293 results about "Instantaneous amplitude" patented technology

An RF signal source receives wideband digital signals representing the instantaneous amplitude of the desired RF, and ΣΔ converts to ternary ΣΔ signals. The ternary ΣΔ signals are delayed in a transversal filter and the delayed signals are converted to currents for application to a traveling-wave summer for producing the desired RF. The traveling-wave summer may be a transmission line. The currents may be weighted with a filter response in order to limit the bandwidth of the RF signals. The RF may be coupled out through an antialiasing filter. The digital clocks may be delayed to correspond with the delays of the combiner, to impart directionality to the summed signals.

The invention discloses a new method for performing fault line selection by using the characteristic of a non-power frequency transient state fault component for extracting single-phase fault zero-sequence current by using Hilbert-Huang transform (HHT). The method comprises the following steps: performing EMD decomposition on fault zero-sequence current of each feeder line by using the HHT to acquire an IMF component of the zero-sequence current of each feeder line; constructing a resolving signal according to Hilbert transform, solving instantaneous amplitude and frequency of the zero-sequence current of each feeder line, and eliminating a power frequency component in the instantaneous frequency of the zero-sequence current of each feeder line to acquire a non-power frequency fault component; and forming a line selection criterion according to the characteristics that the amplitude of the non-power frequency fault component of the fault line is greater than that of the non-fault line and the polarity of the fault line is opposite to that of the non-fault line. The line selection method is not affected by an arc suppression coil, and is applicable to non-grounded, arc suppression coil grounded and high-resistor grounded systems, overhead lines or cable lines and cable overhead mixed lines; and the line selection result shows that the method applied to low-current single-phase grounding fault line selection is accurate and reliable.

A multiattribute frequency division imaging method based on wavelet transformation comprises the following steps: 1) preprocessing the seismic data; 2) executing fine frequency spectrum analysis to the seismic data for confirming the effective frequency spectrum range of seismic data; 3) executing horizon interpretation to the seismic data, and executing processions of interpolation, smoothing, etc.; 4) using seismic data and interpreted seismic horizon as input, adopting a wavelet analyzing technique executing wavelet transformation to the seismic data for obtaining a wavelet transformation frequency division result; 5) computing the instantaneous amplitude, instantaneous frequency or instantaneous phase of wavelet field under the meaning of frequency division (ai-1, ai) according to interpretation requirement; 6) reconstructing the instantaneous amplitude, instantaneous frequency or instantaneous phase data of wavelet field according to interpretation requirement; 7) repeating the steps of 4) to 6) to each seismic channel for obtaining the computing results of all seismic channels; and 8) drafting the computed instantaneous wavelet frequency division data. The method of the invention can increase the recognition capability and detection capability of lithologic trap, stratigraphic trap and small discontinuous object for further increasing the fine prediction precision of container horizon.

The invention provides a gear fault diagnosis method based on improving multivariable predictive models. The method comprises the following steps: measuring the vibration signal of a fault object; extracting a fault characteristic value from the vibration signal, namely the instantaneous amplitude entropy of local characteristic scale decomposition; dividing the fault characteristic value into a training sample and a test sample; respectively carrying out training of multivariable predictive models based on a support vector regression machine method to the training sample to establish the best variable predictive model, and classifying the test sample according to the best variable predictive model; and distinguishing the operating state and the fault type of the fault object according to the classification result. The gear fault diagnosis method based on improving the multivariable predictive models has higher resolution in the model recognition process.

The present invention relates to a device (22) for processing input signals (34a, 34b) related to a vital sign of a subject (10), comprising an interface (24) for receiving a non-invasively detected input signal (34a, 34b), a feature extraction module (26) for extracting at least one feature of the input signal (34a, 34b), said at least one feature including an instantaneous frequency representation (40a, 40b) of the input signal (34a, 34b) and/or an instantaneous amplitude representation (42a, 42b) of the input signal (34a, 34b), a processing module (28) for determining a signal information content parameter (52) for the input signal (34a, 34b) based on the at least one extracted feature, said signal information content parameter (52) being indicative of information on a vital sign of the subject (10) included in the input signal (34a, 34b) and a combination module (30) for combining a plurality of input signals based on the signal information content parameters (52) of the plurality of input signals into a combined output signal characterizing the vital sign of the subject (10). The present invention further relates to a corresponding method and to a monitoring system (12) for remotely monitoring a vital sign of a subject (10).

The invention provides an electrocardiosignal data acquisition and processing method and system. The electrocardiosignal data acquisition and processing system comprises a data processing unit which comprises a data analysis unit. The data analysis unit comprises an RR interphase preprocessing unit and an instantaneous feature extraction unit. The RR interphase preprocessing unit is used for preprocessing an RR interphase sequence, so that a signal bandwidth of an RR interphase is enabled to fall in a frequency bandwidth requested by the best amplitude frequency features of the Hilbert transform. The instantaneous feature extraction unit is used for extracting instantaneous amplitude features and instantaneous frequency features of the RR interphase sequence. The electrocardiosignal data acquisition and processing system has the advantages that through acquisition and processing of the electrocardiosignals, complexity of data analysis is reduced, and amount of calculation is decreased; the system is simple in design, low in cost and convenient to apply.

The invention discloses a fault diagnosis method for a rolling bearing based on LCD-MF (Local Characteristic Scale Decomposition)-(Multifractal). The fault diagnosis method comprises the following steps: firstly, obtaining a plurality of intrinsic scale components (ISC) by using LCD, and selecting useful ISCs to respectively calculate Teager energy operators (TEO) to obtain instantaneous amplitude and instantaneous frequency corresponding to each ISC; secondly, analyzing the instantaneous amplitude of each ISC by using multifractal detrended fluctuation analysis (MFDFA), and extracting a generalized Hurst index of each ISC as the multifractal characteristic of each ISC; thirdly, performing dimension reduction by using principal component analysis (PCA) and taking a result obtained by the PCA as a fault feature vector; acquiring a vibration signal of the rolling bearing during work under variable working conditions in real time, processing the acquired vibration signal according to the steps to obtain a corresponding fault feature vector M, and identifying the fault feature vector to realize fault detection and fault positioning of the rolling bearing.

A method is provided for determining a threshold (81, 82) for spike (12) detection in an electrophysiological signal (11). The method comprises a step of determining an estimated envelope (31) of the electrophysiological signal (11), a step of, based on the estimated envelope (31), determining an estimated Gaussian noise, a step of determining a distribution (51) of instantaneous amplitudes of the estimated Gaussian noise, a step of determining a mode (61) of the distribution (51) of instantaneous amplitudes, and determining the threshold (81, 82) based on the mode (61) of the distribution (51) of instantaneous amplitudes.

The invention discloses a novel digital demodulation identification method based on generalized fractional fourier transform (GFRFT) and fractional lower Wigner-Ville distribution (FLOWVD). The novel digital demodulation identification method comprises the steps as follows: received signals are sampled firstly, and then complex envelopes of the signals are restored through Hilbert transform; the maximum value of zero center normalization instantaneous amplitude spectrum density for the signals based on the GFRFT is calculated; the maximum value of the FLOWVD amplitude for the signals is calculated and is taken as characteristic quantity; and in addition, a sorter based on a decision tree is adopted, and the signals adopting different demodulation manners can be identified through data processing, threshold setting and comparison decision. Under the non-gaussian Alpha stable distribution noise, the novel digital demodulation identification method achieves the performance that remarkably surpasses the traditional method, and also achieves higher identification rate and favorable robustness.

The invention provides a method for identifying digital modulation signals in the presence of complicated noise. The method comprises the following steps that fraction low-order rapid independent composite analysis is conducted on an observation vector x of a received signal, and the received signal is divided into a transmitting signal and Alpha stable distribution noise; ensemble average LMD based on interpolation is conducted on the separated transmitting signal s(n), and the transmitting signal is decomposed into multiple components; the first classification feature, namely the subsection instantaneous frequency standard deviation sigmaf of a component, is extracted, and a corresponding threshold delta 1 is set; a second classification feature, namely subsection instantaneous amplitude standard deviation sigmaa of the components, is extracted and a corresponding threshold delta1 and a corresponding threshold delta2 are set; a signal set {MSK, 2ASK, QPSK and 16QAM} is divided into two types, namely the signal set {MSK} and the signal set {2ASK, QPSK and 16QAM} through the threshold delta1, and signals in the signal set {2ASK, QPSK and 16QAM} are identified through the threshold delta 2 and the threshold delta3. By the adoption of the method for identifying digital modulation signals in the presence of complicated noise, the performance of identifying signals existing in a low-signal-to-noise-ratio environment in the presence of the Alpha stable distribution noise is high, and the stability is high.

The invention discloses a time-varying non-stable-signal time-frequency analyzing method. The method comprises the steps of providing definitions of single-frequency signals and i(th)-level local extremum, conducting single-frequency signal decomposition on the time-varying non-stable signals according to the definitions so as to obtain a plurality of single-frequency components, then obtaining a plurality of instantaneous frequency and instantaneous amplitude of the components by Hilbert, further, drawing the amplitude on a time-frequency plane in an amplitude-frequency-time three-dimensional space to obtain a Hilbert amplitude spectrum, and conducting integration on the time through the amplitude spectrum to obtain a Hilbert marginal spectrum. According to the method, the complete adaptability is realized; meanwhile, the method is not restricted by uncertainty principle, and the time and the frequency have high resolution ratio; the decomposition is thorough, so that the respectively independent single-frequency components, namely the substantive characteristics, are obtained, and cross terms among all the components do not exist; and the calculating time is saved as the accelerating method is adopted in the decomposition process.

The invention discloses a wind generating set wind wheel unbalance monitoring method. The wind generating set wind wheel unbalance monitoring method comprises the steps of measuring vibration acceleration instantaneous values of two symmetrical points of the axis in a tower; carrying out weighting calculation and fast Fourier transform treatment on the two vibration acceleration instantaneous values to obtain corresponding frequency domain signals; enabling the frequency domain signals to pass through a peak value detector to obtain the instantaneous frequency and the instantaneous amplitude value signal at a vane doubled wind wheel rotating frequency; obtaining the base frequency at the vane doubled wind wheel rotating frequency according to a generator rotating speed signal and a gearbox speed change ratio; comparing the instantaneous frequency with the base frequency to obtain a difference value, giving an alarm and carrying out machine halt if the difference value exceeds a preset error threshold value range, judging whether the instantaneous amplitude value signal is within an amplitude threshold value range or not if the difference value is within the error threshold value range, and giving an alarm and carrying out machine halt if the instantaneous amplitude value signal is without the amplitude threshold value range. Whether unbalance occurs or not is calculated and judged according to vibration acceleration signals measured by a sensor in a cabin. The wind generating set wind wheel unbalance monitoring method is used for generator rotating speed signal checking, wind wheel unbalance monitoring and the like, and safe and effective operation of a set is guaranteed.

In a method to determine a control sequence for a magnetic resonance imaging system in order to acquire echo signal-based raw magnetic resonance data in k-space along one or more trajectories on the basis of the control sequence, the control sequence is optimized so that, to control a gradient magnetic field for at least a predetermined portion of the control sequence, a change of an attribute of the gradient magnetic field is limited. The limitation takes place so that a momentary amplitude change rate of the gradient magnetic field falls below a predetermined amplitude change rate limit value, and/or so that a momentary direction change rate of the gradient magnetic field falls below a predetermined direction change rate limit value, and/or so that a momentary gradient change rate of the gradient magnetic field that is based on a combination of the momentary amplitude change rate and the momentary direction change rate falls below a predetermined gradient change rate limit value.

The invention discloses an unconformity strong-reflection self-adaptive separation method based on matching pursuit. Firstly seismic data is leveled along an unconformity layer interpretation result, and overlapped to acquire an overlapped seismic trace, phase and frequency at the maximum instantaneous amplitude are selected through calculating by using three instantaneous attributes, and a waveform width parameter is scanned, so that the best matched atom is acquired. A spectral factorization method is introduced, the best waveform atom is acquired by matching pursuit to be mutually correlated with original seismic records, strong reflection is information is extracted and separated from the original seismic records, and thus the seismic records highlighting effective reflection of reservoirs are acquired. The method provided by the invention is high in computation efficiency and strong in stability, and provides an effective data base for subsequent reservoir prediction and fluid identification based on prestack seismic inversion.

The invention relates to a method for generating the P-wave time-domain high-precision converted wave profile in the geophysical prospecting, comprising the following steps: picking up the location of a selected index bed on the P-wave seismic profile and the converted wave seismic profile; calculating the compression ratio of the converted wave in relation to the P-wave; re-sampling and compressing, and transforming the converted wave time domain into the P-wave time domain; extracting the seismic attributes of the instantaneous amplitude, the instantaneous phase and the instantaneous frequency; calculating the cross-correlation coefficients of the compressed converted wave seismic attributes to evaluate the arithmetic mean value of the cross-correlation coefficients; determining the points with the cross-correlation coefficients greater than the threshold value as the changeable control points; evaluating the compression ratio of the converted wave between two adjacent control points, relative to the P-wave; and converting the seismic trace to obtain the high-precision conversion wave profile of the P-wave time domain. The invention makes use of the reflection intensity, instantaneous phase, instantaneous frequency and other attributes, and takes into account the advantages of the standard horizon method and the cross-correlation method so as to achieve highly precise and strongly reliable results.

The invention provides an arc suppression coil grounding system single-phase grounding line selection method. The method comprises a step of collecting bus three-phase voltage, bus zero sequence voltage and branch circuit zero sequence current of different times, a step of calculating the fundamental component instantaneous value u'0(t) of the bus zero sequence voltage according to the bus zero sequence voltage, and judging a phase with a fault according to the amplitudes of the bus three-phase voltage of different times and the fundamental component instantaneous value u'0(t), a step of calculating zero sequence current fundamental component instantaneous amplitude values I0M,x of different times and different positions of a branch circuit of a phase with the fault, and calculating the zero sequence current fundamental component instantaneous amplitude values I'0M, x of the branch circuit after the arc suppression coil compensation effect is completed, and judging whether the branch circuit has a grounding fault according to the zero sequence current instantaneous amplitudes before and after arc suppression coil compensation. A line with an arc suppression coil single-phase grounding fault can be accurately selected without the influences of a field and the polarity of a zero sequence current transformer, the accurate of line selection is effectively improved, the group judgment is not needed, and a line selection process is greatly simplified.

The invention relates to a low-voltage AC arc fault detection method based on characteristic mode component energy analysis. The method includes the steps: utilizing empirical mode decomposition for analysis to obtain an intrinsic mode function of a sampled current, decomposing the intrinsic mode function to obtain the characteristic mode component I1 of an original sequence, extracting the instantaneous amplitude distribution of the characteristic mode component I1 through Hilbert transform, calculating energy of the half periods of the characteristic mode component I1, and identifying a series arc fault semi-cycle by comparing the energy of the half periods with a set reference threshold. Compared with the prior art, the method has the advantages of being high in identification, suitable for various loads, etc.

The invention discloses a pseudo-random code estimation method of a direct sequence spread spectrum system, which mainly overcomes the shortcomings of high requirement on sampling precision, great error of carrier frequency estimation and poor universality of the prior method. The invention comprises the following steps: 1. obtaining a pseudo code period and a symbol period from a secondary spectrum and a secondary moment of related functions; 2. determining a starting point of the pseudo code of a period according to an autocorrelation matrix of a received signal; 3. taking a pseudo code starting point as the starting point for the received signal, taking a plurality of pseudo code periods as window size for subsection, carrying out inherent time-scale decomposition on each section through carrier frequency and a first zero-crossing point frequency, and obtaining respective instantaneous amplitude value; 4. subtracting one instantaneous amplitude value from the other instantaneous amplitude value to obtain a difference signal, and making a column diagram to set two thresholds; 5. recording the moment when a minimum point is lower than a smaller threshold in a pseudo code period ofthe difference signal; and 6. setting the polarity of starting code strip, determining the polarity of each code strip from the second code strip, and determining the pseudo code according to pseudocode property. The invention has the advantages of high estimation precision and strong universality, and is used for monitoring frequency spectrum.

The invention discloses a nonlinear frequency modulation component decomposition-based time-frequency domain modal parameter identification method comprising steps of (1) acquiring a dynamic response signal of a to-be-identified structure and setting sampling time and frequency, (2) building a redundancy Fourier model including the response signal, instantaneous frequency and instantaneous amplitude via Fourier series, (3) extracting instantaneous frequency information of the response signal according to generalized parameterization time-frequency transform, (4) extracting instantaneous amplitude information of the response signal according to a regularized least square method according to the acquired instantaneous frequency information, (5) conducting structure modal parameter identification via a linear least square fitting algorithm based on the instantaneous frequency information and slope information of the instantaneous amplitude logarithm and (6) analyzing errors of an identification result. Signal analysis and modal parameter identification are conducted according to a structure vibration response signal, so simple and easy use can be achieved; modal parameter identification precision for high-compact modal structures can be effectively improved; and strong adaptability and interference resistance capacity can be provided.