4results about How to "Prevent disappearing" patented technology

The invention discloses a flash memory controller which is used for accessing a flash memory module. The flash memory controller comprises a buffer memory, a microprocessor and a counter. A partial area of the buffer memory is used for temporarily storing write-read command program codes associated with processing write-in or read-out in system internal program codes. The counter generates a count value in response to the number of times the microprocessor continuously receives write or read commands. Wherein the microprocessor sets a partial area of the buffer memory as a non-holding memory, and after a sleep command is received and when a count value is greater than or equal to a pre-designed value, the microprocessor sets the partial area of the buffer memory as a holding memory, then the flash memory controller enters a power-saving mode, and the non-holding memory is closed. The invention further discloses a control method of the flash memory controller and an electronic device applying the control method.

This application provides a training method, a spiking neural network, and an apparatus for a spiking neural network. The method includes: iteratively training the spiking neural network using training data of the object to be processed, wherein the spiking neural network contains at least one neuron model; calculating a task loss function based on the training results of the spiking neural network and updating the weight parameters; updating the current discharge threshold parameter of the neuron model based on the historical discharge frequency of the neuron model, wherein the value of the current discharge threshold parameter increases with the increase of the historical discharge frequency; and obtaining the trained spiking neural network model using the updated weight parameters and the updated discharge threshold parameter after multiple iterations of training. This method facilitates the training of a sparsely discharging spiking neural network, reduces the energy consumption during spiking neural network applications, and improves model energy efficiency by using the trained spiking neural network model to process the data of the object to be processed.

The invention discloses an HUD virtual image and real environment polarization matching detection method. The method comprises the following steps: when an HUD projects a virtual image, collecting a virtual polarization parameter set of a plurality of preset test points in an HUD virtual image picture; synchronously acquiring a real environment polarization parameter set of real environment light in corresponding directions of the plurality of preset test points; inputting the virtual polarization parameter set and the real environment polarization parameter set into a preset polarization matching degree evaluation model, and calculating a matching degree score of each preset test point; when at least one of the matching degree scores of the preset test points is lower than a preset threshold value, it is judged that the test points have polarization conflicts, and polarization conflict types and conflict position information are output. According to the method and the device, the visibility of the virtual image under the polarized sunglasses and the visual consistency of the virtual image and the real ambient light are ensured, so that the darkening, disappearance, glare and visual fatigue of the image are avoided, and the driving safety and the user experience are remarkably improved.

The invention relates to the technical field of image processing, in particular to a tunnel lining defect ground penetrating radar image enhancement method, medium and equipment, and the method comprises the following steps: constructing a data set; constructing a generative adversarial network model based on improved deep convolution; the obtained tunnel lining structure defect ground penetrating radar image data set is used for training in the constructed improved deep convolution-based generative adversarial network model, and hyper-parameters of a generator and a discriminator are continuously adjusted through a test result, so that the adversarial network model is globally optimal; and generating a tunnel lining structure defect ground penetrating radar image by using the improved deep convolution generative adversarial network model. According to the invention, the technical problem that the difference between the image generated by the existing image processing method and the real ground penetrating radar image is large for the ground penetrating radar image of the internal defect of the tunnel lining is solved.