• Journal of Convergence for Information Technology
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• v.11 no.10
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• pp.45-52
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• 2021

In this paper, we proposed an Atrous Residual U-Net (AR-UNet) to improve the segmentation accuracy of semantic segmentation method based on U-Net. The U-Net is mainly used in fields such as medical image analysis, autonomous vehicles, and remote sensing images. The conventional U-Net lacks extracted features due to the small number of convolution layers in the encoder part. The extracted features are essential for classifying object categories, and if they are insufficient, it causes a problem of lowering the segmentation accuracy. Therefore, to improve this problem, we proposed the AR-UNet using residual learning and ASPP in the encoder. Residual learning improves feature extraction ability and is effective in preventing feature loss and vanishing gradient problems caused by continuous convolutions. In addition, ASPP enables additional feature extraction without reducing the resolution of the feature map. Experiments verified the effectiveness of the AR-UNet with Cityscapes dataset. The experimental results showed that the AR-UNet showed improved segmentation results compared to the conventional U-Net. In this way, AR-UNet can contribute to the advancement of many applications where accuracy is important.

• Journal of the Korea Society of Computer and Information
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• v.28 no.5
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• pp.189-196
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• 2023

This study aims to show that "Computer-assisted language learning (CALL)" and "Mobile-based language learning (MALL)" actually influence education, deviating from the traditional "drill and practice" method in foreign language education and learning due to the development of information and communication technology (IT). Specifically, for first-year college students who have relatively poor English skills and do not feel enough motivation for English learning, I will produce educational video content using multimedia authoring tools and upload it to the e-learning system. Video content is configured to be accessed and utilized through various media such as computers, smartphones, tablets, laptops, etc. Ultimately, an exploration of educational value behind the utilization of IT devices in English language Teaching(ELT) and the Second Language Acquisition (SLA) theory behind effective instructional use of such technology are presented. That is to say, the effectiveness of language learning using information and communication technology (IT) is introduced. The article closes by suggesting how to use computers and mobile media for 'Flipped Learning'.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.717-718
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• 2022

본 연구에서는 예비교사의 인공지능 융합교육 역량 강화를 위한 캡스톤 디자인 기법 활용 인공지능 융합교육 프로그램을 개발하고 효과를 검증하였다. 개발된 교육 프로그램은 예비교사들이 스크래치 프로그래밍과 머신러닝포키즈, 캡스톤 디자인의 이해를 바탕으로, 인공지능 활용 융합 수업을 위한 주제 선정, 수업 설계 및 개발 후, 마이크로티칭을 하고 동료 평가 및 피드백을 하도록 조직되었다. 이는 2022년 1학기 K대학의 교양 강좌를 수강하는 예비교사들에게 처치되었다. 그 결과, 실험 대상자들의 인공지능 교수효능감의 사전-사후 t-검정에서 통계적으로 유의한 효과가 있음을 확인되었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.680-684
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• 2020

최근 국내에서는 식품에 대한 관심도가 높아짐에 따라 먹거리에 건강·환경·미래지향적 가치가 부여되고 있으며 식품 산업에서도 신규 식품 개발이 증가하는 추세이다. 식단을 구성할 때 기준이 되는 식품교환표는 개정과정에서 많은 인력과 시간이 소요되기 때문에 식품 섭취 변화를 신속하게 반영하기 어렵다. 본 논문에서는 식품교환표의 활용도를 높이기 위한 식품교환표 갱신 기법을 제안한다. 제안 기법은 의사결정트리 모델을 학습하여 새롭게 추가된 식품의 정보를 바탕으로 식품군을 분류하여 식품교환표를 갱신한다. 이는 영양 관리가 필요한 당뇨병 환자 등에게 실용적이며 기호성·다양성이 높은 식단을 구성하는 데 도움을 준다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.788-789
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• 2023

기존 도어락의 핀 인증 방식은 사용자에 따라 불편함을 느끼기도 한다. 이에 대한 솔루션으로 안면인식 도어락을 제안하며 Jetson nano에 face recognition 딥러닝 모델을 적용한 도어락을 제작한다. 거주자의 이미지를 촬영한 뒤 얼굴의 특징점을 분석하여 저장한다. 사용자가 도어락의 카메라에 인식되었을 때 저장된 안면 정보를 바탕으로 사용자가 거주자의 특징점과 일치하는지 확인한다. 거주자임이 인지되었을 때 도어락은 unlock되어 열리게 된다. 안면 인식 도어락 사용 시 보안상 취약해질 수 있는 기존 도어락의 핀 인증 방식에 대해 보안을 강화할 수 있으며 카메라를 통해 촬영된 사용자의 사진 및 영상을 활용하여 여러 서비스를 적용할 수 있다.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.197-208
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• 2023

In this study, we attempted to examine the changing ways of thinking about lecture evaluation before and after COVID-19. To this end, decision tree analysis(Decision Tree) was used among data mining techniques based on lecture evaluation data for liberal arts and major classes conducted before and after COVID-19 for A university. According to the results of the study, liberal arts changed from 'method' to 'content', and 'knowledge improvement' was an important factor both before and after majors. In particular, 'Assignment' was found to be an important factor after the COVID-19 in common in the evaluation of lectures in the liberal arts department, which means that in the future, professors will be provided with appropriate teaching methods during class, interaction with students, and feedback on assignments or test results, indicates the need for competence. Based on the results of this study, a plan to improve communication with students and activation of blended learning was suggested.

• Information Systems Review
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• v.24 no.1
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• pp.105-123
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• 2022

Thanks to the remarkable success of Artificial Intelligence (A.I.) techniques, a new possibility for its application on the real-world problem has begun. One of the prominent applications is the bankruptcy prediction model as it is often used as a basic knowledge base for credit scoring models in the financial industry. As a result, there has been extensive research on how to improve the prediction accuracy of the model. However, despite its impressive performance, it is difficult to implement machine learning (ML)-based models due to its intrinsic trait of obscurity, especially when the field requires or values an explanation about the result obtained by the model. The financial domain is one of the areas where explanation matters to stakeholders such as domain experts and customers. In this paper, we propose a novel approach to incorporate financial domain knowledge into local rule generation to provide explanations for the bankruptcy prediction model at instance level. The result shows the proposed method successfully selects and classifies the extracted rules based on the feasibility and information they convey to the users.

• Information Systems Review
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• v.23 no.3
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• pp.51-75
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• 2021

Global e-commerce websites offer personalized recommendation services to gain sustainable competitiveness. Existing studies have offered personalized recommendation services using quantitative preferences such as ratings. However, offering personalized recommendation services using only quantitative data has raised the problem of decreasing recommendation performance. For example, a user gave a five-star rating but wrote a review that the user was unsatisfied with hotel service and cleanliness. In such cases, has problems where quantitative and qualitative preferences are inconsistent. Recently, a growing number of studies have considered review data simultaneously to improve the limitations of existing personalized recommendation service studies. Therefore, in this study, we identify review and rating mismatches and build a new user profile to offer personalized recommendation services. To this end, we use deep learning algorithms such as CNN, LSTM, CNN + LSTM, which have been widely used in sentiment analysis studies. And extract sentiment features from reviews and compare with quantitative preferences. To evaluate the performance of the proposed methodology in this study, we collect user preference information using real-world hotel data from the world's largest travel platform TripAdvisor. Experiments show that the proposed methodology in this study outperforms the existing other methodologies, using only existing quantitative preferences.

• Information Systems Review
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• v.22 no.3
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• pp.81-97
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• 2020

Recent trends in sentiment analysis have been focused on applying single label classification approaches. However, when considering the fact that a review comment by one person is usually composed of several topics or aspects, it would be better to classify sentiments for those aspects respectively. This paper has two purposes. First, based on the fact that there are various aspects in one sentence, aspect mining is performed to classify the emotions by each aspect. Second, we apply the multiple label classification method to analyze two or more dependent variables (output values) at once. To prove our proposed approach's validity, online review comments about musical performances were garnered from domestic online platform, and the multi-label classification approach was applied to the dataset. Results were promising, and potentials of our proposed approach were discussed.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.477-491
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• 2013

In order to live up to public expectations, universities dedicate their best efforts to cultivating all Essential Competencies for outstanding individuals, especially reorganizing and improving their general education curricula and methods of teaching and learning, in light of the fact that a cutting-edge technology in a specific field does not bear a long span in the modern society. Through a Grounded Theory approach, the aim of this research is to study undergraduates' Experience Process of Action Learning designed to foster their Essential Competencies. With broadly selected 15 students from the courses for Essential Competencies, the method of theoretical sampling was employed so as to secure the diversity of the subjects' characteristics and backgrounds. After in-depth interviews, the data from the subjects were analyzed on the basis of Grounded theory approach of Strauss and Corbin. The conclusions of this analysis are as follows; Firstly, a learning coach should play a different role depending on the levels of Action Learning. Secondly, some time for introspection should be taken for the effective operation of Action Learning. Thirdly, learners ought to solve the problems faced during the learning process on their own. Fourthly, the aims of courses for Essential Competencies are also needed to be focused.