Shape Classification Using a Single Seal-Whisker-Style Sensor Based on the Neural Network Method
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Setup and Procedure
2.2. CNN Structure and Settings
3. Results
3.1. Validation of the CNN Model
3.2. Experimental Results of Seal-Whisker-Style Sensor Force Signals
3.3. Results of the CNN Model
4. Discussion
4.1. The Impact of Vortex Shedding Frequency on Force Signals
4.2. The Influence of Sample Length and Filtering on the CNN Model
4.3. Lift Signal Spectra and Their Impact on Target Recognition
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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C1 | C2 | C3 | T1 | T2 | S1 | S2 | D1 | D2 | No | |
---|---|---|---|---|---|---|---|---|---|---|
Case 1 , | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ |
Case 2 , | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ |
Case 3 , | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ |
Case 4 , | √ | √ | √ | √ | √ | √ | √ | √ | √ | - |
Case 5 , | √ | √ | √ | √ | √ | √ | √ | √ | √ | - |
Network Structure | Parameters | |
---|---|---|
Convolution Block 1 | Conv1d + Leaky ReLU | filter size: 256; stride: 64; channel: 64; padding: no padding; negative slope: 0.01 |
Batch Normalization | - | |
Max Pooling | pooling size: 4; stride: 1; padding: no padding | |
Convolution Block 2 | Conv1d + Leaky ReLU | filter size: 7; stride: 1; channel: 64; padding: no padding; negative slope: 0.01 |
Batch Normalization | - | |
Max Pooling | pooling size: 4; stride: 1; padding: no padding | |
Convolution Block 3 | Conv1d + Leaky ReLU | filter size: 7; stride: 1; channel: 64; padding: no padding; negative slope: 0.01 |
Batch Normalization | - | |
Max Pooling | pooling size: 4; stride: 1; padding: no padding | |
Flatten | - | |
Full Connect + Leaky ReLU | node number: 120; dropout: 0.2; negative slope: 0.01 | |
Full Connect + Leaky ReLU | node number: 80; dropout: 0.2; negative slope: 0.01 | |
Full Connect + Softmax | node number = number of classes |
Sample Sets | Length | Time Step | Filtering | Channel | Train Set | Validation Set | Test Set |
---|---|---|---|---|---|---|---|
Sample Set 1 | 2048 | unfiltered | lift and drag | 13,363 | 3341 | 8352 | |
Sample Set 2 | 2048 | unfiltered | lift and drag | 12,902 | 3226 | 8064 | |
Sample Set 3 | 2048 | unfiltered | lift and drag | 11,981 | 2995 | 7488 | |
Sample Set 4 | 2048 | unfiltered | lift and drag | 10,138 | 2534 | 6336 | |
Sample Set 5 | 2048 | 50 Hz low-pass | lift and drag | 11,981 | 2995 | 7488 | |
Sample Set 6 | 2048 | 30 Hz low-pass | lift and drag | 11,981 | 2995 | 7488 | |
Sample Set 7 | 2048 | 10 Hz low-pass | lift and drag | 11,981 | 2995 | 7488 | |
Sample Set 8 | 2048 | 5 Hz low-pass | lift and drag | 11,981 | 2995 | 7488 | |
Sample Set 9 | 2048 | 5 Hz high-pass | lift and drag | 11,981 | 2995 | 7488 | |
Sample Set 10 | 2048 | unfiltered | lift only | 11,981 | 2995 | 7488 | |
Sample Set 11 | 2048 | unfiltered | drag only | 11,981 | 2995 | 7488 |
Learning Settings | |
---|---|
Optimizer | Adam |
Loss function | Categorical cross-entropy function |
Learning rate | 0.0001 |
Batch size | 32 |
Maximum number of training epochs | 75 |
Patience of early stopping | 7 |
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Share and Cite
Mao, Y.; Lv, Y.; Wang, Y.; Yuan, D.; Liu, L.; Song, Z.; Ji, C. Shape Classification Using a Single Seal-Whisker-Style Sensor Based on the Neural Network Method. Sensors 2024, 24, 5418. https://doi.org/10.3390/s24165418
Mao Y, Lv Y, Wang Y, Yuan D, Liu L, Song Z, Ji C. Shape Classification Using a Single Seal-Whisker-Style Sensor Based on the Neural Network Method. Sensors. 2024; 24(16):5418. https://doi.org/10.3390/s24165418
Chicago/Turabian StyleMao, Yitian, Yingxue Lv, Yaohong Wang, Dekui Yuan, Luyao Liu, Ziyu Song, and Chunning Ji. 2024. "Shape Classification Using a Single Seal-Whisker-Style Sensor Based on the Neural Network Method" Sensors 24, no. 16: 5418. https://doi.org/10.3390/s24165418