A multiple-blockage identification scheme for buried pipeline via acoustic signature model and SqueezeNet

Zhu, Xuefeng; Feng, Zao; Fan, Yugang; Ma, Jun

doi:10.1016/j.measurement.2022.111671

Cited by 6 publications

(2 citation statements)

References 21 publications

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“…Federated learning mainly transmits model parameters, and the large number of parameters in existing deep learning models may cause the failure of federated learning. In 2017, a lightweight network model called SqueezeNet was proposed, with parameters nearly 1/50th the size of AlexNet, yet demonstrating similar accuracy [24]. Zhong et al further reduced model complexity and dependence on samples by introducing a selfattention integrated lightweight model combined with transfer learning [25].…”

Section: Introductionmentioning

confidence: 99%

Improved lightweight federated learning network for fault feature extraction of reciprocating machinery

Zhang,

Duan,

et al. 2024

Meas. Sci. Technol.

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he working environment of reciprocating machinery is complex, characterized by nonlinear and non-stationary signals. Deep learning can solve the above problems, but it has its own problems such as complex model and large amount of parameters. Additionally, privacy considerations among enterprises prevent data sharing, leading to the emergence of "data islands" and inadequate training of deep learning models. Based on the above analysis, this paper proposes a reciprocating mechanical feature extraction method based on an improved federated lightweight network. A lightweight network SqueezeNet model is used to solve the problems such as long training time of deep learning. By establishing a federated learning framework, the reciprocating mechanical data can be collectively diagnosed across various enterprises, thereby addressing the problem of limited model training caused by insufficient data. Furthermore, to enhance the accuracy of network training and diagnosis, modifications are made to the SqueezeNet network to reduce the number of model parameters while increasing the number and variety of feature extractions. Experimental results demonstrate that when the number of 1×1 and 3×3 channels is 1 to 7, the fault diagnosis accuracy is the highest, about 97.96%, which enriches the categories of feature extraction. The number of parameters in In-SqueezeNet is 56% of that in SqueezeNet network model, and the training time is reduced by nearly 15%. The fault diagnosis accuracy is increased from 95.1% to 97.3%, and the diversity of extracted features is increased. Compared with other network models such as ResNet, the improved lightweight federated learning network has a fault diagnosis accuracy of 96.6%, an improvement of 10.6%. At the same time, the training time was reduced to 1982s, a reduction of about 41.5%. The validity of the proposed model is further verified.

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Section: Introductionmentioning

confidence: 99%

Improved lightweight federated learning network for fault feature extraction of reciprocating machinery

Zhang,

Duan,

et al. 2024

Meas. Sci. Technol.

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“…Using an existing model as a starting point reduces the time and computing resources needed to train a model from scratch. SqueezeNet, a neural network architecture designed for image recognition, contains fewer parameters than AlexNet, VGG, and ResNet [32][33][34]. The SqueezeNet framework develops models with low parameter counts and great image recognition accuracy.…”

Section: Introductionmentioning

confidence: 99%

Recyclable Waste Classification using SquezeeNet and XGBoost

Yulita,

Ardiansyah,

Prabuwono

et al. 2023

IJACSA

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The unregulated buildup of waste results in the occurrence of flames. This phenomenon poses a substantial threat to both the ecological system and human welfare. To tackle this problem, the current study proposes the implementation of Machine Learning technology to automate the sorting of waste. The methodology being examined incorporates the utilization of SqueezeNet as an image embedding method in conjunction with XGBoost as the final classifier. This work examines the efficacy of the aforementioned technique by doing a comparative analysis with many alternative final classifiers, including LightGBM, XGBoost, CatBoost, Random Forest, SVM, Naïve Bayes, KNN, and Decision Tree. The experimental results indicate that the integration of SqueezeNet and XGBoost produces the highest level of performance in the field of garbage categorization, as supported by an F1-score of 0.931. SqueezeNet is a method employed for image embedding that enables the extraction of salient features from images. This procedure enables the recognition of unique characteristics linked to different classes. Therefore, XGBoost may be utilized to enhance classification tasks. XGBoost has the ability to generate a feature importance score. Therefore, enabling the recognition of the most prominent attributes. This methodology possesses the capacity to alleviate the risk of fire that arises due to the accumulation of unregulated trash. This work makes a substantial contribution to environmental conservation and the improvement of public safety.

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Squeeze-Inception V3 with Slime Mould algorithm-based CNN features for lung cancer detection

Lakshmi G,

Nagaraj

2025

Biomedical Signal Processing and Control

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A multiple-blockage identification scheme for buried pipeline via acoustic signature model and SqueezeNet

Cited by 6 publications

References 21 publications

Improved lightweight federated learning network for fault feature extraction of reciprocating machinery

Improved lightweight federated learning network for fault feature extraction of reciprocating machinery

Recyclable Waste Classification using SquezeeNet and XGBoost

Squeeze-Inception V3 with Slime Mould algorithm-based CNN features for lung cancer detection

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