Displacement-Sensing Method Based on Residual Scaling for One-Shot MMF Specklegram Prediction
A high-resolution and wide measurement range displacement sensing method based on multimode fiber (MMF) is proposed. To achieve a high-resolution displacement detection model, a one-shot dataset was constructed by collecting MMF specklegram images for 1801 displacements with resolution of 0.01 mm. T...
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MDPI AG
2025-02-01
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| author | Bohao Shen Jianzhi Li |
| author_facet | Bohao Shen Jianzhi Li |
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| description | A high-resolution and wide measurement range displacement sensing method based on multimode fiber (MMF) is proposed. To achieve a high-resolution displacement detection model, a one-shot dataset was constructed by collecting MMF specklegram images for 1801 displacements with resolution of 0.01 mm. This work modifies the fully connected layer of a residual network (ResNet) to achieve displacement prediction and applies residual scaling to reduce prediction errors in the one-shot learning task. Under stable environmental conditions, experimental results show that this method achieves an average error as low as 0.0083 mm in displacement prediction with resolution of 0.01 mm; meanwhile, the measurement range reaches 18 mm. Additionally, the model trained on a 0.01 mm resolution dataset was evaluated on a specklegram dataset with a resolution of 0.005 mm for its generalization ability, yielding an average error of 0.0138 mm. Regression evaluation metrics demonstrate that the proposed model has a significant improvement over other displacement-sensing methods based on MMF specklegrams, with prediction errors approximately three times lower than ResNet. Additionally, temperature immunity was studied within an 18 mm measurement range under a temperature range from 21.25 °C to 22.35 °C; the MMF displacement sensor demonstrates a dispersion of 5.08%, an average nonlinearity of 7.71% and a hysteresis of 6.13%. These findings demonstrate the potential of this method for high-performance displacement-sensing in practical applications. |
| format | Article |
| id | doaj-art-bf4ebf0dbdb54ce5b348428004710f52 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-bf4ebf0dbdb54ce5b348428004710f522025-08-20T02:59:15ZengMDPI AGSensors1424-82202025-02-01255143410.3390/s25051434Displacement-Sensing Method Based on Residual Scaling for One-Shot MMF Specklegram PredictionBohao Shen0Jianzhi Li1School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaKey Laboratory of Structural Health Monitoring and Control, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaA high-resolution and wide measurement range displacement sensing method based on multimode fiber (MMF) is proposed. To achieve a high-resolution displacement detection model, a one-shot dataset was constructed by collecting MMF specklegram images for 1801 displacements with resolution of 0.01 mm. This work modifies the fully connected layer of a residual network (ResNet) to achieve displacement prediction and applies residual scaling to reduce prediction errors in the one-shot learning task. Under stable environmental conditions, experimental results show that this method achieves an average error as low as 0.0083 mm in displacement prediction with resolution of 0.01 mm; meanwhile, the measurement range reaches 18 mm. Additionally, the model trained on a 0.01 mm resolution dataset was evaluated on a specklegram dataset with a resolution of 0.005 mm for its generalization ability, yielding an average error of 0.0138 mm. Regression evaluation metrics demonstrate that the proposed model has a significant improvement over other displacement-sensing methods based on MMF specklegrams, with prediction errors approximately three times lower than ResNet. Additionally, temperature immunity was studied within an 18 mm measurement range under a temperature range from 21.25 °C to 22.35 °C; the MMF displacement sensor demonstrates a dispersion of 5.08%, an average nonlinearity of 7.71% and a hysteresis of 6.13%. These findings demonstrate the potential of this method for high-performance displacement-sensing in practical applications.https://www.mdpi.com/1424-8220/25/5/1434multimode fiber specklegramdisplacement sensorhigh resolution and wide rangedeep learningresidual scalingone-shot prediction |
| spellingShingle | Bohao Shen Jianzhi Li Displacement-Sensing Method Based on Residual Scaling for One-Shot MMF Specklegram Prediction Sensors multimode fiber specklegram displacement sensor high resolution and wide range deep learning residual scaling one-shot prediction |
| title | Displacement-Sensing Method Based on Residual Scaling for One-Shot MMF Specklegram Prediction |
| title_full | Displacement-Sensing Method Based on Residual Scaling for One-Shot MMF Specklegram Prediction |
| title_fullStr | Displacement-Sensing Method Based on Residual Scaling for One-Shot MMF Specklegram Prediction |
| title_full_unstemmed | Displacement-Sensing Method Based on Residual Scaling for One-Shot MMF Specklegram Prediction |
| title_short | Displacement-Sensing Method Based on Residual Scaling for One-Shot MMF Specklegram Prediction |
| title_sort | displacement sensing method based on residual scaling for one shot mmf specklegram prediction |
| topic | multimode fiber specklegram displacement sensor high resolution and wide range deep learning residual scaling one-shot prediction |
| url | https://www.mdpi.com/1424-8220/25/5/1434 |
| work_keys_str_mv | AT bohaoshen displacementsensingmethodbasedonresidualscalingforoneshotmmfspecklegramprediction AT jianzhili displacementsensingmethodbasedonresidualscalingforoneshotmmfspecklegramprediction |