Segmentação automática de cicatrizes de deslizamento de terra em imagens de sensores remotos utilizando aprendizagem profunda de máquina (Deep Learning)

Soares, Lucas Pedrosa

doi:10.11606/d.44.2022.tde-08072022-080909

Cited by 1 publication

(1 citation statement)

References 54 publications

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“…Later, some scholars proposed the complementary ensemble empirical mode decomposition (CEEMD) method [14], which can suppress the residual white noise in EEMD. Artificial neural networks are frequently employed in a variety of industries, including monitoring product quality in production and forecasting some key factors in the industry [15][16][17][18]. In addition, the convolutional neural network (CNN) is crucial for computer vision, object detection, complicated network classification, pattern recognition, and other fields of study [19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A Fiber Vibration Signal Recognition Method Based on CNN-CBAM-LSTM

Huang

2022

Applied Sciences

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By trying to solve the issue of identifying multiple types of intrusion vibration signals collected by distributed vibrating fiber optic sensors, this study investigates the signal identification and feature extraction of intrusion signals, and proposes an optical fiber vibration signal (OFVS) identification method based on deep learning. The external vibration signal is collected by the Sagnac fiber optic interferometer, and then denoised by spectral subtraction. Endpoint detection is carried out by combining the short-time logarithmic energy method and the spectral entropy method. Finally, the equal-length signal containing valid information is intercepted and the corresponding preprocessing is carried out. The method for feature processing incorporates the strong feature learning capability of the long-short-term memory (LSTM) and the great short-term feature extraction capability of the convolutional neural network (CNN). At the same time, to further enhance the signal feature identification, a convolutional block attention module (CBAM) is introduced to perform adaptive feature refinement on the signal. In summary, a network model combining CNN, LSTM, and CBAM is proposed to process the signal features, and finally, the multi-layer perceptron (MLP) is used to complete the task of classification and recognition of multi-type intrusion signals. The experimental findings indicate that the OFVS method of CNN-CBAM-LSTM can effectively identify four kinds of OFVS, and the overall average recognition accuracy reaches 97.9%. Walking and knocking signals among them are recognized with over 99% accuracy.

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