3 results about "Volterra series" patented technology

A distortion apparatus for a power amplifier comprises: pre-distortion circuitry that comprises a memory polynomial look-up-table (LUT) circuit; and post-distortion circuitry that updates the pre-distortion circuitry based on an output of the power amplifier. The pre-distortion circuitry comprises a plurality of LUTs. Each LUT corresponds to a different memory depth of a memory polynomial of a Volterra series. Each LUT indexed by an instantaneous power of an input sample delayed by an amount corresponds to a respective memory depth of each LUT. An output of the memory polynomial LUT circuit corresponds to a summation of an output of each LUT multiplied by the input sample delayed by the amount corresponding to the respective memory depth of each LUT, and an output of the pre-distortion circuitry is provided to the power amplifier.

This invention discloses a distributed photovoltaic (PV) power quality assessment method based on an improved Volterra series and adaptive interval type-two fuzzy logic. The method involves acquiring and preprocessing raw voltage signals from key nodes of the distributed PV system to obtain real-time PV operating parameters, calculating a light intensity weighting factor, and then calculating the Volterra output and extracting core features based on the light intensity weighting factor, the preprocessed voltage signals, and pre-calculated first-order and symmetric second-order kernels. These core features are mapped to IT2FS, and the Mamdani inference method is used to obtain the IT2FS inference results from a PV power quality rule base. The centroid method is then used to reduce the type of perturbation and its severity level for the core features. The source of the perturbation is determined based on the feature threshold and the core feature value. This invention achieves accurate assessment of the type, severity level, and source of power quality perturbations in distributed PV systems.

The invention provides an underground cable three-dimensional image detection method based on terahertz data, and the method comprises the steps: obtaining the terahertz data of an underground cable, and generating the dielectric constant distribution of the underground cable according to the terahertz data through a multi-physics coupling waveguide model; compressing the dielectric constant distribution into a complex signal by using a quantum derivative compressed sensing algorithm, extracting an electric field time-domain signal from the complex signal, and performing nonlinear phase compensation on the electric field time-domain signal by using a preset Volterra series model to obtain a compensated electric field time-domain signal; performing tensor decomposition on the compensated electric field time domain signal to obtain a feature matrix, and performing Bayesian inversion on the feature matrix to obtain optimal cable parameters of the underground cable; and generating an optimal three-dimensional parameter field according to the optimal cable parameters, and drawing a three-dimensional image of the underground cable according to the optimal three-dimensional parameter field. In this way, the problem that existing metal detection, low-frequency electromagnetic waves and radar imaging are limited in penetrating power and resolution in an underground complex environment is solved.