34 results about "Spline wavelet" patented technology

A photoacoustic probe for port wine stain (PWS), burn and melanin depth measurements is comprised of optical fibers for laser light delivery and a piezoelectric element for acoustic detection. The probe induced and measured photoacoustic waves in acryl amide tissue phantoms and PWS skin in vivo. Acoustic waves were denoised using spline wavelet transforms, then deconvolved with the impulse response of the probe to yield initial subsurface pressure distributions in phantoms and skin. The waves were then analyzed for epidermal melanin concentration, using a photoacoustic melanin index (PAMI) related to the amount of laser energy absorbed by melanin. Propagation time of the photoacoustic wave was used to determine the depth of blood perfusion underlying necrotic, burned tissue. Thus, the photoacoustic probe can be used for determining PWS, burn and melanin depth for most patients receiving laser therapy.

The invention discloses a method for detecting and tracking a faint target of a high frequency ground wave radar with a non-linear dynamics characteristic of a wave echo. The method comprises the following steps: recording intermediate frequency data in an echo signal in real time; canceling impact interference and the first-order data of an echo; establishing a chaotic dynamics forecasting model of the wave echo; and executing detection and tracking of the faint target, wherein the steps of canceling are as follows: implementing distance conversion and speed conversion to the intermediate frequency data; eliminating impact interference on a speed spectral domain through a spline wavelet filter, and eliminating a strong sea clutter through a track matrix wave of a reconstructing attractor. In addition, the invention discloses an apparatus for detecting and tracking a faint target of a high frequency ground wave radar with a non-linear dynamics characteristic of a wave echo.

The invention relates to an electrocardiogram signal classifying method based on one-dimensional convolutional neural network. The method comprises the following steps: firstly, conducting denoising processing on an electrocardiogram signal by virtue of a wavelet fusion method; then, detecting an R wave peak point by a QRS wave group recognition algorithm based on biorthogonal spline wavelet, and completing division and dimensionality reduction of the electrocardiogram signal with the R point as a datum, so that a plurality of R wave candidate bands are obtained; then, establishing and optimizing an electrocardiogram signal oriented one-dimensional convolutional neural network model; and finally, with the processed R wave candidate bands as input data of the model, automatically completing characteristic extraction and classification of the electrocardiogram signal. The method provided by the invention, with the adoption of the wavelet fusion method, can simultaneously remove high-frequency noise and low-frequency noise, so that extracted signal characteristics are more conducive to recognition; by establishing the electrocardiogram signal oriented one-dimensional convolutional neural network model, the problem that electrocardiogram signal characteristic points must be precisely located is solved, and moreover, the problem that a conventional method, which selects an algorithm to extract characteristics firstly, and then selects an algorithm to complete classification, is complex in computation is solved.

A fabric defect detection method based on B-spline wavelets and deep neural network includes deep neural network model training and defect image detecting. The deep neural network model training is characterized in that by learning existing sample libraries, the defect-free reconstructed images of samples under the premise that defect images are input. The defect image detecting showed in the picture is characterized in that to-be-detected fabric images are subjected to multiple times of wavelet transformation to obtain compressed images with most of texture information reserved, and the compressed images are saved; the compressed images are input into a trained deep neural network input end, calculation is performed, and the defect-free reconstructed images are obtained at an output end; margin calculation is performed on the reconstructed images and the saved compressed images to obtain the images which only contain defects; the features of the defect images are extracted to analyze whether the fabric contains defects or not, the categories of the defects, and the like.

A selective protection method for single-phase earthing of generator stator uses three times of B-spline wavelet as an analysing tool to directly analyse the measured voltage at two sides of generator end and neutral point as well as zero sequence current, and it can be sure that 100% of earthing fault of generator stator can be identified reliably by this method which works as using standard connection form for voltage and current mutual-inductor, calibrating the phase of zero sequence current at generator end, using mode maximum value as a start element after zero sequence voltage being analysed by B-spline wavelet, to judge whether the earthing fault of internal single plase of generator stator is produced or not by checking that the mode maximum value of zero sequence current at generator end and zero sequence voltage of generator end and neutral point side is in an uniformity or not through the analysis by B-spline wavelet.

The invention discloses a coronary heart disease self-diagnosis system based on electrocardiographic monitoring and back-propagation neural network, comprising an electrocardiographic collection terminal and a hospital monitoring center computer system, wherein the electrocardiographic collection terminal is composed of an electrocardiographic monitoring collector and a data transmission module based on wired or wireless data transmission. By means of multi-scale features of wavelet transformation, the system of the invention completes the extraction of wave peak points and the detection of ST segment in different scale decomposition coefficients by adopting a quadratic spline wavelet transformation method, thus the electrocardiographic waveform of the clinical patient can be accurately extracted. On the basis of correctively extracting characteristic points, an electrocardiogram ST segment pattern recognition model is set up by using a BP (Back-Propagation) neutral network in order to successfully recognize the pattern of the ST segment, and the initial weight and the threshold of the BP neutral network are optimized by using genetic algorithm and DNA (deoxyribonucleic acid) algorithm, thereby problem that the BP neutral network is liable to fall into local optimum in the process of training is solved, and the pattern recognition of ST segment and the diagnosis of coronary heart disease in the manner of artificial experience before are replaced.

The invention discloses an image fusion method based on Riesz-Lap transformation and PCNN, Riesz transformation has the capability of separating low-level features of an image, and important information perceived by human vision also comes from the low-level features of the image. According to the invention, a to-be-fused source image is decomposed into a Riesz transformation domain; the fractional order Laplace operator and multi-harmonic spline wavelet transform are performed on the image sequence of the Riesz transform domain to enable image representation to have direction characteristicsand multi-resolution characteristics, and the fusion is carried out by combining global coupling characteristics of a pulse coupling neural network on the basis of Riesz Laplace wavelets. Through multi-focus, medical CT and nuclear magnetic resonance, remote sensing panchromatic and multispectral image various application scene tests, the obtained image fusion result is good in visual effect, thecontrast ratio and definition are improved, and the operation efficiency is high.

The invention discloses a coronary heart disease self-diagnosis system with electrocardiographic monitoring and self-adaptive fuzzy reasoning network, which comprises an electrocardiogram acquisition terminal and a hospital monitoring center computer system. The electrocardiogram acquisition terminal is composed of an electrocardiographic monitoring and acquiring instrument and a data transmission module based on wired or wireless data transmission. Based on multi-dimension feature of wavelet transformation, the system of the invention completes the extraction for crest value points and the detection for ST segment in different dimensional decomposition coefficients by using quadratic spline wavelet transformation, thus electrocardiographic waveform of clinical patients can be accurately extracted. Based upon corrective extraction of characteristic points, six electrocardiogram ST segment morphologic models constructed by using self-adaptive fuzzy reasoning network training method succeed in ST segment morphologic recognition and replace the former human experience way for ST segment morphologic recognition and diagnosis of coronary heart disease.

The invention relates to a multi-scale wavelet antivibration design method of a high-speed main shaft of a machine tool, which comprises the steps of: 1. by using interval cubic Hermite spline wavelets as the multi-scale interpolation basis, establishing a multi-scale wavelet numerical value solving model influenced by comprehensive gyroscopic couple, transverse shear deformation, main shaft rotary inertia, material delay and viscous damping, bearing cross rigidity and cross damping; and 2. carrying out the dynamic characteristic analysis on the high-speed main shaft of the machine tool by using the computation model established in step 1 to obtain a rotation speed range during smooth running and designing the running speed of the main shaft of the machine tool. The experimental result indicates that the invention can greatly enhance the precision of the analysis on the critical eddy speed and the half-speed eddy speed of the emulated high-speed main shaft of the computer, and avoids the resonance region and the unstable region for carrying out the antivibration design of the main shaft rotation speed of the high-speed machine tool according to the acquired dynamic characteristic data , thereby ensuring the machining quality. The invention has the advantages of high design efficiency and low cost.

The invention provides a fault positioning method and system based on cubic B-spline wavelets and an interpolation algorithm. The method comprises the following steps: obtaining current traveling wavesignals at two ends of a line, and extracting line mode components of the traveling wave signals; constructing a cubic B-spline wavelet as a wavelet basis, and carrying out wavelet decomposition on the extracted linear mode component to obtain a harmonic component with the highest frequency; calculating the instantaneous energy of the harmonic component with the highest frequency, and obtaining the abrupt change moment of the energy; extracting a plurality of points near the first mutation energy, performing Hermite interpolation calculation to construct a curve, identifying a maximum value point of the curve, and determining a traveling wave arrival moment; and positioning a fault position according to the traveling wave arrival time. The cubic B-spline wavelet is constructed to serve asa wavelet basis for wavelet decomposition, the arrival time of a wave head is determined through a Teager energy operator and a Hermite interpolation method, a practical algorithm for power transmission line fault positioning is formed, and the accuracy of high-frequency signal first mutation point recognition can be greatly improved.

The invention provides a neutron transport equation processing method based on discontinuous finite elements and a program product. The processing method comprises the following steps: constructing a shape function and a conversion matrix of an interval B spline wavelet unit; on the basis of the discrete longitudinal standard neutron transport equation, deducing the discrete longitudinal standard neutron transport equation of the conventional discontinuous finite element; on the basis of a discrete longitudinal standard neutron transport equation of a conventional discontinuous finite element, deriving a discrete longitudinal standard neutron transport equation of an interval B-spline wavelet discontinuous finite element according to the constructed shape function and the transformation matrix of the interval B-spline wavelet unit; solving a discrete longitudinal standard neutron transport equation of interval B spline discontinuous finite elements; and guiding the design of the nuclear reactor according to a solving result. The technical problems that when a neutron transport equation is solved through a conventional discontinuous finite element at present, calculation is time-consuming if the number of grids is large, and calculation precision is not high and convergence speed is low if the number of grids is small can be solved.

The invention provides a Smith predictive compensation method based on a sixth-order B-spline wavelet neural network, which solves such problems of a Smith predictor as low model precision and unsatisfactory interference suppression effects. The method is characterized in that a differential equation is established for a tested object and subjected to discretization processing, so that system state quantity sampling intervals and learning samples of the sixth-order B-spline wavelet neural network are obtained; and after a structure, input layer weights, a hidden layer function and the number of hidden layer nodes of the neural network are determined, iterative training is carried out to obtain an output layer weight vector and a neural network expression, so that a mathematic model of a Smith predictive compensator is obtained. The method provided by the invention has the advantages that a non-linear controlled object can be modelled; the precision of a process model can be effectivelyimproved; and meanwhile, due to the characteristic of the wavelet neural network that the frequency band is limited, satisfactory interference suppression effects can be achieved.

The invention relates to a single-end travelling wave range finding method for a common-tower double-loop T-joint power transmission line, and belongs to the technical field of power transmission linefault positioning. The method comprises the steps: reading current travelling waves of two loop lines measured by a measurement-end high-speed collection device; employing a third spline wavelet function for detecting and verifying the obtained current travelling waves, comparing the maximum values of the current travelling waves of the measurement ends of the two loop lines, and extracting the traveling waves in a time window min {[t1, t1 + 2l2 / v], [t1, t1 + 2l3 / v]}, judging the amplitudes and extreme values of the traveling waves of the two loop lines in the time window, judging whetherthere is a healthy loop line initial traveling wave or not: determining that a fault happens to an MT segment or a QT segment if there is no healthy loop line initial traveling wave, determining thatthere is definitely a traveling wave reflecting the total length NT of the line in the neighbourhood of the moment of t1+2l2/v if a fault happens to a QT branch circuit, or else determining that thefault happens to an MT branch circuit; determining that the fault happens to the NT segment or a QT segment if there is the fault; determining that there is definitely a traveling wave reflecting thetotal length QT of the line in the neighbourhood at the moment of t1+2l3/v if the fault happens to the NT segment, or else determining that the fault happens to the QT branch circuit, and calculatinga fault distance through a single-end traveling wave range-finding formula.

The invention discloses a control method of a direct-drive gantry motion platform, and relates to the field of identification and control of direct-drive gantry motion platforms. The problem that an existing control algorithm is insufficient in compensation for the dynamic characteristics of the direct-drive gantry motion platform, and consequently the control precision of the direct-drive gantry motion platform is poor is solved. The method comprises the steps of updating a model through a neural network weight, generating a neural network weight, and sending the neural network weight to a B-spline wavelet neural network online identification model; the B-spline wavelet neural network online identification model being used for generating a feedforward compensation signal unn (t); a robust feedback controller being used for generating a robust feedback compensation signal us (t); and summing the feedforward compensation signal unn (t) and the robust feedback compensation signal us (t) to obtain a control signal u (t), and controlling the direct-drive gantry motion platform by using the control signal u (t). The control method is mainly used for controlling the direct-drive gantry motion platform.

The invention relates to an image edge detection method based on a six-order spline scale function, and solves the problem that the resolution of wavelet transform and a signal processing area are difficult to adjust in real time according to image change in traditional wavelet transform. According to the invention, the wavelet edge extraction capability and the adaptive capability of the neural network are combined; firstly, matrixes Psi 1, Psi 2, Phi1 and Phi2 are obtained through a six-order spline scale function and a six-order spline wavelet function, then the matrixes Psi 1, Psi 2, Phi1and Phi2 are obtained, matrixes Q1 and Q2 are used for obtaining the matrixes, then the matrixes are obtained based on an image matrix M, and finally an image edge B is obtained through use. And dynamic adjustment of the wavelet transform resolution and the signal processing area is realized in the image edge extraction process, so that finer and more accurate edges can be obtained.

The invention discloses a weighing system for a medical suspension bed, and a method by using the same. The system consists of a bed pedestal unit, a tension-compression sensor support unit, a silicasand supporting unit, a silica sand storage cabin, a master control module and a communication module; the tension-compression sensor support unit adopts six high-precision tension-compression sensorswhich are placed on a same horizontal plane and uniformly distributed to the supporting points of a suspension bed, so that weighing data can be collected in real time; the weighing data can be processed by applying a spline wavelet analysis method to eliminate noise, so that the weighing data which can assist clinic can be output; according to clinical demands, the increasing and decreasing amount of the body weight of a patient can be recorded and stored in a set time range, so that corresponding acousto-optic alarm can be sounded; and nurses can perform fluid supplementing or adjusting thespeed of fluid supplementing according to the suggests of an internal expert system. Thus, with the help of the high-precision weighing system and the internal expert system, the nursing personnel can be assisted to perform timely fluid supplementing, so that the patient can be effectively prevented from shocking due to excessive fluid loss, and the working strength of the nursing personnel can be reduced.

The invention provides a novel robust mesh digital watermark method based on a biorthogonal non-uniform B-spline wavelet (BNBW), wherein a watermark is embedded through a watermark modulating wavelet coefficient vector on a frequency domain and the watermark is extracted through the relevance among coefficients. The watermark can be embedded into an irregular mesh through the novel robust mesh digital watermark method so that the three-dimensional wavelet analysis becomes possible. The method can resist the common attack of a three-dimensional model, such as simplified polygon meshes, the addition of random noise, rotation, scaling and the combination of various attacks and can detect and position a distorted vertex.