6 results about "Time averaging" patented technology

The application discloses a kind of fast online unmanned plane trajectory planning method, unmanned plane trajectory planning technical field, it includes establishing unmanned plane 3DOF dynamics model, design objective function, utilize accurate penalty function to handle constraint condition, and the objective function and state quantity are discretized, solve objective function to obtain flight trajectory.The present application can be through accurate penalty function and the collaborative design of discretization, significantly improve the accuracy and computational efficiency of complex constraint processing, single step calculation time average is 108ms, longest 160ms, satisfy real-time requirement, applicable to small unmanned plane embedded platform online trajectory planning.

This invention discloses a method and system for identifying layered cloud melting layers based on cloud radar, belonging to the field of cloud radar identification technology. The method includes: acquiring airborne cloud radar observation data; resampling the airborne cloud radar observation data to a unified geographic grid; for each sample, extracting continuous time periods with radar bright band characteristics and performing time averaging to obtain a comprehensive average reflectivity factor profile; calculating the vertical gradient of the comprehensive average reflectivity factor profile; and identifying the effective melting layer based on the vertical gradient. This invention automatically identifies the height of the top and bottom of the melting layer by analyzing the vertical gradient changes of reflectivity factor and vertical velocity, exhibiting high accuracy and strong adaptability.

An electronic device according to an example embodiment includes: at least one wireless communication module comprising communication circuitry configured to transmit and receive a wireless signal, at least one processor operatively connected to the at least one wireless communication module, and a memory electrically connected to the processor and configured to store instructions executable by the processor, wherein, in response to the instructions being executed by the processor, the processor may be configured to: determine whether there is a target wake time (TWT) parameter for a TWT service, determine a time-averaged specific absorption rate (TAS) backoff regulation value for a time window based on the TWT parameter, and control the at least one wireless communication module to perform communication during the time window based on the TAS backoff regulation value and the TWT parameter.

This invention relates to a white-box adversarial example generation method and system for liquid state machines, belonging to the field of neural network security. It aims to address the problem that existing methods cannot effectively handle non-differentiable cyclic components and gradient calculation failures caused by random pulse coding in liquid state machines. The method constructs a computable and stable gradient propagation path from model loss to the original input through gradient splitting and time-averaged gradient approximation. It includes a cyclic process of forward propagation and backward gradient calculation. The corresponding system includes a data input and preprocessing module, a target model loading and inference module, a gradient calculation module, an attack algorithm integration module, and an adversarial example synthesis and feedback module. This invention achieves an effective white-box attack on liquid state machines for the first time, with advantages such as high attack success rate, good perturbation concealment, and strong scalability, providing a powerful tool for evaluating the security of spiking neural networks.

A method includes performing background data collection in a first region at a first time, the performing background data collection in the first region includes determining a first set of time-averaged reference signal received power (RSRP) data for each beam identification (ID) number and a first set of standard deviation data of the time-averaged RSRP for each beam ID number based on a first set of RSRP data. The method further includes performing RSRP data collection in the first region for a second time and performing RSRP data processing for a first database, and performing k-nearest neighbor (kNN) crowd counting based on at least the RSRP data processing. The performing kNN crowd counting includes estimating a number of people in the first region based on at least a second set of normalized statistical features.

This application relates to a collaborative charging scheduling method and system for heterogeneous unmanned platforms. Based on a charging behavior model constructed from the charging data of each unmanned platform, a charging feature vector is determined for each platform, including charging urgency and charging duration. Based on these feature vectors, multiple unmanned platforms are divided into at least one cluster. A scheduling objective function is set to minimize the time-averaged error between the actual total load of the cluster and the grid reference load. Simultaneously, a first virtual queue representing the backlog of charging demand and a second virtual queue representing priority based on charging urgency are constructed. Based on the first and second virtual queues, the scheduling objective function, and the Lyapunov drift penalty algorithm, the charging scheduling strategy for the current scheduling slot is solved. Thus, this application achieves real-time tracking of the grid reference load without requiring precise prediction of future charging demand and load changes, while prioritizing the charging needs of unmanned platforms with high urgency.