• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.45-48
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• 2022

건축물 안전 점검은 대부분 전문가의 현장 방문을 통한 육안검사다. 그중 균열 검사는 건물 위험도를 나타내는 중요한 지표로써 발생 위치, 진행성, 크기를 조사하는데, 최근 균열 조사 방식에 대해 객관성과 체계성을 보완할 딥러닝 개발이 활발하다. 그러나 균열 이미지는 외부 현장에 모양, 규모도 많은 종류라 도메인이 다양해야 하는데 대부분 제한된 환경과 실제적인 균열 검사와는 무관한 데이터로 구성되어 실효적이지 않다. 본 연구에서는 균열 조사에 적합하고 Wild 환경에 적용 가능한 POC 데이터셋을 소개한다. 기존 균열 공인 데이터셋 4종의 특징과 한계점을 분석을 토대로 고해상도 이미지로써 균열의 세부 특징을 담았고 균열 유사 환경과 조건들을 추가 촬영해 균열 검출에 강인하게 학습되도록 지향하였다. 정제 및 라벨링 작업을 거친 POC 데이터 셋은 균열 검출모델인 YOLO-v5으로 성능을 실험하였고, mAP(mean Average Precision) 75.5%로 높은 검출률을 보였다. POC 데이터셋으로 더욱 도메인에 적응적(Domain-adapted)인 인공지능 모델을 개발하여 건물, 댐, 교량 등 각종 대형 건축물에 대한 안전하고 효과적인 안전 관리 도구로써 활용할 것을 기대한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.570-573
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• 2024

본 연구는 대형 언어 모델 (LLM) 시대에 공감적 대화 생성을 위한 감정 인식의 필요성을 확인하고 소형 언어 모델 (SLM)을 통한 미세 조정 학습이 고비용 LLM, 특히 ChatGPT의 대안이 될 수 있는지를 탐구한다. 이를 위해 KoBERT 미세 조정 모델과 ChatGPT를 사용하여 사용자 감정을 인식하고, Polyglot-Ko 미세 조정 모델 및 ChatGPT를 활용하여 공감적 응답을 생성하는 비교 실험을 진행하였다. 실험 결과, KoBERT 기반의 감정 분류기는 ChatGPT의 zero-shot 접근 방식보다 뛰어난 성능을 보였으며, 정확한 감정 분류가 공감적 대화의 질을 개선하는 데 기여함을 확인하였다. 이는 공감적 대화 생성을 위해 감정 인식이 여전히 필요하며, SLM의 미세 조정이 고비용 LLM의 실용적 대체 수단이 될 수 있음을 시사한다.

• Journal of Satellite, Information and Communications
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• v.12 no.2
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• pp.42-45
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• 2017

It is known that power line communication systems have more noise than general wired communication systems due to the high voltage that flows in power line cables, and the noise causes a serious performance degradation. In order to mitigate performance degradation due to such noise, this paper proposes an artificial intelligence algorithm based on polynomial regression, which detects signals in the impulse noise environment in the power line communication system. The polynomial regression method is used to predict the original transmitted signal from the impulse noise signal. Simulation results show that the signal detection performance in the impulse noise environment of the power line communication is improved through the artificial intelligence algorithm proposed in this paper.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2241-2258
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• 2023

This study developed digital literacy instruments and tested their effectiveness on college students' perceptions of AI technologies. In creating a new digital literacy test tool, we reviewed the concept and scale of digital literacy based on previous studies that identified the characteristics and measurement of AI literacy. We developed 23 preliminary questions for our research instrument and used a quantitative approach to survey 318 undergraduates. After conducting exploratory and confirmatory factor analysis, we found that digital literacy in the age of AI had four ability sub-factors: critical understanding, artificial intelligence social impact recognition, artificial intelligence technology utilization, and ethical behavior. Then we tested the sub-factors' predictive powers on the perception of AI's usefulness and ease of use. The regression result shows that the most common powerful predictor of the usefulness and ease of use of AI technology was the ability to use AI technology. This finding implies that for college students, the ability to use various tools based on AI technology is an essential competency in the AI era.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.5
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• pp.187-193
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• 2020

The purpose of this study is to study the methodology of deriving a policy that activates artificial intelligence from the governmental perspective in order to induce corporate growth by effectively grafting artificial intelligence technology into society and thereby improve individual and national competitiveness by creating new jobs. In order to derive activation plans, 1) detailed investigation of the domestic environment, 2) discovery of priority support fields and models that can be applied to artificial intelligence, 3) preparation of guidelines for activation and introduction, 4) specific methods for promoting and activating artificial intelligence Should be presented. The proposed artificial intelligence activation method performs a procedure to verify and confirm the effectiveness of artificial intelligence nurturing through a multi-faceted approach. The multi-faceted analysis approach includes business ecosystem aspects, industry-specific aspects including companies, technology fields, policy aspects, public and non-public services aspects, government-led and private-led aspects. Therefore, it can be reviewed as a method of inducing activation in various forms. In the future research field, it is necessary to prove the effectiveness of the proposed activation plan based on empirical data on artificial intelligence-based services. The expected effect of this study is to contribute to support the development of artificial intelligence technology and to establish related policies.

• 한국어정보학회:학술대회논문집
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• 2017.10a
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• pp.225-227
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• 2017

본 논문은 한국어 서브워드 기반 워드 임베딩 기술을 다룬다. 미등록어 문제를 가진 기존 워드 임베딩 기술을 대체할 수 있는 새로운 워드 임베딩 기술을 한국어에 적용하기 위해, 음소열 기반 서브워드 자질 검증을 진행한다. 기존 서브워드 자질은 문자 n-gram을 사용한다. 한국어의 경우 특정 단음절 발음은 단어에 따라 달라진다. 여기서 음소열 n-gram은 특정 서브워드 자질의 변별력을 확보할 수 있다는 장점이 있다. 본 논문은 서브워드 임베딩 기술을 재구현하여, 영어 환경에서 기존 워드 임베딩 사례와 비교하여 성능 우위를 확보한다. 또한, 한국어 음소열 자질을 활용한 실험 결과에서 의미적으로 보다 유사한 어휘를 벡터 공간상에 근접시키는 결과를 보여 준다.

• Knowledge Management Research
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• v.9 no.2
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• pp.1-13
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• 2008

The purpose of this study is to investigate the use of elements related to the competitive intelligence(CI) process, methods, and infrastructure in accordance with the degree of CI effectiveness of domestic firms and to propose a guideline for designing and operating an effective CI program in Korean films. The results of this study reveal that, for the elevation of CI effectiveness of Korean firms, it is important to actively utilize the overall CI process, including undisclosed information through human networks, public information through various media, and quantitative and qualitative analyses, an independent CI unit, various CI support systems, such as information and reward systems, and organizational culture of CI openness within an organization. However, CI outsourcing, CI primary objectives, and CI scale do not seem to have a significant influence on CI effectiveness of Korean firms. Based on these results, this research presents some important implications for effective competitive intelligence for Korean firms.

• Nuclear Engineering and Technology
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• v.37 no.2
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• pp.127-138
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• 2005

Approaches to several recent issues in the operation of nuclear power plants using computational intelligence are discussed. These issues include 1) noise analysis techniques, 2) on-line monitoring and sensor validation, 3) regularization of ill-posed surveillance and diagnostic measurements, 4) transient identification, 5) artificial intelligence-based core monitoring and diagnostic system, 6) continuous efficiency improvement of nuclear power plants, and 7) autonomous anticipatory control and intelligent-agents. Several changes to the focus of Computational Intelligence in Nuclear Engineering have occurred in the past few years. With earlier activities focusing on the development of condition monitoring and diagnostic techniques for current nuclear power plants, recent activities have focused on the implementation of those methods and the development of methods for next generation plants and space reactors. These advanced techniques are expected to become increasingly important as current generation nuclear power plants have their licenses extended to 60 years and next generation reactors are being designed to operate for extended fuel cycles (up to 25 years), with less operator oversight, and especially for nuclear plants operating in severe environments such as space or ice-bound locations.

• East Asian mathematical journal
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• v.39 no.2
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• pp.229-250
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• 2023

The purpose of this study is to investigate the impact of project learning using chatbots on artificial intelligence literacy. The subjects of the study were a total of 41 students from 1st to 3rd grade of general high school in Gyeonggi-do. Classes were held after school for a total of 6 hours, and the contents of the classes consisted of the concept and characteristics of artificial intelligence, the concept and expression of knowledge, OBT application for Kakao i open builder, guidance on how to create chatbots, and chatbot production practice. As a result of the pre- and post-test of the experimental group, the quantitative value of artificial intelligence literacy increased in all three grades. In the case of second-year students who set up a comparison group, when compared with the results of the comparison group, there was a significant positive effect on the AI literacy result, and female students were found to be more effective than male students.