• Annual Conference of KIPS
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• 2024.10a
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• pp.884-885
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• 2024

최근 배드민턴 동호인의 증가와 함께 경기 후 셔틀콕 수집 작업의 번거로움이 문제로 제기되고 있다. 본 논문에서는 이러한 문제를 해결하기 위해 AI 기반의 셔틀콕 자동 수집 로봇 시스템을 제안한다. 효과적인 작동을 위해 YOLOv8 모델을 이용해 셔틀콕을 빠르게 인식하고, Turtlebot3 를 활용해 자동으로 셔틀콕을 수집하는 하드웨어 시스템을 구축하였다. 모델 학습에는 1295 장의 데이터를 사용하여 mAP50-95 0.963 수준의 셔틀콕 인식 성능을 달성했으며, 실제 테스트에서 셔틀콕을 정확하게 인식하고 수집하는 결과를 얻었다.

• The Transactions of the Korea Information Processing Society
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• v.13 no.5
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• pp.209-216
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• 2024

Real estate market prices are determined by various factors, including macroeconomic variables, as well as the influence of a variety of unstructured text data such as news articles and social media. News articles are a crucial factor in predicting real estate transaction prices as they reflect the economic sentiment of the public. This study utilizes sentiment analysis on news articles to generate a News Sentiment Index score, which is then seamlessly integrated into a real estate price prediction model. To calculate the sentiment index, the content of the articles is first summarized. Then, using AI, the summaries are categorized into positive, negative, and neutral sentiments, and a total score is calculated. This score is then applied to the real estate price prediction model. The models used for real estate price prediction include the Multi-head attention LSTM model and the Vector Auto Regression model. The LSTM prediction model, without applying the News Sentiment Index (NSI), showed Root Mean Square Error (RMSE) values of 0.60, 0.872, and 1.117 for the 1-month, 2-month, and 3-month forecasts, respectively. With the NSI applied, the RMSE values were reduced to 0.40, 0.724, and 1.03 for the same forecast periods. Similarly, the VAR prediction model without the NSI showed RMSE values of 1.6484, 0.6254, and 0.9220 for the 1-month, 2-month, and 3-month forecasts, respectively, while applying the NSI led to RMSE values of 1.1315, 0.3413, and 1.6227 for these periods. These results demonstrate the effectiveness of the proposed model in predicting apartment transaction price index and its ability to forecast real estate market price fluctuations that reflect socio-economic trends.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.694-702
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• 2022

This paper is the UX-based technology strategy research which is a solution to how conversational AI can be ethically evolved in the Metaverse environment. Since conversational AI influences people's on-offline decision-making factors through interaction with people, the Metaverse AI ethics must be reflected. In the machine learning process of conversational AI, cultural codes along with user's personal experience data must be included and considered to reduce the error value of user experience. Through this, the super-personalized Metaverse service can evolve ethically with social values. With above hypothesis as a result of the study, a conceptual model of a forward-looking perspective was developed and proposed by adding user experience data to the machine learning (ML) process for context-based interactive AI in the Metaverse service environment.

• Journal of Industrial Convergence
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• v.22 no.8
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• pp.1-17
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• 2024

Recently, interest in edutech has been focused on its fusion with AI technology, and the market in this field is expanding. This study aims to analyze the technological competitiveness and key technological areas of major countries in the AI-edutech field. Additionally, considering that AI-edutech is a convergence of AI technology and edutech, the study seeks to examine the path dependence of AI-edutech in each country to determine whether they are based on existing AI technologies or edutech. To this end, AI-edutech patents were collected and competitiveness was analyzed using patent activity, patent impact, and market acquisition indicators. Path dependence for each country was analyzed using the hurdle negative binomial regression model. The analysis results indicate that the major countries in the AI-edutech field are China, South Korea, the United States, India, and Japan. In terms of patent activity, China had the highest level, followed by South Korea. In terms of patent impact and market securing power, the United States was high in both aspects, Japan had high market securing power, and South Korea had high patent influence. The results of the hurdle negative binomial analysis presented unique findings. The logit part results indicated that the possession of existing AI and edutech did not positively affect the emergence of current AI-edutech, but the count part results showed a positive influence. This suggests that, overall, it is difficult to assert that current AI-edutechs are based on past AI and edutechs. However, once some AI-edutechs based on existing AI and edutechs emerge, they are influenced by the existing technologies. These findings provide implications for future research and technological strategies in this field.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.5
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• pp.875-884
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• 2024

The proliferation of IoT networks has led to an increase in cyber attacks, highlighting the importance of Network Intrusion Detection Systems (NIDS). To overcome the limitations of traditional NIDS and cope with more sophisticated cyber attacks, there is a trend towards integrating artificial intelligence models into NIDS. However, AI-based NIDS are vulnerable to adversarial attacks, which exploit the weaknesses of algorithm. Model Type Inference Attack is one of the types of attacks that infer information inside the model. This paper proposes an optimized framework for Model Type Inference attacks against NIDS models, applying more realistic assumptions. The proposed method successfully trained an attack model to infer the type of NIDS models with an accuracy of approximately 0.92, presenting a new security threat to AI-based NIDS and emphasizing the importance of developing defence method against such attacks.

• Journal of the Korea Society of Computer and Information
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• v.29 no.11
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• pp.113-118
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• 2024

This paper proposes an optimized fruit set prediction model for cucumbers using an AI-based automatic growth control system. Based on data collected from experimental farms at Sunchon National University and Suncheon Bay cucumber farms, we constructed and compared the performance of models using three machine learning algorithms: Random Forest, XGBoost, and LightGBM. The models were trained using 19 environmental and growth-related variables, including temperature, humidity, and CO2 concentration. The LightGBM model showed the best performance (RMSE: 1.9803, R-squared: 0.5891). However, all models had R-squared values below 0.6, indicating limitations in capturing data nonlinearity and temporal dependencies. The study identified key factors influencing cucumber fruit set prediction through feature importance analysis. Future research should focus on collecting additional data, applying complex feature engineering, introducing time series analysis techniques, and considering data augmentation and normalization to improve model performance. This study contributes to the practical application of smart farm technology and the development of data-driven agricultural decision support systems.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.771-780
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• 2024

In this study, we implemented a real-time pest detection and prediction system for a strawberry farm using a computer vision model based on the YOLOv5 architecture and an Isolation Forest Classifier. The model performance evaluation showed that the YOLOv5 model achieved a mean average precision (mAP 0.5) of 78.7%, an accuracy of 92.8%, a recall of 90.0%, and an F1-score of 76%, indicating high predictive performance. This system was designed to be applicable not only to strawberry farms but also to other crops and various environments. Based on data collected from a tomato farm, a new AI model was trained, resulting in a prediction accuracy of over 85% for major diseases such as late blight and yellow leaf curl virus. Compared to the previous model, this represented an improvement of more than 10% in prediction accuracy.

• Annual Conference of KIPS
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• 2018.05a
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• pp.449-452
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• 2018

최근 AI 스피커를 비롯한 지능형 비서 서비스들이 빠르게 등장하고 있으며, AI 시장에서도 특히 챗봇 구축이 가장 활발하게 진행되고 있다. 건국봇은 건국대학교 학생들에게 필요한 정보를 제공하는 대화형 서비스이다. 본 논문에서는 대표적인 챗봇 구현 방법인 검색모델과 생성모델의 장단점을 분석하고, 건국봇에 적용한 사례를 소개한다. 궁극적으로, 질의문의 의도를 단어의 가중치를 고려해 추론함으로써 Unknown 추론을 강화하고 의도되지 않은 문장의 처리 관점에서 성능을 향상시키는 방법을 제안한다.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.97-106
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• 2020

Due to the rapid development and dissemination of 5G communication and IoT technologies, there are increasing demands for big data analysis techniques and service systems. In particular, explosively growing demands on AI technology adoption are also causing high competitions to take advantages of machine/deep-learning models to extract novel values from enormously collected data. In order to adopt AI technology to various research and application domains, it is necessary to prepare high-performance GPU-equipped systems and perform complicated settings to utilze deep learning models. To relieve the efforts and lower the barrier to utilize AI techniques, AIaaS(AI as a service) platform is attracting a great deal of attention as a promising on-line service, where the complexity of preparation and operation can be hidden behind the cloud side and service developers only need to utilize the high-level AI services easily. In this paper, we propose an AIaaS system which can support the creation of AI services based on Docker and OpenFaaS from the registration of models to the on-line operation. We also describe a case study to show how AI services can be easily generated by the proposed system.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.17-27
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• 2023

Today, as AI (Artificial Intelligence) technology develops and its practicality increases, it is widely used in various application fields in real life. At this time, the AI model is basically learned based on various statistical properties of the learning data and then distributed to the system, but unexpected changes in the data in a rapidly changing data situation cause a decrease in the model's performance. In particular, as it becomes important to find drift signals of deployed models in order to respond to new and unknown attacks that are constantly created in the security field, the need for lifecycle management of the entire model is gradually emerging. In general, it can be detected through performance changes in the model's accuracy and error rate (loss), but there are limitations in the usage environment in that an actual label for the model prediction result is required, and the detection of the point where the actual drift occurs is uncertain. there is. This is because the model's error rate is greatly influenced by various external environmental factors, model selection and parameter settings, and new input data, so it is necessary to precisely determine when actual drift in the data occurs based only on the corresponding value. There are limits to this. Therefore, this paper proposes a method to detect when actual drift occurs through an Anomaly analysis technique based on XAI (eXplainable Artificial Intelligence). As a result of testing a classification model that detects DGA (Domain Generation Algorithm), anomaly scores were extracted through the SHAP(Shapley Additive exPlanations) Value of the data after distribution, and as a result, it was confirmed that efficient drift point detection was possible.