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Found 18 papers, 5 papers with code
An interpretable deep learning method for bearing fault diagnosis
During the model evaluation process, the proposed approach retrieves prediction basis samples from the health library according to the similarity of the feature importance.
Predicting Battery Lifetime Under Varying Usage Conditions from Early Aging Data
Using a newly generated dataset from 225 nickel-manganese-cobalt/graphite Li-ion cells aged under a wide range of conditions, we demonstrate a lifetime prediction of in-distribution cells with 15. 1% mean absolute percentage error using no more than the first 15% of data, for most cells.
When SAM Meets Sonar Images
Notably, there is a lack of research on the application of SAM to sonar imaging.
Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial
In this tutorial, we aim to provide a holistic lens on emerging UQ methods for ML models with a particular focus on neural networks and the applications of these UQ methods in tackling engineering design as well as prognostics and health management problems.
Federated Learning with Uncertainty-Based Client Clustering for Fleet-Wide Fault Diagnosis
To quantify dataset similarity between clients without explicitly sharing data, each client sets aside a local test dataset and evaluates the other clients' model prediction accuracy and uncertainty on this test dataset.
HyperAttack: Multi-Gradient-Guided White-box Adversarial Structure Attack of Hypergraph Neural Networks
Hypergraph neural networks (HGNN) have shown superior performance in various deep learning tasks, leveraging the high-order representation ability to formulate complex correlations among data by connecting two or more nodes through hyperedge modeling.
A Lightweight Reconstruction Network for Surface Defect Inspection
Currently, most deep learning methods cannot solve the problem of scarcity of industrial product defect samples and significant differences in characteristics.
Multi-scale Feature Imitation for Unsupervised Anomaly Localization
The unsupervised anomaly localization task faces the challenge of missing anomaly sample training, detecting multiple types of anomalies, and dealing with the proportion of the area of multiple anomalies.
IDMS: Instance Depth for Multi-scale Monocular 3D Object Detection
Firstly, to enhance the model's processing ability for different scale targets, a multi-scale perception module based on dilated convolution is designed, and the depth features containing multi-scale information are re-refined from both spatial and channel directions considering the inconsistency between feature maps of different scales.
Data Augmentation Vision Transformer for Fine-grained Image Classification
Recently, the vision transformer (ViT) has made breakthroughs in image recognition.
Pedestrian Spatio-Temporal Information Fusion For Video Anomaly Detection
Aiming at the problem that the current video anomaly detection cannot fully use the temporal information and ignore the diversity of normal behavior, an anomaly detection method is proposed to integrate the spatiotemporal information of pedestrians.
Efficient Unsupervised Video Object Segmentation Network Based on Motion Guidance
Then, the semantic features of the motion representation are obtained through the local attention mechanism in the motion guidance module to obtain the high-level semantic features of the appearance representation.
Spatio-Temporal-based Context Fusion for Video Anomaly Detection
Motion features are used multiple targets in the video frame to construct spatial context simultaneously, re-encoding the target appearance and motion features, and finally reconstructing the above features through the spatio-temporal dual-stream network, and using the reconstruction error to represent the abnormal score.
A Comprehensive Review of Digital Twin -- Part 2: Roles of Uncertainty Quantification and Optimization, a Battery Digital Twin, and Perspectives
This second paper presents a literature review of key enabling technologies of digital twins, with an emphasis on uncertainty quantification, optimization methods, open source datasets and tools, major findings, challenges, and future directions.
A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies
In part two of this review, the role of uncertainty quantification and optimization are discussed, a battery digital twin is demonstrated, and more perspectives on the future of digital twin are shared.
Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images
The proposed model exploits three lightweight plug-and-play modules, namely dense feature pyramid network (DenseFPN), spatial context pyramid (SCP), and hierarchical region of interest extractor (HRoIE), to aggregate global visual context at feature, spatial, and instance domains, respectively.
Normal Learning in Videos with Attention Prototype Network
With models trained on the normal data, the reconstruction errors of anomalous scenes are usually much larger than those of normal ones.
S&CNet: Monocular Depth Completion for Autonomous Systems and 3D Reconstruction
In this paper, a lightweight yet efficient network (S\&CNet) is proposed to obtain a good trade-off between efficiency and accuracy for the dense depth completion.
