• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.5
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• pp.675-682
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• 2021

Fire accidents on land and at sea can cause serious casualties; specifically, owing to the nature of marine plants and ships, the mortality rate at sea from suffocation in confined spaces is significantly higher than that on land. To prevent such cases of asphyxiation, it is essential to install ventilation fans that can outwardly direct these toxic gases from fires; however, considering the scale of marine fires, the installation of large ventilation fans is not easy owing to the nature of marine structures. Therefore, in this study, we developed a new concept for fire safety technology to control toxic gases generated by fires from applied direct current (DC) electric fields. In the event of a fire, most flames contain large numbers of positive and negative charges from chemi-ionization, which generates an "ionic wind" by Lorentz forces through the applied electric fields. Using these ionic winds, an experimental study was performed to artificially control the fire smoke caused by burning paper and styrofoam, which are commonly used as insulation materials in general buildings and ships. The experiments showed that a fire smoke could be artificially controlled by applying a DC voltage in excess of ±5 kV and that relatively effective control was possible by applying a negative voltage rather than a positive voltage.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.4
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• pp.413-418
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• 2020

It is necessary to manage the prediction accuracy of the machine learning model to prevent the decrease in the performance of the grid network condition prediction model due to overfitting of the initial training data and to continuously utilize the prediction model in the field by maintaining the prediction accuracy. In this paper, we propose an automation technique for maintaining the performance of the model, which increases the accuracy and reliability of the prediction model by considering the characteristics of the power grid state data that constantly changes due to various factors, and enables quality maintenance at a level applicable to the field. The proposed technique modeled a series of tasks for maintaining the performance of the power grid condition prediction model through the application of the workflow management technology in the form of a workflow, and then automated it to make the work more efficient. In addition, the reliability of the performance result is secured by evaluating the performance of the prediction model taking into account both the degree of change in the statistical characteristics of the data and the level of generalization of the prediction, which has not been attempted in the existing technology. Through this, the accuracy of the prediction model is maintained at a certain level, and further new development of predictive models with excellent performance is possible. As a result, the proposed technique not only solves the problem of performance degradation of the predictive model, but also improves the field utilization of the condition prediction model in a complex power grid system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.43-50
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• 2019

Block-chain enjoys technical advantages such as "robust security," owing to the structural characteristic that forgery is impossible, decentralization through sharing the ledger between participants, and the hyper-connectivity connecting Internet of Things, robots, and Artificial Intelligence. As a result, public organizations have highly positive attitudes toward the adoption of technology using block-chain, and the design of university information services is no exception. Universities are also considering the application of block-chain technology to foundations that implement various information services within a university. Through case studies of block-chain applications across various industries, this study designs an empirical model of an integrated information service platform that integrates information systems in a university. A basic road map of university information services is constructed based on block-chain technology, from planning to the actual service design stage. Furthermore, an actual empirical model of an integrated information service in a university is designed based on block-chain by applying this framework.

• The Journal of the Korea Contents Association
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• v.19 no.9
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• pp.81-91
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• 2019

Recently, the nation has been suffering from a variety of problems, such as the rapid aging of the population and the weakening of the family's role due to rapid industrialization, such as the problem of supporting the elderly or the decline in the quality of supporting them. Among them, the issue of supporting the sentiment of the elderly is a major issue in terms of the quality of the stimulus. The best solution would be to resolve this issue of emotional support through various physical and human support. However, due to various limitations, access to efficient utilization of resources is being sought, among which support efforts through the convergence of digital technologies need to be noted. In this study, we took note of the problems of aging population shortage due to aging phenomenon and the problems of the emotional side of the problem of declining quality of the service, and analyzed the types of digital technology applied to support the emotional side through the convergence of digital technology. Among them, the Commission proposed emotional support through smart speakers, confirming the possibility of supporting the elderly through smart speakers. In addition, the Commission proposed guidelines for smart speaker communication systems to support the sentiment of older adults by conducting an in-depth interview with the In-Depth interview with the evaluation of the usability of smart speakers for older people. Based on the results of this study, it is expected that it will be the basic data for designing a communication system when developing smart speakers to support the emotions of the elderly.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.199-208
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• 2021

In this paper, the effect of artificial intelligence convergence education program that provides STEAM education using machine learning platform on elementary school students' STEAM literacy and learning flow was analyzed. A homogeneous group of 44 elementary school 6th graders was divided into an experimental group and a control group. The control group received 10 lessons of general subject convergence class, and the experimental group received 10 lessons of STEAM-based artificial intelligence convergence education using Machine learning for Kids. To develop the artificial intelligence convergence education program, the goals, achievement standards, and content elements of the 2015 revised curriculum to select subjects and class contents is analyzed. As a result of the STEAM literacy test and the learning flow test, there was a significant difference between the experimental group and the control group. In particular, it can be confirmed that the coding environment in which the artificial intelligence function is expanded has a positive effect on learners' learning flow and STEAM literacy. Among the sub-elements of convergence talent literacy, significant differences were found in the areas of personal competence such as convergence and creativity. Among the sub-elements of learning flow, significant differences were found in the areas such as harmony of challenge and ability, clear goals, focus on tasks, and self-purposed experiences. If further expanded research is conducted in the future, it will be a basic research for more effective education for the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.283-289
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• 2021

Many people with disabilities have shown interest in artificial intelligence speakers that serves as the main hub of the smart home. Therefore, the purpose of this study was to identify the intention of people with disabilities to use such speakers. The focus is on those with physical disabilities, a segment that accounts for the largest number of disability types. Based on the theoretical model of technology acceptance, the effect of perceived ease of use and perceived usefulness of artificial intelligence speakers by people with disabilities was analyzed using Structural Equation Modeling (SEM). Research has confirmed that the technology acceptance model is suitable for identifying the intention to use artificial intelligence speakers by people with disabilities, and specifically that the perceived ease of use has a significant impact on usefulness. Furthermore, the perceived ease of use for people with disabilities did not have a statistically significant effect on their intent to use whereas the perceived usefulness was shown to have a significant effect on the same. This study is meaningful as a foundation for developing customized artificial intelligence speaker services and improving the use of artificial intelligence speakers by people with disabilities.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1331-1340
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• 2020

Object detection in maritime means that the captain detects floating objects that has a risk of colliding with the ship using the computer automatically and as accurately as human eyes. In conventional ships, the presence and distance of objects are determined through radar waves. However, it cannot identify the shape and type. In contrast, with the development of AI, cameras help accurately identify obstacles on the sea route with excellent performance in detecting or recognizing objects. The computer must calculate high-volume pixels to analyze digital images. However, the CPU is specialized for sequential processing; the processing speed is very slow, and smooth service support or security is not guaranteed. Accordingly, this study developed maritime object detection software and implemented it with FPGA to accelerate the processing of large-scale computations. Additionally, the system implementation was improved through embedded boards and FPGA interface, achieving 30 times faster performance than the existing algorithm and a three-times faster entire system.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.93-100
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• 2022

This study aimed to understand the current status of science and engineering university(SEU) R&D operations depending on the research project characteristics(e.g., stages and characteristics), then provide implications for future university R&D support systems and related policies. Hence, an online survey targeting SEU R&D recipients was conducted between October 4th to November 5th, 2021. Analyzing 445 valid data using the Apriori algorithm, 16 association rules for R&D operation according to the research project characteristics show that regardless of research characteristics, SEU's R&D projects, particularly in applied research, were funded or operated under the leadership of government or public institutions. For basic research, individual researchers had a higher level of autonomy in determining research topics; yet, they had a short duration (3 years) and a unit of evaluation period of more than 3 years. These findings can be empirical evidence for revealing the relationship among various variables in operating SEUs' R&D.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.2
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• pp.116-126
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• 2022

Although the 'Act on Urban Parks and Green Spaces' was revised in 2016 to provide a legal foundation for national urban parks, there was no further discussion or follow-up research for the implementation of national urban parks. This study investigates Korea's park and green space regulations and national urban park cases from across the world. It aims to analyze worldwide cases and set a course for a viable national urban park system in Korea. The importance and characteristics of national urban parks were evident after reviewing the cases of Japan, Sweden, Finland, and Canada, which have national urban park systems, and the United States and Germany, which aid city parks with national budgets. Each country determined the plans and procedures by assessing the scope of government intervention and the government's role. The importance of communication between the state and municipal governments and private sector participation and governance was recognized. A system was also formed in which local governments actively participate in the nomination, establishment, administration, and management of national urban parks. The results of this study are as follows. First, the concept of equitable national development should be implemented to activate the national urban park system. Second, the national urban park should be a land management tool that may be used to balance development and preservation. Third, a specific method of securing, constructing, administering, and operating national urban parks should be supplemented by the current legislative framework amendment. Furthermore, the establishment of a sustainable research institute is needed to comprehensively analyze parks and green space systems and make appropriate decisions.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.2
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• pp.91-108
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• 2022

In most cases, optical images have been used as training data of DL (Deep Learning) models for object detection, recognition, identification, classification, semantic segmentation, and instance segmentation. However, properties of 3D objects in the real-world could not be fully explored with 2D images. One of the major sources of the 3D geospatial information is DSM (Digital Surface Model). In this matter, characteristic information derived from DSM would be effective to analyze 3D terrain features. Especially, man-made objects such as buildings having geometrically unique shape could be described by geometric elements that are obtained from 3D geospatial data. The background and motivation of this paper were drawn from concept of the intrinsic image that is involved in high-level visual information processing. This paper aims to extract buildings after classifying terrain features by training DL model with DSM-derived information including slope, aspect, and SRI (Shaded Relief Image). The experiments were carried out using DSM and label dataset provided by ISPRS (International Society for Photogrammetry and Remote Sensing) for CNN-based SegNet model. In particular, experiments focus on combining multi-source information to improve training performance and synergistic effect of the DL model. The results demonstrate that buildings were effectively classified and extracted by the proposed approach.