4results about How to "Improve picking accuracy" patented technology

The invention relates to a seismic phase pickup method based on time-frequency analysis and deep learning, and belongs to the field of seismology, a deep neural network comprising an empirical mode decomposition module, a residual shrinkage module, an LSTM layer, a transformer layer, an up-sampling layer and a convolutional layer is established, a linear rectification function is adopted as an activation function, an Adam adaptive optimization function is adopted for back propagation, and a seismic phase is obtained. And taking a binary cross entropy loss function as a loss function, and taking a mean absolute error function, an error standard deviation, an accuracy rate, a recall rate and an F1 score as evaluation indexes to obtain a deep neural network model. When the frequencies of the noise, the P wave and the S wave are different, empirical mode decomposition can effectively separate the noise, the P wave and the S wave, so that the network is helped to better identify the P wave and the S wave. Even for broadband noise, the noise can be decomposed into different intrinsic mode functions, so that the noise content in the intrinsic mode functions where P waves and S waves are located is reduced. The time-frequency analysis method can effectively improve the anti-noise capability of the network.

The invention proposes a seismic signal P-wave detection method based on a PPNet network, belonging to the technical field of seismic signal processing; the network of the invention can realize the seismic P-wave picking with higher precision and lower error; combined with UNet++ encoding and decoding, a lightweight P-wave arrival time picking network PPNet (P‑wave Pyramid Network) with seismic phase feature analysis and fusion capability is designed by integrating feature filters; the encoder module adopts a convolution layer with a large convolution kernel and a low number of channels to perform deep feature extraction on the input seismic signal, enhance the phase arrival time weight in the signal sequence, and suppress and filter other interference information; a feature fusion mechanism is added in the feature restoration process of the decoder module to complete the feature information and realize the accurate identification of each time step in the seismic signal sequence; feature filters are added only to the three downsampling modules after the encoder to process the output feature sequence, refine the P-wave arrival time feature, and improve the arrival time picking accuracy; the invention is applied to seismic P-wave picking.

The utility model provides a portable fruit picker belongs to fruit picking technical field, this portable fruit picker includes the assembling cylinder, and the movable plate is slidably connected in the assembling cylinder, the adjusting lever is fixedly connected in the lower extreme of movable plate, and the lower extreme of adjusting lever is active and is penetrated the lower extreme of assembling cylinder, the connecting rod is fixedly connected in the lower extreme of movable plate, the moving groove is established in the circumference inner wall of assembling cylinder, and the upper and lower inner wall between moving groove is rotatably connected with the screw rod, and the movable plate is driven to go up and down through screw rod - nut mechanism and second motor, realizes the stepless length adjustment of picking pole, and is suitable for different fruit tree height, and the cutting knife is directly integrated in the storage cylinder side wall, and the fruit after cutting can automatically fall into the storage cylinder or the fruit pipe through the conduction groove, avoids the fruit damage, and the camera is linked with the display screen, monitors the cutting position in real time, and improves the picking accuracy.

The invention belongs to the technical field of earthquake phase arrival time picking and image processing, and is an earthquake P wave picking method based on a SAIP-Unet network; the SAIP-Unet network is established on the basis of a codec structure; firstly, a network encoder uses a multi-scale residual module to deeply mine effective features and fuses and outputs them through a multi-scale feature extraction module to accelerate the network convergence speed; then, a spatiotemporal attention mechanism is added to the decoder to generate a high-precision arrival sequence and then perform point-by-point prediction, thereby enhancing the network's perception of arrival features and improving recognition accuracy; compared with traditional methods and deep learning methods, the invention has higher arrival time picking accuracy; and solves the problems of poor efficiency, low accuracy, weak robustness, etc. of existing earthquake arrival time picking algorithms.