10 results about "Nonlinear signal processing" patented technology

The invention relates to the technical field of electromagnetic environment detection, and discloses a multi-mode electromagnetic environment detection system and method. The system acquires multi-modal data such as electromagnetic field intensity and spectrum distribution through a multi-modal sensor array to generate a data set, and realizes interference source identification and dynamic adjustment of detection parameters through nonlinear signal processing, depth feature extraction based on a convolutional neural network, topology analysis of a graph neural network and adaptive optimization of reinforcement learning. And an electromagnetic field simulation module is also arranged to simulate electromagnetic wave propagation and early warn abnormal radiation, and an abnormal mode library is constructed to identify potential interference types. The system can comprehensively and accurately detect an electromagnetic environment, efficiently position an interference source, adaptively adjust detection parameters, improve detection accuracy, real-time performance and system stability, and effectively meet complex electromagnetic environment monitoring requirements.

A system configured to reduce loudspeaker distortion by performing nonlinear signal processing is provided. A device may include preprocessing component(s) that apply nonlinear signal correction prior to sending a playback audio signal to a driver in order to compensate for a nonlinear response of the driver. While the driver response may be nonlinear, a combination of the preprocessing and the nonlinear driver response results in a combined response that is linear and / or compensates for the nonlinear driver response. For example, applying the nonlinear driver response to a processed audio signal may result in output audio generated by the driver accurately reproducing the playback audio signal input to the preprocessing components. To train the preprocessing components to apply the nonlinear signal correction, a deep neural network (DNN) is trained to model the driver response.

The invention discloses a running-in and wear dynamic behavior characterization method based on a gray texture recurrence plot, and relates to the technical field of complex nonlinear signal processing, and the method comprises the steps: S1, carrying out the preprocessing of a friction signal outputted in a running-in and wear process, obtaining a pure friction signal, and storing the pure friction signal in a computer in a time sequence format; s2, setting a window length, and decomposing all the friction signal time sequences into a plurality of continuous and non-overlapped calculation windows; s3, time delay and embedded dimension parameters are calculated for the friction signals in each window, the frequency of two sets of parameters in all the windows is counted, the parameter with the highest occurrence frequency is selected as the same parameter, and phase-space reconstruction is conducted on the friction signal time sequence in all the windows. According to the macroscopic mode and quantitative parameters, the dynamic characterization research of the running-in wear behavior is carried out from the tribology-dynamics coupling angle, and a new perspective is provided for running-in state identification.

The invention discloses a biphase space mapping symplectic geometric mode decomposition method and system for nonlinear signal processing, and belongs to the technical field of signal processing. The method comprises five steps of nonlinear signal acquisition, adaptive time delay parameter selection, symplectic geometric mode decomposition, mode screening based on phase space trajectory geometric characteristics and signal reconstruction. According to the method, a phase space analysis idea is applied to two key stages of symplectic geometric mode decomposition; in the decomposition stage, the phase space reconstruction quality is optimized by adaptively selecting time delay parameters; in the reconstruction stage, component automatic screening is carried out by using phase space trajectory geometric features. According to the method, the limitation of a traditional signal processing method in a complex environment is overcome, and the feature extraction precision of the nonlinear non-stationary signal is effectively improved. Experimental verification shows that the method has excellent performance in the aspect of processing the acoustic emission damage signal of the blade of the wind driven generator, and provides reliable technical support for engineering application.

The invention provides a tidal reach water level prediction method, device, equipment and medium, and the prediction method comprises the following steps: collecting historical data and current time period data, the historical data and the current time period data comprising actually measured runoff data, actually measured water level data and actually measured tidal range data; lagging adjustment is conducted on the actually measured runoff data and the actually measured tidal range data with the actually measured water level data as the benchmark through a cross-correlation analysis method; performing EEMD decomposition to obtain respective intrinsic mode functions and residual errors; and outputting the eigenmode function of the water level data of the analysis station in the future time period and the predicted value of the residual error through the LSTM model, and carrying out superposition to obtain the predicted value of the water level of the tidal reach. According to the method, the water level data, the runoff data and the tidal range data of the tidal reach are subjected to conjoint analysis, the problems of nonlinear signal processing and low precision of the water level of the tidal reach are effectively solved through EEMD decomposition and the LSTM model, and the prediction accuracy of the water level of the tidal reach is greatly improved.

The application discloses a power supply equipment state monitoring system and method based on AI power spectrum, and relates to the technical field of electrical intelligent sensing; the method comprises the following steps: obtaining original analog signals of the power supply equipment through real-time synchronous acquisition, and preprocessing the original analog signals to obtain digital waveform data frames; performing time domain feature calculation on the digital waveform data frames to obtain a time domain feature set; performing frequency spectrum feature extraction on the digital waveform data frames to obtain a frequency domain feature set; performing high-order statistical analysis and nonlinear signal processing on the digital waveform data frames to obtain a nonlinear feature vector; based on the frequency domain feature set, the nonlinear feature vector and the time domain feature set, combining a pre-trained artificial intelligence model, outputting a fault type identifier and a corresponding fault probability, and calculating an abnormal score based on the fault type identifier and the corresponding fault probability; the application can effectively identify the camouflage fault of the power supply equipment and reduce the cumulative risk of potential faults.

The application provides a tidal river reach water level prediction method, device, equipment and medium, wherein the prediction method comprises the following steps: collecting historical data and current period data, the historical data and the current period data both comprise measured runoff data, measured water level data and measured tidal range data; using cross-correlation analysis method to adjust the measured runoff data and the measured tidal range data with the measured water level data as the benchmark; performing EEMD decomposition to obtain respective intrinsic mode functions and residuals; outputting the prediction values of the intrinsic mode functions and the residuals of the water level data of the analysis station in the future period through the LSTM model and superimposing them to obtain the prediction value of the water level of the tidal river reach. The application carries out joint analysis on the water level data, runoff data and tidal range data of the tidal river reach, effectively solves the nonlinear signal processing problem and the low precision problem of the water level of the tidal river reach through EEMD decomposition and the LSTM model, and greatly improves the prediction accuracy of the water level of the tidal river reach.

The invention belongs to the technical field of new energy power prediction and photoelectric nonlinear signal processing, and discloses a new energy power prediction method and system based on an electro-optical differential feedback chaos reservoir, and the method comprises the steps: collecting and preprocessing historical power data and meteorological data, and constructing a prediction model comprising an input layer, a reservoir layer and an output layer; wherein the reserve pool layer comprises an electro-optical chaos ring with differential feedback, and high-dimensional nonlinear mapping and time memory expansion of an input signal are realized; the output layer solves the optimal output layer weight through ridge regression, and linear mapping of the historical state and the future power is achieved. According to the method, the complexity of chaotic dynamics is improved by introducing a differential feedback link, and the nonlinear mapping capability and short-term memory performance of the reserve pool are remarkably enhanced, so that the precision and real-time performance of new energy power prediction are improved. The method is suitable for scenes of wind-solar power prediction, power grid dispatching, distributed energy management and the like.

The invention relates to the technical field of satellite height measurement data processing, and discloses a height measurement echo waveform redetermination method and system based on pole symmetry mode decomposition, and the method comprises the steps: introducing a pole symmetry mode decomposition method, and carrying out the multi-mode decomposition of an echo waveform signal of satellite height measurement data, so as to complete the waveform denoising; constructing a dual-mode fusion adaptive waveform analysis model, and reconstructing a complete height measurement waveform sequence; and through a Bayesian algorithm, waveform parameters are optimized in a combined manner, a neural network model is combined, adaptive echo waveform redetermination is carried out, and waveform classification and processing are completed. According to the method, a pole symmetry mode decomposition method is introduced, multi-scale intrinsic mode decomposition is carried out on echo waveform signals, high-frequency noise modes are accurately separated, high-fidelity reconstruction of the signals is achieved, the method has excellent nonlinear signal processing capacity and local adaptivity, the denoising effect is remarkably improved, and by combining adaptive echo waveform reconstruction, the denoising effect is greatly improved. The waveform reconstruction precision in complex terrains and severe environments is improved, and the high-precision satellite height measurement data processing requirement is met.

The present application discloses signal processing in a hearing device. A method for defining and setting non-linear signal processing of a hearing device by machine learning includes: providing at least one electrical input signal representing at least one input sound signal from the user environment of the hearing device; determining a normal hearing representation of the at least one electrical input signal based on a normal hearing auditory model; determining a hearing-impaired representation of the at least one electrical input signal based on a hearing-impaired auditory model; determining optimized training parameters by machine learning, where determining the optimized training parameters includes repeatedly adjusting the training parameters and comparing the normal hearing representation and the hearing-impaired representation to determine a matching degree between the normal hearing representation and the hearing-impaired representation until the matching degree meets a predetermined requirement; and when the matching degree meets the predetermined requirement, determining corresponding signal processing parameters of the hearing device based on the optimized training parameters.