An uncertainty-incorporated active data diffusion learning framework for few-shot equipment RUL prediction
C Zhang, D Gong, G Xue - Reliability Engineering & System Safety, 2025 - Elsevier
In predicting the remaining useful life (RUL) of critical equipment, the challenge of obtaining
degradation data and the limitation of data volume lead to few-shot problems that …
degradation data and the limitation of data volume lead to few-shot problems that …
Prompting and Tuning: In-Band Interference Segmentation Using Segment Anything Model
This letter explores potential of a segment anything model (SAM), the first promptable image
segmentation system, in detecting wireless interference based on time-frequency images …
segmentation system, in detecting wireless interference based on time-frequency images …
Wireless Interference Recognition with Multimodal Learning
In non-cooperative communications, malicious electromagnetic interference attacks
communication systems and causes higher probability of communication disruption. In order …
communication systems and causes higher probability of communication disruption. In order …
Edge-and-Mask Integration-Driven Diffusion Models for Medical Image Segmentation
Q Tang, Q Zhu, Y Xiong, Y Xu… - IEEE Signal Processing …, 2024 - ieeexplore.ieee.org
Denoising diffusion probabilistic models (DDPMs) exhibit significant potential in the realm of
medical image segmentation. Nevertheless, current DDPM implementations rely on original …
medical image segmentation. Nevertheless, current DDPM implementations rely on original …
Open-Set Jamming Pattern Recognition via Generated Unknown Jamming Data
G Wang, Y Gao - IEEE Signal Processing Letters, 2024 - ieeexplore.ieee.org
Jamming pattern recognition (JPR) is extensively investigated as a crucial aspect of anti-
jamming in wireless communication. However, with the emergence of unknown malicious …
jamming in wireless communication. However, with the emergence of unknown malicious …