119 results about "Noise correction" patented technology

The invention discloses a method and a system for automatically classifying heterogeneous data based on business deep learning, particularly relates to the technical field of data classification, and is used for solving the problems of limited classification accuracy and insufficient interpretability caused by cross-modal feature entanglement in the existing method. The method comprises the following steps: decoupling and separating an independent feature and a redundant feature vector through a modal implicit feature, generating a noise correction instruction based on orthogonality residual evaluation, carrying out cross-modal noise distribution consistency correction on the redundant feature, quantifying a gradient direction conflict rate in combination with a vector space projection relation, and dynamically adjusting a back propagation path. Fusion path switching is triggered through semantic matching degree and contribution degree balance monitoring, a self-adaptive classification decision boundary function is constructed, and high-precision classification of heterogeneous data and strong generalization adaptation of service scenes are achieved.

The invention discloses a circuit and a method for ultrasonic flow detection, the method is a signal processing method, a system main controller is provided with a double-path driving channel and a double-path ADC acquisition channel, and a switching circuit and a signal conditioning path are dynamically controlled by a microcontroller, so that the flow of ultrasonic flow is detected. Through a real-time noise correction and self-calibration mechanism running in parallel with signal acquisition, a circuit system comprises a microcontroller, a switching circuit A1, a switching circuit A2, a driving signal conditioning circuit, a receiving signal conditioning circuit, a band elimination filter circuit and ultrasonic probes Y1 and Y2, multi-channel cooperative work and error compensation are realized; through a two-way switching architecture, a switching strategy and a dynamic calibration mechanism, the problems of zero drift accumulation, environmental noise interference and difficult calibration circuit signal deviation existing in a traditional single-channel design are effectively solved, and the precision, stability and environmental adaptability of flow measurement are remarkably improved.

The invention discloses a wireless communication ranging system and method, electronic equipment and a storage medium. According to the wireless communication distance measurement method, a star flash SLE communication module is adopted, and the star flash SLE communication module is replaced in a communication module of a vehicle door controller and a wireless vehicle key and is used for transmitting and receiving wireless signals; the Kalman filter is connected with the satellite flash SLE communication module and is used for carrying out noise correction on the RSSI value; and the Gaussian regression process model is connected with the Kalman filter and is used for establishing a mathematical relationship between the optimized RSSI value and the distance so as to realize distance estimation. According to the invention, the technical problem of inaccurate distance measurement caused by low stability of vehicle-mounted wireless communication in the prior art is solved.

The invention discloses an online self-calibration filtering method for inertial navigation, and relates to the technical field of inertial navigation, and the method comprises the following steps: obtaining multi-modal data, and establishing an inertial device error mathematical model and an inertial navigation system error mathematical model; carrying out recursive estimation on the state variables by the extended Kalman filtering model to obtain error parameters of the inertial device and the navigation system; constructing a fuzzy adaptive model based on the error parameters, generating an observation noise correction factor through a fuzzy logic rule, and correcting an observation noise covariance matrix in the extended Kalman filtering model in real time; carrying out online estimation and compensation on error parameters of the inertial navigation system by utilizing an extended Kalman filtering model, and outputting compensation parameters containing zero offset and scale factors in real time; correcting the original data based on the compensation parameters, injecting the original data into the navigation solution, and updating the fuzzy adaptive model according to the compensated error data.

The invention discloses an automobile active noise reduction method and system based on exhaust noise prediction, and relates to the technical field of automobile noise reduction. An automobile active noise reduction system based on exhaust noise prediction comprises a data acquisition module, a driving prediction module, a noise mapping module, a noise correction module, an active noise reduction module and an associated noise reduction module. According to the method, the vehicle exhaust noise influence information and the driving behavior prediction information are fused, a multi-source state sensing and prediction mechanism is constructed, and the perspectiveness and accuracy of exhaust noise modeling and control are remarkably improved; by constructing the vehicle motion prediction model based on the driving style, high-precision deduction of the dynamic state of the vehicle in the future is realized, and an advanced control basis can be provided for active noise reduction, so that the response speed and the noise reduction precision are improved.

The invention discloses a remote sensing image aircraft fine-grained classification method based on multi-dimensional part feature modulation, and relates to the technical field of target detection and recognition. A self-supervised pre-trained ViT model is adopted as a backbone network, and remote sensing image set noise correction and dimension adjustment are input to generate a basic feature tensor containing part information; defining a key part prototype and a background prototype, calculating a similarity distance and adding noise, generating an attention map through convolution, calculating a local feature vector and a global feature vector, and splicing and reducing dimensions to obtain fusion features; and constructing a composite loss function, calculating a category score in combination with fusion features, converting the category score into probability distribution through Softmax, and selecting a category with the maximum probability as a final fine-grained classification result. Through multi-scale feature fusion and feature modulation of an aircraft target, the fine-grained classification precision can be effectively improved, and the limitation of poor performance under a complex background and a weak feature target is overcome.

The invention discloses a convolutional neural network training method for label noise image processing, electronic equipment and a storage medium, and belongs to the technical field of label noise image processing. In order to improve the stability of the training process and improve the label noise filtering performance, the time average neural network is introduced, a more stable sample weight is generated, the stability of the training process is improved, and the label noise filtering performance is improved. For the identified noise data set, label correction is carried out by using the time averaging neural network, and the stability of the label noise correction method is improved because the mutual influence of the deep neural network and the label noise correction part is decoupled. On the whole, the robustness and generalization ability of the model on the data with the noise label are effectively improved. Compared with a traditional method, the method shows remarkable superiority when the label noise problem is processed.

The invention relates to the technical field of aquaculture water quality prediction, in particular to a procambarus clarkii aquaculture water quality prediction method fusing LSTM and an Internet of Things sensing network. According to the technical scheme, the method comprises the steps that time sequence water quality parameters of a culture pond are obtained, dynamic sliding window preprocessing is conducted on data, an LSTM prediction model of a mixed gating mechanism is constructed, and a culture equipment control instruction is generated according to a prediction result. Through fusion of the Internet of Things sensing network and the improved LSTM model, the problems of insufficient utilization of spatio-temporal data, weak model adaptability and disjunction of prediction control in a traditional method are effectively solved, spatio-temporal correlation fusion and noise correction of multi-source water quality data are realized, water quality parameters are accurately predicted, the ammonia nitrogen mutation risk is synchronously detected, and the method is suitable for large-scale popularization and application. Therefore, artificial experience driving of breeding management is promoted to be converted into accurate data driving, a real-time dynamic environment regulation and control scheme is provided for healthy growth of shrimps, the breeding risk is remarkably reduced, and the management efficiency is improved.

The invention relates to the technical field of hyperspectral alteration, in particular to a satellite hyperspectral alteration mineral remote sensing quantitative recognition method based on deep learning. The method comprises the following steps: acquiring high-resolution 5B satellite hyperspectral data, DEM data and atmospheric parameter data; performing multi-source noise suppression processing on the high-resolution 5B satellite hyperspectral data to obtain a noise correction data set; performing terrain shadow correction based on the noise correction data set and the DEM data, and performing atmospheric correction in combination with the atmospheric parameter data to obtain a pre-corrected hyperspectral data set; through multi-source data fusion, noise and environment interference correction, key wave band screening and multi-dimensional depth feature extraction and fusion, the quality and characterization capability of hyperspectral data are effectively improved, and a solid data basis is provided for subsequent refined mineral recognition and analysis.

The invention discloses an underwater high-speed three-dimensional measurement method based on stripe structured light, and relates to the technical field of underwater optical three-dimensional imaging, in an underwater operation scene of an FPP system, projecting a three-step phase shift sine stripe and a Gray code pattern to an object to be measured, and respectively calculating a wrapped phase 1, a wrapped phase 2 and an auxiliary order phase; analyzing the light intensity information received by the camera, and carrying out water body influence analysis in combination with an underwater light transmission model; the initial wrapped phase divides the order phase into a stable region and a non-stable region, the stable region is combined with the wrapped phase 1 to calculate a preliminary absolute phase, and the non-stable region is combined with the wrapped phase 2 to calculate an absolute phase through an island processing method. According to the method, a phase bisection complementary decoding strategy is adopted, a jump error region in an order phase is effectively identified and corrected by constructing complementary wrapped phase information, a wrong order phase is corrected by using a noise correction algorithm, and the measurement stability in a dynamic scene is remarkably improved.

In one example, an imaging device includes a light-receiving pixel, a first coupling terminal, a first voltage generation circuit, a drive circuit, a reference signal generation circuit, a noise correction circuit, a comparison circuit, and a processing circuit. The light-receiving pixel generates a pixel signal. The drive circuit is configured to drive the light-receiving pixel on the basis of a voltage from the first voltage generation circuit at the first coupling terminal. The reference signal generation circuit generates a reference signal having a ramp waveform. The noise correction circuit generates a noise correction signal corresponding to the voltage at the first coupling terminal, and superimposes the noise correction signal on the reference signal. The comparison circuit compares the pixel signal and the reference signal with the superimposed noise correction signal. The processing circuit calculates a pixel value based on a result of the comparison.

The invention relates to a preprocessing method for an infrared focal plane image sequence, which comprises the following steps of: averaging each pixel point of an image in a time domain to obtain a time domain mean value; then local nonlinear filtering is used, the maximum value and the minimum value in a window are removed in the sliding window of the image, dynamic threshold detection is carried out on the time domain mean value of each point, the local mean value of the window and the maximum value and the minimum value in the window, and pixels exceeding the threshold are marked as blind pixels; after median filtering compensation is carried out on blind pixels, a time-space domain difference operator non-uniformity correction model is established for an infrared image sequence, and the infrared image sequence is solved by using an alternating direction operator method, so that the infrared image sequence is pre-processed in a self-adaptive manner. When the method is used for preprocessing, the accuracy of blind pixel compensation is high, the non-uniformity noise correction effect is good, and image artifacts are effectively reduced.

The invention discloses a film thickness measurement method and system based on a line scanning hyperspectral camera, and relates to the technical field of hyperspectral imaging, and the method comprises the steps: obtaining a reflection or transmission spectrum image and a dark noise image, and carrying out the denoising processing; using a hyperspectral camera and an integrating sphere to carry out radiation correction on the reflected or transmitted spectral image; acquiring a film reflectivity or transmissivity spectrum according to the transmission spectrum without the film or the standard specular reflection spectrum; and determining the number of global interference periods according to a reflectivity or transmissivity curve, and calculating by using a fast Fourier transform method or a spectrum fitting method to obtain the film thickness. Image data with space and spectrum information is acquired through a hyperspectral camera, high-precision film thickness measurement under global imaging and dynamic scanning is realized through an algorithm combining fast Fourier transform and spectrum fitting, and meanwhile, the film thickness measurement precision is improved by adopting dark noise correction and integrating sphere radiation correction. The quantitative accuracy of incident light and sample reflection or transmission light is ensured, and the detection efficiency is greatly improved.

Disclosed in the present invention are an infrared non-uniformity noise correction method and system. The method comprises the following steps: determining whether an infrared imaging device has moved, if yes, acquiring non-uniformity correction values and non-uniformity correction value change amounts of pixel points on the basis of a gradient matrix and a truth value matrix, and performing corresponding updating processing on the pixel points; completing iterative calculation of a bias matrix of the current image frame on the basis of the updated pixel points; and performing non-uniformity correction on the current image frame on the basis of the bias matrix. The present invention first extracts a clear non-uniform noise pattern by means of the bias matrix, and then corrects the non-uniform noise by means of the bias matrix, so that the non-uniform noise in the infrared image can be significantly reduced, and the details of a target object are not lost, and thus, the image contrast and quality are greatly improved.

A time error vector is determined using pairs of two closest points of input-referred noise data that straddle respective crossing times indicating when a clock signal representation crosses a threshold value, a slew rate of the clock signal representation, and the crossing times. A system filter is applied to the time error vector in the frequency domain. A first RMS value is determined indicating a jitter value present in the filtered time error vector. A raw clock signal time error vector of the clock signal under test is generated, the system filter is applied to the raw clock signal time error vector in the frequency domain, and a second RMS value indicating a jitter content of the filtered raw clock signal time error vector is determined. The second RMS value is corrected using the first RMS value to thereby generate a jitter measurement compensated for input-referred noise.

The invention provides a non-uniform noise correction method, electronic equipment and a computer program product. In the application, after the output of the infrared imaging equipment at the current temperature is corrected each time, the non-uniform noise fpnM is learned based on the correction result, and the background data corresponding to the highest temperature value and the background data corresponding to the lowest temperature value in the temperature interval of the temperature are adjusted according to the learned non-uniform noise fpnM. According to the mode of updating the dynamically learned non-uniform noise data into the background data in real time, the background data in the stored background-temperature data can be updated in real time based on the non-uniform noise data, and the stored background-temperature data is used for correcting the output of the infrared imaging equipment at a temperature, so that the background data can be updated in real time based on the non-uniform noise data. Therefore, the non-uniform noise correction of the output of the infrared imaging equipment at the temperature is realized equivalently.

The invention relates to the technical field of pet food spectrum detection, and discloses a pet food spectrum detection device and a detection method. Sampling at equal intervals in a preset wavelength range, and eliminating noise interference through dark current and standard whiteboard calibration; taking a natural logarithm of the reflectivity to balance a dynamic range; constructing a linear spline basis in an equidistant and segmented manner, and adaptively calculating a regularization coefficient according to the energy ratio of the signal to the basis function; constructing an augmented matrix according to a basis function and a signal inner product, and solving a spline coefficient through Gaussian elimination; reconstructing a spectrum by using the coefficient and quantifying a residual error; establishing a reference model based on the coefficient mean value and the standard deviation of the multiple qualified samples; comparing the Euclidean distance between the to-be-detected sample coefficient and the model mean value with a threshold value, and generating a residual error, a distance, a threshold value and a judgment report; empirical parameters or manual adjustment and optimization are not needed in the whole process, online adaptive detection in batches can be achieved, and noise correction, feature enhancement, over-fitting suppression and traceable rapid and accurate detection are achieved.

A method for correcting fixed-pattern noise in at least one image is disclosed. The method includes the following steps:—acquiring at least N substantially distinct and regular images by means of at least one rotating image sensor such that the combination of the N images forms a panorama of a surrounding scene, the fixed-pattern noise in each of the N acquired images being substantially the same;—determining at least one correction parameter for correcting the fixed-pattern noise by means of a processing module from the at least N acquired images, the at least one correction parameter minimizing a functional;—correcting the fixed-pattern noise in each of the N images acquired by the processing module using the at least one determined fixed-pattern noise correction parameter.

The invention discloses an amphibious step chariot muzzle attitude measurement method and device based on process noise adaptive filtering, and the method specifically comprises the steps: determining the installation positions of an optical fiber inertial navigation device, an MEMS inertial navigation device and a photoelectric encoder according to an amphibious step chariot body; according to roll, pitch and course angle information, provided by optical fiber inertial navigation, of a gun and pitch angle and course angle information, provided by a photoelectric encoder, of a barrel of the gun, attitude information of virtual optical fiber inertial navigation is obtained through calculation; carrying out calculation by utilizing MEMS inertial navigation to obtain self attitude information; measuring the difference between the attitude information of the MEMS inertial navigation and the attitude information of the virtual optical fiber inertial navigation; a dynamic lever arm correction method and a flexural deflection noise correction method are adopted, and Sage-Husa process noise self-adaptive Kalman filtering is combined to estimate muzzle attitude information. The system has the capacity of self-adaption to amphibious environments, has the advantages of being convenient to install, stable in performance, high in measurement precision and high in real-time performance, and can effectively cope with complex and dynamic battlefield environments.

The invention discloses a speech enhancement and noise reduction system based on a generative adversarial network, and the system comprises a time-frequency alignment module which is used for collecting and preprocessing original noise speech; the three-component coding module is used for extracting voice content features, voiceprint features and noise characterization; the dual-domain collaborative generation module is used for generating candidate enhanced waveform frequency spectrums and performing collaborative correction; the multi-head discrimination module is used for generating a noise correction factor through a time domain discriminator, a frequency domain discriminator and an index discrimination head; the noise re-projection module is used for correcting noise representation, generating heavy noise voice and obtaining a noise self-adaptive enhancement result set; and the consistency reconstruction module is used for spectrum consistency correction and phase reconstruction. According to the method, the double-domain three-decoupling VoiceGAN is used for voice enhancement and noise reduction, and the method has the advantages of being high in naturalness, high in intelligibility and good in generalization.

To provide an X-ray phase imaging apparatus and an image processing method for improving contrast and visibility of an image photographed by a phase contrast method.SOLUTION: According to an aspect of the present invention, there is provided an X-ray phase imaging apparatus comprising an X-ray source, a detector configured to detect X-rays emitted from the X-ray source to a subject, and an image processing unit configured to generate an image of the subject, the image processing unit includes a storage unit in which a plurality of noise correction images for correcting a luminance variation between pixels are stored separately for brightness and darkness, and an image generation unit that generates a luminance image of the subject by irradiating the subject with X-rays, generates a plurality of corrected images of the subject by correcting the luminance image according to the noise correction image, and generates one subject image by selecting the plurality of corrected images according to a threshold value.SELECTED DRAWING: Figure 5

The invention discloses a multi-camera correction method and system and a medium, and the method comprises the steps: controlling a sampling condition, and analyzing the system gain of each camera; based on the ratio of the standard gain to the system gain, the correction coefficient of each camera is determined to correct each camera, so that the responsivity of each camera after correction is consistent; the method comprises the following steps of: extracting multiple frames of images acquired by the same camera, calculating a time domain noise value of each camera, determining a noise correction coefficient of each camera by adopting a ratio of a standard time domain noise value to the time domain noise value of each camera, and performing noise filtering on a gray value of each pixel point in the images acquired by the cameras according to the noise correction coefficient of each camera, therefore, the noise consistency adjustment of each camera is realized. According to the method, the effect that the noise level is consistent on the premise that the responsivity of the cameras is consistent is achieved, and the universality of the cameras of the same model or different models is improved.

This invention provides a fan speed control method, device, electronic device, and computer-readable storage medium, applied to a range hood controller, and relating to the field of household appliance technology. The method includes: first, responding to user operation and acquiring the initial speed setting and its adjustment value; then, determining the noise variation range; furthermore, real-time monitoring of the range hood's back pressure, determining the noise correction coefficient corresponding to the back pressure using a preset noise correction coefficient table, and adjusting the noise variation range based on the noise correction coefficient; if the noise variation range is large, dividing it into multiple speed control intervals; adjusting the fan's operating state based on the rotational speed corresponding to the endpoints of the speed control intervals and a preset speed control time; by determining the noise variation range, dividing it, and performing linear speed control within the divided speed control intervals, the overall noise variation during the entire speed control process remains within an acceptable range for the user, improving the user experience.

The application relates to a fixed-pattern noise correction method of an infrared detector and belongs to the technical field of infrared imaging. The application can eliminate the fixed-pattern noise of the infrared detector in a real-time image by taking the fixed-pattern noise phenomenon of the infrared detector as a template and calculating the fixed-pattern noise intensity in the real-time image through correlation.

The invention relates to the technical field of noise prediction, in particular to a vibration noise correction method and device based on a structural rigid body mode. The method comprises the following steps: constructing a primary function of elastic displacement to form a first vibration mode matrix, and introducing a rigid body mode into the first vibration mode matrix to form a second vibration mode matrix; and obtaining a structural modal mass matrix, a modal stiffness matrix and a modal force by using the second vibration mode matrix, and deducing a modal participation coefficient and a modal frequency. Performing a simulation test in combination with the deduced modal parameter coefficient and the complete vibration mode matrix to obtain vibration responses of the structure under different frequencies, and predicting the structure according to the vibration responses to obtain simulation data; and dynamically correcting the modal matrix according to the difference between the actual test data and the simulation data of the structure. By introducing the degree of freedom of rigid body motion, the displacement function of the structure can more accurately describe the actual behavior of the structure, so that a more accurate basis is provided for simulating the actual vibration behavior.

The present invention provides a noise label correction method for unsupervised domain adaptive person re-identification, which relates to the field of unsupervised domain adaptive person re-identification. The noise label correction method for unsupervised domain adaptive person re-identification uses a source domain dataset and its corresponding labels to train a source model; uses a clustering-based unsupervised domain adaptive person re-identification method to fine-tune the source model, and uses the noise label correction framework proposed in this article to correct the noise labels of the fine-tuned domain adaptation model. Compared with previous noise label correction methods, this method uses a noise label corrector to process noise labels as variables, and updates the probability label variables through back propagation, gradually correcting the noise labels while updating the variables. This method can significantly improve model performance and is a plug-and-play noise correction method.

This invention relates to the field of pet food spectral detection technology, and discloses a pet food spectral detection device and method. The method involves sampling at equal intervals within a preset wavelength range, eliminating noise interference through dark current and standard white plate calibration; taking the natural logarithm of reflectance to balance the dynamic range; constructing a linear spline basis at equal intervals, and adaptively calculating the regularization coefficient based on the energy ratio of the signal and basis functions; constructing an augmented matrix based on the inner product of the basis functions and the signal, and solving for the spline coefficients using Gaussian elimination; reconstructing the spectrum using the coefficients and quantifying the residuals; establishing a reference model based on the mean and standard deviation of multiple qualified sample coefficients; comparing the Euclidean distance between the coefficients of the sample to be tested and the model mean with a threshold, and generating residuals, distances, thresholds, and a judgment report; the entire process requires no empirical parameters or manual tuning, enabling batch online adaptive detection, achieving rapid and accurate detection with noise correction, feature enhancement, overfitting suppression, and traceability.

The application relates to the technical field of video data processing, and relates to an audio-video synchronous noise reduction method and system based on AI visual perception, which comprises the following steps: pre-processing audio data to obtain pre-processed audio data; performing time-frequency analysis on the pre-processed audio data to obtain a speech feature set and a background sound feature set; performing noise reduction on video data to obtain primary noise reduction video data, performing visual perception on the primary noise reduction video data to obtain a video feature set; performing time axis correction operation on the speech feature set based on a mouth shape feature according to the video feature set to obtain an updated time axis; performing active noise reduction operation on the progress correction audio data according to the updated time axis and a pre-constructed background sound adaptation degree sequence to obtain noise correction audio data; and performing merging operation on the noise correction audio data and the primary noise reduction video data to obtain synchronous noise reduction audio-video. The application can improve the clarity of images and sounds in a video.

This invention discloses a direct snow depth inversion method based on a spaceborne photon-counting lidar, comprising: extracting the target snow-covered area, removing areas with thick cloud cover and non-target surface point clouds, aligning the surface, and performing noise correction; performing deconvolution to obtain a corrected snow attenuation backscattering profile; setting an initial snow absorption coefficient, inverting the backscattering path length distribution of non-absorbing snow, and calculating the first and second moments of this distribution; based on the first and second moments, simultaneously inverting snow depth, snow albedo, and diffuse scattering coefficient; combining the snow albedo and diffuse scattering coefficient to calculate an updated snow absorption coefficient; if the updated snow absorption coefficient does not meet the set convergence threshold, using the updated snow absorption coefficient as the new initial snow absorption coefficient, and repeating the iteration; if it meets the threshold, outputting the final snow depth. Using this invention, along-track snow depth inversion can be achieved directly without relying on any other data.

To accurately infer a higher-level operation from a recognition result of a series of atomic operations by a worker.SOLUTION: A production line analysis device 1 comprises: a data acquisition section 11 that acquires operation sequence data 31 representing a series of operations of a worker in a production line having a plurality of work stations; a noise detection section 12 that detects noise from the operation sequence data 31 on the basis of operation constitution information 32 representing a relationship between atomic operations by the worker and a higher-level operation comprising a combination of the plurality of atomic operations; a noise correction section 13 that corrects the operation sequence data 31 so as to reduce the noise; and an operation inference section 14 that infers, on the basis of the operation sequence data 34 after the correction by the noise correction section 13, the higher-level operation corresponding to a work item executed by the worker at each of the work stations of the production line.SELECTED DRAWING: Figure 3