169 results about "Waveform sampling" patented technology

The invention relates to a load fault diagnosis detection method and a device thereof. Rise phase current waveform sampling is driven through a load, comparison with a preset threshold is carried out, logic overturn of a diagnosis signal which exceeds the threshold occurs, a time of a logic overturn moment of the diagnosis signal is detected, the time and a preset benchmark value are contrasted and analyzed, hereby whether the load has short circuit, circuit break and load over-limit faults is determined, and simultaneously, whether the short circuit fault is short circuit to ground or short circuit to a power supply can be determined; through measuring time of drive starting moment and ending moment of diagnosis signal overturn, comparison with a driving signal is carried out, and hereby whether pulse width output of the driving signal has abnormity or not is determined. According to the method, only a diagnosis circuit is added based on a load performer driving circuit, additional cost is low, protection and control reliability in a load performer driving process can be raised substantially, and a foundation for load driving analysis and fault diagnosis is established.

The invention discloses an intelligent electrocardiogram classification method. The invention performs 3 Hz and 4 Hz sampling on the raw data of I lead ECG waveform sampling points with a sampling rate of 500 Hz in 30 seconds, and performs 3 Hz sampling on the raw data of the I lead ECG waveform sampling points. 45 Hz band-pass filtering, R-peak recognition algorithm based on wavelet transform isused to extract R-peak position, extracting a heart beat template of 195 sampling points based on the R peak position, and then extracting the maximum amplitude of the ECG waveform, Minimum, Mean, Logarithmic entropy, amplitude and position of PQRS complex and other traditional ECG signal characteristics, using the original ECG waveform, corresponding classification labeling, gradient descent training deep residual network superposition long-short time memory network to extract the deep-level features of ECG waveform, and finally input all the extracted features into the stochastic forest model for classification and diagnosis. The invention can largely remove noise interference, reduce the dependence on artificial feature identification, and greatly improve the speed and accuracy of electrocardiogram classification.

Systems and methods are provided for ultrafast optical waveform sampling based on temporal stretching of an input signal waveform. Temporal stretching is performed using a time lens based on four-wave mixing in a nonlinear medium. The signal is passed through an input dispersive element. The dispersed signal is sent into the time lens, which comprises a chirped pump pulse and a nonlinear medium. The chirped pump pulse is combined with the signal. The four-wave mixing process occurs in the nonlinear device or medium, which results in the generation of a signal at a new optical frequency (idler). The idler is spectrally separated from the signal and pump pulse using a bandpass filter and sent into an output dispersive element. The output dis persive element is longer than the input dispersive element and the temporal stretching factor is given by the ratio between the dispersions of these two elements.

There are disclosed an inspection system for and a method of confirming the soundness of a transported object while the object is being transported and handled. Waveform sampling sensors are mounted on the transported object and a transportation container, and a GPS and a CCD camera are mounted on a transportation vehicle. Sampled data are sent via the Internet Web in real-time to a remote transported object monitoring office at all times while the object is being transported based on bidirectional communications between the transportation vehicle and the remote transported object monitoring office. Various items of information including generated accelerations, times at which the accelerations are generated, raw waveforms of the accelerations are successively displayed on a traveling route on a map displayed on a personal computer for bidirectional confirmation of the soundness of the transported object in real-time.

The invention discloses a method for recognizing the fault type of a feed line of a power distribution line, and the method comprises the steps: obtaining waveform sampling data, and sequentially carrying out the Hilbert-Huang transformation and band-pass filtering of the waveform sampling data; reconfiguring a time frequency matrix according to the band-pass filtering data, solving the singular value of the time frequency matrix, and forming feature vector matrixes; carrying out the normalization processing of all feature vector matrixes, and enabling the feature vector matrixes to serve as the input samples of a multi-stage support vector machine after normalization processing, so as to recognize the fault type of the feed line of the power distribution line. The invention also provides a device for recognizing the fault type of the feed line of the power distribution line. The multi-stage support vector machine is good in performance, is clear in logic, is simple, and can recognize four types of power grid faults: single-phase grounding faults, two-phase grounding faults, two-phase short-circuit faults and three-phase short-circuit faults. The method provided by the invention is stronger in adaptive capability, and still has a high recognition rate of fault types under the impact of noise.

The invention relates to a power distribution network feeder fault type identification method and device. According to the method and device, the transient information of post-fault related electric quantity is mainly utilized to realize fault identification. The method includes the following steps that: waveform sampling data are obtained at first, local feature scale decomposition, Hilbert transform and band-pass filtering are sequentially performed on the waveform sampling data, so that a reconstructed time-frequency matrix can be obtained; and the singular spectrum of the time-frequency matrix is solved, and the distribution parameters of the singular spectrum are extracted so as to construct feature vector matrixes, normalized processing is performed on all the feature vector matrixes, and the normalized feature vector matrixes are adopted as the input samples of a multi-level support vector machine, so that feeder fault type identification can be realized. The power distribution network feeder fault type identification method and device of the invention have the advantages of high fault type recognition accuracy and strong adaptability under conditions such as a noise interference condition.

The invention provides a method for recognizing over-voltage fault types of a power distribution network. The method comprises the following steps: acquiring zero sequence voltage waveform sampling data x(t); taking an IMF (Intermediate Frequency) signal with a maximum correlation coefficient C as a principle mode component y(t); calculating a marginal spectrum h(omega) of the y(t); judging that a frequency dividing resonance fault occurs when an energy ratio of a frequency band of 10-40Hz in h(omega) exceeds 50%; judging that a high-frequency resonance fault occurs when an energy ratio of a frequency band of 100-150Hz in h(omega) exceeds 50%; otherwise calculating overall similarity S of the x(t) and standard single-phase earth fault zero sequence voltage waveform; judging that a fundamental resonance fault occurs when S is smaller than a preset similarity threshold K, otherwise judging that a single-phase earth fault occurs. The method disclosed by the invention is capable of automatically recognizing the over-voltage resonance type and the single-phase earth fault, simple in algorithm, less in calculation time, high in anti-jamming capability and high in recognition accuracy.

The invention discloses a wave control array ultra-wideband radar antenna array based on array element feed waveform control, and belongs to the field of ultra-wideband radar technology. In free space, beam scanning is achieved through digital sampling delay. In complex electromagnetic scattering environments, beam scanning or focusing is achieved by changing the excitation waveform of each array element. The excitation waveform of each array element is generated first by obtaining a source waveform by A/D sampling and then by D/A after transform processing in a digital domain. The wave control array radar of the invention does not need a radio frequency phase shifter for beam scanning, but generates the focusing beam and achieves the focused beam scanning in the time domain or the ultra-wideband range by controlling the excitation waveform of each array element and the waveform sampling delay thereof. The antenna of the invention belongs to an ultra-wideband radar array, and the coverage of the working frequency range approaches the highest frequencies of DC to A/D and D/A digital sampling signals.

The present invention relates to a compression system and method for electrical power system periodic waveform synchronous sampling data, wherein the system comprises a synchronous sampling unit, a comparator, a synchronous frequency doubling unit, a time-mark generator, a data-processing unit and a encode unit; the method comprises the following steps: 1. convert periodic signal to square wave signal through the comparator; 2. generate synchronous sampling pulse through the synchronous frequency doubling unit and the synchronous sampling unit performs synchronous digifax conversion under synchronization of synchronous sampling pulse and inputs convertion result into the data-processing unit, meanwhile, the time-mark generator records start moment of each frequency current and input time mark into the data-processing unit; 3. preliminary treatment to information through the data-processing unit; 4. encode to form a compressed file through the encode unit. The present invention performs preliminary treatment to sampled data before encode, enhances compression ratio, realizes high sampling frequency, overcomes influence to non-integer-period sampling, and improves data consistency.

The invention discloses a digitized high-power microwave diode reversed dynamic waveform and loss power testing system. The system comprises a low-noise power circuit used for providing direct voltage and applying reverse bias voltage to a diode to be tested, a forward adjustable current source circuit used for providing multiple adjustable forward currents for the diode to be tested, an edge adjustable pulse generating circuit used for providing multiple adjustable pulse modulation signals for the diode to be tested, a reverse dynamic current and voltage waveform testing and peak detection circuit used for acquiring the reverse dynamic current and voltage waveforms and peak signals of the diode to be tested, a dynamic current waveform sampling circuit used for processing the peak signals into analog signals, a C parameter testing circuit used for measuring the ratio of reverse peak voltage to bias voltage, and a central processing unit used for data processing. According to the system, by detecting performance parameters, including the reverse dynamic current, reverse dynamic voltage and loss power, of the diode, the purposes of well selecting and using the diode and improving the reliability of the diode are realized.

From a storage device storing waveform sample data sampled at a given sampling rate, the waveform sample data are read out asynchronously with the given sampling rate and temporarily stored in an input buffer. The thus-stored waveform sample data are read out from the input buffer and converted to an inner sampling rate selected from among a plurality of predetermined inner sampling rates. Predetermined arithmetic processing is performed on the waveform sample data converted to the selected inner sampling rate. Then, the waveform sample data having undergone the predetermined arithmetic processing are converted to a predetermined output sampling rate and written into an output buffer. The thus-written waveform sample data are then sequentially read out at an sampling frequency corresponding to the output sampling rate, to thereby generate tone data in accordance with the output sampling frequency.

The invention discloses a laser ranging method and a laser ranging device based on synchronous sampling and multiple phase measurement. A system sends out a starting signal so that a laser device driving unit generates a semicontinuous sinusoidal signal with period being integral multiples of a global clock, the semicontinuous sinusoidal signal is used for modulating intensity of emitted laser, echo light reflected by a target is converted into an electric signal and then sampled by an analog-to-digital converter driven by the global clock, and a modulation frequency and a sampling interval are in a homologous frequency multiplication relationship. Number of the global clocks between a rising edge of the starting signal to a previous moment of a first echo waveform sampling point is recorded to obtain a rough distance, the sampled digital waveforms are subjected to Fourier transform, phase difference between echo sinusoidal waveform to a previous moment of its arrival is obtained by utilizing frequency domain phase discrimination, multiple phase measurement is carried out on echoes after adopting oversampling and series decomposition to increase precision, the average of phase measurement results is converted into precise distance according to a sinusoidal modulation period, and distance information is derived by adding the rough distance to the precise distance. The laser ranging method and the laser ranging device have the advantages of being free of ranging distance limitation, easy hardware implementation and the like.

The invention provides a control method of a DC brushless machine, comprising a voltage waveform sampling circuit, an inverter and a microcontroller, wherein the method comprises: detecting the back electromotive force voltage waveform of the DC brushless machine in certain given rotating speed, storing the voltage waveform data in the microcontroller, building and storing the proportional relation data of the rotating speed and the back electromotive force by the microcontroller; after outputting the voltage from the inverter to the DC brushless machine, sampling the rotating speed in the DC brushless machine by the voltage waveform sampling circuit and the voltage waveform data outputted by the inverter, and transmitting to the microcontroller; computing the corresponding back electromotive force voltage waveform of the DC brushless machine and the error threshold range in the current rotating speed by the microcontroller, and judging whether the voltage waveform data input by the inverter exceeds the error threshold range, if true, the microcontroller emits the control signal to the inverter. The DC brushless machine of the invention has small vibration and advanced efficiency.

The invention relates to implementable medical equipment with a ventricular tachycardia diagnosis function. According to the implementable medical equipment with the ventricular tachycardia diagnosisfunction, ventricular tachycardia counting and template matching algorithms are combined, and heart rate suddenness judgement and multistage ventricular tachycardia counting thresholds are set; ventricular tachycardia counting is conducted if a sudden heart rate is acquired, and when a ventricular tachycardia count value reaches a sampling threshold, QRS waveform sampling is conducted; when the ventricular tachycardia count value reaches a matching threshold, sampled real-time heart rate QRS waveforms match sinus heart rate QRS waveform templates; and when the ventricular tachycardia count value reaches a diagnosis threshold, if the number of sampled QRS waveforms which cannot match QRS waveform templates reaches a ventricular tachycardia threshold, ventricular tachycardia is diagnosed. Bymeans of the scheme, supraventricular tachycardia and ventricular tachycardia can be distinguished, so that therapeutic errors are prevented.

The invention provides a digital triggering system of an oscilloscope. An ADC module is used for converting analog signals from an analog channel of the oscilloscope into digital signals. A data reconstitution module is used for receiving four 8-bit 625 Mbps data streams output by the ADC module in parallel and outputting x(n) to an up-sampler module in a buffering storage mode. The up-sampler module is used for receiving signals x(n) of the data reconstitution module, so that pixel points of a screen and waveform sampling points are kept consistent. A digital comparator compares signals y(n) output by an up-sampler with a specified triggering threshold value ranging from 0 to 255, an output level is changed, and triggering signals are output. By the adoption of the scheme, the digital triggering system is completely achieved in an FPGA; by the adoption of the digital interpolation mode, the precision of time interval measurement is improved, triggering jittering of the oscilloscope is reduced, and the processing speed and the refresh rate are greatly increased under the circumstance that digital post-processing compensation for jittering is not needed.

Servo demodulation is performed by appropriately compensating for demodulation errors caused by fluctuation of the replayed signal amplitude of a phase servo pattern. A servo demodulating circuit comprises single waveform sampling means for reading into a register a single waveform of a servo burst signal included in a phase servo signal based on a sampling clock synchronized by clock synchronization means, single waveform digitizing means for calculating an amplitude value of the single waveform of the servo burst signal from peak and bottom of the single waveform of the servo burst signal read into the register, and for standardizing a value of the register based on an amplitude value of the single waveform of the servo burst signal, and phase information calculation means for calculating phase information of the single waveform of the servo burst signal based on the value of the register standardized by the waveform digitizing means.

The invention discloses a laser pulse transmission time widening and channel transfer function measuring method in a turbulent atmospheric channel, and belongs to the technical field of wireless laser communication. The method includes the steps that a narrow-band gauss optical pulse sequence is emitted to the turbulent atmospheric channel for transmission, detection and waveform sampling are carried out on the optical pulse sequence at a receiving end, and sampling data are stored as files; on the basis that the pulse waveform sampling data are subjected to ensemble average, ensemble average gauss pulse waveform parameters are calculated through the data fitting method, and then the transfer function of the turbulent atmospheric channel is solved. An analytical expression of the transfer function of the turbulent atmospheric channel can be obtained by using the method, so that theoretical analysis performed on characteristics of the turbulent atmospheric channel based on actual measurement data becomes easier.

The invention discloses a time-of-flight mass spectrometer electronics reading method which can improve time resolution and a quantitative performance of a time-of-flight mass spectrometer at the same time and is different from an electronics reading scheme of a traditional time-of-flight mass spectrometer by an ADC (Analog to Digital Converter) waveform sampling or a TDC (Time to Digital Converter) pulse time measuring. The pulse front edge time measurement and a pulse forming sampling scheme are combined; the front edge time measured by the TDC is corrected by amplitude information obtained by a middle and low speed ADC for sampling a formed pulse, so that a time resolution effect is obviously improved, and the quantitative performance of an apparatus is improved. According to the reading method, an expensive high-speed ADC (over 1G SPS (samples per second) ) chip is replaced by a pulse forming circuit and the middle and low-speed ADC (about 100M of SPS); a constant fraction discriminator (CFD) circuit in a traditional time-of-flight mass spectrometer reading electronics is replaced by utilizing direct pulse front edge screening, and apparatus cost is reduced when a performance is improved.

The invention discloses an identification method of a fault indicator installation phase sequence, which includes the steps of: monitoring a second pulse signal rising edge of a GPS clock signal; sending synchronization commands to indicators by a communication unit when the second pulse signal rising edge appears, and controlling each indicator to carry out voltage waveform sampling on respectively-installed phase sequence; and copying and reading sampling data of each indicator, and judging the phase sequence installed on each indicator according to a phase relationship of a three-phase voltage of an electric power system. The identification method of the fault indicator installation phase sequence uses the GPS clock signal to provide the uniform synchronization signal, uses the synchronization signal to control each indicator for the voltage waveform sampling, and judges the phase sequences where the indicators are according to the sampling waveforms. Therefore the method is automatically realized through the fault indicators all the way without human intervention. The efficiency is extremely high. The mistakes and errors possibly brought by manual operation are avoided. The information of the phase sequences where the indicators are is re-checked and corrected regularly and irregularly. The information correctness of the phase sequences where the indicators are is guaranteed.

One or more waveform generators impose an input waveform across touch sensor components. Phase shift detection logic measures a phase shift between the input waveform and an output waveform component associated with each waveform generator appearing across a touch sensor component. Amplitude ratio detection logic measures an output-to-input waveform magnitude ratio associated with each waveform generator. Translator logic converts the phase shift(s) and optionally the output-to-input waveform magnitude ratio(s) to capacitance values. Validation logic compares the resulting capacitance values to each other to determine consistency and validity during a particular waveform sampling time. The phase shift detection logic and the amplitude ratio detection logic are optionally implemented with discrete Fourier transform logic which includes narrow-band filtering to exclude electromagnetic interference frequency components from touch sensor signal measurements.

Various embodiments provide methods, apparatuses, and systems for sampling a waveform using relatively prime sampling methods. A waveform with a first plurality of cycles and a first frequency may be sampled with a waveform sampling device. Sampling the waveform may include sampling the waveform at a sampling rate of the waveform sampling device. Sampling the waveform may include taking a sample number of samples of the waveform where the sample number may be relatively prime with respect to the number of cycles of the waveform. The sample number of samples of the waveform may be interleaved with a controller.

The present invention provides a method for sampling and rebuilding waveform of oscillograph including: calculating the number of burst noise in each period of wave signal in oscillograph, sampling uniformly according to a certain frequency after signal passed a lowpass and storing in oscillograph. In the signal rebuilding process, firstly resuming burst noise by new arithmetic and resuming whole signal waveforms containing burst noise by rebuilding method of band-limited signal based on the known burst noise. The sampling rate can be reduced and the storage capacity of oscillograph is improved using periodicity of signal. The signal in oscillograph with burst noise isn't band-limited signal any more, and can't be resumed original signal exactly by classical sampling method of band-limited signal, so the new method of waveforms sampling and rebuilding is provided to resume original signal exactly and the method has a good robustness.

The signal source consists of controller, waveform sampling and processing module, program controlled clock module, frequency measurement and keyboard control module and LCD module. The controller is connected to waveform sampling and processing module, program controlled clock module, frequency measurement and keyboard control module and LCD module respectively. Input signal is inputted to waveform sampling and processing module and frequency measurement module. The program controlled clock module is connected to waveform sampling and processing module. Less key button are placed on panel. Besides frequency, amplitude of signal even waveform of signal can be displayed without need of using oscilloscope.

The embodiment of the invention provides a machine learning model training method and system for power load identification. Specifically, actually measured electrical parameter data is taken as a basis; basic electrical parameter data is unified in format, trained and input into a neural network model for continuous optimization; parameters of the model are continuously adjusted by verifying a data set so as to select an optimal model; meanwhile, the performance of the model is evaluated by utilizing the test data set; the optimal effect is achieved; and the model can be further applied to a power load identification system. According to the method, the model can be trained according to the input sampling data, so that the electric equipment in use can be identified according to waveform sampling data of specific voltage, current and active power; therefore, manual parameter adjustment and feature extraction are not needed, and the feature parameters required for identifying the powerload can be autonomously learned and automatically obtained, so that the application range of the model is expanded, and the accuracy of power load identification is improved.