In the article, the application of the electrical spike, instead of the tone-burst, to the ultrasonic pulsed-Doppler velocity profiling the first time is presented. To generate ultrasound, the ultrasonic sensor is excited by the spike. For the Doppler method, the spike excitation was not used previously. It has been used with wideband signal processing methods. In our study, first, the fluid-velocity profile measurement using spike excitation has been carried out for single-phase pipe flow. The received data have been validated by the logarithmic law of the wall, for turbulent flow regime. The flow rate calculated by using the mean velocity profile well coincides with the flow-meter data. Furthermore, the spike signal and the Doppler method are used in the multiwave ultrasonic velocity profile (UVP) method, for the bubbly flow measurement. The experiments show that, if the spike signal is slowly damped, the generated ultrasonic wave is similar to the tone-burst that is required for the Doppler method, but in this case, usually the pulse length is smaller and depends on the damping and sensor characteristics. The autocorrelation technique is used for echo signal processing. The derived UVP systems inherit the advantages from both the spike excitation and Doppler method.
This paper proposes a machine vision system for the surface inspection of black rubber rollers in manufacturing processes. The system aims to enhance the surface quality of the rollers by detecting and classifying defects. A lighting system is installed to highlight surface defects. Two algorithms are proposed for defect detection: a traditional-based method and a deep learning-based method. The former is fast but limited to surface defect detection, while the latter is slower but capable of detecting and classifying defects. The accuracy of the algorithms is verified through experiments, with the traditional-based method achieving near-perfect accuracy of approximately 98% for defect detection, and the deep learning-based method achieving an accuracy of approximately 95.2% for defect detection and 96% for defect classification. The proposed machine vision system can significantly improve the surface inspection of black rubber rollers, thereby ensuring high-quality production.
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