• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.837-842
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• 2023

This study analyzed the awareness and actual condition on metaverse utilization in education for design major students. An online survey was conducted for 14 days from May 10 to 23, 2023, targeting 120 students majoring in design at D University. The evaluation method of the questionnaire was a nominal scale and a 5-point scale, and the questionnaire results were analyzed through SPSS 29.0. First, it is necessary to sufficiently share the advantages of metaverse utilization in education with students, and to provide basic literacy programs utilizing the characteristics of metaverse and supporting class activities. Second, students' response will be higher in studio classes where practical training is conducted rather than information delivery or understanding-oriented lectures. Third, in order for the metaverse to become a means of education in the digital transformation era rather than just a temporarily medium in COVID-19 era, specific and systematic design education programs reflecting the characteristics of the metaverse need to be continuously developed. In addition, it is important for instructors to actively review the use of the metaverse and search for various ways to utilize it.

• Journal of the Korea Society for Simulation
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• v.33 no.3
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• pp.13-26
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• 2024

The CBRN(Chemical, Biological, Radiological, and Nuclear) hazard prediction model is a system that supports commanders in making better decisions by creating contamination distribution and damage prediction areas based on the weapons used, terrain, and weather information in the events of biochemical and radiological accidents. NBC_RAMS(Nuclear, Biological and Chemical Reporting And Modeling S/W System) developed by ADD (Agency for Defense Development) is used not only supporting for decision making plan for various military operations and exercises but also for post analyzing CBRN related events. With the NBC_RAMS's core engine, we introduced a CBR hazard assessment scenario analysis system that can generate contaminant distribution prediction results reflecting various CBR scenarios, and described how to apply it in specific purposes in terms of input information, meteorological data, land data with land coverage and DEM, and building data with pologon form. As a practical use case, a technology development case is addressed that tracks the origin location of contaminant source with artificial intelligence and a technology that selects the optimal location of a CBR detection sensor with score data by analyzing large amounts of data generated using the CBRN scenario analysis system. Through this system, it is possible to generate AI-specialized CBRN related to training and analysis data and support planning of operation and exercise by predicting battle field.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.79-104
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• 2020

Recently, as deep learning has attracted attention, the use of deep learning is being considered as a method for solving problems in various fields. In particular, deep learning is known to have excellent performance when applied to applying unstructured data such as text, sound and images, and many studies have proven its effectiveness. Owing to the remarkable development of text and image deep learning technology, interests in image captioning technology and its application is rapidly increasing. Image captioning is a technique that automatically generates relevant captions for a given image by handling both image comprehension and text generation simultaneously. In spite of the high entry barrier of image captioning that analysts should be able to process both image and text data, image captioning has established itself as one of the key fields in the A.I. research owing to its various applicability. In addition, many researches have been conducted to improve the performance of image captioning in various aspects. Recent researches attempt to create advanced captions that can not only describe an image accurately, but also convey the information contained in the image more sophisticatedly. Despite many recent efforts to improve the performance of image captioning, it is difficult to find any researches to interpret images from the perspective of domain experts in each field not from the perspective of the general public. Even for the same image, the part of interests may differ according to the professional field of the person who has encountered the image. Moreover, the way of interpreting and expressing the image also differs according to the level of expertise. The public tends to recognize the image from a holistic and general perspective, that is, from the perspective of identifying the image's constituent objects and their relationships. On the contrary, the domain experts tend to recognize the image by focusing on some specific elements necessary to interpret the given image based on their expertise. It implies that meaningful parts of an image are mutually different depending on viewers' perspective even for the same image. So, image captioning needs to implement this phenomenon. Therefore, in this study, we propose a method to generate captions specialized in each domain for the image by utilizing the expertise of experts in the corresponding domain. Specifically, after performing pre-training on a large amount of general data, the expertise in the field is transplanted through transfer-learning with a small amount of expertise data. However, simple adaption of transfer learning using expertise data may invoke another type of problems. Simultaneous learning with captions of various characteristics may invoke so-called 'inter-observation interference' problem, which make it difficult to perform pure learning of each characteristic point of view. For learning with vast amount of data, most of this interference is self-purified and has little impact on learning results. On the contrary, in the case of fine-tuning where learning is performed on a small amount of data, the impact of such interference on learning can be relatively large. To solve this problem, therefore, we propose a novel 'Character-Independent Transfer-learning' that performs transfer learning independently for each character. In order to confirm the feasibility of the proposed methodology, we performed experiments utilizing the results of pre-training on MSCOCO dataset which is comprised of 120,000 images and about 600,000 general captions. Additionally, according to the advice of an art therapist, about 300 pairs of 'image / expertise captions' were created, and the data was used for the experiments of expertise transplantation. As a result of the experiment, it was confirmed that the caption generated according to the proposed methodology generates captions from the perspective of implanted expertise whereas the caption generated through learning on general data contains a number of contents irrelevant to expertise interpretation. In this paper, we propose a novel approach of specialized image interpretation. To achieve this goal, we present a method to use transfer learning and generate captions specialized in the specific domain. In the future, by applying the proposed methodology to expertise transplant in various fields, we expected that many researches will be actively conducted to solve the problem of lack of expertise data and to improve performance of image captioning.

• Journal of Educational Research in Mathematics
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• v.12 no.1
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• pp.49-70
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• 2002

There have recently been increasing exchanges between South and North Korea in many areas of society, involving politics, economics, culture, education. In response to these developments, research activities are more strongly demanded in each of these areas to help prepare for the final unification of the two parts of the nation. In the area of mathematics education, scholars have started to conduct comparative studies of mathematics education in South and North Korea. As a response to the growing demand of the time, in this thesis we compared the secondary mathematics education in South Korea with that in North Korea. To begin with, we examined the background of education, in North Korea, particularly predominant ideological, epistemological and teaching theoretical aspects of education in North Korea. Thereafter, we compared the mathematics curriculum of South Korea with that of North Korea. On the basis of these examinations, we compared the secondary school mathematics textbooks of South and North Korea, and we attempted to suggest a guideline for researches preparing for the unification of the mathematics curriculum of South and North Korea. As a communist society, North Korea awards the socialist ideology the supreme rank and treats all school subjects as instrumental tools that are subordinated to the dominant communist ideology. On the other hand, under the socialist ideology North Korea also emphasizes the achievement of the objective of socialist economic development by expanding the production of material wealth. As such, mathematics in North Korea is seen as a tool subject for training skilled technical hands and fostering science and technology, hence promoting the socialist material production and economic development. Hence, the mathematics education of North Korea adopts a so-called "awakening teaching method," and emphasizes the approaches that combine intuition with logical explanation using materials related with the ideology or actual life. These basic viewpoints of North Korea on mathematics education are different from those of South Korea, which emphasize the problem-solving ability and acquisition of academic mathematical knowledge, and which focus on organizing as well as discovering knowledge of learners' own accord. In comparison of the secondary school mathematics textbooks used in South and North Korea, we looked through external forms, contents, quantity of each area of school mathematics, viewpoints of teaching, and term. We have identified similarities in algebra area and differences in geometry area especially in teaching sequence and approaching method. Many differences are also found in mathematical terms. Especially, it is found that North Korea uses mathematical terms in Hangul more actively than South Korea. We examined the specific topics that are treated in both South and North Korea, "outer-center & inner-center of triangle" and "mathematical induction", and identified such differences more concretely. Through this comparison, it was found that the concrete heterogeneity in the textbooks largely derive from the differences in the basic ideological viewpoints between South and North Korea. On the basis of the above findings, we attempted to make some suggestions for the researches preparing for the unification in the area of secondary mathematics education.

• Journal of National Security and Military Science
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• s.4
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• pp.83-118
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• 2006

This study is to develop digital leadership in a field of national defense. Today, korean society is facing the crisis of national security. But national defense leadership is not show in the circumstance of national security crisis. As you know, national defense leadership is a process that make use of influence. Which means it converges people's interest and demands well and also show people the right vision of national defense and make them to comply the policy about national security. Because of the environmental change, our national defense leadership is having a new turning point. First, international order, which is under post-cold war, raises possibility of guarantee of peace and security in international society but also, cause the increase of multiple uncertainty and small size troubles in security circumstance. In addition, Korean society is rushing into democratization and localization period by success in peaceful change of political power went through about three times. The issue of political neutralization of military is stepping into settlement but still, negative inheritance of old military regime is worrying about it. In this situation, we can't expect rise in estimation about the importance of security and military's reason for being. So, military have to give their concern to not only internal maintenance of order and control and growth of soldiers but also developing external leadership to strength influence to society and military's the reason for being. So for these alternative I'm suggesting a digital leadership of national defense which fits digital era. This digital leadership is the leadership which can accept and understand digital technology and lead the digital organization. To construct digital national defense we need a practical leadership. The leadership has to be digital leadership with digital competence that can direct vision of digital national defense and carry out the policy. A leader who ha s digital leadership can lead the digital society. The ultimate key to construct digital government, digital corporate and digital citizen depends on digital leader with digital mind. To be more specific, digital leadership has network leadership, next generation leadership, knowledge driven management leadership, innovation oriented leadership. A leader with this kind of leadership is the real person with digital leadership. From now on, to rise this, we have to build up human resource development strategy and develop educational training program.

• Journal of Agricultural Extension & Community Development
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• v.3 no.1
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• pp.141-155
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• 1996

The purpose of this study was to investigate the sole of local newspaper for community development in Korea. Specific objectives of the study were : 1) to identify the role of local newspaper in community development, 2) to analyze problems and situations on local newspapers in Korea, and 3) to suggest desirable roles of local newspaper for the community development. To attain the above objectives, this study was carried out through literature review, contest analysis of selected local newspapers, and the questionnaire survey of selected readers of local newspaper. The data were collected from 267 readers of local newspaper who participated in the farmers` training session in the Rural Development Administration, by using questionnaires developed by the researcher, and total of 263 questionnaires were analyzed. The statistical techniques used for the study were frequence, percentile, standard deviation utilizing the $SPSS/PC^+$. The major findings of the study were as follows : 1. The major roles of functions of local newspapers identified in this study were summarized as to reinforce the community consciousness, to form community opinion, to watch and to monitor community environment, to provide education and entertainment, and to contribute to the conveyance and promotion of community culture. 2. The general characteristic of local newspaper readers surveyed were; a) average distance from town to their village was 9.3 Km, b) average age was 29 years, c) about 75% of them were senior high school graduates, and d) about 96% of them participated in group activities. 3. About 45% of the respondents used television or radio for their moor sources of local news were TV or radio, while about 31% respondents used immunity newspaper for their moor sources of local news. About 67% of the respondents kept their readership over two years, and about 40% of them read community newspaper regularly at their home. 4. The results of content analysis showed drat the local newspapers were dealing with articles on cultural affairs, community consciousness, administrative and civic affairs, however, there were not enough educational news, and various kinds of general news in the community. 5. Survey also showed the most needed news were ; 1) political news including administrative and civic affairs, 2) economic news including sales and distribution, 3) social news including social problems on environmental pollution and community development works, 4) educational news including technology and information, 5) cultural news including guide to cultural and historical sites and local brief news. 6. In the evaluation of local newspaper, the readers were generally positive in valued roles of local newspaper in community relations, community development, promoting community cohesion and understanding of community members, and about 40% of the respondents were positive in predicting the bright future perspectives of local newspapers. 7. In the readers` evaluation of local newspaper, readers responded that local newspapers were very closely related to the community and residents, reflecting the current concerns of local population and recognizing the value of community media, and the contents of local newspaper were positively related to daily lives of community residents and opinions of overall community.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Journal of Korea Water Resources Association
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• v.54 no.5
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• pp.301-309
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• 2021

In this study, we tried to improve the performance of the existing U-net-based deep learning rainfall prediction model, which can weaken the meaning of time series order. For this, ConvLSTM2D U-Net structure model considering temporal consistency of data was applied, and we evaluated accuracy of the ConvLSTM2D U-Net model using a RainNet model and an extrapolation-based advection model. In addition, we tried to improve the uncertainty in the model training process by performing learning not only with a single model but also with 10 ensemble models. The trained neural network rainfall prediction model was optimized to generate 10-minute advance prediction data using four consecutive data of the past 30 minutes from the present. The results of deep learning rainfall prediction models are difficult to identify schematically distinct differences, but with ConvLSTM2D U-Net, the magnitude of the prediction error is the smallest and the location of rainfall is relatively accurate. In particular, the ensemble ConvLSTM2D U-Net showed high CSI, low MAE, and a narrow error range, and predicted rainfall more accurately and stable prediction performance than other models. However, the prediction performance for a specific point was very low compared to the prediction performance for the entire area, and the deep learning rainfall prediction model also had limitations. Through this study, it was confirmed that the ConvLSTM2D U-Net neural network structure to account for the change of time could increase the prediction accuracy, but there is still a limitation of the convolution deep neural network model due to spatial smoothing in the strong rainfall region or detailed rainfall prediction.

• Economic and Environmental Geology
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• v.54 no.1
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• pp.127-136
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• 2021

This study was carried out to understand China's earthquake governance and role-sharing, and to strategically use it for research cooperation in related fields with China. The characteristics of China's national earthquake governance and role-sharing are detailed in this study. First, unlike Korea, China's geoscience and earthquake research fields are separate, and are clearly distinguished from other fields of science and technology. They hold a higher status compared to other fields in China. Second, China's provincial earthquake agencies simultaneously carry out related tasks under the dual supervisory management system of the central and provincial governments. Third, the China Earthquake Administration (CEA) has the authority to do research and development, manpower training, and degree conferment, which are centered on directly affiliated institutions. Fourth, China carries out similar functions in directly affiliated institutions of the CEA and the China Geological Survey (CGS), and affiliated institutions of the Chinese Academy of Sciences (CAS), respectively. Fifth, the CEA is continuously expanding the seismic observation network that connects the vast land of the country. Sixth, China is considered to have detailed structures of earthquake-related laws and regulations. Given China's earthquake governance and role-sharing, it is considered that the possibility of success in research cooperation is high if Korea first determines whether it is under the jurisdiction of the CGS, CEA, and CAS, depending on the specific field.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.77-86
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• 2022

The Republic of Korea is a country in which coastal surveillance is an imperative national task as it is surrounded by seas on three sides under the confrontation between South and North Korea. However, due to Defense Reform 2.0, the number of R/D (Radar) operating personnel has decreased, and the period of service has also been shortened. Moreover, there is always a possibility that a human error will occur. This paper presents specific guidelines for developing an AI learning model for the intelligent coastal surveillance system. We present a three-step strategy to realize the guidelines. The first stage is a typical stage of building an AI learning model, including data collection, storage, filtering, purification, and data transformation. In the second stage, R/D signal analysis is first performed. Subsequently, AI learning model development for classifying real and false images, coastal area analysis, and vulnerable area/time analysis are performed. In the final stage, validation, visualization, and demonstration of the AI learning model are performed. Through this research, the first achievement of making the existing weapon system intelligent by applying the application of AI technology was achieved.