18results about How to "Remove random noise" patented technology

The invention discloses a map navigation method and system based on integrated navigation, and particularly relates to the field of integrated navigation, comprising compass data acquisition, magnetic declination correction, map integration, navigation system data acquisition, navigation system data analysis and integrated navigation. According to the method, the precision and robustness of the navigation system are remarkably improved, different environments can be dynamically adapted, and the error influence is reduced; rich geographic information support is provided by presetting an electronic map and POI marks; the real-time navigation service provides visual road condition information and path planning, the user experience is enhanced, and on the whole, more accurate, reliable and convenient navigation service is provided for the user.

The invention relates to the technical field of RFID chip manufacturing, in particular to an RFID chip image anomaly detection method and system based on intelligent learning. The method comprises the following steps: acquiring a real-time RFID chip sampling image set, performing feature alignment based on a registration template, generating an average grey-scale map by using an alignment image set, and obtaining a chip edge mask image set by using an edge search algorithm; segmenting the edge mask image set of the RFID chip to obtain an effective edge mask variance image set of the RFID chip; integrating the variance image set to obtain a pre-generated variance image; acquiring field environment data of the RFID chip and performing sensitivity coefficient estimation to obtain an edge sensitivity coefficient; and performing convolution filtering smoothing processing on the pre-generated variance image to obtain a chip edge variance image, and constructing an RFID chip image anomaly detection model based on the edge variance image and the average grey-scale image. According to the invention, the detection accuracy and stability are improved.

The invention discloses a grinding workpiece edge shape detection method and system based on machine learning, and the method comprises the steps: obtaining an original image of a grinding workpiece, and carrying out the preliminary image preprocessing; denoising the image and enhancing edge information by combining image filtering and mathematical morphology image processing; based on a second-order differential operator, edge detection is carried out in combination with the morphological gradient, and a workpiece edge contour is obtained; performing feature extraction on the image by using a convolutional neural network, automatically learning workpiece edge features, and constructing and training an edge detection model; acquiring a workpiece image in real time, inputting the workpiece image into the edge detection model for detection, and dynamically correcting the model; and S6, outputting a final edge detection result. According to the invention, by combining image preprocessing, composite image filtering, second-order differential operator and morphological gradient edge detection and feature extraction and model construction of the convolutional neural network, high-precision and robust detection of the edge of the grinding workpiece is realized.

The invention relates to the field of industrial equipment monitoring, and discloses a multi-sensor integrated system for remote monitoring of industrial equipment, which comprises a data acquisition module, a data processing module, a fault prediction module, a self-adaptive control module and a monitoring feedback module, the data acquisition module acquires multi-dimensional data from sensor arrays of a plurality of devices; the data processing module carries out noise reduction processing on the data and extracts key features; the fault prediction module predicts a short-term fault of the equipment and predicts a long-term operation trend; the self-adaptive control module adjusts control parameters of the equipment according to the prediction result and optimizes a control strategy; and the monitoring feedback module generates real-time fault alarm information by realizing remote monitoring and feedback of the equipment. The system can improve the safety and stability of equipment operation, optimize equipment management and reduce the equipment maintenance cost, and has high real-time performance, accuracy and intelligent level.

The invention discloses a low-illumination image enhancement algorithm based on deep convolution, which is suitable for the field of image processing, and the core of the algorithm lies in an efficient noise reduction process and accurate decomposition of an illumination image and a reflection image. Comprising the following steps that firstly, a channel noise reduction module works cooperatively through multiple noise reduction strategies, noise in an image is effectively removed, and a foundation is laid for follow-up processing; then, the illumination image decomposition network and the reflection image decomposition network accurately separate the brightness information, texture and color information of the image based on a deep learning structure. And finally, by ingeniously fusing the illumination image and the reflection image, reconstructing a high-quality enhanced image, remarkably improving the brightness, contrast and details of the image, and meanwhile, retaining the natural color of the image. The algorithm has the advantages of being high in noise reduction capacity, accurate in illumination image decomposition, efficient in reflection image decomposition, good in enhancement effect and the like, and an effective and practical solution is provided for image enhancement under the low-illumination condition.

The invention discloses an underwater distorted image restoration method based on nonlinear reconstruction and random image block optimization. The method comprises the following steps: eliminating periodic distortion in a video sequence according to nonlinear reconstruction; random image block optimization is carried out on the video sequence after periodic distortion elimination; fusing and splicing the selected image blocks to form a complete image; and repeating the steps until a restored image sequence is obtained. According to the method, nonlinear reconstruction and random image block optimization are creatively fused into a whole, the method is extremely sensitive to obvious periodic distortion, and the details and definition of the image are improved by selecting and fusing the optimal block in the image, so that large-amplitude distortion in the image is restored, and the restoration effect of the underwater distorted image is greatly improved.

The invention discloses a display screen flat field correction method, device and system and a storage medium, which are used for improving the uniformity of a display screen image after flat field correction. Acquiring a plurality of frames of initial bright field images through acquisition equipment; performing gray average processing on the plurality of frames of initial bright field images to generate an average bright field image; selecting an ROI region on the average bright field image according to the gray level uniformity of the average bright field image; generating an ROI region mean value according to the pixel point information of the ROI region; generating a flat field correction coefficient graph according to the average bright field image and the ROI mean value; and collecting a to-be-corrected display screen through the collection device, and carrying out flat field correction processing on the collected to-be-corrected image according to the flat field correction coefficient graph.

The invention provides a circuit board life prediction method and system based on mass failure welding spot data and a risk matrix, electronic equipment and a storage medium, and relates to the field of welding spot failure. The method comprises the following steps: acquiring massive invalid welding spot data, and extracting welding spot failure mechanism characteristics from the invalid welding spot data based on expert knowledge to obtain welding spot failure mechanism characteristics; based on the welding spot failure mechanism characteristics, welding spot failure characteristic factors are determined; acquiring welding spot failure characteristic factor data and circuit board data of a to-be-predicted circuit board, and determining welding spot failure data of the circuit board based on the welding spot failure characteristic factor data; and determining a welding spot failure type based on the welding spot failure data, and inputting the circuit board data and the welding spot failure type into a preset life prediction model to obtain a life prediction value. By means of the method, mass data are deeply utilized, essential reasons influencing welding spot faults are found out, and accurate evaluation and prediction of the residual life of the circuit board are achieved.

The invention discloses a remote detection method for soot blowing system pipe structure defects, and relates to the technical field of thermal equipment detection. The problems that in the prior art, detection needs to be stopped, real-time monitoring cannot be achieved, and defect information is not visually presented can be at least partially solved. The method comprises the following steps: deploying an electromagnetic ultrasonic guided wave sensor array around a welding seam to perform signal excitation and synchronous acquisition; performing dual noise reduction processing by adopting synchronous average and wavelet transform to extract defect signals; constructing a space model to position defects based on the signal arrival time difference; and generating a defect image by utilizing C-scanning imaging and performing quantitative evaluation. According to the invention, on-line remote detection without shutdown is realized, the signal-to-noise ratio is obviously improved through dual noise reduction, accurate positioning and visual display of defects are realized by combining time difference analysis and C scanning imaging, and safe operation of a unit is guaranteed.

The invention discloses an SVD (Singular Value Decomposition) seismic fracture-cavity body enhancement method based on structural constraint, which comprises the following steps of: taking a seismic data sub-body in a set surface element from single-channel post-stack seismic data, and performing singular value decomposition on the seismic data sub-body to obtain a singular value and a singular vector; performing data reconstruction according to the size sequence of the singular values, performing similarity structure evaluation on the reconstructed data and the original data, gradually determining a fracture-cavity response threshold value based on a similarity coefficient, and determining a noise response threshold value according to an energy retention principle; designing a soft threshold window function according to the fracture-cavity response threshold and the noise response threshold to construct a fracture-cavity enhancement operator; applying a fracture-cavity enhancement operator to the singular value matrix to obtain reconstructed fracture-cavity enhanced seismic data of the single-channel data; and repeating the process on all single-channel seismic data to obtain a final fracture-cavity enhanced seismic data volume. According to the method, the seismic response characteristics of the fracture-cavity body can be effectively enhanced, the weak signals of the reservoir are highlighted, and the interpretation precision is further improved.

The invention provides a positioning and leveling method, system and equipment of an underwater leveling machine and the underwater leveling machine, and relates to the technical field of ocean engineering. The method comprises the following steps: acquiring an original ranging data sequence of a fixed reference node; performing deviation compensation and correction on the original ranging data sequence to generate a target ranging value; performing time sequence filtering processing on the target distance measurement value of the distance measurement link between the underwater leveling machine and each fixed reference node to obtain a smooth distance measurement sequence; selecting data at multiple moments from the smooth ranging sequence to construct a time window input matrix, taking the time window input matrix as the input of the target deep learning model, and outputting the predicted position of the underwater leveling machine; and generating a control instruction based on the predicted position so as to control the underwater leveling machine to carry out positioning and leveling. According to the method, by combining multi-stage data processing and deep learning prediction, distance measurement errors and environmental interference are eliminated, so that the positioning and leveling accuracy of the underwater leveling machine can be improved.

The present invention provides an AI-based three-light image type fire detector, which relates to the technical field of data processing and includes: a flame spectrum acquisition module, which is used to obtain multi-spectral image data of a monitoring area; a preprocessing module, which is used to preprocess the multi-spectral image data and generate multi-spectral data; a feature extraction module, which is used to generate multi-dimensional feature data according to the multi-spectral data; an environmental condition acquisition module, which is used to collect environmental condition data of the monitoring area; an AI classification module, which is used to perform layer-by-layer analysis and classification processing on the multi-dimensional feature data by a multi-layer neural network model to generate a preliminary fire identification result; a background adaptation module, which is used to receive the preliminary fire identification result and the environmental condition data, and process the preliminary fire identification result to obtain fire identification data; an alarm triggering module, which is used to receive the fire identification data and determine whether the preset fire alarm conditions are met; the present invention has a good detection effect.

This invention discloses a spectral analysis method, apparatus, device, and storage medium. It includes: acquiring raw spectral data to be analyzed; preprocessing the raw spectral data to obtain preprocessed spectral data; performing multi-peak fitting on the preprocessed spectral data using an iterative residual peak-finding fitting algorithm to obtain a final fitted spectrum and a final residual spectrum; and generating spectral analysis results based on the final fitted spectrum and the final residual spectrum. Preprocessing the raw spectral data effectively eliminates background interference, cosmic ray pseudo-signals, and random noise, improving the signal-to-noise ratio of the spectral data. Using the iterative residual peak-finding fitting algorithm for multi-peak fitting accurately identifies and fits all significant spectral peaks, avoiding underfitting and overfitting problems, and improving the accuracy of multi-peak fitting. The generated spectral analysis results can accurately extract core feature indicators, and the fitting quality can be verified through residual spectroscopy, ensuring that the analysis results are accurate and reliable, truly reflecting the structural characteristics of the material.

The invention relates to the technical field of image display, in particular to a metal metallographic structure image classification display method and system. The method comprises the following steps: preprocessing a metal metallographic structure image, calculating and evaluating an image feature index Tzls, quantifying the overall quality of the metal metallographic structure image, automatically screening low-quality and repeated images, and reducing stored and processed invalid data; classifying the metal metallographic structure images by using two characteristics of structural factors and defect factors of the metal metallographic structure; the metal metallographic structure image is classified and graded by calculating and evaluating a structure characteristic index Jtzs and a defect characteristic index Qtzs; and all grade division results are transmitted to the image classification display module, so that timely and accurate data transmission is ensured, a user searches and filters according to a structure grade or a defect grade, a required image is quickly found, and the working efficiency is improved.

The invention relates to a UWB radar target motion prediction method and system based on a multi-convolutional neural network, and the method comprises the steps: obtaining echo signal features collected by a UWB radar in real time and corresponding target motion parameters, and obtaining an initial data set through time series data division and normalization processing; extracting and splicing the initial data set based on a local mean decomposition algorithm to obtain standard input feature data; constructing a motion parameter prediction model based on the echo signal features, optimizing the motion parameter prediction model by adopting a genetic algorithm, and performing training through standard input feature data; and eliminating multi-path and noise interference from echo signal characteristics corresponding to a to-be-detected target through local mean decomposition, and inputting the echo signal characteristics into the trained motion parameter prediction model to obtain predicted motion parameters of the to-be-detected target. The UWB radar motion parameter prediction precision and generalization ability can be improved through LMD interference removal and CNN combined with attention and gating and genetic algorithm optimization.

The application provides a college major talent outflow trend prediction method based on multi-channel feature fusion and adaptive weighting random forest, relates to the talent flow prediction and machine learning application technical field, and comprises the following steps: collecting college major enrollment data and corresponding macroeconomic data of each year, and preprocessing missing data; adopting a multi-channel feature decoupling and fusion mechanism; the multi-channel feature decoupling and fusion mechanism adopted by the application effectively solves the defects of weak feature expression ability and neglecting external economic driving factors in the prior art, decouples internal trends and external driving factors into three channels, comprehensively extracts enrollment sequence characteristics, regional macroeconomic characteristics and regional economic difference characteristics, realizes deep fusion of internal and external information, enhances the model's recognition ability to complex patterns, can fully capture the influence of macroeconomic variables on professional talent flow, and greatly improves the prediction accuracy.

The present invention relates to the field of spectrum detection technology, and in particular to a method, device and Raman spectrometer for denoising a Raman spectrum signal. The method for denoising a Raman spectrum signal comprises step S100, sampling a reset signal N times, and performing an addition operation on the reset signals obtained by sampling to obtain a first signal; step S200, sampling a valid signal N times, and performing an addition operation on the valid signals obtained by sampling to obtain a second signal; step S300, performing a subtraction operation on the first signal and the second signal and dividing by N to obtain an actual pixel signal. By collecting the reset signal and the valid signal multiple times and adding and then subtracting them, the reset noise can be eliminated while eliminating random noise, thereby reducing the interference of noise on the spectrum signal, improving the signal-to-noise ratio, and then effectively improving the accuracy of Raman spectrum data for material analysis.

The invention provides a pixel parasitic light sensitivity effect suppression method and an image sensor. The method comprises the following steps: acquiring a plurality of frames of basic images; calculating the signal intensity of each row of pixels by using the multiple frames of basic images to obtain a signal intensity data set; obtaining a parasitic light correction coefficient of each row of pixels according to the signal intensity data set; and performing parasitic light correction on the input original image by using the parasitic light correction coefficient. The parasitic light correction coefficient corresponding to each line is calculated according to the signal intensity of each line of the multi-frame basic image, so that the parasitic light correction coefficient of each line can be utilized to perform line-by-line parasitic light correction on the input original image, and the parasitic light sensitivity effect is effectively inhibited in a dynamic correction mode. Meanwhile, due to the fact that parasitic light correction is carried out line by line, random noise in pixel signals of the current line can be eliminated, image consistency is guaranteed, image quality is improved, and the problem of how to effectively suppress the parasitic light sensitivity effect to improve the image quality is solved.