7 results about "Signal variance" patented technology

A test and / or measurement system comprising a test and / or measurement device is described. The test and / or measurement device includes a first measurement module, which is configured to digitize a first measurement signal, thereby obtaining a first digital measurement signal. The test and / or measurement device further comprises a signal processing module (24), which includes a mean value detector module (66), an RMS detector module (68), and a variance detector module (70). The mean value detector module (66) is configured to average a first digital baseband signal over a detector interval of the mean value detector module (66), thereby obtaining a first detector signal. The RMS detector module (68) is configured to determine an RMS value of the first digital baseband signal over a detector interval of the RMS detector module (68), thereby obtaining a second detector signal.The variance detector module (70) is configured to determine at least one variance parameter of the first digital baseband signal based on the first detector signal and the second detector signal, wherein the at least one variance parameter is a measure of the deviation of the first digital baseband signal from its mean value. Furthermore, a measurement procedure is described.

This application relates to the field of data processing technology and discloses a method for processing heterogeneous multi-scale data in Gaussian process regression. The method first acquires the raw data and performs feature transformation, standardization, and dimensionality reduction preprocessing to construct a multi-level validation dataset. Second, it reconstructs the Gaussian process regression kernel function, introducing a learnable Minkowski distance parameter to replace the traditional fixed Euclidean distance metric. Next, it uses a tree-structured Parzen estimator strategy to perform global iterative optimization on the validation set with the goal of minimizing prediction error, determining the optimal Minkowski distance parameter. Subsequently, it trains the model based on the optimal parameter and an automatic correlation determination mechanism, jointly optimizing hyperparameters such as signal variance and length scale through maximum likelihood estimation. Finally, it performs prediction and outputs the mean and variance. This invention, through adaptive parameterization of the kernel function metric, effectively overcomes the shortcomings of traditional models in adapting to non-spherical distributions and multi-dimensional heterogeneous features, significantly improving prediction accuracy and generalization performance.

The application discloses a laser triangular displacement signal deconvolution and symmetric reconstruction method and a laser displacement detection device, acquires optical point spread functions of one-dimensional linear array photoelectric detectors in a measuring range of the device at different physical positions, and constructs a space time-varying optical point spread function reference library; a one-dimensional original light intensity signal of a measured surface is collected, corresponding optical point spread functions are called from the reference library after initial positioning of a light spot, iterative deconvolution is performed on the original light intensity signal, termination iteration is determined by a normalized residual change rate and a preset maximum iteration number, and a physical sharpened light spot signal is obtained; signal variance and statistical kurtosis of a main peak local area are calculated, and a dynamic pixel window width is adaptively adjusted; an asymmetric index is calculated in the dynamic pixel window, forced symmetric reconstruction is performed when the asymmetric index is greater than a preset truncation threshold, sub-pixel centroid coordinates are extracted based on a reconstructed symmetric waveform, and an actual displacement value is calculated.

The application discloses a kind of hybrid feature fault diagnosis method and system based on low-voltage intelligent circuit breaker, belong to low-voltage intelligent circuit breaker fault diagnosis field.Adopt fuzzy entropy, variance, energy moment, the ratio of the variance of first-order difference signal and original signal variance and the ratio of the mobility of first-order derivative and the mobility of original signal to extract fault feature, and singular value decomposition is carried out to the parameter matrix that variance, energy moment, the ratio of the variance of first-order difference signal and original signal variance and the ratio of the mobility of first-order derivative and the mobility of original signal, to avoid feature redundancy, compared with single feature detection, greatly improve the fault diagnosis accuracy.Through the feature of the vibration signal of multiple low-voltage intelligent circuit breaker is collected, overcome the problem that traditional low-voltage intelligent circuit breaker fault diagnosis research is less.Can accurately identify the fault of low-voltage intelligent circuit breaker, realize the fault diagnosis of low-voltage intelligent circuit breaker.

The present application relates to the field of tire multi-working condition dynamics parameter detection technology, and more particularly to a tire multi-working condition dynamics parameter detection method and system, comprising collecting original signals inside the rim, wheel core acceleration signals and external six-component force data; establishing a reference angle domain coordinate to generate a synchronous analysis matrix; extracting a prior centrifugal load characteristic quantity and fitting a centrifugal bias baseline sequence; extracting a high-frequency nonlinear response component, a stick-slip high-frequency vibration energy ratio and a non-contact area segment signal variance; using the nonlinear increment change of the tire wall tension to reconstruct the order of rheological calculus and compensate for the amount of intermodulation distortion, to generate a net response signal sequence; constructing a time-varying reliability attenuation flow form constraint inversion weight to solve and generate tire body structure dynamic stiffness characteristic parameters. The present application solves the problem of nonlinear response intermodulation distortion caused by centrifugal bias drift and transient impact, and improves the inversion accuracy of dynamics parameters under complex excitation.

The invention is suitable for the technical field of underwater target detection, and provides a broadband signal DOA estimation method based on fast sparse Bayesian learning. The method comprises the following steps: acquiring frequency domain array receiving signal data and establishing a sparse signal model; constructing a Bayesian probability model based on the sparse signal model; deducing a fixed point updating formula of the hyper-parameter, and performing parameter iterative calculation by using the formula to obtain estimation of a sound source signal space energy distribution vector; according to the method, by using the fixed point updating formula, the convergence rate of parameter iteration of sparse Bayesian learning is remarkably increased, and the real-time processing capacity of the technical scheme is effectively improved; by using parameterized signal variance in sub-band signal prior distribution modeling, effective description of spatial sparsity of different sub-band signals is realized, and the frequency band adaptability is improved; the angle refinement processing based on the marginal likelihood maximum criterion is designed for optimizing the DOA coarse estimation, the DOA estimation with higher precision is obtained, and the DOA estimation precision is effectively improved.

The invention discloses a signal abrupt change fault threshold calculation method and a channel switching control process thereof. The method comprises the following steps: step 1, detecting the type of an input signal; step 2, scheme selection; 3, according to the variances of the signals to be detected calculated by different schemes, determining a composite variance and setting a signal abrupt change fault threshold value; and 4, according to the abrupt change threshold obtained in the step 3, when an abrupt change fault switching condition is met, stopping using the signal and switching to a standby signal channel, and completing signal abrupt change fault alarm and channel switching. The technical problem to be solved by the invention is to provide the signal abrupt change fault threshold calculation method and the channel switching control process thereof, so that the detection and identification of sensor signal abrupt change faults are realized, the inherent problems of a traditional signal abrupt change fault identification mode are solved, the system reliability and the effectiveness of output signals are ensured, and the reliability of the system is improved. And the working performance of the system is optimized.