• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.206-214
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• 2022

The way to measure the extent of damage from floods and droughts is to identify changes in the extent of water systems. In order to effectively grasp this at a glance, satellite images are used. KOMPSAT-5 uses Synthetic Aperture Radar (SAR) to capture images regardless of weather conditions such as clouds and rain. In this paper, various deep learning models are applied to perform semantic segmentation of the water system in this SAR image and the performance is compared. The models used are U-net, V-Net, U2-Net, UNet 3+, PSPNet, Deeplab-V3, Deeplab-V3+ and PAN. In addition, performance comparison was performed when the data was augmented by applying elastic deformation to the existing SAR image dataset. As a result, without data augmentation, U-Net was the best with IoU of 97.25% and pixel accuracy of 98.53%. In case of data augmentation, Deeplab-V3 showed IoU of 95.15% and V-Net showed the best pixel accuracy of 96.86%.

• Journal of the Korean Society for Heat Treatment
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• v.36 no.6
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• pp.374-381
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• 2023

This study develops and evaluates a deep learning model for predicting oxide and nitride layers based on plasma process data. We introduce a novel deep learning-based Varying Coefficient Regressor (VCR) by adapting the VCR, which previously relied on an existing unique function. This model is employed to forecast the oxide and nitride layers within the plasma. Through comparative experiments, the proposed VCR-based model exhibits superior performance compared to Long Short-Term Memory, Random Forest, and other methods, showcasing its excellence in predicting time series data. This study indicates the potential for advancing prediction models through deep learning in the domain of plasma processing and highlights its application prospects in industrial settings.

• Science of Emotion and Sensibility
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• v.26 no.4
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• pp.115-124
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• 2023

This study explores the intersection of literary studies and artificial intelligence (AI), focusing on the common theme of human emotions to foster complementary advancements in both fields. By adopting a comparative perspective, the paper investigates emotion as a shared focal point, analyzing various emotion-related concepts from both literary and AI perspectives. Despite the scarcity of research on the fusion of AI and literary studies, this study pioneers an interdisciplinary approach within the humanities, anticipating future developments in AI. It proposes that literary sensibility can contribute to AI by formalizing subjective literary emotions, thereby enhancing AI's understanding of complex human emotions. This paper's methodology involves the terminology-centered extraction of emotions, aiming to blend subjective imagination with objective technology. This fusion is expected to not only deepen AI's comprehension of human complexities but also broaden literary research by rapidly analyzing diverse human data. The study emphasizes the need for a collaborative dialogue between literature and engineering, recognizing each field's limitations while pursuing a convergent enhancement that transcends these boundaries.

• Journal of Digital Convergence
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• v.18 no.5
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• pp.75-87
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• 2020

The present study determined new value that can be created through the convergence between artificial intelligence technology (AIT) and all industries by deriving and thoroughly analyzing major issues related to artificial intelligence (AI). This study analyzes domestic articles related to AI using topic modeling method based on LDA algorithm. Keywords were extracted from 3,889 articles of eleven metropolitan newspapers, eight business newspapers and major broadcasting companies; articles were selected by searching for the keyword "artificial intelligence". Keywords were extracted by optimizing the relevance parameter λ to improve the measure of pointwise mutual information (PMI), which shows the association among the keywords of each topic, and topic names were inferred from keywords based on valid evidence. The extracted topics widely showed changes occurring throughout society, economy, industries, culture, and the support policy and vision of the government.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1249-1254
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• 2021

Computing technology and networking technology in the era of the 4th industrial revolution are rapidly evolving into an intelligent information society. AR, VR, and MR technologies, which are dual immersive media fields, are being applied in many convergence technologies, especially! The development of the health and healthcare field is actively progressing. In the field of health and healthcare, there are many problems due to aging of the population, increase in chronic progress, lack of infrastructure, and lack of professional manpower. services in the field are adopted. Therefore, this study applies cognitive evaluation through a computing system to the mild cognitive impairment, designs and develops a cognitive judgment platform using augmented reality based on the cognitive judgment technology system design, and integrates AI and BigData-based intelligent cognitive rehabilitation in the future. It is used as basic data for service platform development.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1207-1212
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• 2022

Realistic media, the next-generation media technology in the era of the 4th industrial revolution, is becoming an issue as a technology to experience through an environment that optimizes user experience, especially! It is rapidly developing into the health and healthcare convergence and complex fields. Realistic media technologies and services are being adopted to solve the problems of the increase in chronic diseases due to the increase in the elderly population and the lack of infrastructure and professional manpower in the fields of cognitive training and rehabilitation. Therefore, in this study, a cognitive training system was designed and implemented for the purpose of improving cognitive ability and daily life activity in subjects with mild cognitive impairment (MCI) who require cognitive rehabilitation. In the future, an integrated service platform with interactive communication and immediate feedback as an intelligent cognitive rehabilitation integrated platform based on AI and BigData is left as a research project.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.35-43
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• 2023

This study tried to find out the applicability and effectiveness of the AI-based adaptive learning system in university classes by operating an AI-based adaptive learning system on a pilot basis. To this end, an AI-based adaptive learning system was applied to analyze the operation results of 42 learners who participated in basic mathematics classes, and a survey and in-depth interviews were conducted with students and professors. As a result of the study, the use of an AI-based customized learning system improved students' academic achievement. Both instructors and learners seem to contribute to improving learning performance in basic concept learning, and through this, the AI-based adaptive learning system is expected to be an effective way to enhance self-directed learning and strengthen knowledge through concept learning. It is expected to be used as basic data related to the introduction and application of basic science subjects for AI-based adaptive learning systems. In the future, we suggest a strategy study on how to use the analyzed data and to verify the effect of linking the learning process and analyzed data provided to students in AI-based customized learning to face-to-face classes.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.761-768
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• 2023

In the 4th Industrial Revolution, AI(Artificial Intelligence) and IoT(Internet of Things) technologies are being applied to across various fields, with particularly prominence in asset management, disaster management, and meteorological observation. In these fields, it is necessary to accurately determine the real-time and precise tracking of the object's location and status, and to collect various data even in situations that are difficult to detect with existing sensors. In order to address these demands, the use of GNSS(Global Navigation Satellite System) is essential, and this technology enables the efficient management of assets, disaster prevent and response, and accurate weather forecasting. In this paper, we provide the investigated results for the latest trends in the application of GNSS in the fields of asset management, disaster management, and weather observation, among various fields incorporating AI and IoT and analyze them.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.539-544
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• 2024

This paper examines the necessity and direction of digital literacy education as university education in the AI era. Digital literacy can be considered universal education about everyday culture in a digital environment, and its scope is expanding to cultivate the competencies necessary for citizens of a digital society, rather than simply the ability to use digital devices. In this paper, the university liberal arts curriculum has strengthened the information literacy area to reflect the changes of the times, but it is presented as a problem that it is still focused on the technical aspects of learning how to use digital devices and specific programs. It was suggested that the direction of digital literacy education in universities should not be limited to the technical and instrumental aspects of using digital devices, but that it would be desirable to focus on digital ethics considering the social impacts that may arise from the use of digital devices.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.579-582
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• 2022

청소년의 성격유형을 분석할 때 소셜미디어 데이터를 활용하여 텍스트 처리로 분석하는 연구는 많이 알려져 있다. 그러나 이미지를 사용하여 성격유형을 분석한 연구는 미비하다. 본 연구는 청소년의 발테그 그림검사로 표현된 이미지를 데이터로 사용하고, CNN을 활용하여 MMTIC의 16가지 청소년의 성격유형을 예측한다. 연구 대상은 중학교 재학생을 대상으로 한다. MMTIC에서 U-band를 제외한 340명의 학생으로 2012년 4월부터 2013년 3월까지 조사하였다. 연구 결과 CNN을 사용하였을 때 21.6% 예측율을 보였으며, CNN Ensemble을 적용하였을 때 23.1%로 2.5%가 증가한다.