Power-Efficient Trainable Neural Networks towards Accurate Measurement of Irregular Cavity Volume
Abstract
:1. Introduction
1.1. Related Work
1.2. Contributions
- (1)
- We design a micro-compressed air method to collect parameters related to the irregular cavity volume. To ensure that the read-to-measure parts are not damaged, the closed atmospheric air in the irregular cavity parts is slightly compressed, and the measurement parameters are collected.
- (2)
- We propose a method to analyze the main controlling factors affecting the volume detection of irregular cavity parts. We screen seven main characteristic parameters: pressure, temperature, humidity, gas equilibration time, etc. We carried out linear and nonlinear correlation analysis, feature selection, and normalization processing for the characteristic parameters. On this basis, we establish an irregular cavity volume measurement model based on FCNNs and the HSIC.
- (3)
- During the training process, we propose a new training scheme based on the HSIC. This method solves the challenges existing in the traditional BP-based methods. This method can reduce the error as much as possible and make the predicted value closer to the ground truth.
- (4)
- We conduct extensive experiments to evaluate the proposed neural network. We build a dataset for irregular volume measurement. The samples are collected in real-world applications. The results show the effectiveness and outperformance of the proposed method.
2. Preliminary
- (1)
- Under the environment of normal temperature and pressure, the parts of the irregular cavity to be tested are filled with air of normal pressure;
- (2)
- Seal the air in the irregular cavity components to be tested. Record the ambient atmospheric pressure , the stable differential pressure of the gas in the cavity of the component to be tested, and the temperature ;
- (3)
- The precision piston is controlled to extend completely into the cavity of the part to be tested, and the gas is slightly compressed. The volume of the piston completely entering the irregular cavity part to be tested is recorded as ;
- (4)
- After thermodynamic equilibrium is achieved, experimental data are recorded including the ambient atmospheric pressure , the stable differential pressure of the gas in the parts, and the temperature ;
3. Method
3.1. Preprocessing of Feature Data for Volume Prediction of Irregular Cavity Parts
3.2. Establishment of the Volume Prediction Model of Irregular Cavity Components with Fully Connected Neural Network
3.3. HSIC Bottleneck Method
4. Experiments
4.1. Experimental Settings
4.2. Ablation Studies
4.3. Comparison and Application
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Group | V0 mL | V-15 mL | V-20 mL | V-30 mL |
---|---|---|---|---|
1 | 2326 | 2326.16 | 2323.62 | 2327.63 |
2 | 2326 | 2318.77 | 2322.04 | 2326.60 |
3 | 2326 | 2320.37 | 2321.49 | 2325.88 |
4 | 2326 | 2321.07 | 2322.45 | 2325.59 |
5 | 2326 | 2321.78 | 2321.84 | 2326.32 |
6 | 2326 | 2319.85 | 2322.10 | 2325.80 |
7 | 2326 | 2322.14 | 2321.98 | 2325.99 |
8 | 2326 | 2321.99 | 2322.51 | 2325.66 |
9 | 2326 | 2324.02 | 2323.65 | 2326.01 |
10 | 2326 | 2319.89 | 2321.21 | 2325.45 |
Number | Feature Parameters |
---|---|
1 | Atmospheric pressure |
2 | Atmospheric humidity |
3 | Temperature before micro-compression |
4 | Stable differential pressure before micro-compression |
5 | Temperature after micro-compression |
6 | Stable differential pressure after micro-compression |
7 | Gas equilibration time |
lr | 0.0005 | 0.001 | 0.002 | 0.003 | 0.004 | 0.005 | 0.006 | 0.007 | 0.008 |
MAE | 0.011 | 0.010 | 0.009 | 0.007 | 0.006 | 0.006 | 0.005 | 0.006 | 0.006 |
Layers | Parameters | MAE |
---|---|---|
4 | 4096 | 1.112 |
6 | 20,480 | 0.005 |
8 | 86,016 | 0.005 |
Method | Per Step CPU Time | Accuracy in Test Set |
---|---|---|
SVM | 0.326125 s | 0.76 |
FCNN | 0.576233 s | 0.85 |
Proposed | 0.176329 s | 0.99 |
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Zhang, X.; Jiang, Y.; Gao, H.; Yang, W.; Liang, Z.; Liu, B. Power-Efficient Trainable Neural Networks towards Accurate Measurement of Irregular Cavity Volume. Electronics 2022, 11, 2073. https://doi.org/10.3390/electronics11132073
Zhang X, Jiang Y, Gao H, Yang W, Liang Z, Liu B. Power-Efficient Trainable Neural Networks towards Accurate Measurement of Irregular Cavity Volume. Electronics. 2022; 11(13):2073. https://doi.org/10.3390/electronics11132073
Chicago/Turabian StyleZhang, Xin, Yueqiu Jiang, Hongwei Gao, Wei Yang, Zhihong Liang, and Bo Liu. 2022. "Power-Efficient Trainable Neural Networks towards Accurate Measurement of Irregular Cavity Volume" Electronics 11, no. 13: 2073. https://doi.org/10.3390/electronics11132073
APA StyleZhang, X., Jiang, Y., Gao, H., Yang, W., Liang, Z., & Liu, B. (2022). Power-Efficient Trainable Neural Networks towards Accurate Measurement of Irregular Cavity Volume. Electronics, 11(13), 2073. https://doi.org/10.3390/electronics11132073