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1610 results about "White noise" patented technology

In signal processing, white noise is a random signal having equal intensity at different frequencies, giving it a constant power spectral density. The term is used, with this or similar meanings, in many scientific and technical disciplines, including physics, acoustical engineering, telecommunications, and statistical forecasting. White noise refers to a statistical model for signals and signal sources, rather than to any specific signal. White noise draws its name from white light, although light that appears white generally does not have a flat power spectral density over the visible band.

Apparatus and method of in-service audio/video synchronization testing

An apparatus and method provide non-intrusive in-service testing of audio/video synchronization testing without using traditional audio marker tones. The network includes an A/V synchronous test signal generator which injects video and audio markers into the video and audio non-intrusively and routes the two signals into a switch where they are switched into a channel for encoding and transmission via the ATM network. At the distant end the signal is decoded and routed by a switch into the A/V test generator and measurement set where the markers are detected and the A/V skew calculated, after which the audio and video are routed to the subscriber. The A/V test set signal generator includes a Video Blanking Interval (VBI) test signal generator and a white noise generator, the former injecting a marker into the video signal and the later injecting an audio marker into the audio signal. The video marker is injected into the VBI and broadband, background audio noise to measure the delay between the audio and video components of a broadcast. The marking of the audio is accomplished by gradually injecting white noise into the audio channel until the noise level is 6 dB above the noise floor of the audio receiver. As a precursor A/V sync signal, a small spectrum of the white noise is notched or removed. This signature precludes inadvertent recognition of program audio noise as the audio marker.
Owner:IBM CORP

Extraction method for early failure sensitive characteristics based on ensemble empirical mode decomposition (EEMD) and wavelet packet transform

InactiveCN103091096AGuaranteed Adaptive Accurate PartitioningAdaptive Precise Partition PreciseMachine gearing/transmission testingMachine bearings testingNODALDecomposition
The invention relates to an extraction method for early failure sensitive characteristics based on ensemble empirical mode decomposition (EEMD) and wavelet packet transform. The extraction method for the early failure sensitive characteristics based on the EEMD and the wavelet packet transform includes the following steps: (1), collected original vibration signals of mechanical and electrical equipment are decomposed according to the EEMD, white noise is added, and intrinsic mode function (IMF) components are obtained through decomposition; (2), the sensitive IMF components closely related to failure are chosen, and other irrelative IMF components are ignored; (3), the sensitive IMF components chosen through step (2) are decomposed in an orthogonal wavelet packet mode, and a wavelet coefficient of each node is obtained; and (4), envelopes are extracted from the obtained wavelet packet coefficients by adoption of the Hilbert transform and the Fourier transform, power spectrums are calculated, the power spectrum corresponding to each wavelet packet coefficient is obtained and serves as the early failure sensitive characteristic , and the sensitive characteristics are automatically obtained. Self-adapting signals can be decomposed, the sensitive characteristics can be convenient to obtain automatically, diagnosis precision and speed are improved, and a mechanical and electrical system can be diagnosed quickly, accurately and stably. The extraction method for the early failure sensitive characteristics based on the EEMD and the wavelet packet transform can be applied to the field of mechanical and electrical equipment failure diagnosis.
Owner:BEIJING INFORMATION SCI & TECH UNIV

Diagnosis method for fault position and performance degradation degree of rolling bearing

The invention discloses a diagnosis method for the fault position and the performance degradation degree of a rolling bearing, belonging to the technical field of fault diagnosis for bearings, and solving the problems of low accuracy of diagnosis for fault position and performance degradation degree, and high time consumption of training existing in an intelligent diagnosis method for a rolling bearing in the prior art. A white noise criterion is added in the disclosed integrated empirical mode decomposition method, so that artificial determination for decomposition parameters can be avoided, and the decomposition efficiency can be increased; and via the disclosed nuclear parameter optimization method based on a hypersphere centre distance, the small and effective search region of nuclear parameters in a multi-classification condition can be determined, so that training time is reduced, and the final state hypersphere model of a classifier is given. The intelligent diagnosis method based on parameter-optimized integrated empirical mode decomposition and singular value decomposition, and combined with a nuclear parameter-optimized hypersphere multi-class support vector machine based on the hypersphere centre distance is higher in identification rate compared with the existing diagnosis method. The diagnosis method disclosed by the invention is mainly applied to intelligent diagnosis on the fault position and the performance degradation degree of the rolling bearing.
Owner:HARBIN UNIV OF SCI & TECH

Wind turbine generator bearing fault diagnosis method under variable speed

InactiveCN105784366AEliminate the effects of analysisImprove accuracyMachine bearings testingElectricityBearing vibration
The invention discloses a wind turbine generator bearing fault diagnosis method under the variable speed. According to the method, a rotation angle change curve is drafted according to the bearing rotation speed; equal angle division for the rotation angle change curve is carried out, and an equal angle re-sampling time sequence is determined; interpolation for a bearing vibration signal is carried out according to the time sequence, a random Gauss white noise sequence is added to an angle domain vibration signal, and the signal added with the white noise is processed by utilizing an empirical mode decomposition (EMD) algorithm to acquire multiple sets of IMF; a kurtosis value of each IMF component is calculated; the IMF with the largest kurtosis value is selected and taken as a sensitive IMF; Hilbert envelope demodulation for the sensitive IMF is carried out to obtain an envelope signal, the envelope signal is processed by utilizing Fourier transform to obtain order envelope spectrum of the sensitive IMF, the fault characteristic frequency is extracted, and fault diagnosis on the wind turbine generator bearing is realized. The method is advantaged in that influence of rotation speed change on vibration signal analysis can be eliminated, and accuracy and validity of fault diagnosis can be improved.
Owner:NORTH CHINA ELECTRIC POWER UNIV (BAODING)

Gaussian process regression-based method for predicting state of health (SOH) of lithium batteries

The invention discloses a Gaussian process regression-based method for predicting state of health (SOH) of lithium batteries, relates to a method for predicting the SOH of the lithium batteries, belongs to the fields of electrochemistry and analytic chemistry and aims at the problem that the traditional lithium batteries are bad in health condition prediction adaptability. The method provided by the invention is realized according to the following steps of: I. drawing a relation curve of the SOH of a lithium battery and a charge-discharge period; II, selecting a covariance function according to a degenerated curve with a regeneration phenomenon and a constraint condition; III, carrying out iteration according to a conjugate gradient method, then determining the optimal value of a hyper-parameter and bringing initial value thereof into prior distribution; IV, obtaining posterior distribution according to the prior part; V, obtaining the mean value and variance of predicted output f' without Gaussian white noise; and VI, together bringing the practically predicted SOH of the battery and the predicted SOH obtained in the step V into training data y to obtain the f', then determining the prediction confidence interval and predicting the SOH of the lithium battery. The method provided by the invention is used for detecting lithium batteries.
Owner:HARBIN INST OF TECH

Multicarrier transmitter and multicarrier receiver

A multicarrier transmitter and a multicarrier receiver both enabling improvement of the reception characteristic of hierarchical modulation multiplex communication. A base station (100) transmits a multicarrier signal which is a superposition of a modulated signal addressed to a far user and a modulated signal addressed to a near user and modulated with a modulation multivalued number different from that of the modulated signal addressed to the far user. The base station (100) comprises a DFT section (120), an S/P section (125), and a combining section (145). The DFT section (120) separates the modulated signal addressed to the far user into signals in frequency ranges for each symbol. The S/P section (125) serial/parallel-transforms the modulated signal addressed to the near user to generate N1 parallel signals. The combining section (145) combines the N1 frequency components generated by the DFT section (120) and the N1 parallel signals. Since the far user receiver can change the modulated signal addressed to the near user into white noise by IDFT, the far user receiver can demodulate the modulated signal addressed to the far user with high accuracy. The near user receiver can acquire the signal addressed thereto with high accuracy by subtracting the modulated signal addressed to the far user, which is an interference signal, from the received signal.
Owner:PANASONIC CORP
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