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- W2887131025 abstract "The leakage aperture cannot be easily identified, when an oil pipeline has small leaks. To address this issue, a leak aperture recognition method based on wavelet packet analysis (WPA) and a deep belief network (DBN) with independent component regression (ICR) is proposed. WPA is used to remove the noise in the collected sound velocity of the ultrasonic signal. Next, the denoised sound velocity of the ultrasonic signal is input into the deep belief network with independent component regression (<mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M1><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant=normal>D</mml:mi><mml:mi mathvariant=normal>B</mml:mi><mml:mi mathvariant=normal>N</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=normal>I</mml:mi><mml:mi mathvariant=normal>C</mml:mi><mml:mi mathvariant=normal>R</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>) to recognize different leak apertures. Because the optimization of the weights of the DBN with the gradient leads to a local optimum and a slow learning rate, ICR is used to replace the gradient fine-tuning method in conventional DBN for improving the classification accuracy, and a Lyapunov function is constructed to prove the convergence of the <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M2><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant=normal>D</mml:mi><mml:mi mathvariant=normal>B</mml:mi><mml:mi mathvariant=normal>N</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=normal>I</mml:mi><mml:mi mathvariant=normal>C</mml:mi><mml:mi mathvariant=normal>R</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> learning process. By analyzing the acquired ultrasonic sound velocity of different leak apertures, the results show that the proposed method can quickly and effectively identify different leakage apertures." @default.
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- W2887131025 date "2018-08-16" @default.
- W2887131025 modified "2023-10-14" @default.
- W2887131025 title "Pipeline Leak Aperture Recognition Based on Wavelet Packet Analysis and a Deep Belief Network with ICR" @default.
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- W2887131025 doi "https://doi.org/10.1155/2018/6934825" @default.
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