A Novel Deep Offline-to-Online Transfer Learning Framework for Pipeline Leakage Detection With Small Samples

Wang, Chuang; Wang, Zidong; Liu, Weibo; Shen, Yuxuan; Dong, Hongli

doi:10.1109/tim.2022.3220302

Cited by 11 publications

(2 citation statements)

References 63 publications

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“…Mei et al [10] utilized compressed sensing to represent the data coefficients and fed the processed data into a deep residual neural network (ResNet) as a two-dimensional matrix, which greatly reduced the training time. Wang et al [11] introduced migration learning into long-distance pipeline leakage detection and accomplished online detection from a real monitoring pipeline. Although these models achieved good results, they incurred a lot of computational costs as the number of network layers increased.…”

Section: Introductionmentioning

confidence: 99%

Dy-GNet: a lightweight and efficient 1DCNN-based network for leakage aperture identification

Zhang,

Li,

Liu

et al. 2024

Meas. Sci. Technol.

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The deep neural network's gas leakage aperture recognition method has high accuracy, but its low computational efficiency due to its complex structure greatly limit its application in resource-limited industrial environments and real-time processing. In this paper, we proposed an efficient recognition method based on 1D convolutional neural network. First, wavelet scattering coefficients with time-frequency information are obtained using the wavelet scattering transform. Second, the dynamic convolution is used to deepen the feature extraction and PConv to speed up the inference time, and the efficient Dy-G module is constructed. Finally, the Dy-G module is stacked to construct Dy-GNet to achieve the classification task. The performance of the model is verified in a noisy environment, and the results show that the model FLOPs are 17.24 M and 83.94% accuracy is achieved at SNR=0 dB, which guarantees the accuracy while speeding up the inference speed with high efficiency.

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Section: Introductionmentioning

confidence: 99%

Dy-GNet: a lightweight and efficient 1DCNN-based network for leakage aperture identification

Zhang,

Li,

Liu

et al. 2024

Meas. Sci. Technol.

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“…Empirical evidence suggests that such a well-designed architecture can partially alleviate the issue of overfitting. Besides, in terms of the amount of data required, a substantial dataset is typically required for effective training and accurate generalization [13]- [16].…”

Section: Introductionmentioning

confidence: 99%

Spatial Variation Generation Algorithm for Motor Imagery Data Augmentation: Increasing the Density of Sample Vicinity

Qin,

Yang,

Huang

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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The imbalanced development between deep learning-based model design and motor imagery (MI) data acquisition raises concerns about the potential overfitting issuemodels can identify training data well but fail to generalize test data. In this study, a Spatial Variation Generation (SVG) algorithm for MI data augmentation is proposed to alleviate the overfitting issue. In essence, SVG generates MI data using variations of electrode placement and brain spatial pattern, ultimately elevating the density of the raw sample vicinity. The proposed SVG prevents models from memorizing the training data by replacing the raw samples with the proper vicinal distribution. Moreover, SVG generates a uniform distribution and stabilizes the training process of models. In comparison studies involving five deep learning-based models across eight datasets, the proposed SVG algorithm exhibited a notable improvement of 0.021 in the area under the receiver operating characteristic curve (AUC). The improvement achieved by SVG outperforms other data augmentation algorithms. Further results from the ablation study verify the effectiveness of each component of SVG. Finally, the studies in the control group with varying numbers of samples show that the SVG algorithm consistently improves the AUC, with improvements ranging from approximately 0.02 to 0.15.

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Estimating the State of Health for Lithium-ion Batteries: A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach

Wang

Liu

et al. 2023

IEEE/CAA J. Autom. Sinica

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A Novel Deep Offline-to-Online Transfer Learning Framework for Pipeline Leakage Detection With Small Samples

Cited by 11 publications

References 63 publications

Dy-GNet: a lightweight and efficient 1DCNN-based network for leakage aperture identification

Dy-GNet: a lightweight and efficient 1DCNN-based network for leakage aperture identification

Spatial Variation Generation Algorithm for Motor Imagery Data Augmentation: Increasing the Density of Sample Vicinity

Estimating the State of Health for Lithium-ion Batteries: A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach

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