Dual prototypical contrastive network: a novel self-supervised method for cross-domain few-shot fault diagnosis
Data-driven methods have pushed mechanical fault diagnostics to an unprecedented height
recently. However, their satisfactory performance heavily relies on the availability of …
recently. However, their satisfactory performance heavily relies on the availability of …
Fast and efficient computing for deep learning-based defect detection models in lightweight devices
Defect anomaly detection is beneficial in the production cycle of various industries. It is
widely used in areas such as metal surface and fabric industries. This paper focuses on …
widely used in areas such as metal surface and fabric industries. This paper focuses on …
DL4ALL: Multi-task cross-dataset transfer learning for Acute Lymphoblastic Leukemia detection
Methods for the detection of Acute Lymphoblastic (or Lymphocytic) Leukemia (ALL) are
increasingly considering Deep Learning (DL) due to its high accuracy in several fields …
increasingly considering Deep Learning (DL) due to its high accuracy in several fields …
Few-Shot Defect Detection of Catheter Products via Enlarged Scale Feature Pyramid and Contrastive Proposal Memory Bank
The automatic detection of defects in manufacturing catheter production assumes a crucial
role in ensuring safety within the downstream healthcare industry. However, existing deep …
role in ensuring safety within the downstream healthcare industry. However, existing deep …
Knowledge distillation-based information sharing for online process monitoring in decentralized manufacturing system
In advanced manufacturing, the incorporation of sensing technology provides an opportunity
to achieve efficient in situ process monitoring using machine learning methods. Meanwhile …
to achieve efficient in situ process monitoring using machine learning methods. Meanwhile …
Research on Forest Flame Detection Algorithm Based on a Lightweight Neural Network
Y Chen, T Wang, H Lin - Forests, 2023 - mdpi.com
To solve the problem of the poor performance of a flame detection algorithm in a complex
forest background, such as poor detection performance, insensitivity to small targets, and …
forest background, such as poor detection performance, insensitivity to small targets, and …
HG-XAI: human-guided tool wear identification approach through augmentation of explainable artificial intelligence with machine vision
Identifying tool wear state is essential for machine operators as it assists in informed
decisions for timely tool replacement and subsequent machining operations. As each wear …
decisions for timely tool replacement and subsequent machining operations. As each wear …
A multi-scale graph pyramid attention network with knowledge distillation towards edge computing robotic fault diagnosis
C Chen, T Wang, D Mao, Y Liu, L Cheng - Expert Systems with Applications, 2024 - Elsevier
The advanced graph neural networks (GNNs) algorithms and the abundant monitoring data
have greatly improved the performance of fault diagnosis of industrial robots. However …
have greatly improved the performance of fault diagnosis of industrial robots. However …
[HTML][HTML] ODNet: A High Real-Time Network Using Orthogonal Decomposition for Few-Shot Strip Steel Surface Defect Classification
Strip steel plays a crucial role in modern industrial production, where enhancing the
accuracy and real-time capabilities of surface defect classification is essential. However …
accuracy and real-time capabilities of surface defect classification is essential. However …
A Novel Feature Extraction Method Based on Legendre Multi-Wavelet Transform and Auto-Encoder for Steel Surface Defect Classification
X Zheng, W Liu, Y Huang - IEEE Access, 2024 - ieeexplore.ieee.org
Effective steel surface defect classification with low computational cost is essential for online
quality inspection. The challenge of this task is large intra-class differences and unclear inter …
quality inspection. The challenge of this task is large intra-class differences and unclear inter …