1,628 research outputs found
Deep Residual Shrinkage Networks for EMG-based Gesture Identification
This work introduces a method for high-accuracy EMG based gesture
identification. A newly developed deep learning method, namely, deep residual
shrinkage network is applied to perform gesture identification. Based on the
feature of EMG signal resulting from gestures, optimizations are made to
improve the identification accuracy. Finally, three different algorithms are
applied to compare the accuracy of EMG signal recognition with that of DRSN.
The result shows that DRSN excel traditional neural networks in terms of EMG
recognition accuracy. This paper provides a reliable way to classify EMG
signals, as well as exploring possible applications of DRSN
Dynamic noise reduction with deep residual shrinkage networks for online fault classification
Fault signals in high-voltage (HV) power plant assets are captured using the electromagnetic interference (EMI) technique. The extracted EMI signals are taken under different conditions, introducing varying noise levels to the signals. The aim of this work is to address the varying noise levels found in captured EMI fault signals, using a deep-residual-shrinkage-network (DRSN) that implements shrinkage methods with learned thresholds to carry out de-noising for classification, along with a time-frequency signal decomposition method for feature engineering of raw time-series signals. The approach will be to train and validate several alternative DRSN architectures with previously expertly labeled EMI fault signals, with architectures then being tested on previously unseen data, the signals used will firstly be de-noised and a controlled amount of noise will be added to the signals at various levels. DRSN architectures are assessed based on their testing accuracy in the varying controlled noise levels. Results show DRSN architectures using the newly proposed residual-shrinkage-building-unit-2 (RSBU-2) to outperform the residual-shrinkage-building-unit-1 (RSBU-1) architectures in low signal-to-noise ratios. The findings show that implementing thresholding methods in noise environments provides attractive results and their methods prove to work well with real-world EMI fault signals, proving them to be sufficient for real-world EMI fault classification and condition monitoring
Difference-based Deep Convolutional Neural Network for Simulation-to-reality UAV Fault Diagnosis
Identifying the fault in propellers is important to keep quadrotors operating
safely and efficiently. The simulation-to-reality (sim-to-real) UAV fault
diagnosis methods provide a cost-effective and safe approach to detect the
propeller faults. However, due to the gap between simulation and reality,
classifiers trained with simulated data usually underperform in real flights.
In this work, a new deep neural network (DNN) model is presented to address the
above issue. It uses the difference features extracted by deep convolutional
neural networks (DDCNN) to reduce the sim-to-real gap. Moreover, a new domain
adaptation method is presented to further bring the distribution of the
real-flight data closer to that of the simulation data. The experimental
results show that the proposed approach can achieve an accuracy of 97.9\% in
detecting propeller faults in real flight. Feature visualization was performed
to help better understand our DDCNN model.Comment: 7 pages, 8 figure
Intelligent Feature Extraction, Data Fusion and Detection of Concrete Bridge Cracks: Current Development and Challenges
As a common appearance defect of concrete bridges, cracks are important
indices for bridge structure health assessment. Although there has been much
research on crack identification, research on the evolution mechanism of bridge
cracks is still far from practical applications. In this paper, the
state-of-the-art research on intelligent theories and methodologies for
intelligent feature extraction, data fusion and crack detection based on
data-driven approaches is comprehensively reviewed. The research is discussed
from three aspects: the feature extraction level of the multimodal parameters
of bridge cracks, the description level and the diagnosis level of the bridge
crack damage states. We focus on previous research concerning the quantitative
characterization problems of multimodal parameters of bridge cracks and their
implementation in crack identification, while highlighting some of their major
drawbacks. In addition, the current challenges and potential future research
directions are discussed.Comment: Published at Intelligence & Robotics; Its copyright belongs to
author
Constructive Incremental Learning for Fault Diagnosis of Rolling Bearings with Ensemble Domain Adaptation
Given the prevalence of rolling bearing fault diagnosis as a practical issue
across various working conditions, the limited availability of samples
compounds the challenge. Additionally, the complexity of the external
environment and the structure of rolling bearings often manifests faults
characterized by randomness and fuzziness, hindering the effective extraction
of fault characteristics and restricting the accuracy of fault diagnosis. To
overcome these problems, this paper presents a novel approach termed
constructive Incremental learning-based ensemble domain adaptation (CIL-EDA)
approach. Specifically, it is implemented on stochastic configuration networks
(SCN) to constructively improve its adaptive performance in multi-domains.
Concretely, a cloud feature extraction method is employed in conjunction with
wavelet packet decomposition (WPD) to capture the uncertainty of fault
information from multiple resolution aspects. Subsequently, constructive
Incremental learning-based domain adaptation (CIL-DA) is firstly developed to
enhance the cross-domain learning capability of each hidden node through domain
matching and construct a robust fault classifier by leveraging limited labeled
data from both target and source domains. Finally, fault diagnosis results are
obtained by a majority voting of CIL-EDA which integrates CIL-DA and parallel
ensemble learning. Experimental results demonstrate that our CIL-DA outperforms
several domain adaptation methods and CIL-EDA consistently outperforms
state-of-art fault diagnosis methods in few-shot scenarios
Defect Analysis of 3D Printed Cylinder Object Using Transfer Learning Approaches
Additive manufacturing (AM) is gaining attention across various industries
like healthcare, aerospace, and automotive. However, identifying defects early
in the AM process can reduce production costs and improve productivity - a key
challenge. This study explored the effectiveness of machine learning (ML)
approaches, specifically transfer learning (TL) models, for defect detection in
3D-printed cylinders. Images of cylinders were analyzed using models including
VGG16, VGG19, ResNet50, ResNet101, InceptionResNetV2, and MobileNetV2.
Performance was compared across two datasets using accuracy, precision, recall,
and F1-score metrics. In the first study, VGG16, InceptionResNetV2, and
MobileNetV2 achieved perfect scores. In contrast, ResNet50 had the lowest
performance, with an average F1-score of 0.32. Similarly, in the second study,
MobileNetV2 correctly classified all instances, while ResNet50 struggled with
more false positives and fewer true positives, resulting in an F1-score of
0.75. Overall, the findings suggest certain TL models like MobileNetV2 can
deliver high accuracy for AM defect classification, although performance varies
across algorithms. The results provide insights into model optimization and
integration needs for reliable automated defect analysis during 3D printing. By
identifying the top-performing TL techniques, this study aims to enhance AM
product quality through robust image-based monitoring and inspection
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