• Journal of the Korea Society of Computer and Information
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• v.25 no.11
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• pp.9-16
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• 2020

The TA-50 aircraft is conducting simulated training on various situations, including air-to-air and air-to-ground fire training, in preparation for air warfare. It is also used for pilot training before actual deployment. However, the TA-50 does not have the ability to operate smart weapon forces, limiting training. Therefore, the purpose of this study is to implement the TA-50 aircraft to enable virtual training of one of the smart weapons, the Point Direct Attack Munition (JDAM). First, JDAM functions implemented in FA-50 aircraft, a model similar to TA-50 aircraft, were analyzed. In addition, since functions implemented in FA-50 aircraft cannot be directly utilized by source code, algorithms were extracted using machine learning techniques(TensorFlow). The implementation of this function is expected to enable realistic training without actually having to be armed. Finally, based on the results of this study, we would like to propose ways to supplement the limitations of the research so that it can be implemented in the same way as it is.

• The Journal of the Korea Contents Association
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• v.11 no.11
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• pp.514-520
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• 2011

Technological development, a diverse range of media adapting to new technology has been developing. Augmented Reality Technology provides realistic information through the stereoscopic 3D images and it has been riding a wave of learner interest as an educational media which can broaden their learning experience through direct operating activity. The biggest reason why the Augmented Reality has gained such attention is because of its unique way of suggesting learning information that unlike the existing educational media, the Augmented Reality provides additional digitalized information while its users are looking at the actual object. Based on the domestic and foreign cases of development for educational contents using the Augmented Reality Technology, this thesis suggests the utilization of Pop-up books and multi-augmented Digilog Books that are ways to maximize the educational effects of the Augmented Reality. Through the development of smartphone applications that are propagating lately at a rapid rate, users can now use the Augmented Reality educational contents in a way that is easier than ever.

• Journal of the Korea Society of Computer and Information
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• v.16 no.8
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• pp.67-76
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• 2011

This study is to analyze the effects on using robots in elementary regular classes. The robots and PC-linked applications were developed considering the school subjects' academic goals and the learners' responses were carefully examined while applying the robots in the class. The image profile after the robot application shows positive feedbacks in most of the contents and some significant differences were found in contents such as 'theoretical-practical', 'uncooperative-cooperative'(p<.05). Moreover, the analysis on the learners' image drawings shows that more robot images are formed related to the concrete learning environment. The learners have answered in an interview to evaluate their understanding of Robot based instruction that both direct and indirect robot experience induce positive participations and provide authentic learning experience. During the robot based instruction, the learners cooperate with their group members and recognize the cooperation as a positive activity. It proves that the robot based instruction can lead to a possibility of new education paradigm.

• Journal of the Korean Institute of Educational Facilities
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• v.27 no.3
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• pp.3-14
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• 2020

The purpose of this study is to discuss the planning direction of educational spaces to support children's healthy and creative learning based on bio_philic theory. This study analyzed the characteristics of the application of biophilic patterns in children's education space through case analysis. The conclusion of this study is summarized as follows. As a result of the analysis of children's classroom space, the pattern of 'A(Visual connection with nature), F(Dynamic & Diffuse Light), K(Prospect)' shows high application rate, but the pattern of 'C(Non-Rhythmic Sensory Stimuli), G(Connection with Natural Systems), I(Material Connection with Nature)' shows low application rate. In particular, there is a lack of connection with patterns such as hearing, smell, touch, taste stimulation and water experience, and curiosity through exploration of nature about 'B(Non-visual connection with nature), E(Presence of Water), N(Risk/Peril)' changes in nature and ecosystem. In the corridor and rest space, the pattern of 'A(Visual connection with nature), D(Thermal & Airflow Variability), F(Dynamic & Diffuse Light), G(Connection with Natural Systems), K(Prospect)' shows high application rate, but 'B(Non-visual connection with nature)' shows low application rate. In addition, the application of patterns related to the stimulation of curiosity through direct exploration of nature and the exploration of the patterns of 'E(Presence of Water), N(Risk/Peril)' is insufficient. Therefore, in the case of classroom spaces, the active use of nature as it is should be considered within the scope that does not cause visual confusion, and it should provide an area that can be experienced through the five senses. And corridors and rest spaces should be designed to introduce more active natural elements as spaces to recover stress caused by learning. In other words, the characteristics of children's education facilities need to be connected between classroom space, corridor, rest space and external space. This study is meaningful in that it analyzes and derives the application characteristics of 'biophilic design' which affects the 'Attention Restoration' of children's educational spaces through foreign cases.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.8
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• pp.1743-1750
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• 2003

DC motor requires a rotor speed sensor for accurate speed control. The speed sensors such as resolvers and encoders are used as a speed detector, but they increase cost and size of the motor and restrict the industrial drive applications. So in these days, many papers have reported in the sensorless operation of DC motor〔3­5〕. This paper presents a new sensorless strategy using neural networks〔6­8〕. Neural network has three layers which are input layer, hidden layer and output layer. The optimal neural network structure was tracked down by trial and error, and it was found that 4­16­1 neural network structure has given suitable results for the instantaneous rotor speed. Also, learning method is very important in neural network. Supervised learning methods〔8〕 are typically used to train the neural network for learning the input/output pattern presented. The back­propagation technique adjusts the neural network weights during training. The rotor speed is gained by weights and four inputs to the neural network. The experimental results were found satisfactory in both the independency on machine parameters and the insensitivity to the load condition.

• Journal of Educational Research in Mathematics
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• v.26 no.3
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• pp.543-563
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• 2016

This study was designed as a meta-analysis to investigate the research trends in mathematics learning disabilities(MLD) area. The results of this study were as follows: The 201 researches targeted for the analysis can be categorized 4: characteristic of students with MLD, screening students with MLD, interventional teaching for students with MLD, and et cetera. Also, the outcomes of researches regarding intervention in MLD determined to have a large effect resulted in a total average of 0.958. Especially, as a result of analysing the effect size in accordance with teaching method variables in group-case designed researches, the effect was largest when direct instruction and strategy instruction was given. The effect was largest when the frequency of intervention was over 16 and under 20. The results in this study be summed up as follows. MLD can be served as a foundation in setting a direction for further research to improve in Korea.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.2
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• pp.18-28
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• 2006

We consider a Direct Input Output Manufacturing System(DIOMS) which has a munber of machine centers placed along a built-in Automated Storage/Retrieval System(AS/RS). The Storage/Retrieval (S/R) machine handles parts placed on pallets for the operational aspect of DIOMS and determines the optimal operating policy by combining computer simulation and genetic algorithm. The operational problem includes: input sequencing control, dispatching rule of the S/R machine, machine center-based part type selection rule, and storage assignment policy. For each operating policy, several different policies are considered based on the known research results. In this paper, using the computer simulation and genetic algorithm we suggest a method which gives the optimal configuration of operating policies within reasonable computation time.

• Journal of Multimedia Information System
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• v.9 no.2
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• pp.113-120
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• 2022

Mongolian folk songs are inspired by Mongolian labor songs and are classified into long and short songs. Mongolian long songs have ancient origins, are rich in legends, and are a great source of folklore. So it was inscribed by UNESCO in 2008. Mongolian written literature is formed under the direct influence of oral literature. Mongolian long song has 3 classes: ayzam, suman, and besreg by their lyrics and structure. In ayzam long song, the world perfectly embodies the philosophical nature of world phenomena and the nature of human life. Suman long song has a wide range of topics such as the common way of life, respect for ancestors, respect for fathers, respect for mountains and water, livestock and animal husbandry, as well as the history of Mongolia. Besreg long songs are dominated by commanded and trained characters. In this paper, we proposed a method to classify their 3 types of long songs using machine learning, based on their lyrics structures without semantic information. We collected lyrics of over 80 long songs and extracted 11 features from every single song. The features are the name of a song, number of the verse, number of lines, number of words, general value, double value, elapsed time of verse, elapsed time of 5 words, and the longest elapsed time of 1 word, full text, and type label. In experimental results, our proposed features show on average 78% recognition rates in function type machine learning methods, to classify the ayzam, suman, and besreg classes.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.91-98
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• 2022

The purpose of this study is to investigate the effect of online basic clinical training on critical thinking disposition, self determination motivation and learning satisfaction for nursing student in the COVID-19 situation. This study is one group pre-test and post-test design. The subjects of this study were collected using an online questionnaire for third-year nursing students located in G city, and 41 students participated in the final. The results of this study showed that critical thinking disposition(p=.013) and self determination motivation(p=.007) increased statistically significantly after the online basic clinical training. This findings indicate that online basic clinical training is effective in improving critical thinking disposition, self determination motivation. As it is difficult to perform direct nursing in the COVID-19 medical field recently, it is expected that the limitations of online clinical practice can be overcome by using various online contents.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.6
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• pp.245-254
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• 2022

Proteins are the basic unit of all life activities, and understanding them is essential for studying life phenomena. Since the emergence of the machine learning methodology using artificial neural networks, many researchers have tried to predict the function of proteins using only protein sequences. Many combinations of deep learning models have been reported to academia, but the methods are different and there is no formal methodology, and they are tailored to different data, so there has never been a direct comparative analysis of which algorithms are more suitable for handling protein data. In this paper, the single model performance of each algorithm was compared and evaluated based on accuracy and speed by applying the same data to CNN, LSTM, and GRU models, which are the most frequently used representative algorithms in the convergence research field of predicting protein functions, and the final evaluation scale is presented as Micro Precision, Recall, and F1-score. The combined models CNN-LSTM and CNN-GRU models also were evaluated in the same way. Through this study, it was confirmed that the performance of LSTM as a single model is good in simple classification problems, overlapping CNN was suitable as a single model in complex classification problems, and the CNN-LSTM was relatively better as a combination model.