Sound source ranging using a feed-forward neural network trained with fitting-based early stopping

Chi, Jing; Li, Xiaolei; Wang, Haozhong; Gao, Dazhi; Gerstoft, Peter

doi:10.1121/1.5126115

Cited by 43 publications

(12 citation statements)

References 14 publications

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“…Features learning for classification uses a fully connected network of 300, 200, and 100 nodes for each layer, respectively, with a ReLU activation function and ends with a classification layer Softmax using a trial and error search technique. The training, tuned by a grid search mechanism [40], is done under the CPU with an early stopping option and batch size equal to 10 samples using Adam optimizer [43].…”

Section: Bearing Fault Detection and Diagnosismentioning

confidence: 99%

Bearing Fault Event-Triggered Diagnosis Using a Variational Mode Decomposition-Based Machine Learning Approach

Habbouche

Amirat

Benkedjouh

et al. 2022

IEEE Trans. Energy Convers.

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The monitoring of rolling element bearing is indexed as a critical task for condition-based maintenance in various industrial applications. It allows avoiding unscheduled maintenance operations while decreasing their cost. For this purpose, various methodologies were developed to ensure accurate and efficient monitoring. In this context, this paper proposes an approach for bearing fault early diagnosis based on the variational mode decomposition (VMD), used as a notch filter for dominant mode cancellation, and a machine learning approach, namely the onedimensional convolution neural network (1D-CNN), for detection and diagnosis purposes. Specifically, the proposed approach first performs features extraction using VMD for fault detection, and then triggers to multi-scale features extraction using CNN convolution and pooling layers for classification and diagnosis.The proposed bearing fault detection and diagnosis approach is evaluated, in terms of robustness and performances, using the well-known Case Western Reserve University experimental dataset. In addition, performances are evaluated versus wellestablished demodulation techniques, in terms of fault detection, and machine learning strategies, in terms of fault diagnosis. The achieved results show that the proposed VMD notch filter-based 1D-CNN approach is clearly promising for bearing degradation monitoring.

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Section: Bearing Fault Detection and Diagnosismentioning

confidence: 99%

Bearing Fault Event-Triggered Diagnosis Using a Variational Mode Decomposition-Based Machine Learning Approach

Habbouche

Amirat

Benkedjouh

et al. 2022

IEEE Trans. Energy Convers.

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“…Early stopping is a method that informs when to stop running iterative algorithms during the training process, which improves the general performance of CNN models by reducing model overfitting and removing small test errors that are not visible during the training process [63,64]. The validation loss is the average model error of the validation data from a specified loss function, which tells the CNN model when to stop training.…”

Section: Model Optimizationmentioning

confidence: 99%

A Novel Tropical Cyclone Size Estimation Model Based on a Convolutional Neural Network Using Geostationary Satellite Imagery

Baek

Moon

et al. 2022

Remote Sensing

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A novel tropical cyclone (TC) size estimation model (TC-SEM) in the western North Pacific was developed based on a convolutional neural network (CNN) using geostationary satellite infrared (IR) images. The proposed TC-SEM was tested using three CNN schemes: a single-task regression model that separately estimated the radius of maximum wind (RMW) and the radius of 34 kt wind (R34) of the TC, a multi-task regression model that estimated the RMW and R34 simultaneously, and a multi-task regression model using best-track TC intensity information. For model training, validation, and testing, 29,730, 2505, and 11,624 geostationary satellite images of the region around the center of the TC, respectively, were used, each containing four IR bands: short-wavelength IR (3.7 µm), water vapor (6.7 µm), IR1 (10.8 µm), and IR2 (12.0 µm). The results showed that the multi-task model performed better than the single-task model due to knowledge sharing and its ability to solve multiple interrelated tasks simultaneously. The inclusion of TC intensity information in the multi-task model further improved the performance of the RMW and R34 estimations, with correlations (mean absolute errors) of 0.95 (2.05 nmi) and 0.93 (9.77 nmi), respectively, which represent significant improvements over the performance of existing linear regression statistical methods. The results suggested that this CNN model using geostationary satellite images may be a powerful tool for estimating TC sizes in operational TC forecasts.

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“…This may not yield superior performance on the test data. Chi et al (2019) prevented the classifier from being overfitted to the training data by adding regularization to the test data while training on ship range estimation. As the input, they used the covariance matrix of a vectorized sound field proposed by Niu et al (2017a).…”

Section: Passive Target Localizationmentioning

confidence: 99%

Underwater Acoustic Research Trends with Machine Learning: Passive SONAR Applications

Yang

Lee

Choo

et al. 2020

J. Ocean Eng. Technol.

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Underwater acoustics is a scientific domain that involves the study of the phenomena of sound waves in water, including their generation, propagation, and reception. Specifically, the sound navigation and ranging (SONAR) system is utilized to investigate underwater communication and target detection and to study marine resources and the environment; further, it is utilized to measure and analyze sound sources in water. The main objective of underwater acoustics-based remote sensing is the indirect acquisition of information on underwater targets of interest using acoustic data. At present, highly advanced data-driven machine-learning techniques are being applied in various ways for extracting information from acoustic data. The techniques closely related to these applications are introduced in the first part of this paper (Yang et al., 2020). This paper presents a detailed review of the applications of machine learning in underwater acoustics and passive SONAR signal processing. 2. Passive SONAR Signal Processing 2.1 Passive Target Detection and Identification Signals measured by a passive SONAR system exhibit fluctuations owing to irregular noises in the ocean. This hinders target signal detection. The conventional signal processing method for detecting target signals is based on the Neyman-Pearson criterion (Nielsen, 1991). As the probability distribution of the received signals, including the target signals, differs from that of the noise signals, the probability ratio that is set according to the presence of the target signal at the time of observation is compared with a preset value. This helps determine whether the target signal is included in the observed time period. This technique can be expanded to detect the target signal by comprehensively analyzing all the signals measured in the time domain of interest as well as signals observed at a specific time. In general, techniques for detecting a target signal through comparison with a threshold value have a disadvantage: false alarms can occur frequently, particularly in the scenario of a low signal

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Sound source ranging using a feed-forward neural network trained with fitting-based early stopping

Cited by 43 publications

References 14 publications

Bearing Fault Event-Triggered Diagnosis Using a Variational Mode Decomposition-Based Machine Learning Approach

Bearing Fault Event-Triggered Diagnosis Using a Variational Mode Decomposition-Based Machine Learning Approach

A Novel Tropical Cyclone Size Estimation Model Based on a Convolutional Neural Network Using Geostationary Satellite Imagery

Underwater Acoustic Research Trends with Machine Learning: Passive SONAR Applications

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