• Genomics & Informatics
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• v.16 no.4
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• pp.31.1-31.7
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• 2018

The prevalence of metabolic syndrome (MS) in the nonobese population is not low. However, the identification and risk mitigation of MS are not easy in this population. We aimed to develop an MS prediction model using genetic and clinical factors of nonobese Koreans through machine learning methods. A prediction model for MS was designed for a nonobese population using clinical and genetic polymorphism information with five machine learning algorithms, including naïve Bayes classification (NB). The analysis was performed in two stages (training and test sets). Model A was designed with only clinical information (age, sex, body mass index, smoking status, alcohol consumption status, and exercise status), and for model B, genetic information (for 10 polymorphisms) was added to model A. Of the 7,502 nonobese participants, 647 (8.6%) had MS. In the test set analysis, for the maximum sensitivity criterion, NB showed the highest sensitivity: 0.38 for model A and 0.42 for model B. The specificity of NB was 0.79 for model A and 0.80 for model B. In a comparison of the performances of models A and B by NB, model B (area under the receiver operating characteristic curve [AUC] = 0.69, clinical and genetic information input) showed better performance than model A (AUC = 0.65, clinical information only input). We designed a prediction model for MS in a nonobese population using clinical and genetic information. With this model, we might convince nonobese MS individuals to undergo health checks and adopt behaviors associated with a preventive lifestyle.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.1
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• pp.83-94
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• 2018

UNESCO has continued to work in Africa, especially in the six southern sub-Saharan countries, and Asia, where international cooperation is needed. The CLC (Rwanda Community Learning Center) covered in this study aims to create a regional learning center in Rwanda and to recover local communities and provide learning environment. During the course of this study, we conducted field trips for actual planning and reviewed the current state of educational and cultural facilities that recently opened and are operated, and found implications. In consultation with the Rwandan Educational Commission, the site for CLC was decided, the building was designed, and the construction is about to start. The results of this study are as follows. First, in addition to the efforts of the activists in the village, which can be considered the smallest unit of a local community, the approach for establishing an architectural space and active education and community environment can be evaluated as a result of experimental efforts. Second, we can pay attention to the attempts to realize local communities. The bridge business is based on the multi-purposes such as early childhood education, technical education for adults, and community restoration of local residents and it reflects space and program plans for this purposes. It also reflects detailed plans such as differentiating the flow planning depending on users' time of use. Third, we can explain the characteristics of architectural planning considering local characteristics such as active use of local materials. Due to the characteristics of a developing country, there were significant considerations on maintenance, and to this end, the plan included plans for the environment and use of materials that are easily maintained. In addition, the participation of local residents in the process of establishment was suggested as a possibility to serve an educational role.

• Journal of Sasang Constitutional Medicine
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• v.33 no.4
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• pp.1-9
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• 2021

Objectives The purpose of this study was to check whether the classification of the individual's Body Mass Index (BMI) could be predicted by analyzing the voice data constructed at the Korean medicine data center (KDC) using machine learning. Methods In this study, we proposed a convolutional neural network (CNN)-based BMI classification model. The subjects of this study were Korean adults who had completed voice recording and BMI measurement in 2006-2015 among the data established at the Korean Medicine Data Center. Among them, 2,825 data were used for training to build the model, and 566 data were used to assess the performance of the model. As an input feature of CNN, Mel-frequency cepstral coefficient (MFCC) extracted from vowel utterances was used. A model was constructed to predict a total of four groups according to gender and BMI criteria: overweight male, normal male, overweight female, and normal female. Results & Conclusions Performance evaluation was conducted using F1-score and Accuracy. As a result of the prediction for four groups, The average accuracy was 0.6016, and the average F1-score was 0.5922. Although it showed good performance in gender discrimination, it is judged that performance improvement through follow-up studies is necessary for distinguishing BMI within gender. As research on deep learning is active, performance improvement is expected through future research.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.2
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• pp.681-687
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• 2013

This study examined whether the combination of the indoor temperature on the learning environment and the colors of the learning materials affect the learning performance. To do this, the condition of indoor temperature was divided into three conditions: the neutral condition which is the appropriate temperature condition of the learning activities ($22.5{\sim}24^{\circ}C$), the high-temperature condition (> $24^{\circ}C$), and the low-temperature condition (< $22.5^{\circ}C$). In addition, colors of red, blue, black, and green were used as the warm, cold, and neutral colors, and the verbal-working memory task was used as the learning task. As a result, it was not significant differences in the response time of the learning task, whereas, in the accuracy rate of the learning task, the performance was more accurate in red- and black-color conditions. These results could be interpreted as the saliency and color-temperature of the red color, and the familiarity and specificity of the black color.

• Journal of Advanced Navigation Technology
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• v.16 no.1
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• pp.76-81
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• 2012

With the spread of the various mobile devices, the studies on the learning management system based on the u-learning are actively proceeding. The u-learning-based learning management system is very convenient in that there are no restrictions on the various access devices as well as the access time and place. However, the judgments on the authentication for the user and whether learning is focused on are difficult. In this paper, the voice and user face capture interface rather than the common user event oriented interface was applied to the learning management system. When a user is accessing the learning management system, user's registered password is input and login as voice, and the user's learning attitude is judged through the response utterance of simple words during the process of learning through contents. As a result of evaluating the proposed learning management system, the user's learning achievement and concentration were improved, thus enabling the manager to monitor the user's abnormal learning attitude.

• Journal of Engineering Education Research
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• v.24 no.5
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• pp.3-14
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• 2021

This study deals with the case of online-based project learning, which was designed for the purpose of university educational innovation and enhancing learners' competencies required by society, operated during the COVID-19 pandemic. The course was applied Python programming language, team-based project learning, and intensive course system, which is required by our society and companies in the era of the 4th industrial revolution. Also it was operated as a non-face-to-face online class, which would have been operated in an offline class if it had not been for Covid 19 pandemic, to explore the possibilities and educational effects of online learning. To do this, 32 university students participated in online-based project learning during 8 weeks, and then conducted a survey. The survey results were analyzed in terms of i) non-face-to-face online learning, ii) team-based project learning, and iii) application of the intensive course system. Results say that most of the learners were satisfied with the online learning, team-based project learning, and the intensive semester system applied in this course at a high level, and also they clearly presented the reasons. Thereby, it has been confirmed that the learners were already well aware of the pros and cons of each learning method. Based on these results, the implications were discussed.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.4
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• pp.11-18
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• 2021

We propose a method that combines learning in a virtual environment and a real environment for indoor autonomous driving through reinforcement learning. In case of learning only in the real environment, it takes about 80 hours, but in case of learning in both the real and virtual environments, it takes 40 hours. There is an advantage in that it is possible to obtain optimized parameters through various experiments through fast learning while learning in a virtual environment and a real environment in parallel. After configuring a virtual environment using indoor hallway images, prior learning was carried out on the desktop, and learning in the real environment was conducted by connecting various sensors based on Jetson Xavier. In addition, in order to solve the accuracy problem according to the repeated texture of the indoor corridor environment, it was possible to determine the corridor wall object and increase the accuracy by learning the feature point detection that emphasizes the lower line of the corridor wall. As the learning progresses, the experimental vehicle drives based on the center of the corridor in an indoor corridor environment and moves through an average of 70 steering commands.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.742-756
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• 2022

A flood of information has occurred with the rise of the internet and digital devices in the fourth industrial revolution era. Every millisecond, massive amounts of structured and unstructured data are generated; smartphones, wearable devices, sensors, and self-driving cars are just a few examples of devices that currently generate massive amounts of data in our daily. Machine learning has been considered an approach to support and recognize patterns in data in many areas to provide a convenient way to other sectors, including the healthcare sector, government sector, banks, military sector, and more. However, the conventional machine learning model requires the data owner to upload their information to train the model in one central location to perform the model training. This classical model has caused data owners to worry about the risks of transferring private information because traditional machine learning is required to push their data to the cloud to process the model training. Furthermore, the training of machine learning and deep learning models requires massive computing resources. Thus, many researchers have jumped to a new model known as "Federated Learning". Federated learning is emerging to train Artificial Intelligence models over distributed clients, and it provides secure privacy information to the data owner. Hence, this paper implements Federated Averaging with a Deep Neural Network to classify the handwriting image and protect the sensitive data. Moreover, we compare the centralized machine learning model with federated averaging. The result shows the centralized machine learning model outperforms federated learning in terms of accuracy, but this classical model produces another risk, like privacy concern, due to the data being stored in the data center. The MNIST dataset was used in this experiment.

• Journal of the Korean Society of Earth Science Education
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• v.10 no.1
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• pp.26-37
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• 2017

The purpose of this study was to investigate secondary pre-service science teachers' misconceptions of tide and to develop a learning module that can change misconceptions into scientific concepts and to examine the effect of the learning module for conceptual change. In order to achieve the purpose of the study, the researcher developed the test tool of tidal phenomena and the learning module using cognitive conflict strategy. The subjects of this study were 40 first year students who majored science education at a college of education in G metropolitan city. The results of this study are as follows. First, secondary pre-service science teachers had various misconceptions about tidal phenomena. Second, the developed learning module was effective in changing misconceptions about tide of pre-service science teachers into scientific concepts. However, some students had misconceptions about tidal phenomena after learning the developed module. The typical misconception was that they could not distinguish the centrifugal force generated when the earth and the moon revolve about the center of common mass as the center of rotation and the centrifugal force generated by the earth's rotation. And they did not know that they should not consider the earth's rotation while the earth was revolving around the center of common mass.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.320-330
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• 2020

Fire is difficult to achieve good performance in image detection using deep learning because of its high irregularity. In particular, there is little data on fire detection in underground utility facilities, which have poor light conditions and many objects similar to fire. These make fire detection challenging and cause low performance of deep learning models. Therefore, this study proposed a fire detection model using deep learning and estimated the performance of the model. The proposed model was designed using a combination of a basic convolutional neural network, Inception block of GoogleNet, and Skip connection of ResNet to optimize the deep learning model for fire detection under underground utility facilities. In addition, a training technique for the model was proposed. To examine the effectiveness of the method, the trained model was applied to fire images, which included fire and non-fire (which can be misunderstood as a fire) objects under the underground facilities or similar conditions, and results were analyzed. Metrics, such as precision and recall from deep learning models of other studies, were compared with those of the proposed model to estimate the model performance qualitatively. The results showed that the proposed model has high precision and recall for fire detection under low light intensity and both low erroneous and missing detection capabilities for things similar to fire.