costs, education and sharing of literacy skills for teachers at the school, annual operation of literacy education need to be supported to succeed literacy education in elderly learners. In conclusion, the research shows the need to increase literacy teachers' education, to use assistant teachers, to activate accreditation of literacy curriculum.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.133-144
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• 2020

The number of elderly people with dementia is increasing as fast as the proportion of older people due to aging, which creates a social and economic burden. In particular, dementia care costs, including indirect costs such as increased care costs due to lost caregiver hours and caregivers, have grown exponentially over the years. In order to reduce these costs, it is urgent to introduce a management system to care for dementia patients. Therefore, this study proposes a sensor-based abnormal behavior detection system to manage dementia patients who live alone or in an environment where they cannot always take care of dementia patients. Existing studies were merely evaluating behavior or evaluating normal behavior, and there were studies that perceived behavior by processing images, not data from sensors. In this study, we recognized the limitation of real data collection and used both the auto-encoder, the unsupervised learning model, and the LSTM, the supervised learning model. Autoencoder, an unsupervised learning model, trained normal behavioral data to learn patterns for normal behavior, and LSTM further refined classification by learning behaviors that could be perceived by sensors. The test results show that each model has about 96% and 98% accuracy and is designed to pass the LSTM model when the autoencoder outlier has more than 3%. The system is expected to effectively manage the elderly and dementia patients who live alone and reduce the cost of caring.

• International Journal of Advanced Culture Technology
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• v.5 no.3
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• pp.11-23
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• 2017

Anatomy is one of the basic subjects of the nursing course, which is included in the curriculum of the nursing. Anatomy is a basic course for understanding major in nursing and it is the first gateway to acquire expertise. It is mainly opened in nursing and first to second grades. Therefore, students who have advanced to the nursing department have great interest and expectation on the anatomy subjects. In general, nursing students are studying anatomy with theories and models, and some universities practice on tour after dissection of medical consortium for short time. This is called the Cadaver practice. This study was carried out to investigate the thoughts and experiences of bioethics through nursing students' Cadaver practice. The interview data were processed through the analysis and interpretation process using the phenomenological research method, Giorgi method. As a result, 48 semantic units were derived, and then they were divided into 10 subcomponents and divided into 6 categories. As a result of the analysis, the experience of nursing students' bioethics was tied to the topic of six questions. For example, 'what about the respect and responsibility of nursing students in Cadaver?', 'What about your experience with the Cadaver experiment and bioethics?', 'What was the academic achievement of the actual human body structure viewing experience with cadaver?', 'What was the connection with theory?', 'What was your intention to recommend to others?', 'What was your perception and interest in the Cadaver experiment?'. Analyzes were integrated into 10 structures; "Thank you for your donation", "want others to refuse donation", "Burden of practice", "Good opportunities for learning", "Understand better", "Should study harder", "Memory is better", "Compared to theory", "Good experience", "Want to rejoin". The general structural description of the participants' meanings is summarized as follows. Nursing college students who participated in the Cadaver had a gratitude for the donor, but they said they would like to talk to others about donating organs. Before they went to practice, they felt a lot of pressure on the dissection of the cadaver, but they went to the practice and thought that it was a good opportunity to learn by doing well. Specifically, they understood that they had better understanding than the theoretical lesson, and that they were more eager to study their major through practice. In other words, most of them were more memorable and they would like to participate again if the opportunity comes next time. The results of this study show that the practice of Cadaver in nursing college students is very positive in terms of educational effectiveness. However, in terms of bioethics, it can be seen that the education process is somewhat unsatisfactory. Therefore, the systematic bioethics education should be prepared before the practice in the nursing college students' Cadaver practice.

• The Journal of the Korea Contents Association
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• v.15 no.4
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• pp.29-44
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• 2015

Digital Signage, a way of advertising or delivering information to viewers through digital displays, has expanded from being just an advertising channel in public places. Recently, it has become widely prevalent in restaurants and retail stores. Despite its wide expansion, digital signage is limited to specific usages and services and the devices it uses are also quite expensive. This study introduces a stick-type digital signage product that operates on Android OS, which addresses all the weaknesses of digital signage with much more reasonable pricing and stable operation. For stability, performance tests were executed on the hardware and applications. The results for hardware performance were extremely promising, as each scenario's maximum performance results, measured by Load Runner programs, reached target indexes. Also, as a result of the usability test, all participants, including non-digital signage system users (novices), were able to easily learn all the tasks. As a result of user satisfaction survey, positive responses were exhibited for ease of learning and usability (LEU), helpfulness and problem solving capabilities (HPSC), affective aspect and multimedia properties (AAMP), commands and minimal memory load (CMML), and control and efficiency (CE).

• Journal of the Korean School Mathematics Society
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• v.6 no.1
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• pp.135-161
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• 2003

This research is how students and teacher apprehend mathematics education, pointing out problem areas as a basis on how to improve students understanding of mathematics through improved guidance by teachers in the future. 1107 high school students and 105 teachers from around Daejeon and Choongnam province were surveyed and the results were as follows. 1. 77 %( 852) of students viewed the "application of problem solving methods" as understanding mathematic problems. 2. Replies to the question on understanding the study of mathematics resulted in 85.7% of teachers saying "it is the understanding of the basic concept to which you solve the problems" 3. For questions relating to the large difference in-class mathematics achievements and mock University entrance exam achievements, students' response that "for in-class tests you only have to learn problems with similar form but the mock tests are not like that" pointed out the problem in the area of mathematics education. 4. For future mathematic education teachers will have to "explain better and more completely the basic principles and concepts before solving problems" , and make an effort to stimulate students by "creating a more fun atmosphere" . There will also be the need to prevent as much as possible, the use of "formula or memory driven problems" and encourage students to initiate problem solving for themselves.; and encourage students to initiate problem solving for themselves.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.4 s.310
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• pp.46-54
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• 2006

Recently, the important of a personal identification is increasing according to expansion using each on-line commercial transaction and personal ID-card. Although a personal ID-card embedded RFID(Radio Frequency Identification) tag is gradually increased, the way for a person's identification is deficiency. So we need automatic methods. Because RFID tag is vary small storage capacity of memory, it needs effective feature extraction method to store personal biometrics information. We need new recognition method to compare each feature. In this paper, we studied the face verification system using Hippocampal neuron modeling algorithm which can remodel the hippocampal neuron as a principle of a man's brain in engineering, then it can learn the feature vector of the face images very fast. and construct the optimized feature each image. The system is composed of two parts mainly. One is feature extraction using NMF(Non-negative Matrix Factorization) and LDA(Linear Discriminants Analysis) mixture algorithm and the other is hippocampal neuron modeling and recognition simulation experiments confirm the each recognition rate, that are face changes, pose changes and low-level quality image. The results of experiments, we can compare a feature extraction and learning method proposed in this paper of any other methods, and we can confirm that the proposed method is superior to the existing method.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.429-440
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• 2021

With the recent development of deep convolutional neural network learning, deep learning techniques applied to single image super-resolution are showing good results. One of the existing deep learning-based super-resolution techniques is RDN(Residual Dense Network), in which the initial feature information is transmitted to the last layer using residual dense blocks, and subsequent layers are restored using input information of previous layers. However, if all hierarchical features are connected and learned and a large number of residual dense blocks are stacked, despite good performance, a large number of parameters and huge computational load are needed, so it takes a lot of time to learn a network and a slow processing speed, and it is not applicable to a mobile system. In this paper, we use the residual dense structure, which is a continuous memory structure that reuses previous information, and the residual dense channel attention block using the channel attention method that determines the importance according to the feature map of the image. We propose a method that can increase the depth to obtain a large receptive field and maintain a concise model at the same time. As a result of the experiment, the proposed network obtained PSNR as low as 0.205dB on average at 4× magnification compared to RDN, but about 1.8 times faster processing speed, about 10 times less number of parameters and about 1.74 times less computation.

• The Journal of the Korea Contents Association
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• v.5 no.5
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• pp.316-324
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• 2005

e-Learning has been mushrooming with wide range of teaming groups from pedagogy to andragogy As e-teaming opportunities increase, many people raise question about whether e-teaming show positive teaming effects. The related research emphasized that e-learning would be a failure in terms of understanding of e-Learners and activating intuitive teaming activities from learner's long-term memory span. The e-teaming strategies based on the traditional classroom and resulted boring and ineffective teaming outcomes, should be changed to provide authentic and effective teaming results. This paper analyzed that how learners have received e-Learning for the last few years from the research and explained what could be the failing aspects in e-Learning. To be successful, e-loaming should consider the e-learner's individualized teaming style and thinking patterns. When considering of various e-Learning components, the quality of e-teaming should not be focused on any specific single factor, but develop every individual factor to be integrated into high level of quality. In conclusion, this paper suggest that it is needed new understandings of e-Loaming and e-Learner. Also the e-Learning strategies should be examined throughly whether they are on the side of learners and realized how they learn from e-Learning. Finally, we should add enormous imagination into e-loaming for next generation because new generation's teaming patterns significantly differ from their parent's generation.

• 재활복지
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• v.21 no.3
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• pp.129-146
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• 2017

In an event of an emergency, such as fire in a building, visually impaired and blind people are prone to exposed to a level of danger that is greater than that of normal people, for they cannot be aware of it quickly. Current fire detection methods such as smoke detector is very slow and unreliable because it usually uses chemical sensor based technology to detect fire particles. But by using vision sensor instead, fire can be proven to be detected much faster as we show in our experiments. Previous studies have applied various image processing and machine learning techniques to detect fire, but they usually don't work very well because these techniques require hand-crafted features that do not generalize well to various scenarios. But with the help of recent advancement in the field of deep learning, this research can be conducted to help solve this problem by using deep learning-based object detector that can detect fire using images from security camera. Deep learning based approach can learn features automatically so they can usually generalize well to various scenes. In order to ensure maximum capacity, we applied the latest technologies in the field of computer vision such as YOLO detector in order to solve this task. Considering the trade-off between recall vs. complexity, we introduced two convolutional neural networks with slightly different model's complexity to detect fire at different recall rate. Both models can detect fire at 99% average precision, but one model has 76% recall at 30 FPS while another has 61% recall at 50 FPS. We also compare our model memory consumption with each other and show our models robustness by testing on various real-world scenarios.

• 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.