• Smart Media Journal
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• v.11 no.11
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• pp.92-98
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• 2022

Identifying defects in textiles is a key procedure for quality control. This study attempted to create a model that detects defects by analyzing the images of the fabrics. The models used in the study were deep learning-based VGGNet and ResNet, and the defect detection performance of the two models was compared and evaluated. The accuracy of the VGGNet and the ResNet model was 0.859 and 0.893, respectively, which showed the higher accuracy of the ResNet. In addition, the region of attention of the model was derived by using the Grad-CAM algorithm, an eXplainable Artificial Intelligence (XAI) technique, to find out the location of the region that the deep learning model recognized as a defect in the fabric image. As a result, it was confirmed that the region recognized by the deep learning model as a defect in the fabric was actually defective even with the naked eyes. The results of this study are expected to reduce the time and cost incurred in the fabric production process by utilizing deep learning-based artificial intelligence in the defect detection of the textile industry.

• The Journal of Bigdata
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• v.7 no.2
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• pp.195-204
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• 2022

The tourism industry is facing a crisis due to the recent COVID-19 pandemic, and it is vital to improving profitability to overcome it. In situations such as COVID-19, it would be more efficient to sell additional products other than guest rooms to customers who have visited to increase the unit price rather than adopting an aggressive sales strategy to increase room occupancy to increase profits. Previous tourism studies have used machine learning techniques for demand forecasting, but there have been few studies on cross-selling forecasting. Also, in a broader sense, a resort is the same accommodation industry as a hotel. However, there is no study specialized in the resort industry, which is operated based on a membership system and has facilities suitable for lodging and cooking. Therefore, in this study, we propose a cross-selling prediction model using various machine learning techniques with an actual resort company's accommodation data. In addition, by applying the explainable artificial intelligence XAI(eXplainable AI) technique, we intend to interpret what factors affect cross-selling and confirm how they affect cross-selling through empirical analysis.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1122-1137
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• 2021

PM_2.5 concentration in Seoul could be predicted by deep neural network model. In this paper, the contribution of input factors to the model's prediction results is analyzed using the LRP(Layer-wise Relevance Propagation) technique. LRP analysis is performed by dividing the input data by time and PM concentration, respectively. As a result of the analysis by time, the contribution of the measurement factors is high in the forecast for the day, and those of the forecast factors are high in the forecast for the tomorrow and the day after tomorrow. In the case of the PM concentration analysis, the contribution of the weather factors is high in the low-concentration pattern, and that of the air quality factors is high in the high-concentration pattern. In addition, the date and the temperature factors contribute significantly regardless of time and concentration.

• Journal of Information Technology Services
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• v.21 no.2
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• pp.85-95
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• 2022

This study analyzed the translation error factors of machine translation services such as Naver Papago and Google Translate through Self-Attention path visualization. Self-Attention is a key method of the Transformer and BERT NLP models and recently widely used in machine translation. We propose a method to explain translation error factors of machine translation algorithms by comparison the Self-Attention paths between ST(source text) and ST'(transformed ST) of which meaning is not changed, but the translation output is more accurate. Through this method, it is possible to gain explainability to analyze a machine translation algorithm's inside process, which is invisible like a black box. In our experiment, it was possible to explore the factors that caused translation errors by analyzing the difference in key word's attention path. The study used the XLM-RoBERTa multilingual NLP model provided by exBERT for Self-Attention visualization, and it was applied to two examples of Korean-Chinese and Korean-English translations.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.75-77
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• 2021

전력 생산의 효율을 높이고 지속적인 공정관리를 위해 전력 설비 데이터의 패턴을 분석하고 원인이 되는 주요 변수를 찾는 것이 중요하다. 따라서, 본 연구에서는 전력 설비 데이터의 패턴을 분석하기 위해 데이터를 군집화하고 연구 방법으로 Decision Tree, Random Forest와 ResNet을 이용하여 패턴을 분류하였다. Class Activation Map을 이용하여 설비데이터의 원인이 되는 주요 변수를 확인하였다. 본 연구를 통해 전력 설비 데이터의 분류 및 원인 분석이 가능한 통합적 솔루션을 제시하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.830-833
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• 2021

인공지능 기술이 사회 전반에 적용되면서 인공지능에 대한 인간의 이해도 역시 중요해지고 있다. 이러한 필요성을 기반으로 설명 가능한 인공지능(XAI) 분야 연구가 현재 활발히 진행되고 있다. 이 중 입력의 변화를 통하여 반사실적 대안을 제시하는 반사실적 예제 기반의 설명은 피쳐수가 많아지는 이미지 데이터에서 연산량이 크게 증가하는 단점이 있다. 본 연구에서는 이러한 단점을 해결하고자 이미지의 추상화된 피쳐 영역에서 프로토타입 피쳐를 이용한 반사실적 예제를 생성하는 기법을 제안한다. 나아가 이러한 이미지 형식의 반사실적 예제를 활용할 분야를 제시하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.700-702
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• 2022

코로나 바이러스-19 감염증 상황이 지속됨에 따라 영유아 비대면 상담이 증가하였다. 비대면이라는 제한된 환경에서, 보다 정확한 상담을 위해 영유아의 감정을 예측하는 보조도구로써 CNN 학습모델을 이용한 감정분석 결과를 활용할 수 있다. 하지만, 대부분의 감정분석 CNN 모델은 성인 데이터를 위주로 학습이 진행되므로 영유아의 감정인식률은 상대적으로 낮다. 본 논문에서는 영유아와 청소년 데이터의 감정분석 정확도 차이의 원인을 XAI 기법 중 하나인 LIME을 사용해 시각화하여 분석하고, 분석 결과를 근거로 영유아 데이터에 대한 감정인식 성능을 향상시킬 수 있는 방법을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.370-372
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• 2023

신용카드 부정 사용은 고객 및 기업의 신용과 재산에 막대한 손실을 미치고 있다. 이에 따라 금융사들은 이상금융거래탐지시스템을 도입하였으나 이상 거래 발생 여부를 지속적으로 모니터링하고 있기 때문에 시스템 유지에 많은 비용이 따른다. 따라서 본 논문에서는 컴퓨팅 리소스를 절약함과 동시에 성능 개선 효과를 보인 신용카드 이상 거래 탐지 알고리즘을 제안한다. CTGAN 을 활용하여 정상 거래와 이상 거래의 비율을 일부 완화하였고 XAI 기법인 SHAP 를 활용하여 유의미한 속성값을 선택하였다. 이것을 기반으로 LSTM Autoencoder를 사용하여 이상데이터를 탐지하였다. 그 결과 전통적인 비지도 학습 기법에 비해 제안 알고리즘이 우수한 성능을 보였음을 확인하였다.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.803-812
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• 2021

This study explored the concept of artificial intelligence and the problem-solving process that can be explained through literature research. Through this study, the educational meaning and application plan of artificial intelligence that can be explained were presented. XAI education is a human-centered artificial intelligence education that deals with human-related artificial intelligence problems, and students can cultivate problem-solving skills. In addition, through algorithmic education, it is possible to understand the principles of artificial intelligence, explain artificial intelligence models related to real-life problem situations, and expand to the field of application of artificial intelligence. In order for such XAI education to be applied in elementary schools, examples related to real world must be used, and it is recommended to utilize those that the algorithm itself has interpretability. In addition, various teaching and learning methods and tools should be used for understanding to move toward explanation. Ahead of the introduction of artificial intelligence in the revised curriculum in 2022, we hope that this study will be meaningfully used as the basis for actual classes.

• Journal of The Korean Association of Information Education
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• v.24 no.5
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• pp.495-509
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• 2020

This study is a theoretical research to explore ways to effectively learn AI in the age of intelligent information driven by artificial intelligence (hereinafter referred to as AI). The emphasis is on presenting a teaching method to make AI education accessible not only to students majoring in mathematics, statistics, or computer science, but also to other majors such as humanities and social sciences and the general public. Given the need for 'Explainable AI(XAI: eXplainable AI)' and 'the importance of storytelling for a sensible and intelligent machine(AI)' by Patrick Winston at the MIT AI Institute [33], we can find the significance of research on AI storytelling learning model. To this end, we discuss the possibility through a pilot study targeting general students of an university in Daegu. First, we introduce the AI storytelling(AI+ST) learning method[30], and review the educational goals, the system of contents, the learning methodology and the use of new AI tools in the method. Then, the results of the learners are compared and analyzed, focusing on research questions: 1) Can the AI+ST learning method complement algorithm-driven or developer-centered learning methods? 2) Whether the AI+ST learning method is effective for students and thus help them to develop their AI comprehension, interest and application skills.