Search Results for author:
Found 7 papers, 7 papers with code
Classifier-guided neural blind deconvolution: a physics-informed denoising module for bearing fault diagnosis under heavy noise
Blind deconvolution (BD) has been demonstrated as an efficacious approach for extracting bearing fault-specific features from vibration signals under strong background noise.
EEG-DG: A Multi-Source Domain Generalization Framework for Motor Imagery EEG Classification
In this paper, we propose a novel multi-source domain generalization framework called EEG-DG, which leverages multiple source domains with different statistical distributions to build generalizable models on unseen target EEG data.
A class-weighted supervised contrastive learning long-tailed bearing fault diagnosis approach using quadratic neural network
In this paper, we propose a supervised contrastive learning approach with a class-aware loss function to enhance the feature extraction capability of neural networks for fault diagnosis.
BearingPGA-Net: A Lightweight and Deployable Bearing Fault Diagnosis Network via Decoupled Knowledge Distillation and FPGA Acceleration
To the best of our knowledge, this is the first instance of deploying a CNN-based bearing fault diagnosis model on an FPGA.
One Neuron Saved Is One Neuron Earned: On Parametric Efficiency of Quadratic Networks
In this paper, with theoretical and empirical studies, we show that quadratic networks enjoy parametric efficiency, thereby confirming that the superior performance of quadratic networks is due to the intrinsic expressive capability.
Attention-embedded Quadratic Network (Qttention) for Effective and Interpretable Bearing Fault Diagnosis
Bearing fault diagnosis is of great importance to decrease the damage risk of rotating machines and further improve economic profits.
Quadratic Neuron-empowered Heterogeneous Autoencoder for Unsupervised Anomaly Detection
Encouraged by this inspiring theoretical result on heterogeneous networks, we directly integrate conventional and quadratic neurons in an autoencoder to make a new type of heterogeneous autoencoders.
