A novel method is proposed in this paper for simultaneously locating and quantifying damage in composite plates by employing Lamb waves and the algorithm of convolution neural network. The interaction between Lamb wave and damage of different degrees is also studied by simulation. The experiments on Lamb wave are carried out by employing a square array which is composed of 4 piezoelectric wafers. First of all, the sensor array collects response signals of Lamb wave as training data, and then de-noises them adopting the method of wavelet transform. In the process, the damage caused to composite can be realized through mass blocks. Besides, the Fourier transform is applied for the extraction of the characteristics shown by the signals. After that, the spectrum with the characteristics of damage and corresponding damage modes are employed as input and output of convolutional neural network respectively, and accordingly the model of damage identification is established. Finally, 191 samples (from a total of 192) were identified accurately and the correct recognition rate achieved is 99.5%, which consequently demonstrates that convolution neural network can be employed to establish the complex mapping relationship between signal and damage, and further proves that the proposed method performs well in high accuracy and great potential in simultaneous localization and quantitative identification of damage existing in composite plate.

A wearable, fast-healing, and self-adhesive hydrogel is prepared to realize the monitoring of full-range human motion and give an impressive thermal response in human body temperature detection for health information collection, as well as tunable photothermal property.

This work reports a refractive index sensor made of plastic optical fiber (POF) with tapered structure. Transmission loss is measured when the external environment’s refractive index changes from 1.33 to 1.41. Three wavelengths (532, 633, and 780 nm) are used to evaluate the sensitivity of the sensor, and results indicate that 633 nm is the best sensing wavelength due to the increased levels of sensitivity achieved at this wavelength. A biconical sensing structure is designed to enhance the sensitivity of the sensor. A sensitivity of $950\mathrm{\mu W}/\mathrm{RIU}$ at 633 nm is obtained for a biconical sensing structure when launched power is 1 mW. Due to its sensitivity to the refractive index and simple construction, POF with tapered structure has potential applications in the biosensing field.

The D-POF Hyperbolic Metamaterial SPR sensor has excellent detection performance based on the unique multimode response and high K conduction mode of Ag and MgF₂ structure.