Rapid coal-rock identification is one of the key technologies for intelligent and unmanned coal mining.Currently,the existing image recognition algorithms cannot satisfy practical needs in terms of recognition speed a...Rapid coal-rock identification is one of the key technologies for intelligent and unmanned coal mining.Currently,the existing image recognition algorithms cannot satisfy practical needs in terms of recognition speed and accuracy.In view of the evident differences between coal and rock in visual attributes such as color,gloss and texture,the complete local binary pattern(CLBP)image feature descriptor is introduced for coal and rock image recognition.Given that the original algorithm oversimplifies local texture features by ignoring imaging information from higher-order pixels and the concave and convex areas between adjacent sampling points,this paper proposes a higher-order differential median CLBP image feature descriptor to replace the original CLBP center pixel gray with a local gray median,and replace the binary differential with a second-order differential.Meanwhile,for the high dimensionality of CLBP descriptor histogram and feature redundancy,deep learning perceptual field theory is introduced to realize data nonlinear dimensionality reduction and deep feature extraction.With relevant experiments conducted,the following conclusion can be drawn:(1)Compared with that of the original CLBP,the recognition accuracy of the improved CLBP algorithm is greatly improved and finally stabilized above 94.3%under strong noise interference;(2)Compared with that of the original CLBP model,the single image recognition time of the coal rock image recognition model fusing the improved CLBP and the receptive field theory is 0.0035 s,a reduction of 71.0%;compared with the improved CLBP model(without the fusion of receptive field theory),it can shorten the recognition time by 97.0%,but the accuracy rate still maintains more than 98.5%.The method offers a valuable technical reference for the fields of mineral development and deep mining.展开更多
Detecting low-frequency underwater acoustic signals can be a challenge for marine applications.Inspired by the notably strong response of the auditory organs of pectis jellyfish to ultralow frequencies,a kind of otoli...Detecting low-frequency underwater acoustic signals can be a challenge for marine applications.Inspired by the notably strong response of the auditory organs of pectis jellyfish to ultralow frequencies,a kind of otolith-inspired vector hydrophone(OVH)is developed,enabled by hollow buoyant spheres atop cilia.Full parametric analysis is performed to optimize the cilium structure in order to balance the resonance frequency and sensitivity.After the structural parameters of the OVH are determined,the stress distributions of various vector hydrophones are simulated and analyzed.The shock resistance of the OVH is also investigated.Finally,the OVH is fabricated and calibrated.The receiving sensitivity of the OVH is measured to be as high as−202.1 dB@100 Hz(0 dB@1 V/μPa),and the average equivalent pressure sensitivity over the frequency range of interest of the OVH reaches−173.8 dB when the frequency ranges from 20 to 200 Hz.The 3 dB polar width of the directivity pattern for the OVH is measured as 87°.Moreover,the OVH is demonstrated to operate under 10 MPa hydrostatic pressure.These results show that the OVH is promising in low-frequency underwater acoustic detection.展开更多
基金Scientific and technological innovation project of colleges and universities in Shanxi Province,Grant/Award Number:2020L0294Shanxi Province Science Foundation for Youths,Grant/Award Number:201901D211249。
文摘Rapid coal-rock identification is one of the key technologies for intelligent and unmanned coal mining.Currently,the existing image recognition algorithms cannot satisfy practical needs in terms of recognition speed and accuracy.In view of the evident differences between coal and rock in visual attributes such as color,gloss and texture,the complete local binary pattern(CLBP)image feature descriptor is introduced for coal and rock image recognition.Given that the original algorithm oversimplifies local texture features by ignoring imaging information from higher-order pixels and the concave and convex areas between adjacent sampling points,this paper proposes a higher-order differential median CLBP image feature descriptor to replace the original CLBP center pixel gray with a local gray median,and replace the binary differential with a second-order differential.Meanwhile,for the high dimensionality of CLBP descriptor histogram and feature redundancy,deep learning perceptual field theory is introduced to realize data nonlinear dimensionality reduction and deep feature extraction.With relevant experiments conducted,the following conclusion can be drawn:(1)Compared with that of the original CLBP,the recognition accuracy of the improved CLBP algorithm is greatly improved and finally stabilized above 94.3%under strong noise interference;(2)Compared with that of the original CLBP model,the single image recognition time of the coal rock image recognition model fusing the improved CLBP and the receptive field theory is 0.0035 s,a reduction of 71.0%;compared with the improved CLBP model(without the fusion of receptive field theory),it can shorten the recognition time by 97.0%,but the accuracy rate still maintains more than 98.5%.The method offers a valuable technical reference for the fields of mineral development and deep mining.
基金supported by the National Natural Science Foundation of China(Grant 51875535 and 61727806)by 1331KSC,State Key Laboratory of Precision Measuring Technology and Instruments(pilab1805)。
文摘Detecting low-frequency underwater acoustic signals can be a challenge for marine applications.Inspired by the notably strong response of the auditory organs of pectis jellyfish to ultralow frequencies,a kind of otolith-inspired vector hydrophone(OVH)is developed,enabled by hollow buoyant spheres atop cilia.Full parametric analysis is performed to optimize the cilium structure in order to balance the resonance frequency and sensitivity.After the structural parameters of the OVH are determined,the stress distributions of various vector hydrophones are simulated and analyzed.The shock resistance of the OVH is also investigated.Finally,the OVH is fabricated and calibrated.The receiving sensitivity of the OVH is measured to be as high as−202.1 dB@100 Hz(0 dB@1 V/μPa),and the average equivalent pressure sensitivity over the frequency range of interest of the OVH reaches−173.8 dB when the frequency ranges from 20 to 200 Hz.The 3 dB polar width of the directivity pattern for the OVH is measured as 87°.Moreover,the OVH is demonstrated to operate under 10 MPa hydrostatic pressure.These results show that the OVH is promising in low-frequency underwater acoustic detection.
