• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1195-1204
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• 2021

Recently, local torrential rain have become more frequent and severe due to abnormal climate conditions, causing a surge in human and properties damage including infrastructures along the river. In this study, water surface elevation prediction algorithm was developed using the LSTM (Long Short-term Memory) technique specialized for time series data among Machine Learning to estimate and prevent flooding of the facilities. The study area is Jamsu Bridge, the study period is 6 years (2015~2020) of June, July and August and the water surface elevation of the Jamsu Bridge after 3 hours was predicted. Input data set is composed of the water surface elevation of Jamsu Bridge (EL.m), the amount of discharge from Paldang Dam (m³/s), the tide level of Ganghwa Bridge (cm) and the number of tweets in Seoul. Complementary data were constructed by using not only structured data mainly used in precedent research but also unstructured data constructed through wordcloud, and the role of unstructured data was presented through comparison and analysis of whether or not unstructured data was used. When predicting the water surface elevation of the Jamsu Bridge, the accuracy of prediction was improved and realized that complementary data could be conservative alerts to reduce casualties. In this study, it was concluded that the use of complementary data was relatively effective in providing the user's safety and convenience of riverside infrastructure. In the future, more accurate water surface elevation prediction would be expected through the addition of types of unstructured data or detailed pre-processing of input data.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.896-905
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• 2021

In the field of SIEM(Security information and event management), many studies try to use a feedback system to solve lack of completeness of training data and false positives of new attack events that occur in the actual operation. However, the current feedback system requires too much human inputs to improve the running model and even so, those feedback from inexperienced analysts can affect the model performance negatively. Therefore, we propose "active model improving feedback technology" to solve the shortage of security analyst manpower, increasing false positive rates and degrading model performance. First, we cluster similar predicted events during the operation, calculate feedback priorities for those clusters and select and provide representative events from those highly prioritized clusters using XAI (eXplainable AI)-based event visualization. Once these events are feedbacked, we exclude less analogous events and then propagate the feedback throughout the clusters. Finally, these events are incrementally trained by an existing model. To verify the effectiveness of our proposal, we compared three distinct scenarios using PKDD2007 and CSIC2012. As a result, our proposal confirmed a 30% higher performance in all indicators compared to that of the model with no feedback and the current feedback system.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.251-257
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• 2022

Nowadays, the issues of comprehensive formation of a person capable of self-education, self-development and creative self-realization in the conditions of distance education are relevant. There is a need to solve this problem, which is due to social, cultural, and pedagogical factors. This makes it necessary to find effective means of personality formation. In this matter, great importance is attached to the modern method of forming a creative personality - art therapy. Various approaches to the definition of art therapy have been clarified. They consider various forms of art therapy when working with children, adolescents and adults in the context of distance education. The most relevant are the two main forms of work - individual and group art therapy. Art therapy develops the individual's creativity. Therefore, during art therapy, attention is focused on the inner world, experiences, and feelings. Therefore, we believe that in the context of distance education, art therapy has everything for the powerful potential of personality formation. Scientists consider this therapy as therapy by means of art, which is based on experiences, conflicts that can be expressed in the visual arts and music. Art therapy helps to get rid of conflicts and experiences. This happens in the context of distance education through the development of attention to feelings, strengthening one's own personal value and increasing artistic competence. The article describes the signs that characterize art therapy. Art-therapeutic technologies in the context of distance education, which are now actively used by psychologists, teachers and art therapists themselves, are highlighted. The advantages of distance learning are considered. The characteristic features of distance learning and features of the use of art therapy by means of distance education in the process of professional training of specialists are determined.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.649-654
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• 2023

This paper proposes the design of a neural network structure search model using graph convolutional neural networks. Deep learning has a problem of not being able to verify whether the designed model has a structure with optimized performance due to the nature of learning as a black box. The neural network structure search model is composed of a recurrent neural network that creates a model and a convolutional neural network that is the generated network. Conventional neural network structure search models use recurrent neural networks, but in this paper, we propose GC-NAS, which uses graph convolutional neural networks instead of recurrent neural networks to create convolutional neural network models. The proposed GC-NAS uses the Layer Extraction Block to explore depth, and the Hyper Parameter Prediction Block to explore spatial and temporal information (hyper parameters) based on depth information in parallel. Therefore, since the depth information is reflected, the search area is wider, and the purpose of the search area of the model is clear by conducting a parallel search with depth information, so it is judged to be superior in theoretical structure compared to GC-NAS. GC-NAS is expected to solve the problem of the high-dimensional time axis and the range of spatial search of recurrent neural networks in the existing neural network structure search model through the graph convolutional neural network block and graph generation algorithm. In addition, we hope that the GC-NAS proposed in this paper will serve as an opportunity for active research on the application of graph convolutional neural networks to neural network structure search.

• Korean Journal of Environmental Biology
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• v.40 no.2
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• pp.214-223
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• 2022

Among migratory insect pests, Mythimna seperata and Cnaphalocrocis medinalis are invasive pests introduced into South Korea through westerlies from southern China. M. seperata and C. medinalis are insect pests that use rice as a host. They injure rice leaves and inhibit rice growth. To understand the distribution of M. seperata and C. medinalis, it is important to understand environmental factors such as temperature and humidity of their habitat. This study predicted current and future habitat suitability models for understanding the distribution of M. seperata and C. medinalis. Occurrence data, SSPs (Shared Socio-economic Pathways) scenario, and RCP (Representative Concentration Pathway) were applied to MaxEnt (Maximum Entropy), a machine learning model among SDM (Species Distribution Model). As a result, M. seperata and C. medinalis are aggregated on the west and south coasts where they have a host after migration from China. As a result of MaxEnt analysis, the contribution was high in the order of Land-cover data and DEM (Digital Elevation Model). In bioclimatic variables, BIO_4 (Temperature seasonality) was high in M. seperata and BIO_2 (Mean Diurnal Range) was found in C. medinalis. The habitat suitability model predicted that M. seperata and C. medinalis could inhabit most rice paddies.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.119-125
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• 2023

Through the Ukraine-Russia war, the military importance of drones is being reassessed, and North Korea has completed actual verification through a drone provocation towards South Korea at 2022. Furthermore, North Korea is actively integrating artificial intelligence (AI) technology into drones, highlighting the increasing threat posed by drones. In response, the Republic of Korea military has established Drone Operations Command(DOC) and implemented various drone defense systems. However, there is a concern that the efforts to enhance capabilities are disproportionately focused on striking systems, making it challenging to effectively counter swarm drone attacks. Particularly, Air Force bases located adjacent to urban areas face significant limitations in the use of traditional air defense weapons due to concerns about civilian casualties. Therefore, this study proposes a new passive air defense method that aims at disrupting the object detection capabilities of AI models to enhance the survivability of friendly aircraft against the threat posed by AI based swarm drones. Using laser-based adversarial examples, the study seeks to degrade the recognition accuracy of object recognition AI installed on enemy drones. Experimental results using synthetic images and precision-reduced models confirmed that the proposed method decreased the recognition accuracy of object recognition AI, which was initially approximately 95%, to around 0-15% after the application of the proposed method, thereby validating the effectiveness of the proposed method.

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.171-190
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• 2024

This paper conducts a comparative analysis of citation patterns between journals and conferences using bibliometric and social network analysis on references from the 'Journal of the Korea Institute of Information Security and Cryptology (JKIISC)'. The results indicate that conference references slightly exceed journal references, with around 80% being international publications, highlighting Korean researchers' high dependency on overseas publications. Analysis of citation age shows trends of increasing immediacy citation rate, lengthening citing half-life, and shortening peak time, with domestic publications having higher immediacy citation rate and international publications having slower citing half-life. Mapping SCOPUS journals and ICORE conferences revealed that journal citations mainly come from 'Computer science' (32.3%), 'Engineering' (23.5%), 'Mathematics' (16.7%), and 'Social Cciences' (12.8%), along with other research fields (25.6%), while conference citations are predominantly in 'Cybersecurity and Privacy' with recent increases in 'Computer Vision and Multimedia Computation' and 'Machine Learning'. Co-citation network analysis shows higher degree centrality for conference groups and international publications. The co-citation frequency between different types of literature was highest between journals and conferences (36.9%), compared to within journals (34.3%) or within conferences (28.8%). Lastly, network visualization maps are presented to explore the structural connections among co-cited publications and their research fields. The results of this study suggest that the field of information security research in Korea effectively balances the use of journal and conference literature, indicating that the field is developing through a complementary relationship between these sources.

• Journal of The Korean Association For Science Education
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• v.31 no.5
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• pp.788-800
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• 2011

The purpose of this study was to investigate the elementary school teachers' perception of science writing. In this study, 10 elementary school teachers who have taught in the 3rd or 4th grade science lesson in 2010 were selected. Researchers constructed interview guide in three parts including the teachers' understanding of science writing, the status of science writing teaching and the difficulties of science writing in their classes. For the investigation, semi-structured in-depth interviews with 10 elementary school teachers were conducted individually. The results showed that the elementary school teachers were unfamiliar with the word ‘science writing’ and considered science writing as a writing using science learning contents. Also, they think that teaching science writing in their science lessons was not needed and didn't assess and provide detailed feedback with the students' written works. Most teachers needed teaching materials and assessment tools for science writing. To develop elementary teachers' understanding of the value and use of writing for learning in science, they will need to participate in science writing programs for in-service teachers and various teaching materials and assessment tools should also be developed.

• Journal of the Korean Society of Radiology
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• v.16 no.4
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• pp.491-497
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• 2022

In this study, we tried to secure basic data to create an environment necessary for distance learning through a survey on professor demand. Among the 184 full-time faculty members of the university, 73 (39.89%) respondents in 2021 and 87 (47.28%) in 2022 were included. As a result of the research on professor demand, in the 2021 school year, 27 people (37%) were classified as LMS improvement items when checking attendance, 38 people (23.3%) were pin-mics as content development support items, and 26 people (35.6%), 33 people (45.2%) of GOM Mix and 25 people (34.2%) of the distance education support center wanted to learn video editing program as the item of video editing program they are currently using. In the 2022 school year, 27 people (31.03%) said mobile upgrade as an LMS improvement item, 52 people (59.8%) of pin-microphone as a content development support item, 33 people (37.9%), but currently using the content creation intention using a studio. As for the video editing program they are working on, 47 people (54%) of GOM Mix Pro and 23 people (26.4%) of the distance education support center want to learn content creation method. In addition, the intention to produce content using the studio for the 2021 and 2022 academic year and the desired educational topic of the distance education support center in the future appeared insignificant (p > 0.05). In this distance education support center, we are working to solve the class of LMS attendance, upgrade mobile, and plan to distribute pin microphones. We are planning to increase the usability of the studio and provide training on how to use video editing programs and how to create video content. In order for a smooth class to take place in a university distance class, the university authorities should seek ways to support the instructor so that he/she does not have difficulties in performing his/her role as a teaching designer, such as setting learning goals, organizing and organizing content, motivating learning, and establishing effective class participation plans. there is a need

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.149-169
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• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."