4 results about "Pulse flow" patented technology

The application discloses a turbine shaft torsional vibration in-situ detection method under an aero-engine whole machine state, relates to the field of aerospace engines, and comprises the following steps: converting the rotating speed fluctuation generated when the turbine shaft of the aero-engine occurs torsional vibration into the change on the triggered pulse time sequence when the rotor blade reaches the optical sensor, and collecting the blade pulse flow signal through the optical sensor; wherein the optical sensor is fixedly connected with the bore probe structure assembly of the aero-engine; the blade pulse flow signal is high-frequency sampled by using high sampling rate dynamic data sampling, and the rotating speed time domain signal is obtained; the rotating speed time domain signal is subjected to preliminary analysis of turbine shaft torsional vibration, and the torsional angle result of the turbine shaft is determined; and the torsional angle result of the turbine shaft is converted into the torsional vibration result of the turbine shaft based on the strain relationship function between the shaft torsional angle and the torsional vibration. The application can realize efficient and accurate detection of the turbine shaft torsional vibration under the aero-engine whole machine state.

Embodiments of the present application provide an eye movement tracking method and device based on pulse data, equipment and a storage medium. The method comprises determining a tracking feature at a starting time according to a labeled pupil position of the target pupil at the starting time of the target pulse stream and a target visual feature corresponding to the starting time; for each time, determining a tracking feature at the time and a predicted pupil position at the time according to a target visual feature corresponding to the time, a tracking feature at a last adjacent time, a predicted pupil position at the last adjacent time, and a labeled pupil position; and obtaining an eye movement tracking result based on the predicted pupil position at each time. With the help of an end-to-end network architecture and a timing iterative updating mechanism, the tracking drift problem caused by high-speed eye movement is effectively solved. At the same time, the slow changes in the appearance of the pupil during the tracking process are adapted, and the efficiency, stability and accuracy of the tracking are significantly improved by relying on the historical state to smooth the predicted trajectory.

The application discloses a kind of light weight multi-focus pulse fusion reconstruction methods based on compressed sensing, constructs light weight multi-focus pulse fusion reconstruction network, contains multi-focus pulse input module, feature fusion module, sparse coding optimization module, image reconstruction module;Multi-focus pulse input module handles two multi-focus pulse flow data, generates single first fusion feature map and single second fusion feature map, feature fusion module extracts and fuses two fusion feature maps, obtains dictionary matrix and long exposure image, based on dictionary matrix and long exposure image, by iteratively sparse coding optimization module solution, obtain optimal sparse coding, image reconstruction module utilizes dictionary transpose matrix and optimal sparse coding, reconstructs output clear image richer in detail, overall loss function improves the approximation degree of reconstructed image and real image.The application effectively solves the problem of pulse camera imaging depth of field blur.

This invention discloses a spiking neural network (SNN) mapping method and system for congestion awareness on neuromorphic hardware, belonging to the field of neuromorphic computing technology. The method includes: mapping neurons in a spiking neural network model to physical cores of neuromorphic hardware to form an initial mapping scheme; iterating through congestion hotspots and updating the mapping scheme in each iteration to alleviate network communication congestion on the hardware; calculating the distribution of congestion hotspot regions composed of physical cores under the current mapping scheme based on pulse flow evaluation between physical core pairs; for each congestion hotspot region, selecting multiple physical cores with high pulse flow contribution as hub cores to be swapped; determining the optimal swap candidate core for each hub core based on a heuristic swapping decision mechanism; and swapping hub cores with the optimal swap candidate cores to update the mapping scheme. This promotes the efficient deployment of SNNs on neuromorphic hardware and provides key technical support for high-energy-efficiency, low-latency neuromorphic computing.