• Journal of the Korea Society of Computer and Information
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• v.29 no.9
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• pp.307-316
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• 2024

In this paper, we attempted to apply a novel engineering-based artificial intelligence education program to elementary gifted students in computing convergence and confirm changes in their convergence attitudes. To this end, we analyzed previous research on novel engineering and artificial intelligence education for talented students, and based on this, we selected books to learn artificial intelligence concepts and principles for each topic and completed a 15-session educational program. The developed program was applied to 10 giftedness in computing convergence over 15 lessons, and as a result of conducting the same convergence attitude test before and after class, it was confirmed that it had a positive effect on convergence attitude. This study is significant in that it suggests the possibility of novel engineering for artificial intelligence convergence education of gifted students.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.3
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• pp.339-346
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• 2016

Semantic search recently been used in the search field. POI is one of the most essential information that make up the geographic information, and many of the geographic information system has POI search function as a basic. In this study, we propose POI semantic search using collective intelligence. For this, we designed and implemented service that constructs empirical information from tag and image, and provides an intuitive spatial navigation experience. For POI search, collective intelligence platform that many users can participate to collect variety information was designed and implemented.

• Journal of the Optical Society of Korea
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• v.19 no.3
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• pp.248-254
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• 2015

In this paper we propose an improved version of the computational integral imaging reconstruction (CIIR) for visualizing a partially occluded object by utilizing an image inpainting technique. In the proposed method the elemental images for a partially occluded three-dimensional (3D) object are recorded through the integral imaging pickup process. Next, the depth of occlusion within the elemental images is estimated using two different CIIR methods, and the weight mask pattern for occlusion is generated. After that, we apply our image inpainting technique to the recorded elemental images to fill in the occluding area with reliable data, using information from neighboring pixels. Finally, the inpainted elemental images for the occluded region are reconstructed using the CIIR process. To verify the validity of the proposed system, we carry out preliminary experiments in which faces are the objects. The experimental results reveal that the proposed system can dramatically improve the quality of a reconstructed CIIR image.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.279-288
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• 2017

Nowadays, nations cyber security capabilities play an important role in a nation's defense. Security-critical infrastructures such as national defenses, public services, and financial services are now exposed to Advanced Persistent Threats (APT) and their resistance to such attacks effects the nations stability. Currently Cyber Threat Intelligence (CTI) is widely used by organizations to mitigate and deter APT for its ability to proactively protect their assets by using evidence-based knowledge. The evidence-based knowledge information can be exchanged among organizations and used by the receiving party to strengthen their cyber security management. This paper will discuss on the business process reengineering of the CTI information exchange management for a nationwide scaled control and governance by the government to better protect their national information security assets.

• Annual Conference of KIPS
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• 2023.05a
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• pp.493-495
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• 2023

Deep learning methods and attention mechanisms have been incorporated to improve facial emotion recognition, which has recently attracted much attention. The fusion approaches have improved accuracy by combining various types of information. This research proposes a fusion network with self-attention and local attention mechanisms. It uses a multi-layer perceptron network. The network extracts distinguishing characteristics from facial images using pre-trained models on RAF-DB dataset. We outperform the other fusion methods on RAD-DB dataset with impressive results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1690-1707
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• 2021

Precipitation prediction during flood season has been a key task of climate prediction for a long time. This type of prediction is linked with the national economy and people's livelihood, and is also one of the difficult problems in climatology. At present, there are some precipitation forecast models for the flood season, but there are also some deviations from these models, which makes it difficult to forecast accurately. In this paper, based on the measured precipitation data from the flood season from 1993 to 2019 and the precipitation return data of CWRF, ANN cycle modeling and a weighted integration method is used to correct the CWRF used in today's operational systems. The MAE and TCC of the precipitation forecast in the flood season are used to check the prediction performance of the proposed algorithm model. The results demonstrate a good correction effect for the proposed algorithm. In particular, the MAE error of the new algorithm is reduced by about 50%, while the time correlation TCC is improved by about 40%. Therefore, both the generalization of the correction results and the prediction performance are improved.

• Informatization Policy
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• v.29 no.1
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• pp.60-83
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• 2022

The purpose of this study is to empirically analyze the effect of individual perception of artificial intelligence and the level of digital literacy on the acceptance of artificial intelligence-based public services. For empirical analysis, a research model was set up based on the technology acceptance model and planned behavior theory using survey data of 2017 and analyzed through structural equations. To summarize the results of the analysis, firstly, the positive perception of individuals about artificial intelligence technology plays a role in reinforcing attitudes toward benefits and reducing concerns about public service in which artificial intelligence technology has been introduced. Secondly, the level of digital literacy reinforces both benefits and concerns about artificial intelligence technology, but it was found that the intention to use public services was reinforced through the benefits of artificial intelligence technology perceived by individuals, rather than privacy concerns about artificial intelligence technology. Thirdly, it was confirmed that the perceived benefits of individuals on artificial intelligence technology reinforced the intention to use public civil services, and privacy concerns negatively influenced the intention to use. It was confirmed that the influence of a perceived ease of use and usefulness, as opposed to privacy concerns, further reinforces the intention to use. Both citizens' positive perceptions regarding the accuracy and reliability of information provided through artificial intelligence technology and institutional complementation of responsibility for errors caused by artificial intelligence technology are strengthened, and technical problems related to privacy protection are solved.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.211-213
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• 2013

In these days, the delusions of Big Data and apprehension about them are coming into the picture in many business fields. General techniques for preservation, analysis, and utilization of Big Data are falling short of useful techniques for the volume of fast-increasing data. However, there are some assertions that the power of analysis and prediction of Artificial Intelligence would intensify the power of Big Data analysis. This paper studies on business cases to try to graft the Artificial Intelligence technique onto Big Data analysis. We first research on various techniques of Artificial Intelligence and relations between Artificial Intelligence and Big Data. And then, we perform case studies of Big Data with using Artificial Intelligence and propose some roles of Big Data in the future.

• Journal of the Korea Society of Computer and Information
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• v.26 no.8
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• pp.13-21
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• 2021

In this paper, we developed an artificial intelligence literacy education program to strengthen the AI convergence education capacity and cultivate literacy of in-service elementary and secondary teachers, and verify the effect on the degree of interest in artificial intelligence convergence education by applying it. As a test tool, the level of interest questionnaire scale developed by George, Hall & Stiegelbauer(2006) was used based on the center of interest acceptance model of Hall et al.(1979). As a result of analyzing the degree of interest in artificial intelligence convergence education before and after the application of the artificial intelligence literacy education program, the types of non-users were found both before and after the application of the program, but the overall degree of interest increased compared to before application. As a result of analyzing the satisfaction result of the artificial intelligence literacy education program, a response that was satisfied in most areas was derived, but there was a tendency to be somewhat less satisfied with the case of convergence and application of artificial intelligence and industry.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.35 no.4
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• pp.45-65
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• 2024

The aim of this study is to identify research trends related to information technology and artificial intelligence and to analyze changes in these trends over time. To conduct the research, we utilized the Web of Science (WoS) database and collected a total of 4,233 articles from 2011 to June 2024, performing a bibliographic mapping analysis. To observe changes over time, the data was divided into three periods: Period 1 (2011-2015), Period 2 (2016-2020), and Period 3 (2021-June 2024). The analysis revealed that clusters such as 'academic library,' 'information literacy,' and 'librarian' were of consistent interest throughout the entire period. In Period 1, a Web 2.0 cluster emerged, composed of keywords such as Library 2.0, Web 2.0, and social media. In Period 2, the 'bibliometrics' cluster expanded significantly, and keywords like 'big data' and 'deep learning' began to appear. In Period 3, new clusters such as 'artificial intelligence,' 'machine learning,' and 'COVID-19' emerged, highlighting these as key research topics.