Earticle

초록

한국어

In this paper, we investigate the use of Generative AI to enhance and augment datasets within the context of smart factory metaverse platforms. Specifically, we propose a method for generating synthetic abnormal data using Generative Adversarial Networks (GANs) to address the inherent data imbalance issues in PCB (Printed Circuit Board) datasets, where normal data far exceeds abnormal samples. In our study, we demonstrate that generating synthetic data from a minimal set of abnormal samples significantly improves the performance of AI models, such as MobileNet-V3 Large. By augmenting the abnormal dataset from 20 to 500 images, the classification accuracy of the model increased from 74.9% to 97.3%, validating the effectiveness of this approach. This research highlights the potential of combining generative AI with metaverse platforms, enabling real-time guidance and training for users without the limitations of time and space, thus enhancing production efficiency and minimizing human error in smart factory environments.

목차

Abstract
I. INTRODUCTION
II. METHOD
A. Dataset
B. Generative AI Model
C. Generator
D. Discriminator
III. RESULT
IV. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

저자

• Ji-Won Kim [ dept. Convergence Engineering for Artificial Intelligence Sejong University Seoul, Republic of Korea ]
• Jioh Kim [ dept. Convergence Engineering for Software Jangan University Hwaseong, Republic of Korea ]
• Muhammad Fayaz [ dept. Computer Science & Engineering Sejong University Seoul, Republic of Korea ]
• Se-yong Jin [ dept. Artificial Intelligence Sejong University Seoul, Republic of Korea ]
• Geon-Hee Lee [ dept. Artificial Intelligence Sejong University Seoul, Republic of Korea ]
• Hyeonjoon Moon [ dept. Computer Science & Engineering Sejong University Seoul, Republic of Korea ] Corresponding Author

참고문헌