4 results about "Orthogonal complement" patented technology

The application provides a secure alignment method based on a neural network parameter subspace repair, which is applied to a federal learning server side, and private training data of each participant is not accessed in a method execution process. In each round of distributed collaborative training, the following steps are performed: based on a current round global neural network model parameter, a secure parameter increment vector and an unsafe parameter increment vector are respectively obtained; a difference parameter vector is calculated and decomposed to obtain an orthogonal basis matrix of a dangerous parameter subspace; based on the orthogonal basis matrix, a to-be-repaired global parameter update vector is projected to an orthogonal complement space of the dangerous parameter subspace to remove a projection component of the to-be-repaired global parameter update vector on the dangerous subspace; the weighted secure parameter increment vector and the projected parameter update vector are fused to obtain a repaired global parameter update vector to update a parameter of the global neural network model.

The application relates to a four-component unmanned aerial vehicle power estimation method based on orthogonal decoupling. The method introduces a special data-driven residual to compensate for transient dynamics, constructs a four-component power decomposition model containing hovering, horizontal, vertical and additional items, and accurately describes the power of the unmanned aerial vehicle during acceleration, deceleration and complex maneuvering. A sparse identification strategy based on orthogonal projection is introduced, which mathematically forces the data-driven residual to be located in the orthogonal complement space of the linear subspace of the basic physical model, ensuring that the learned residual is a pure supplement to the physical model residual. The application uses a strictly decoupled four-component physical model to achieve high-precision and strong-explanation estimation of the dynamic power of the unmanned aerial vehicle without running a complex black-box neural network.

The invention belongs to the technical field of network security, and particularly relates to a continuous learning network intrusion detection method and system based on gradient projection and concept drift detection. The problems of serious concept drift, disastrous forgetting, new and old knowledge gradient interference and the like of an existing intrusion detection system in a dynamic network environment are solved, network flow data flow is monitored in real time, variance fluctuation of feature dimensions is monitored through a drift detection module based on principal component analysis (PCA), and unsupervised identification of distribution offset is achieved; a strategic sample selection and forgetting mechanism is adopted, a memory buffer area is optimized based on KL divergence, and balanced learning of normal traffic and an evolution abnormal mode is achieved under limited resources; a gradient projection mechanism (GPM) is introduced in a model fine tuning stage, and a parameter updating direction of a new task is limited in an orthogonal complement space of historical knowledge by constructing an orthogonal basis of a historical feature subspace, so that destructive interference between tasks is eliminated on a gradient level.

This invention discloses a method for obtaining beamforming weight vectors in an integrated radar-communication system, within the field of array signal processing technology. The method comprises the following steps: S1: Establishing a receiver signal model for a uniform linear array antenna in the integrated radar-communication system; S2: Introducing orthogonal polynomials and the Gauss-Legendal numerical integration method to perform a low-complexity, fast fitting solution for the interference subspace; S3: Introducing the Schmitt orthogonalization method to recursively solve the orthogonal complement projection operator of the interference subspace and project it to obtain the beamforming weight vector; S4: Introducing an equivalent representation of the beamforming weight vector under a dual-phase shifter architecture to obtain the updated beamforming weight vector value under the dual-phase shifter architecture. This beamforming weight vector acquisition method solves the problems of high computational complexity, poor performance in low-quantization-bit hardware scenarios, and desired signal suppression under high signal-to-interference-plus-noise ratios in existing methods, achieving low-complexity and highly robust beamforming.