• Journal of The Korean Association For Science Education
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• v.43 no.2
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• pp.99-110
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• 2023

In this study, in order to strengthen the AI utilization competency of pre-service secondary science teachers, a project activity in which pre-service teachers directly create an 'AI-based molecular structure customized learning support tool' by using Google's teachable machine was developed and applied. To this end, the program developed for 26 third-grade pre-service teachers enrolled in the Department of Chemistry Education at H University in Chungcheongbuk-do was applied for 14 sessions during extracurricular activities. Then, the perceptions of 'understanding how AI works', 'efficacy of using AI in science classes', and 'plans to utilize AI in science classes' were investigated. As a result of the study, it was found that the program developed in this study was effective in helping pre-service teachers understand the operating principle of AI technology for machine learning at a basic level and learning how to use it. In addition, the program developed in this study was found to be effective in increasing the efficacy of pre-service teachers for the use of AI in science classes. And it was also found that pre-service teachers recognized the aspect of using AI technology as a new teaching·learning strategy and tool that can help students understand science concepts. Accordingly, it was found that the program developed in this study had a positive impact on pre-service teachers' AI utilization competency reinforcement and perception improvement at the basic level. Implications of this were discussed.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.322-323
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• 2023

지속 가능한 에너지인 태양광 발전은 전 세계에서 널리 활용하는 재생 에너지 원천 중 하나로 최근 효율적인 태양광 발전 시스템 운영을 위해 태양광 발전량을 정확하게 예측하기 위한 연구가 활발히 진행되고 있다. 태양광 발전량 예측 모델을 구성하기 위해서는 기상 및 대기 환경을 넘어 태양의 위치에 따른 일사량의 정보가 필수적이나 태양의 실시간 위치 정보를 입력 변수로 활용한 연구가 부족한 실정이다. 그리하여 본 논문에서는 시간과 태양광 발전소 위치를 기반으로 태양의 고도와 방위각을 실시간으로 계산하여 입력 변수로 사용하는 방식을 제안한다. 이를 위해 AutoML 기반의 다양한 기계학습 모델을 구성하여 태양광 발전율을 예측하고 그 성능을 비교 분석하였다. 실험 결과, 태양 위치 정보를 포함한 경우에 환경 변수만을 고려하였을 때보다 예측 성능이 크게 향상되었음을 확인할 수 있었으며, Extra Trees 모델의 경우 태양 위치 정보를 추가하였을 때 MAE(Mean Absolute Error)가 33.90 에서 22.38 까지 낮아지는 결과를 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.366-367
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• 2023

지적 장애인은 제한된 활동 환경 범위의 제약으로 인해 자기 신체 구조에 맞는 운동법을 접할 기회가 적고, 각자의 건강 상태와 신체 구조에 따라 운동할 때 세심한 요구가 필요하다. 본 논문은 지적 장애인을 대상으로 비만 관리에 대한 필요성 인지 및 신체 활동량을 늘리기 위한 개인 맞춤형 운동 루틴 추천 시스템을 제안하였다. 제안한 시스템을 구성하기 위해 먼저 대한장애인체육회에서 제공하는 건강 상태, 신체 정보, 장애 유형 및 등급 등의 데이터를 분석하였다. 또한, 웹 사이트에서 장애인의 입력 정보가 들어오면 TF-IDF 벡터를 산출하고, 다른 사용자와의 코사인 유사성을 분석해 운동 루틴을 제안하였다. 본 연구에서 제안한 추천 시스템을 통해 지적 장애인을 대상으로 맞춤형 건강관리에 대한 인식 향상 및 건강권 보장, 운동 효율 증진 등을 기대할 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.368-369
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• 2023

대화 데이터 기반 광고 추천은 광고 마케팅에서 고객 맞춤형 광고 제공, 마케팅 효과 극대화 등을 위한 중요한 기술로 주목받고 있다. 본 논문에서는 모바일 인스턴스 메신저인 카카오톡 대화창에서 발생한 텍스트 데이터를 기반으로 대화 내용을 분석하여 대화 주제별 적절한 광고 키워드를 제안한다. 이를 위해 주제별 대화 내용을 미용, 식음료, 상거래로 세분하고 KoNLPy 의 Okt 를 이용하여 텍스트 전처리를 수행하고 키워드별로 빈도수를 뽑아 워드 클라우드를 제시한다. 또한, 잠재 디리클레 할당(Latent Dirichlet Allocation, LDA)을 기반으로 대화 주제를 세분화한 뒤 라벨링을 통해 주제별 대화 키워드를 분석한다. 실험 결과, 대화 주제를 온라인 쇼핑, 헤어, 뷰티 관리, 음식으로 나눌 수 있었으며, 토픽별 상위 키워드를 Word2Vec 을 통해 특정 단어와 유사한 키워드를 도출하여 적절한 광고 키워드를 제시할 수 있었다.

• International Journal of Advanced Culture Technology
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• v.9 no.4
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• pp.329-344
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• 2021

AI assistant, a software interface designed to interact with a user in a natural way and perform specific tasks on the user's behalf, receives increasing attention from both scholars and practitioners. While most of the literatures explain about technical aspects, little is known about the social and psychological factors that intimately influence consumers when using it. This study sheds light on the reason people use AI assistant and how perceived values influence on intention of continuous usage. A total of 361 AI assistant users participated in an online survey, and all were recruited from a major online panel in South Korea. The results from the principal component analysis suggest five social and psychological motives: self-expression, quality of life, entertainment, information, and compatibility. In addition, perceived values, informativeness, entertainment, and trustworthiness, positively predict the intention to use AI assistant. This research provides theoretical contributions from finding motivations of AI assistant usage and from the effects of perceived values on the intention to use it. Practical implications should not be overlooked in this ever-expanding AI industry.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.33 no.4
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• pp.412-418
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• 2023

As advances in artificial intelligence (AI) increasingly approach areas once relegated to the realm of science fiction, there is growing public interest in using these technologies for practical everyday tasks in both the home and the workplace. This paper explores the applications of and implications for of using ChatGPT, a conversational AI model based on GPT-3.5 and GPT-4.0, in the field of occupational health and safety. After gaining over one million users within five days of its launch, ChatGPT has shown promise in addressing issues ranging from emergency response to chemical exposure to recommending personal protective equipment. However, despite its potential usefulness, the integration of AI into scientific work and professional settings raises several concerns. These concerns include the ethical dimensions of recognizing AI as a co-author in academic publications, the limitations and biases inherent in the data used to train these models, legal responsibilities in professional contexts, and potential shifts in employment following technological advances. This paper aims to provide a comprehensive overview of these issues and to contribute to the ongoing dialogue on the responsible use of AI in occupational health and safety.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.25-36
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• 2023

Today, as AI (Artificial Intelligence) technology is introduced in various fields, including security, the development of technology is accelerating. However, with the development of AI technology, attack techniques that cleverly bypass malicious behavior detection are also developing. In the classification process of AI models, an Adversarial attack has emerged that induces misclassification and a decrease in reliability through fine adjustment of input values. The attacks that will appear in the future are not new attacks created by an attacker but rather a method of avoiding the detection system by slightly modifying existing attacks, such as Adversarial attacks. Developing a robust model that can respond to these malware variants is necessary. In this paper, we propose two methods of generating Adversarial attacks as efficient Adversarial attack generation techniques for improving Robustness in AI models. The proposed technique is the XAI-based attack technique using the XAI technique and the Reference based attack through the model's decision boundary search. After that, a classification model was constructed through a malicious code dataset to compare performance with the PGD attack, one of the existing Adversarial attacks. In terms of generation speed, XAI-based attack, and reference-based attack take 0.35 seconds and 0.47 seconds, respectively, compared to the existing PGD attack, which takes 20 minutes, showing a very high speed, especially in the case of reference-based attack, 97.7%, which is higher than the existing PGD attack's generation rate of 75.5%. Therefore, the proposed technique enables more efficient Adversarial attacks and is expected to contribute to research to build a robust AI model in the future.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1091-1100
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• 2022

As deep learning technology advances and various high-resolution remote sensing images are available, interest in using deep learning technology and remote sensing big data to detect buildings and change in urban areas is increasing significantly. In this paper, for semantic building segmentation of high-resolution remote sensing images, we propose a new building segmentation model, Convolutional Block Attention Module (CBAM)-DRUNet that uses the DeepResUNet model, which has excellent performance in building segmentation, as the basic structure, improves the residual learning unit and combines a CBAM with the basic structure. In the performance evaluation using WHU dataset and INRIA dataset, the proposed building segmentation model showed excellent performance in terms of F1 score, accuracy and recall compared to ResUNet and DeepResUNet including UNet.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.939-945
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• 2023

This study analyzes the 'performance efficiency' of AI-based reading assistance systems in the field of radiology by measuring their 'time behavior' properties. Due to the increase in medical images and the limited number of radiologists, the adoption of AI-based solutions is escalating, stimulating a multitude of studies in this area. Contrary to the majority of past research which centered on AI's diagnostic precision, this study underlines the significance of time behavior. Using 50 chest X-ray PA images, the system processed images in an average of 15.24 seconds, demonstrating high consistency and reliability, which is on par with leading global AI platforms, suggesting the potential for significant improvements in radiology workflow efficiency. We expect AI technology to play a large role in the field of radiology and help improve overall healthcare quality and efficiency.

• Journal of radiological science and technology
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• v.46 no.4
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• pp.303-313
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• 2023

This study aimed to investigate the relevance of cardiovascular disease risk factors AI and AIP, divided into three groups, among 300 individuals who underwent health checkups at the hospital. Various variables such as Age, Sex, BMI, WC, TC, TG, HDL-C, LDL-C, FBS, HbA1C, SBP, DBP, HR, AI (TC/HDL-C), and AIP (log(TG/HDL-C)) were analyzed using statistical methods including frequency analysis, cross-tabulation, one-way ANOVA, Pearson's correlation analysis, and multiple linear regression analysis. The cross-analysis based on cardiovascular disease risk criteria revealed that men and individuals in their 50s had higher cardiovascular disease risk based on AI and AIP. Significant differences were observed in TG, TC, HDL-C, LDL-C, SBP, DBP, AI (TC/HDL-C), and AIP (log(TG/HDL-C)) according to AI criteria. For the AIP criteria, TG, TC, HDL-C, FBS, HbA1C, HR, AI (TC/HDL-C), and AIP (log(TG/HDL-C)) were identified as cardiovascular disease risk factors. FBS and HbA1c showed the highest positive correlation In the correlation analysis, followed by TC and LDL-C. The lowest positive correlation was observed between LDL-C and DBP. In terms of negative correlation, HDL-C and AI had the highest negative correlation, while LDL-C and TG showed the lowest negative correlation. Multiple regression analysis indicated that the AI and AIP risk criteria had explanatory powers of 73.6% and 72.5%, respectively. HDL-C had the greatest negative effect on the AI risk criterion, while TG had the most significant influence on the AIP risk criterion. In conclusion, while other serological variables are important, managing HDL-C and TG levels may help reduce the risk of cardiovascular disease.