38 results about "Wave group" patented technology

The invention discloses a navigation X-wave-band radar wave group detection method based on a wave theory. The navigation X-wave-band radar wave group detection method based on a wave theory includes the steps: performing empirical orthogonal function decomposition on a navigation X-wave-band radar image sequence, selecting a main modal to reconstruct a wave field, and obtaining the wave surface displacement of different positions in an observed sea area through scaling of the wave field; for the wave surface of one radial direction, utilizing change of the displacement gradient to detect the extreme value of the wave surface, and according to the difference between adjacent two maximum values and minimum values, obtaining the wave height at different positions; utilizing the sum of two sine functions to fit the wave surface between the adjacent minimum wave heights, and identifying the area as a wave group when the fitting parameter accords with the wave theory; and combining with observations at different times, obtaining the parameters of the wave group, such as amplitude, length and group speed. The navigation X-wave-band radar wave group detection method based on a wave theory utilizes the buoy to perform scaling on the main modal of the wave field so as to obtain the wave height information of a large area in the observed sea area, and then utilizes the two sine functions which are approximate in frequency based on the wave theory to determine the wave group without requirements for selecting a threshold according to the experience, and has the advantages of being simple and easy to do, and being wide in the application range.

The invention discloses a method for detecting electrocardio waveform characteristics based on a wavelet transform and logistic regression algorithm. Except for the waveform range, a wider characteristic is applied to judge if one waveform is a real QSR; these characteristics are energy value within a specific frequency scale after wavelet transform, interval with previous suspected QRS wave, width of suspected QPS wave, voltage change rate of suspected QRS wave; every suspected QRS wave is compared with an expert marked QRS wave to judge if they are real QRS waveform; a characteristic matrixacquired in the previous step is input to a logic regression model, the gradient descent is the optimized method; a learning rate is set as 0.02 and iterated until convergence; the training adopts themethod of 1-remained intersection verification. The algorithm can acquire authority of every characteristic parameter from the massive marked patient data, thereby avoiding the demand for a manual definition rule; the algorithm can identify the QRS wave group while acquire reliable signal quality information; thus the method is helpful to reduce the wrong alarm.

The embodiment of the invention provides a method and device for matching longitudinal waves with transverse waves for geophysical exploration. The method includes the steps that full wave train well logging data are utilized to determine phase and wave group characteristics of the longitudinal waves and converted transverse waves in a main interface; well control processing is respectively conducted on the longitudinal waves and the converted transverse waves; the polarity of the longitudinal waves and the polarity of the converted transverse waves are compared and phase correction is conducted; a data body including the speed ratio of the longitudinal waves to the transverse waves is acquired according to prestack data of the longitudinal waves; the data body is utilized to convert the converted transverse waves from the time domain of the converted transverse waves to the time domain of the longitudinal waves; identification layers which can stably track in whole research area are picked up and utilized to conduct fine horizon correction on the converted time domain of the converted transverse waves. The method and device are combined with seismic data processing, forward modeling, inversion and other measures, so that the method and device are reliable in theory and rigorous in process, and the longitudinal waves and the transverse waves can be well matched.

The invention provides a QRS wave group detection method and device. The method comprises that a multi-lead electrocardio signal to be processed is obtained; first characteristic information is extracted from the multi-lead electrocardio signal; and a QRS wave group area of the multi-lead electrocardio signal is determined according to a pre-trained QRS wave group detection model and the first characteristic information. Thus, the QRS wave group area is determined more accurately, and accurate basis is provided for subsequent analysis based on the QRS wave group area.

The invention belongs to the field of seismic inversion technologies and discloses a method for determining a nonlinear mapping relationship between a wave impedance logging curve and a seismic waveform. According to the method, on the basis of establishing corresponding relationships between different frequency logging data and sedimentary microfacies, frequency division is performed on seismic data through a designed frequency division parameter, seismic frequency division attributes are extracted, and seismic attribute data bodies of different frequency bands are generated; frequency division layer calibration is performed, the wave group characteristics of a wave impedance curve are compared with trace integration profiles of different frequency bands, fine tuning is performed, and stretching and compression processing is performed; and after the frequency division attributes are extracted, a support vector machine is used to establish nonlinear mapping relationships between the seismic frequency division attributes and the logging data. The method of the invention can combine the advantages of the high vertical resolution of logging data and the wide lateral extension of seismic data, improve the resolution of the seismic data, provide a basis for explaining the distribution of sedimentary facies and the development of underground sediments in seismic profiling and providea new inversion technique for approximating a geological real model.

The invention discloses a method for detecting SAR (stop and reveres) image wave group parameters based on contourlet conversion, which comprises the specific steps: firstly, SAR scattering images are converted to a wave height chart through an SAR wave spectrum inversion algorithm; next, the contourlet conversion is carried out on the wave height chart to detect the edges of the wave height; the edges of the wave height are connected by adopting a block extraction algorithm and smooth treatment so as to obtain a wave group region with communication; finally, the parameter detection is carried out on the wave group region with the communication as follows: the propagation direction of the wave group is determined through the wave group direction spectrum and the wind direction, and the effective wave height and the wave length of the wave group are calculated through the wave group direction; and the wave peak length of the wave group is calculated by adopting a threshold value method. in the invention, the method for detecting the SAR image wave group parameters based on the contourlet conversion is adopted to carry out the further image treatment on the SAR images so as to obtain more wave group parameters, and the method is suitable for the complicated wave group parameter detection in the SAR images.

Provided is a guided wave and fuzzy algorithm-based functional gradient structural material characteristic inversion method. The guided wave group velocity is obtained through forward calculation by applying a Legendre polynomial method, and a volume distribution function of FGM structural material parameters is subjected to inversion identification by means of a fuzzy algorithm. The method comprises the specific steps that 1, a given FGM structure volume distribution function is selected, and the guided wave group velocity in the FGM structure is obtained through a Legendre polynomial method; 2, a fuzzy controller is designed through a genetic algorithm; 3, a fuzzy controller parameter optimization program based on the genetic algorithm is written; 4, a fuzzy controller program is written; 5, multiple guided wave group velocities at the FGM structure low order mode low frequency are input into the designed fuzzy controller, and the FGM material characteristics are output through inversion. Accordingly, an error between a parameter value in the FGM structure volume distribution function obtained through inversion and a set value is very small, and an effective method is provided for FGM material characteristic detection.

The invention provides a vertical seismic multi-wave data based predication method and system for depth before drilling. The vertical seismic multi-wave data based predication method for the depth before drilling comprises collecting vertical earthquake multi-wave data; processing the vertical earthquake multi-wave data and obtaining the processed vertical earthquake multi-wave data; obtaining an over well ground seismic section; identifying a target stratum position and wave group characteristics on the over well ground seismic section; identifying a direct wave, a converted wave and a reflected wave lineup which is corresponding to the target stratum from the processed vertical seismic multi-wave data according to the target stratum position and the wave group characteristics; establishing a time-depth function according to the direct wave, the converted wave and the reflected wave lineup; confirming the prediction depth and the prediction time according to the time-depth function. The vertical seismic multi-wave data based predication method for the depth before drilling solves the problem that the existing predication method is not enough in accuracy and timely and accurately provides the depth data before drilling of the target stratum for the drilling engineering.

The invention provides a formation overlap-denudation line recognition method based on iteration earthquake DNA detection. The formation overlap-denudation line recognition method includes the steps of firstly, inputting a three-dimensional seismic data body and unconformable surface layer bit data; secondly, obtaining target seismic data; thirdly, converting the target seismic data into character data; fourthly, setting search parameters; fifthly, using wave group feature change positions meeting the search parameters as overlap-denudation points, and recording the overlap-denudation points; sixthly, outputting overlap-denudation lines, and obtaining the distribution of the overlap-denudation lines on a plane. The formation overlap-denudation line recognition method is based on iteration earthquake DNA detection, the accurate and efficient detection result can be obtained, and by means of the method, the labor intensity of geological research personnel can be lowered, and the exploration deployment quality and benefits can be improved.

The invention provides a vertical seismic data based prespud depth predication method. The method includes acquiring stratum segment speed of a target layer by utilizing vertical seismic data of an adjacent well adjacent to a predication well; recognizing a reflection wave event corresponding to the target layer on a vertical seismic section of the predication well through combination of characteristics of seismic section wave groups of through-well ground of the predication well and reading top boundary reflection time of a target layer and bottom boundary reflection time of the target layer;according to the top boundary reflection time of the target layer and the bottom boundary reflection time of the target layer, calculating a stratum reflection time difference of the target layer; utilizing the stratum reflection time difference of the target layer and the stratum segment speed of the target layer, calculating the predication depth of the target layer. The invention can predicatethe depth of the target layer quickly and accurately and provides effective and reliable data support for drilling engineering.

The invention belongs to the technical field of nondestructive detection and discloses a cable fatigue damage detection method based on guided wave group velocity; the method comprises the following steps of (1) carrying out cyclic loading on a standard test piece, acquiring a relation curve between the circulation loading frequency of the standard test piece and the wave guide group velocity, andtaking the relation curve as a calibration curve; (2) carrying out signal acquisition on a cable to be measured by adopting a guided wave instrument so as to obtain a time domain signal of the guidedwave, and then obtaining the propagation time of the guided wave according to the time domain waveform diagram of the time domain signal, and therefore obtaining the guided wave group velocity according to the propagation time of the guided waves and the propagation distance of the guided waves; and (3) judging whether the cable to be tested has fatigue damage or not on the basis of the calibration curve and the wave guide group velocity. According to the method, the fatigue damage detection of the cable is realized by measuring the wave guide group velocity, and the method is easy to implement, relatively high in detection precision and relatively high in applicability.

The invention discloses a clustering center determining method and a clustering center determining system. The method includes the following steps that: a water wave group including a plurality of water waves is constructed; propagation processing is performed on each water wave in the water wave group; and whether the fitness value of each water wave obtained after the propagation processing is performed is greater than the fitness value of the corresponding water wave obtained before the propagation processing is performed judged; if the fitness value of each water wave obtained after the propagation processing is performed is greater than the fitness value of the corresponding water wave obtained before the propagation processing is performed, the water waves obtained after the propagation processing are adopted to replace the corresponding water waves which are obtained before the propagation processing; if the fitness value of each water wave obtained after the propagation processing is performed is not greater than the fitness value of the corresponding water wave obtained before the propagation processing, the wave height and wave length of the water waves in the water wave group which are obtained before the propagation processing are updated, and the locations of the water waves which are obtained before the propagation processing are kept unchanged; the wave group and the number of iterations are updated; whether the current number of iterations is smaller than a set evolution algebra is judged; if the current number of iterations is smaller than the set evolution algebra, the method shifts to previous steps, propagation processing is performed on the water waves in the updated water wave group; and if the current number of iterations is not smaller than the set evolution algebra, water waves in the updated water wave group, which have the largest fitness value, are selected as optimal water waves. According to the method and system provided by the invention, from the perspective of optimization search, a clustering center is optimized from generation to generation by means of propagation processing, and the clustering center can be approximate to an optimal clustering center.

The embodiment of the invention discloses a method, device, equipment for determining the position of a QRS wave group, and a storage medium, and relates to the technical fields of ECG testing. The method comprises the following steps: acquiring a lead electrocardiosignal sequence, wherein the lead electrocardiosignal sequence comprises at least one QRS wave group; coding the lead electrocardiosignal sequence by using a coding neural network model to obtain a first semantic characteristic vector of the lead electrocardiosignal sequence; and decoding the first semantic characteristic vector byusing a decoding neural network model to obtain the position information of the QRS wave group in the lead electrocardiosignal sequence. Through adoption of the method, the technical problem that theanti-noise capability of detection of the QRS wave group is weak due to manual design characteristics in the prior art can be solved. A coding neural network model and a decoding neural network modelare constructed by using a machine learning mode, so that automatic detection of the QRS wave group can be realized, and the model obtained by training does not need manual design characteristics, andhas better performance, better generalization capability and stronger anti-noise capability.

The invention discloses an offshore seismic data determination polarity zero phasing method, device and equipment and a medium. The method comprises the following steps: removing ghost waves and multiples from normal polarity seismic data to obtain seismic data of minimum phase wavelets; performing deconvolution processing on the seismic data of the minimum phase wavelet to obtain deconvolution seismic data; and performing phase difference compensation processing on the deconvolution seismic data to obtain zero-phase seismic data. According to the method, the determined polarity zero phasing of the offshore seismic data is realized, the zero phasing processing effect of the seismic wavelets is obvious, the resolution ratio is high, the wave group characteristics are good, and the method has a good application prospect.

The invention discloses an electrocardiosignal feature point detection method and system. The electrocardiosignal feature point detection method comprises the following steps: obtaining electrocardiosignals in continuous time; segmenting the electrocardiosignals in the continuous time to obtain standard electrocardiosignal segments; and extracting electrocardiosignal features are extracted from the standard electrocardiosignal segments based on a constructed one-dimensional coding and decoding deep learning model, carrying out waveform classification according to the electrocardiosignal features, and obtaining the detection result of each type of waveform boundary feature points according to the obtained waveform classification result. The electrocardiosignal features are automatically extracted by using the deep learning method, and the waveform classification of P waves, QRS waves, T waves and non-wavebands and detection of the boundary points of the P waves, the QRS wave groups and the T waves can be realized at the same time.

A method for enhancing synchronously recorded multichannel electrocardio signals is characterized by comprising the following steps: (1) reading synchronously recorded multichannel electrocardio signals, and setting the multichannel electrocardio signal as X = [x1; x2; ...; xi...; xm], the m channel electrocardio signals can be directly recorded m channel electrocardio signals or can also be m channel electrocardio signals subjected to certain processing, the preprocessing can refer to basic preprocessing such as direct current removal, baseline drift and 50Hz interference, and also can referto comprehensive preprocessing of parent electrocardio signals removal from abdominal wall electricity; (2) performing initial detection or pre-detection of R peaks on the read m channel electrocardiosignals X, and manufacturing m channel electrocardio signal QRS wave group template matrixwa XQRS = [xQRS1; xQRS2; ...; xQRSi; ...; xQRSm] on the basis of the initial detection of the R peaks; (3) solving a maximization problem (shown in the description) to obtain the most advantageous woptimized (line vector); (4) calculating y = woptimizedX, namely enhancing and outputting the signals.