219results about How to "Suppress noise interference" patented technology

The invention discloses a system for visually detecting workpiece appearance defects based on image processing, comprising an industrial personal computer, a coaxial light source, a CCD (charge coupled device) industrial camera, an image acquisition card and a rejecting mechanism; both the coaxial light source and the rejecting mechanism are connected with the industrial personal computer; the CCD industrial camera is connected with the industrial personal computer through the image acquisition card; wherein the coaxial light source is used for providing a diffuse reflection light source for a workpiece to be detected; the CCD industrial camera is used for taking an image of the workpiece at a detection station; the rejecting mechanism is used for rejecting from a production line, workpieces detected to be defective; the industrial personal computer has a defect detection module based on image processing; the system is high in detection efficiency and easy to implement.

The invention discloses a workpiece apparent defect detection method based on machine vision. Firstly, a visual system is used for guiding a robot, the pose of a target workpiece is precisely positioned according to a template matching algorithm based on a gray value, and then workpiece apparent defect detection is carried out and comprises the steps that firstly, a workpiece image is acquired and pretreated through median filtering; secondly, the target workpiece is divided through a global threshold value, and workpiece pose correction is carried out; thirdly, burr interference of the edge of the workpiece is removed through mathematical morphology open operation; fourthly, notches, material sticking, cracking, indentation, needle eyes, scratches and foaming apparent defects are detected. The method solves the problems that the artificial detection speed is slow, the efficiency is low and precision is poor; the problems that according to current vision detection, defect types are singular, the imaging quality is poor, and the false drop rate is high are solved, and the automation degree of precise workpiece production and product quality are improved.

The invention provides a capsule endoscope system having an ultrasonic positioning function and belongs to the technical field of medical instrument auxiliary devices. The capsule endoscope system solves the problem that the complex degree of a positioning system is increased in the prior art. The capsule endoscope system having the ultrasonic positioning function comprises a capsule endoscope and an external control unit. An MCU control circuit, a miniature ultrasonic transmission module and a radio frequency wireless transmission module are arranged in the capsule endoscope, wherein the miniature ultrasonic transmission module and the radio frequency wireless transmission module are connected with the MCU control circuit. The external control unit comprises a microcontroller, a radio frequency receiving module and an ultrasonic receiving and processing unit, wherein the radio frequency receiving module and the ultrasonic receiving and processing unit are connected with the microcontroller, an audio and video signal processing module is connected between the microprocessor and the radio frequency wireless receiving module, and the microcontroller can obtain the accurate position of the capsule endoscope in the body of a patient in real time through a sound velocity and temperature compensation formula and a spatial orientation algorithm. The capsule endoscope system can obtain the accurate position of the capsule endoscope in the body of the patient in real time and is high in positioning accuracy.

The invention discloses a micro-grid system voltage stability control method based on robust control. The control structure of an intelligent micro-grid system is composed of a system control layer, a concentrated control layer and a local control layer, and energy optimization and voltage and frequency stability control of the micro-grid system are realized through multi-layer coordination control. According to the micro-grid system voltage stability control method based on robust control, a comprehensive load module of an induction motor is established, wherein dynamic loads are connected with static loads in parallel. On the premise that the constraint conditions of system performance and robust stability are met, a micro-grid voltage stability robust controller is designed by the adoption of a method of solving the problem of mixed sensitivity so as to improve the micro-grid voltage stability. The micro-grid system voltage stability control method based on robust control can be adapted to wide-range change of load parameters, has high robustness for the situations that due to the factors such as changes of running conditions, influences of external environment and frequency switching of the loads, perturbation is generated in model parameters of a micro-grid and uncertainty exists in the topologic part and the unmolded part and can better restrain parameter drift and noise interference, and meanwhile, the controller is simple in structure and easy to realize.

An in-vehicle power conversion system, which can shield a circuit unit without using a particular shield member and can achieve reduction in cost and size, is obtained. Included are: a conductive housing having an opening; and a multilayer printed wiring board in which circuit units are mounted and one layer is a GND plane. The multilayer printed wiring board is assembled to an opening surface of an opening of the conductive housing to form a closed space; and the GND plane of the multilayer printed wiring board is electrically connected to the conductive housing to shield the circuit units stored in the closed space surrounded by the conductive housing and the multilayer printed wiring board.

The invention discloses a detecting method of a weak periodic signal based on a chaotic system and wavelet threshold denoising, which comprises the following steps of: firstly carrying out wavelet decomposition on collected information, and determining a decomposition scale according to an actual signal-to-noise condition; denoising a wavelet high-frequency coefficient after the wavelet decomposition, wherein in the process of wavelet threshold denoising, the selection of a threshold is an important problem and directly influences a denoising result, so that the invention firstly provides a method for determining the threshold according to the scale for carrying out coefficient threshold processing to improve the denoising effect; reconstructing a signal after denoising, merging the signal to be detected after wavelet denoising reconstruction as one part of the driving force of the chaotic system into a chaotic detecting system, further inhabiting noise interference by utilizing the characteristics of the chaotic system for strong noise immunity, periodic weak signal sensitivity and the like, and effectively extracting the weak signal. The invention improves the detection threshold and the signal-to-noise ratio purely based on the chaotic detecting system.

Disclosed is a blind restoration method for the motion blurred image; the steps are as follows: (1) the image is converted through the cepstrum method to figure out the blur extent and the blur direction of the blurred image; (2) the blur extent and the blur direction of the blurred image figured out in step (1), and the total variation (TV) method are adopted to process the restoration towards the blurred image. The method based on the total variation (TV) includes: the fixed-point iteration used as the outer-loop and the conjugate gradient method used as the inner-loop are adopted for loop iteration to obtain the restored image. The blind restoration method has the advantages of strong self-adaptation, strong anti-noise ability and good robustness; the blind restoration method has validity and practicality according to the processing effects of the simulation picture and the photographed picture.

The invention discloses an underwater robot depth control device based on linear active disturbance rejection technology and a method thereof. The device includes an underwater robot, a cable, a computer, a control board and a sensor. The lower portion of the underwater robot is equipped with the sensor; the computer is connected with the underwater robot via the cable; the control board is arranged on a control motor; and the depth control method of the linear active disturbance rejection technology is stored in the control board. In the implementation process, information including depth, vertical speed, attitude angle is obtained through the corresponding sensor; a depth controller places a model of a system and external disturbance in the same position; the depth control method based on the linear active disturbance rejection technology is used for controlling resolving and control instructions are outputted; and an actuator of the underwater robot executes the control instructions to complete depth motion control of the underwater robot. According to the invention, the underwater robot depth control device based on the linear active disturbance rejection technology and the method thereof can effectively adapt to strong nonlinear dynamics and external disturbance of the underwater robot and achieve precise depth control.

A driving apparatus of a light emitting diode (LED) and a driving method thereof are provided. In the driving method, when the driving apparatus performs dimming and a duty cycle of a dimming signal is smaller than a predetermination value, outputting time of driving currents are equally allotted in a period, and a magnitude of each driving current is regulated correspondingly. When the driving apparatus performs dimming and the duty cycle of the dimming signal is equal to or greater than the predetermination value, the driving currents are simultaneously output in the period, and the magnitude of each driving current is regulated according to the dimming signal. Therefore, an audio noise and an electromagnetic interference caused by excessive variation of a sum of the driving currents are suppressed.

The invention belongs to an oxyhemoglobin saturation detecting technology and relates to a hypoperfusion oxyhemoglobin saturation measuring method based on a self-correlation modeling method. According to the method, firstly, PPG (photoelectric plethysmogram) signals of red light and infrared light are subjected to digital filtering, in addition, the respective direct current components are calculated, then, the filtered signals are subjected to self-correlation, noise components in the signals are inhibited, the signal noise ratio is improved, finally, a parameter calculating method obtained by self-correlation signals through modeling is utilized for calculating the alternating current component, and in addition, the pulse rate, the oxyhemoglobin index and the oxyhemoglobin saturation are calculated. The method has the advantages that the noise interference of oxyhemoglobin signals is effectively inhibited, in addition, the method for parameter calculation through self-correlation signals is realized through modeling, and the accuracy of the pulse rate, oxyhemoglobin index and oxyhemoglobin saturation measurement at hypoperfusion is improved.

The present invention relates to a two-wheel self-balancing robot attitude calculation method based on the improved Extended Kalman Filter algorithm. In the prior art, the existing two-wheel self-balancing robot attitude calculation method can not well meet accuracy, real-time property, simplicity and other requirements. Baed on the problems of the existing two-wheel self-balancing robot attitude calculation method, the method of the present invention utilizes the improved Extended Kalman Filter algorithm so as to effectively combine the inertial sensor attitude measurement data, compensate the gyroscope random drift error and reduce the influence of the displacement acceleration of the two-wheel self-balancing robot on the attitude calculation during moving. The two-wheel self-balancing robot attitude calculation method of the present invention can further be simultaneously applied for the two-wheel self-balancing electric vehicle. The results of static experiments, simulated platform experiments and practical dynamic experiments of the two-wheel self-balancing robot verify that the attitude calculation accuracy of the two-wheel self-balancing robot can be increased with the method of the present invention.

The invention discloses an X-ray pulsar navigation positioning method and system based on nonlinear prediction strong tracking traceless Kalman filtering. The navigation positioning method comprises the following steps of taking a spacecraft position vector and a velocity vector as a navigation state variable, establishing a navigation system state model and obtaining a spacecraft state predictionvalue; determining a pulsar signal observation value and establishing a navigation system observation model; using a non-linear prediction strong tracking traceless Kalman filtering method to processa pulsar signal observation value and a spacecraft state prediction value, in the spacecraft state prediction stage, acquiring a minimum navigation system state model error according to a constraintfunction, and correcting a navigation system state model error in a quasi real-time mode; and in a spacecraft state updating stage, introducing a fading factor to suppress a noise interference, predicting and updating the state of a spacecraft. A spacecraft state model error is estimated and corrected, and simultaneously, the problems of filter divergence and the low precision of X-ray pulsar navigation caused by the noise interference are solved.

The invention discloses a shock echo signal analysis method based on variational mode decomposition, which comprises the following steps: 1) selecting measurement parameters and working parameters ofequipment according to requirements of field measurement environment, and collecting shock echo signals; 2) setting parameters in variational modal decomposition according to the shock echo signals; 3) decomposing the collected shock echo signals into several eigen modulus functions by a variational modal decomposition method to solve the variational problem; 4) using Hilbert transform to obtain the Hilbert time-frequency spectrum of the eigen modulus functions; 5) integrating the Hilbert time-frequency spectrum of different frequencies in the time domain to obtain the final marginal spectrum.The method identifies the defects of the concrete component by using the marginal spectrum of the shock echo signals, and can suppress the noise interference under strong noise, and achieves higher resolution compared with the traditional Fourier transform.

The invention relates to a three-axis micro-mechanical gyroscope. The three-axis micro-mechanical gyroscope comprises a substrate and a gyroscope main body fixedly arranged on the substrate, wherein the gyroscope main body comprises a plane detecting unit and a z-axis detecting unit; the plane detecting unit comprises a first supporting beam, a linking beam, a driving electrode and a first mass block; the z-axis detecting unit comprises eight modules; and four modules are distributed at the two sides of a y-axis mass block along an x axis, the other four modules are distributed at the two sides of the x-axis mass block along a y axis, and each module comprises a second supporting beam, a decoupling beam, a driving beam, a second mass block and a detecting electrode. The three-axis micro-mechanical gyroscope has the advantages that the whole structure adopts a single-driving design and adopts a comb-tooth electrode for driving; the x axis and the y axis on the plane are detected by a variable-gap plate capacitor, the z axis is detected by a comb-tooth capacitor, and the x axis, the y axis and the z axis realize decoupling of driving and detection respectively; and the structure is compact, the manufacturing process is simple, the cost is low, the performance is higher, and good measuring accuracy and the sensitivity can be realized.

The invention discloses an automatic comparison and identification method for an explosive circuit board debris image, and belongs to the technical field of pattern recognition and image processing. The method comprises the following steps that: firstly, carrying out object segmentation on the debris image on the basis of an image segmentation algorithm of a probability graph model, then, adopting a color model based on probability sampling to carry out the object fine positioning of a component, extracting and filtering feature points, comparing and identifying the filtered feature points with circuit board feature points in a retrieval library, and determining a source circuit board to which a debris belongs. The invention puts forward and realizes a method for automatically identifying the primitive plate which has the same type with a printed circuit board according to the explosive circuit board debris image. By use of the comparison and identification method, the component and a character area in the circuit board can be accurately segmented, noise interference can be inhibited, and similarity between feature point description and a space geometry relationship is considered while comparison is carried out. By use of the method, TOP1 accuracy in the automatic identification of the circuit board debris image is higher than 99%.

The invention relates to a surface observation microseism speed model correcting method based on amplitude stack.Starting from the principle of a reverse time migration amplitude stack method, a very fast simulated annealing method is combined, seismic phase first arrival information does not need to be picked up, and a speed model is corrected by monitoring the energy focusing condition on a perforation point; perforation point relocation accuracy is used for judging whether the speed model can be used for follow-up microseism location.The problem that the maximal value and the maximum value are not separated in an existing algorithm can be effectively solved, the maximum value E of energy focusing on the perforation point can be found accurately, the perforation position is located accurately, and the speed module is effectively corrected.Noise interference can be effectively inhibited by adopting the reverse time migration amplitude stack algorithm, and a good location effect can still be obtained with a low signal-to-noise ratio.The more the speed model is close to an actual model, the more accurate perforation location becomes.Whether the speed model can be used for follow-up microseism location or not is judged according to perforation point relocation accuracy.

The invention discloses an eye movement signal identification system and method based on a common spatial pattern. The eye movement signal identification system comprises an eye movement signal preprocessing module, a spatial filter training module and an eye movement signal identification module. The eye movement signal identification method comprises the steps of collecting eye movement data based on an electro-oculogram and preprocessing the eye movement data; dividing all the preprocessed data into training data and testing data; adopting a CSP algorithm to conduct spatial filtering on the training data, and using the result obtained after filtering as feature parameters which are input into an SVM classifier for SVM model training; using the CSP algorithm to conduct feature extraction on the testing data, feeding the result obtained after the feature extraction into the trained SVM classifier for identification, and finally obtaining the identification result of eye movement. The eye movement signal identification system and method have the advantages that the accuracy of eye movement signal identification is higher, the eye movement signal spreading and classification capability is higher, and the application potential is large.

The invention discloses a rolling bearing vibration signal characteristic extracting and analyzing method under early fault state, which includes the following steps: mounting a vibration acceleration transducer and collecting vibration signals through a vibration measurement instrument; performing autocorrelation function on the vibration signals through the processor in the vibration measurement instrument; extracting the partial maximum points in the signals and the corresponding sample point serial numbers, so as to obtain envelope signals; the resampling at the uniform sampling interval through the interpolation method based on the envelope signals and finally performing fast Fourier transform, so as to obtain an envelope spectrum. The vibration signal characteristic analyzing method can remarkably reflect the modulation characteristics in the vibration signals under the early fault state, and is particularly suitable for the analyzing and extracting of the modulation characteristics in the rolling bearing noisy vibration signals under the early weak fault.

An electromagnetic wave absorber for use in the high frequency range above 1 Ghz and a composite member are characterized by the fact that magnetic metal grains are covered with ceramic above 20 volume %. Further, a method of manufacturing the electromagnetic absorber and the composite member is characterized by the fact that composite magnetic particles, in which a plurality of magnetic metal grains and ceramic are unified, are formed through a mechanical alloying method applied to a composite powder composed of magnetic metal powder and ceramic powder. The electromagnetic wave absorber can be used in a semiconductor device, an optical sending module, an optical receiving module, an optical sending and receiving module, an automatic tollgate in which erroneous operation due to electromagnetic wave disturbance is provided by use of the electromagnetic wave absorber.

The invention relates to a target detection method based on spatial domain phase variance weighting. The target detection method comprises the steps that filtering and amplification are performed on data picked up by all array elements in a towed line array sonar, and then the picked up data are sampled so that discrete data are obtained; FFT analysis is performed on the discrete data so that frequency spectrum data are obtained; phase compensation, accumulation and summation are performed on the data of all the array elements related to all frequency units in the frequency spectrum data so that frequency domain narrowband wave beam formation is completed; the spatial domain phase value of each azimuth is obtained in the narrowband wave beam formation of all the frequency units; the received signals are updated, the aforementioned operations are performed repeatedly, and each frequency unit obtains multiple spatial domain phase values; variance calculation is performed on the spatial domain phase values of all the azimuths; and weighting factors are generated by variances, and spatial domain phase variance weighting statistics is performed on all the azimuths by utilizing the weighing factors so that the final spatial spectrum and azimuth estimation value can be obtained.

The invention discloses a gesture identification method. The method comprises: a shape of a to-be-identified gesture is obtained, a closed profile is extracted from a gesture-shaped edge, and all profile points on the profile and a coordinate of each profile point are obtained; the layer number of the profile is determined, and an area parameter, an arc length parameter, and a gravity center parameter that correspond to each profile point at each layer are calculated based on the coordinate of each profile point, wherein the parameters are used as feature parameters of the profile point; with the feature parameters of each profile point, a to-be-identified gesture and a template in a preset template base are matched to obtain an optimal matching template, and the optimal matching template is determined as the to-be-identified gesture. According to the invention, a global feature, a local feature, and a relation between the global feature and the local feature are described; multi-scale and omnibearing analysis expression is carried out; effective extraction and expression of the global feature and the local feature of the to-be-identified gesture shape can be realized; and a phenomenon of low identification accuracy caused by a single feature can be avoided.

The invention discloses an image enhancement algorithm based on wavelet transform and fusing high and low frequency information. The image enhancement algorithm is mainly used for solving the problemof uneven illumination in the fields of medicine, photography and security and protection, comprehensively utilizes an image enhancement method and sequentially comprises the following steps: S1, inputting a color picture into a computer, converting the color picture into a grey-scale map, carrying out two-layer wavelet decomposition of a two-dimensional signal on the grey-scale picture, and converting the image into a frequency domain from a spatial domain; s2, extracting a low-frequency component of the image, and improving high-frequency response in the low-frequency component by adopting homomorphic filtering; s3, converting the filtered low-frequency image into a spatial domain for gray scale conversion to enhance the contrast and brightness of the image; and S4, reconstructing an image according to the enhanced low-frequency component and high-frequency component coefficients, performing wavelet transform-based threshold denoising on the reconstructed image, and finally obtainingan enhanced image. By means of the technical scheme, the low-frequency information, namely the overall brightness of the image, of the image is effectively enhanced, and meanwhile noise interferencein the high-frequency information is restrained.

An SAR image change detection method based on a multi-region convolutional neural network comprises the following steps: performing difference analysis on two SAR images with different time phases atthe same geographic position to obtain a difference image; pre-classifying the differential images to obtain a constructed training data set and a constructed test data set; sending the sample training data set into a proposed multi-region convolutional neural network for training; and testing the trained network the test set so as to obtain the change detection result of the whole same-ground multi-temporal SAR image. According to the method, when the data set is constructed, the number of the data set is doubled by adding Gaussian noise, the diversity of samples is enriched, and the overfitting problem is solved; at the same time, the method uses an attention mechanism for channels and spaces to improve the performance of the network, improves the robustness of SAR image change detectionto noise, and has strong generalization ability.

The invention discloses a high-precision harmonic parameter estimation method based on sliding window DFT (Discrete Fourier Transform), which is suitable for estimating the harmonic components of a power system. After SWDFT is carried out on a power grid signal containing harmonic components in the power system, the transformation result has the same linear relationship as the original time domainsignal, namely, the transformed sequence still retains the mathematical structure of a multi-frequency signal, so that parameters can be estimated by using the existing harmonic analysis model. In addition, SWDFT enhances the signal-to-noise ratio of each component, and suppresses the interference of noise on the estimation performance. In the method, SWDFT is carried out on a harmonic signal inthe time domain first, and then, a Prony algorithm is adopted for the transformation result to obtain the estimated values of the frequency, amplitude and phase of each harmonic component of the signal. In order to enhance the estimation performance and robustness of the algorithm, a complex least squares (PLS) criterion is introduced to expand the original Prony algorithm. Therefore, the precision of parameter estimation is improved.

The invention relates to a novel method for recognizing the rigidity between shearing type structure layers under environmental excitation. Firstly, the free vibration response of a structure is replaced by a cross correlation function of the structure acceleration response under environmental excitation by using a natural excitation technology; then an equation is decoupled by using the free vibration of the shearing type structure, and a vibration equation of the multidegree of freedom shearing type structure represented by the cross correlation function of the acceleration response is decomposed into a forced vibration equation of a plurality of single degree of freedom systems under the action of corresponding virtual dynamic loads; and finally the physical parameter recognition is carried out on each single degree of freedom system under the action of virtual dynamic loads by using the extended kalman filter algorithm to obtain a recognition value of the rigidity between the shearing type structure layers. The invention only needs to collect the vibration response of each shearing type structure layer, can recognize the rigidity between the layers relatively and accurately, has better prospect of engineering application, can be used for safety detection and health monitoring of a building with a mechanical model, such as, a rigid frame, a frame, and the like being simplified into a shearing type model.

The invention discloses an optical fiber intelligent monitoring system and a monitoring method based on a phase signal carrier technology. The optical fiber intelligent monitoring system comprises two-core closed-loop inducting optical cables distributed in an inducting area, an optical package, a signal processing unit and a personal computer (PC) host computer. Signals inducted by the two-core closed-loop inducting optical cables are transmitted to the PC host computer sequentially through the optical package and the signal processing unit. The optical package carries out light splitting and modulation to the optical signals, the two-core closed-loop inducting optical cables generate two lines of interference lights to external inductions, and the interference lights are processed through the signal processing unit and sent to the PC host computer, and alerting signals are generated after the signals are analyzed. And therefore the purposes of high sensitivity, large detecting range and short response time are achieved.

The invention provides a hyperspectral image anomaly detection method using multi-window feature analysis. The hyperspectral image anomaly detection method comprises the following steps: at first, determining the size of detected windows including an inner-layer window, a middle-layer window and an outer-layer window; next, calculating an OSP (Orthogonal Subspace Projection) operator in the outer-layer window, eliminating background interferences in the inner-layer window and the middle-layer window, and effectively removing white noise; then, carrying out background image element selection in the middle-layer window; and then, calculating a KRX (Kernel RX) operator in the inner-layer window, and carrying out anomaly detection on an image element to be detected; finally, outputting a detection result. According to the hyperspectral image anomaly detection method, a detection mode for three layers of windows is skillfully applied, and hyperspectral data is subjected to noise interference elimination at first and then is subjected to anomaly detection by using two layers of local background pixel windows. The interferences or the white noises emitted by uninterested signal sources in the inner-layer window and the middle-layer window are eliminated by using the OSP operator in the outer-layer window, so that the false alarm probability is reduced and better detection effect is obtained. A simulation experiment is carried out by using AVIRIS (Airborne Visible / Infrared Imaging Spectrometer) hyperspectral data, the detection performance of the hyperspectral image anomaly detection method provided by the invention is remarkably superior to the traditional algorithm, the false alarm possibility is reduced, and better detection effect is gained.

The present invention discloses a method and device for gesture identification based on substantial feature point extraction. The device comprises: an extraction module configured to obtain shape of a gesture to be identified, extract an unclosed contour from the edges of the shape of the gesture to be identified and obtain coordinates of all the contour points on the contour; a calculation module configured to calculate the area parameters of each contour point, perform screening of the contour points according to the area parameters, extract the substantial feature points and take the area parameters of a substantial feature point sequence and the point sequence parameters after normalization as the feature parameters of the contour; and a matching module configured to facilitate the feature parameters of the substantial feature points, perform matching of the gestures to be identified and templates in a preset template library, obtain the optimal matching template of the gesture to be identified and determine the type of the optimal matching template as the type of the gesture to be identified. The method and device for gesture identification based on the substantial feature point extraction have good performances such as translation invariance, rotation invariance, scale invariance and hinging invariance while effectively extracting and expressing gesture shape features so as to effectively inhibit noise interference.

The invention discloses a visual inspection method for bottle packaging quality. The visual inspection method comprises the following steps: (1) acquiring a bottle image and carrying out preprocessing; (2) locating a support ring by using the strategy of longitudinal bottom-to-top searching; (3) carrying out self-adaptive calibration of a capping cover, a liquid level and a sprayed-code detection area; and (4) extracting a Blob candidate block by using a gray threshold segmentation algorithm and determining whether the capping cover, the liquid level and the sprayed-code detection area have defects according to determination rules. The visual inspection method overcomes the problems of slow speed, low efficiency and poor precision of artificial inspection; moreover, the method overcomes the problems that conventional visual inspection algorithms are complex, consume a long period of time in inspection and cannot meet demands on inspection of PET bottles on a high-speed production line, and improves the automation degree of bottle inspection.