AAAI 2024technical4 citations
IncepSeqNet: Advancing Signal Classification with Multi-Shape Augmentation (Student Abstract)
Abstract
This work proposes and analyzes IncepSeqNet which is a new model combining the Inception Module with the innovative Multi-Shape Augmentation technique. IncepSeqNet excels in feature extraction from sequence signal data consisting of a number of complex numbers to achieve superior classification accuracy across various SNR(Signal-to-Noise Ratio) environments. Experimental results demonstrate IncepSeqNet’s outperformance of existing models, particularly at low SNR levels. Furthermore, we have confirmed its applicability in practical 5G systems by using real-world signal data.
BibTeX
@article{Kim_Jo_2024, title={IncepSeqNet: Advancing Signal Classification with Multi-Shape Augmentation (Student Abstract)}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/30464}, DOI={10.1609/aaai.v38i21.30464}, abstractNote={This work proposes and analyzes IncepSeqNet which is a new model combining the Inception Module with the innovative Multi-Shape Augmentation technique. IncepSeqNet excels in feature extraction from sequence signal data consisting of a number of complex numbers to achieve superior classification accuracy across various SNR(Signal-to-Noise Ratio) environments. Experimental results demonstrate IncepSeqNet’s outperformance of existing models, particularly at low SNR levels. Furthermore, we have confirmed its applicability in practical 5G systems by using real-world signal data.}, number={21}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Kim, Jongseok and Jo, Ohyun}, year={2024}, month={Mar.}, pages={23542-23543} }