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Compressive electron backscatter diffraction imaging.
J Microsc
January 2025
Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, UK.
Electron backscatter diffraction (EBSD) has developed over the last few decades into a valuable crystallographic characterisation method for a wide range of sample types. Despite these advances, issues such as the complexity of sample preparation, relatively slow acquisition, and damage in beam-sensitive samples, still limit the quantity and quality of interpretable data that can be obtained. To mitigate these issues, here we propose a method based on the subsampling of probe positions and subsequent reconstruction of an incomplete data set.
View Article and Find Full Text PDFScale-Consistent and Temporally Ensembled Unsupervised Domain Adaptation for Object Detection.
Sensors (Basel)
January 2025
School of Mechanical and Electrical Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
Unsupervised Domain Adaptation for Object Detection (UDA-OD) aims to adapt a model trained on a labeled source domain to an unlabeled target domain, addressing challenges posed by domain shifts. However, existing methods often face significant challenges, particularly in detecting small objects and over-relying on classification confidence for pseudo-label selection, which often leads to inaccurate bounding box localization. To address these issues, we propose a novel UDA-OD framework that leverages scale consistency (SC) and Temporal Ensemble Pseudo-Label Selection (TEPLS) to enhance cross-domain robustness and detection performance.
View Article and Find Full Text PDFLearning with noisy labels via clean aware sharpness aware minimization.
Sci Rep
January 2025
School of Mechanical, Electrical, and Information Engineering, Putian University, Putian, 351100, China.
Noise label learning has attracted considerable attention owing to its ability to leverage large amounts of inexpensive and imprecise data. Sharpness aware minimization (SAM) has shown effective improvements in the generalization performance in the presence of noisy labels by introducing adversarial weight perturbations in the model parameter space. However, our experimental observations have shown that the SAM generalization bottleneck primarily stems from the difficulty of finding the correct adversarial perturbation amidst the noisy data.
View Article and Find Full Text PDFA Novel Intelligent Fault Diagnosis Method of Rolling Bearings Based on the ConvNeXt Network with Improved DenseBlock.
Sensors (Basel)
December 2024
College of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot 010051, China.
Rolling bearings are critical rotating components in machinery and equipment; they are essential for the normal operation of such systems. Consequently, there is a pressing need for a highly efficient, applicable, and reliable method for bearing fault diagnosis. Currently, one-dimensional data-driven fault diagnosis methods, which rely on one-dimensional data, represent a mainstream approach in this field.
View Article and Find Full Text PDFAttention activation network for bearing fault diagnosis under various noise environments.
Sci Rep
January 2025
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China.
Bearings are critical in mechanical systems, as their health impacts system reliability. Proactive monitoring and diagnosing of bearing faults can prevent significant safety issues. Among various diagnostic methods that analyze bearing vibration signals, deep learning is notably effective.
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