A Novel Cross-Domain Mechanical Fault Diagnosis Method Fusing Acoustic and Vibration Signals by Vision Transformer.

Changes in operating conditions often cause the distribution of signal features to shift during the bearing fault diagnosis process, which will result in reduced diagnostic accuracy of the model. Therefore, this paper proposes a dual-channel parallel adversarial network (DPAN) based on vision transformer, which extracts features from acoustic and vibration signals through parallel networks and enhances feature robustness through adversarial training during the feature fusion process. In addition, the Wasserstein distance is used to reduce domain differences in the fused features, thereby enhancing the network's generalization ability.

Elimination of Random Mixed Noise in ECG Using Convolutional Denoising Autoencoder With Transformer Encoder.

Electrocardiogram (ECG) signals frequently encounter diverse types of noise, such as baseline wander (BW), electrode motion (EM) artifacts, muscle artifact (MA), and others. These noises often occur in combination during the actual data acquisition process, resulting in erroneous or perplexing interpretations for cardiologists. To suppress random mixed noise (RMN) in ECG with less distortion, we propose a Transformer-based Convolutional Denoising AutoEncoder model (TCDAE) in this study.

An Intelligent Method for Predicting Pacific Oyster () Freshness Using Deep Learning Fused with Malondialdehyde and Total Sulfhydryl Groups Information.

To achieve a non-destructive and rapid detection of oyster freshness, an intelligent method using deep learning fused with malondialdehyde (MDA) and total sulfhydryl groups (SH) information was proposed. In this study, an "MDA-SH-storage days" polynomial fitting model and oyster meat image dataset were first built. AleNet-MDA and AlxNet-SH classification models were then constructed to automatically identify and classify four levels of oyster meat images with overall accuracies of 92.

Diagnosis of atrial fibrillation using self-complementary attentional convolutional neural network.

Background And Objective: Automatic recognition of wearable dynamic electrocardiographic (ECG) signals is a difficult problem in biomedical signal processing. However, with the widespread use of long-range ambulatory ECG, a large number of real-time ECG signals are generated in the clinic, and it is very difficult for clinicians to perform timely atrial fibrillation (AF) diagnosis. Therefore, developing a new AF diagnosis algorithm can relieve the pressure on the healthcare system and improve the efficiency of AF screening.

A New Universal Domain Adaptive Method for Diagnosing Unknown Bearing Faults.

The domain adaptation problem in transfer learning has received extensive attention in recent years. The existing transfer model for solving domain alignment always assumes that the label space is completely shared between domains. However, this assumption is untrue in the actual industry and limits the application scope of the transfer model.

A generic intelligent tomato classification system for practical applications using DenseNet-201 with transfer learning.

A generic intelligent tomato classification system based on DenseNet-201 with transfer learning was proposed and the augmented training sets obtained by data augmentation methods were employed to train the model. The trained model achieved high classification accuracy on the images of different quality, even those containing high levels of noise. Also, the trained model could accurately and efficiently identify and classify a single tomato image with only 29 ms, indicating that the proposed model has great potential value in real-world applications.

A Weighted Subdomain Adaptation Network for Partial Transfer Fault Diagnosis of Rotating Machinery.

Domain adaptation-based models for fault classification under variable working conditions have become a research focus in recent years. Previous domain adaptation approaches generally assume identical label spaces in the source and target domains, however, such an assumption may be no longer legitimate in a more realistic situation that requires adaptation from a larger and more diverse source domain to a smaller target domain with less number of fault classes. To address the above deficiencies, we propose a partial transfer fault diagnosis model based on a weighted subdomain adaptation network (WSAN) in this paper.

Cooperative Game Approach to Robust Control Design for Fuzzy Dynamical Systems.

There is uncertainty in the system, and we consider that uncertainty is (possibly fast) time varying, but with definite bound. Fuzzy set theory is used to describe the inexact boundary and then the problem of robust control of uncertain dynamical systems is studied. Based on two adjustable design parameters, a robust control method for general mechanical systems is proposed.