A miniature hybrid FPI-FBG optical fiber sensor based was proposed to measure refractive index (RI) and temperature simultaneously. The integrated FPI-FBG composite fiber sensor is constructed by cascading an FP interference cavity at one end of the FBG, where the FPI is used to measure the liquid refractive index and the FBG to measure temperature and compensate for FPI. In this paper, Monte Carlo method is used to simulate the sputtering yield of three commonly used metal high reflection films (aluminum, silver and gold). Au film perform as the reflective mirror of the FPI due to its high sputtering yield and high stability. Owing to the different sensitivities, two-dimensional matrix method was constructed to calculating the refractive index (RI) and temperature synchronously. Experimental results show that the temperature and RI sensitivity of spectral dip wavelength for FPI are 0.338 nm/°C and 1669.5 nm/RIU, respectively. The FBG is insensitive to RI and temperature sensitivity of sensor is 9.8 pm/°C.
With the rapid development of aerospace industry, multilayer structural composites are more widely used. Debonding, bubbles, inclusions and other defects in multilayer structural composites are in urgent need of new non-destructive testing methods for detection and identification. In this paper, the terahertz time domain spectroscopy system is used to detect the polymer materials with different embedded defects. In order to improve the accuracy of defect classification and recognition, the terahertz signal is transformed into the form of wavelet time-frequency graph through Morlet wavelet basis, which enrichesthe local feature and structure information. Then the ResNet101 network with residual block structure is used and compared with VGG16 and DenseNet201 networks. The experimental results show that Compared with the VGG16 network model, the ResNet101 network model has more stable training process, higher accuracy, and faster convergence speed. In training, the accuracy is raised to 100%, and the simultaneous loss rate is reduced to 0. Compared with the DenseNet201 network model, it has higher accuracy and more reasonable training process. It is proved that the ResNet101 network model is more suitable for the classification and identification of terahertz signals of defects in multilayer composite materials. This method can provide a new idea for the defect classification of multi-layer structure composites.
With the continuous development of modern industry, high-precision dynamic monitoring systems are essential to ensure the safety and efficiency of production. Therefore, the terahertz radar system based on frequency modulation continuous wave (FMCW) was studied in this work, aiming at realizing high-precision dynamic monitoring of coal levels in coal bunkers. The system adopts the FMCW method to measure the target distance by transmitting continuous linear frequency modulation signals and using the frequency difference and time delay between the received signal and the transmitted signal. The frequency selection algorithm is combined with the phase estimation algorithm, and the spectrum is refined by Zoom-FFT algorithm to improve the range resolution. The phase estimation algorithm is used to supplement and improve the ranging accuracy to ensure the reliability of the ranging results. To verify the feasibility of the system, field tests were conducted at 120-124 GHz frequencies and 4 GHz bandwidths. The system has a maximum operating range of 50 m, a beamwidth of 4 degrees, and a theoretical range resolution of up to 0.5 mm. By integrating optimization algorithm, the terahertz radar system can realize high-precision dynamic monitoring of coal level in the bunker, and show excellent anti-interference ability and stability, showing great practical application value.
Aiming at the technical problems of intelligent recognition and accurate positioning of steel rolling surface defects, a target detection method based on machine vision and depth neural network was proposed. YOLOX_M was introduced as the model of surface defect detection using the weights trained on the COCO dataset as the initial weights. To realize the identification and location of surface defect categories of rolled steel, the YOLOX_M model was further trained using the practical dataset. The performance of YOLOX_M was compared with the other five YOLOX models. The test results show that YOLOX_M can effectively detect six different forms of surface defects, and the test accuracy (P), recall rate (R) and detection mAP can respectively reach 88.81%, 80.88% and 90.12%. The mAP of the YOLOX_M model is higher than 90% and the model size is less than 100 MB, so it can be better applied in the embedded system for real-time detection.
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