• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.82-90
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• 2019

The purpose of this study was to examine early childhood teachers' importance and performance of technology leadership. A survey was conducted on 205 early childhood teachers. Borich's needs model was used to calculate their needs. In the area of director leadership and vision, the early childhood teachers felt the most need for opportunities to participate in conferences or training programs related to the educational utilization of technology. In the area of teaching-learning methods, they called the most for better ways to take advantage of technology considering the characteristics of the activity areas and activity types. In the area of teaching professionalism, the items they asked for the most were building confidence over the educational utilization of technology and case studies of superior teaching and learning. In the area of institutional support, they felt the most need for assistant human resources who could assist in solving possible problems using technology. In the area of evaluation, they called the most for the development of a variety of evaluation tools and methods. Finally, the item they called for the most in the area of social, ethical, and legal support from the institutions to which they belonged was the preparation of guidelines on how to be in good health in times of using technology.

• Journal of the Korea Convergence Society
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• v.10 no.10
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• pp.161-171
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• 2019

This study aims the meta analysis based on the results of the empirical studies related to Job of life-long educators who are working in life-long education center, and the relationship between variables. In order to deliver this objective, articles published in domestic journals from December 2000 to April 2019 were collected, and total of 14 studies and 170 sub data were coded. With those data, the meta-analysis was executed by CMA(Comprehensive Meta Analysis) 3.0 program. The results of this meta-analysis study are as follows. First, the overall effect size associated with Job of life-long educators was 0.767, indicating between medium and large effect size. Second, the effect sizes of dependent variables that are influenced by job of life-long educators were Organization immersion, Job performance, Job satisfaction, and Job stress in order. Third, the effect size of Individual focus variables study was almost twice as big as that of mutual relation focus variables. The implications of this study were suggested based on the analysis results to provide the directivity about how we consider life-long educators related to their job.

• Journal of the Korean Applied Science and Technology
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• v.36 no.3
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• pp.804-812
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• 2019

The purpose of this study was to investigate the effects of different types of exercise training on neurodegeneration and cognitive function in mice with impaired glucose tolerance (IGT). Thirty-six male C57BL/6 mice were randomly assigned to the control (CO, n = 9) and impaired glucose tolerance (IGT, n = 27) groups. The IGT group consumed 45% high fat diet for 4 weeks and received 40 mg/kg of streptozotocin twice in the lower abdomen to induce IGT. After the IGT induction period, the IGT group was subdivided into IGT + sedentary (IGT, n = 9), IGT + endurance exercise (IGTE, n = 9), and IGT + resistance exercise (IGTR, n = 9). The IGTE and IGTR groups performed treadmill and ladder climbing exercises 5 times per week for 8 weeks, respectively. Fasting glucose and glycated hemoglobin (HbA1c) levels were significantly higher in IGT group than in CO, IGTE, and IGTR groups (p < 0.05). HOMA-IR was significantly higher in IGT group than CO group (p < 0.05). Hippocampal catalase (CAT) was significantly lower in IGT group than in CO group (p < 0.05), while beta-amyloid ($A{\beta}$) was significantly higher in IGT group than in CO group (p < 0.05). Hippocampal tau was significantly higher in IGT group than in CO, IGTE, and IGTR groups (p < 0.05). The Y-maze test performance for cognitive function was significantly lower in IGT group than in CO, IGTE, and IGTR groups (p <0.05). These results suggest that IGT induces neurodegeneration and negatively affects cognitive function, while regular exercise may be effective in alleviating neurodegeneration and cognitive decline regardless of exercise type.

• Phonetics and Speech Sciences
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• v.11 no.1
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• pp.41-49
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• 2019

An end-to-end speech recognition model consisting of a single integrated neural network model was recently proposed. The end-to-end model does not need several training steps, and its structure is easy to understand. However, it is difficult to understand how the model recognizes speech internally. In this paper, we visualized and analyzed the attention-based end-to-end model to elucidate its internal mechanisms. We compared the acoustic model of the BLSTM-HMM hybrid model with the encoder of the end-to-end model, and visualized them using t-SNE to examine the difference between neural network layers. As a result, we were able to delineate the difference between the acoustic model and the end-to-end model encoder. Additionally, we analyzed the decoder of the end-to-end model from a language model perspective. Finally, we found that improving end-to-end model decoder is necessary to yield higher performance.

• Physical Therapy Rehabilitation Science
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• v.8 no.1
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• pp.45-51
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• 2019

Objective: The purpose of this study was to investigate the muscle activity ratio of the lower limb according to changes in straight leg raise (SLR) test angles on hamstring muscle shortening during squat exercises. Design: Randomized controlled trial. Methods: The subjects were 14 healthy adults who were informed of and agreed to the method and purpose of the study. The participants were classified into SLR groups according to two angles (over $80^{\circ}$ or under $80^{\circ}$) assessed using the SLR tests. After training and practicing the wall squat posture to be applied to the experiment, electromyography (EMG) was used to measure changes in muscle activity during the performance of a wall squat. After stretching, a sequence of pre-stretch tests were performed again, and the active and passive SLR tests were also reconducted; thereafter, a wall squat was performed again by attaching EMG electrodes. The EMG results before and after stretching were compared. Results: The muscle activity of the vastus lateralis oblique muscle increased in both groups. The muscle activity of the vastus medialis oblique muscle decreased in over both group. Rectus femorus activity increased in the under 80-degree groups but decreased in the over 80-degree group. The muscle activity of the biceps femoris muscle decreased after stretching in the over 80-degree group and increased in the under 80-degree group, and the semitendinosus muscle activity after stretching was decreased. The quadriceps-to-hamstring muscle (Q:H) ratio before and after stretching between groups showed that the hamstring muscle ratio decreased after stretching in both groups. Conclusions: The results of this study showed that the Q:H ratio before and after stretching between groups was not significantly different.

• Journal of Korean Society of Forest Science
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• v.110 no.2
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• pp.155-164
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• 2021

Many studies have been conducted on developing automatic plant identification algorithms using machine learning to various plant features, such as leaves and flowers. Unlike other plant characteristics, barks show only little change regardless of the season and are maintained for a long period. Nevertheless, barks show a complex shape with a large variation depending on the environment, and there are insufficient materials that can be utilized to train algorithms. Here, in addition to the previously published bark image dataset, BarkNet v.1.0, images of barks were collected, and a dataset consisting of 53 tree species that can be easily observed in Korea was presented. A convolutional neural network (CNN) was trained and tested on the dataset, and the factors that interfere with the model's performance were identified. For CNN architecture, VGG-16 and 19 were utilized. As a result, VGG-16 achieved 90.41% and VGG-19 achieved 92.62% accuracy. When tested on new tree images that do not exist in the original dataset but belong to the same genus or family, it was confirmed that more than 80% of cases were successfully identified as the same genus or family. Meanwhile, it was found that the model tended to misclassify when there were distracting features in the image, including leaves, mosses, and knots. In these cases, we propose that random cropping and classification by majority votes are valid for improving possible errors in training and inferences.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.10
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• pp.397-402
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• 2019

Accurately predicting cancer prognosis to provide appropriate treatment strategies for patients is one of the critical challenges in bioinformatics. Many researches have suggested machine learning models to predict patients' outcomes based on their gene expression data. Gene expression data is high-dimensional numerical data containing about 17,000 genes, so traditional researches used feature selection or dimensionality reduction approaches to elevate the performance of prognostic prediction models. These approaches, however, have an issue of making it difficult for the predictive models to grasp any biological interaction between the selected genes because feature selection and model training stages are performed independently. In this paper, we propose a novel two-dimensional image formatting approach for gene expression data to achieve feature selection and prognostic prediction effectively. Node2Vec is exploited to integrate biological interaction network and gene expression data and a convolutional neural network learns the integrated two-dimensional gene expression image data and predicts cancer prognosis. We evaluated our proposed model through double cross-validation and confirmed superior prognostic prediction accuracy to traditional machine learning models based on raw gene expression data. As our proposed approach is able to improve prediction models without loss of information caused by feature selection steps, we expect this will contribute to development of personalized medicine.

• Knowledge Management Research
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• v.22 no.2
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• pp.1-17
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• 2021

In this study, we predicted the bitcoin prices of Bithum and Coinbase, a leading exchange in Korea and USA, using ARIMA and Recurrent Neural Networks(RNNs). And we used news articles from each country to suggest a separated RNN model. The suggested model identifies the datasets based on the changing trend of prices in the training data, and then applies time series prediction technique(RNNs) to create multiple models. Then we used daily news data to create a term-based dictionary for each trend change point. We explored trend change points in the test data using the daily news keyword data of testset and term-based dictionary, and apply a matching model to produce prediction results. With this approach we obtained higher accuracy than the model which predicted price by applying just time series prediction technique. This study presents that the limitations of the time series prediction techniques could be overcome by exploring trend change points using news data and various time series prediction techniques with text mining techniques could be applied to improve the performance of the model in the further research.

• Journal of The Korean Association of Information Education
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• v.24 no.5
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• pp.483-494
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• 2020

With the advent of the 4th Industrial Revolution, interest in AI education is increasing. In order to cultivate talented people with AI competencies who will lead the future, AI education must be conducted in a sound manner at the school site. Although AI education is being conducted at home and abroad, it was determined that the role of the AI education platform is important to implement better AI education, so this study investigated the perception of experts on the AI education platform. A perception survey was conducted based on five criteria: teaching and learning management, educational contents, accessibility, performance of AI education platform, and level suitability of elementary school students. As a results, the number of 103 educational experts selected 'Entry' as the most proper platform among the eight platforms - 'Machine learning for Kids', 'Teachable Machine', 'AI Oceans(code.org)', 'Entry', 'Genie Block', 'Elice', 'mBlock' and etc. Analysis shows that this is because 'Entry' provides quality educational content, has convenient accessibility, is easy to manage teaching and learning, as well as an AI education platform suitable for the level of elementary school. In order to apply various AI education platforms to the school field, it is necessary to train teachers in AI-related training to train them as AI education experts, and to continuously provide opportunities to experience AI education platforms. In this study, there are limitations to what is called 'a population perception survey'. because only 103 people were surveyed, and most of the experts are working in a specific area(Gyeonggi-do). In the future, it is judged that research targeting experts at the national level should be conducted to supplement these limitations.

• Journal of Korean Academy of Dental Administration
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• v.7 no.1
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• pp.39-43
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• 2019

Smartphone usage has become so common that it has reached 2 billion people in the last year. As a result of this, hospitals have started making use of smartphones at various medical sites and research services for patients. This study aimed to establish support for developing a long distance program for patients with implants who have difficulty visiting clinics or with busy modern lives, by using smartphones for oral hygiene management instruction. The data were collected for 12 weeks, from July 24 to October 21, 2015, for patients who agreed to participate in the study. Although the subjects found the process of transferring photos via smartphone to be cumbersome (75%), the satisfaction level of the oral hygiene management program was excellent for all participating patients, and they all wanted to continue with further management using this process. The results from the phone satisfaction survey showed that oral hygiene self-management after oral hygiene control training by smartphones was mostly equal to previous habits (87.5%) or had partially increased but had not decreased. The need for data on more varied age groups and the issues of protecting the security of personal information on smartphones require further study. However, our study confirmed the efficacy of using electronic media (smartphones) for oral hygiene management in patients with a dental implant due to their improvement of oral hygiene performance as evidenced by less bleeding from probing on post-program visit.