6 results about "Spectral amplitude" patented technology

The application discloses a kind of vehicle diesel engine piston ring wear state monitoring method, storage medium and device, it is related to diesel engine state monitoring field, the method includes defining the measured point sensitivity characteristic value for representing the attenuation amount of measured point energy relative to acoustic emission source energy, and determining multiple acoustic emission source positions and measured point positions for installing acoustic emission sensor on cylinder cover;Drive acoustic emission sensor at acoustic emission source to produce excitation, the measured point sensitivity characteristic value between each measured point and acoustic emission source is calculated, determine the measured point for carrying out fault identification among all measured points, as detection measured point;Acoustic emission signal of detection measured point is obtained when piston is located in set position during diesel engine operation, the PSD spectral amplitude of acoustic emission signal is calculated, and compared with the fault threshold under different fault states of piston ring, realize piston ring fault determination.The application can realize the accuracy and effectiveness of piston ring fault diagnosis.

The application discloses a kind of traceable pressure sensor dynamic response calibration method based on absorption spectrum reference pressure input, in shock tube is driven section tail end wall position arrangement to be calibrated pressure sensor, and set optical measurement window in reflected shock action area, synchronous acquisition pressure sensor output signal and absorption spectrum signal;Absorption spectrum signal is preprocessed and spectral line fitting, calculate the gas temperature and collision broadening coefficient after reflected shock wave, and reference pressure amplitude P5 after reflected shock wave is obtained accordingly;Pressure amplitude P1 before incident shock wave is measured using pressure gauge after metrological calibration;According to P1, P5 and pressure mutation time t0 Reference pressure step input signal based on Heaviside step function is constructed;Reference pressure step input signal and pressure sensor output signal are processed, and the ratio of the spectral amplitude of the two is used as the sensitivity of the pressure sensor at the corresponding frequency;Error source in the process of calculating reference pressure amplitude, pressure mutation time and sensitivity is evaluated and calculated, and the sensitivity calibration result of pressure sensor is obtained.The application can obtain the sensitivity characteristics and its uncertainty of pressure sensor.

The application discloses a single-station synthetic aperture passive positioning method and system based on a maximum spectral sharpness criterion, and belongs to the fields of signal processing technology and passive positioning. The method comprises the following steps: controlling a single motion platform to continuously observe a non-cooperative radiation source and collect a time-domain baseband data sequence; constructing a virtual detection grid in a geographical space, and generating a corresponding geometric de-rotation operator for each grid node; performing point-by-point phase stripping processing on the baseband data by using the operator to obtain a residual phase sequence; mapping the residual phase sequence to a frequency domain, calculating an energy concentration norm of a spectral amplitude as a decision statistic of spatial matching; and determining the spatial coordinates of the radiation source and simultaneously inverting a transmitting-receiving frequency deviation by searching for a global maximum value point of the statistic. The application utilizes the physical characteristics that the signal frequency energy is highly concentrated under the spatial matching state, effectively avoids the defocusing problem of a traditional time-domain coherent accumulation algorithm under a frequency non-synchronous condition, and significantly improves the precision and robustness of single-station passive positioning.

This invention discloses an InSAR phase filtering method based on complex interferograms, comprising: preprocessing acquired primary and secondary SAR images to obtain complex InSAR interferograms; dividing the complex InSAR interferograms into square-shaped interferogram patches along the azimuth and range directions; converting each interferogram patch from the spatial domain to the frequency domain signal, constructing a corresponding spectral amplitude map; using K-means clustering to extract an initial low-pass filter from the spectral amplitude map, and constructing a phase filter together with a Kaiser window function; passing the frequency domain signal of the interferogram patch through the phase filter, where the signal frequency power is preserved, noise at high-frequency positions is filtered out, and noise at low frequencies is smoothed out, resulting in a filtered frequency domain signal; converting the filtered frequency domain signal back to the spatial domain to obtain filtered interferogram patches, which are then stitched together to obtain a filtered InSAR interferogram. This invention contributes to the promotion and advancement of current time-series InSAR interferometry technology and its engineering applications.

In accordance with various embodiments, a method for creating a training dataset for an artificial intelligence-based arc-fault circuit interrupter is provided. In some embodiments, the method comprises collecting a number of frames of no-arc data, calculating a spectral amplitude value for each frame of no-arc data and summing the spectral amplitude values; calculating an average of the sums of the spectral amplitude values of the no-arc data; collecting a number of frames of arc data; calculating a spectral amplitude value for each frame of arc data and summing the spectral amplitude values; for each frame of arc data, comparing the sum of the spectral amplitude values to a threshold based on the average of the sums of the spectral amplitude values of the no-arc data; and adding each frame of arc data whose sum of the spectral amplitude values exceeds the threshold to an AI data model training dataset.

The present application belongs to the technical field of neural signal processing, and specifically relates to a neuron spike signal data enhancement method based on VAE-GAN. The present application comprises: constructing an improved variational autoencoder generative adversarial network, integrating a time attention module in the decoder of the generative network to capture and reconstruct the key time dynamic characteristics of the neuron spike signal; introducing a double discriminator architecture for evaluating the fidelity of the generated signal in the frequency domain and suppressing high-frequency artifacts; designing a composite loss function containing a spectral amplitude loss to impose a time-frequency double constraint on the generator during the adversarial training process, ensuring that the generated samples are highly consistent with the real signal in terms of time domain morphology and frequency domain characteristics; and synthesizing high-fidelity spike waveforms in batches for a small number of neuron classes, which are used to expand the original data set and construct a class-balanced training set. Experiments on multiple data sets and downstream classification models prove that the present method can improve the classification performance of neuron spike signals and has high efficiency and universality.