19results about How to "Reduce random noise" patented technology

The invention provides an engine pipeline measurement method based on laser scanning, which belongs to a non-contact measurement technology, and comprises the following steps: before measurement, selecting a three-dimensional laser scanner, configuring and calibrating the three-dimensional laser scanner, carrying out a scanning test, adjusting a scanning environment, and then obtaining pipeline point cloud data; the outlier error of the pipeline point cloud data is denoised by using a KD-tree (K-dimension tree), and the outlier error of the pipeline point cloud data is denoised by using the KD-tree (K-dimension tree); de-noising system errors and random errors of the pipeline point cloud data by using a Gaussian filter; segmenting the point cloud data of the pipeline, and identifying and isolating all parts (linear segments, elbows and joints) of the pipeline; performing reverse modeling on the pipeline point cloud data to obtain a digital model of the pipeline; and comparing and analyzing the original point cloud of the pipeline and the pipeline model, and evaluating the error of pipeline measurement. According to the invention, the feature extraction and processing analysis of the pipeline point cloud are realized, the time cost of engine pipeline assembly is reduced, and the wear of the pipeline is reduced.

The invention relates to the technical field of artificial intelligence, and discloses an artificial intelligence-based multi-modal document analysis system, which consists of an image preprocessing module, a multi-modal feature extraction module, a natural language processing module, a modal fusion and analysis module and a document structure identification module. According to the method, the image quality is improved through image sharpening and noise and blurring removal, text, table and image areas are extracted through masks respectively, different document contents are separated accurately, visual features and extracted area information are combined together, a comprehensive feature vector is generated, the document contents are understood more comprehensively, and the document quality is improved. According to the method, the text extracted from the image is analyzed, semantic analysis and keyword extraction are carried out, so that text content is understood, keywords are extracted from the analyzed text, main topics and key points of the document are recognized, and the beneficial effect that the analysis result is more accurate due to multi-modal analysis of the document is achieved.

The invention provides a hyperspectrum-based tea tree canopy leaf nitrogen estimation method, which comprises the following steps of: firstly, smoothing original spectral data of a tea tree canopy by using a Savitzky-Golay method so as to reduce random noise and improve a signal-to-noise ratio; then, the original spectrum is converted into 600 wave bands at the interval of 1 nm through spline interpolation, so that the continuity and convenience of calculation are ensured; the method comprises the following steps of: firstly, carrying out first-order derivative processing on a spectrum, then carrying out second-order derivative processing on the processed spectrum, calculating a normalized difference spectrum index (NDSI) by using a derivative spectrum, calculating a Pearson's correlation coefficient (PCC) between the NDSI and a measured nitrogen element, carrying out principal component analysis (PCA) on the highest 20 indexes of the correlation coefficient, and finally constructing a multiple linear regression model by using the principal components with the first 10 PCA scores as parameters. The method is used for predicting the nitrogen content (LNC) of tea tree canopy leaves. Prediction experiments of a plurality of tea gardens show that the model shows a good prediction effect which is close to the actual nitrogen content, and the generalization performance is good.

The present application discloses a load balancing control method and system for an energy storage device, and relates to the technical field of energy storage load regulation. The method first obtains the historical operating data of the target energy storage device, and pre-processes it through the first Gaussian processing method to generate the first operating data. Subsequently, the first modal decomposition method is used to further process these data to decompose the multi-scale time series components, thereby generating the second operating data. These data are further input into a hybrid neural network, which includes two parts: the first sub-network extracts the spatial features of the data, and the second sub-network performs time dependency analysis. Based on the first load control information generated by the network, a control strategy for the target energy storage device is formulated. The system can also collect environmental data in real time, and adjust the noise parameters based on these data to optimize data processing and prediction accuracy.

The present application provides a point cloud reconstruction model generation method, a point cloud reconstruction method and an electronic device, and relates to the field of point cloud technology. The method includes: determining at least one target reconstructed ground in the ground to be reconstructed; performing image acquisition on each target reconstructed ground from different perspectives to obtain image pairs corresponding to each target reconstructed ground; determining the local optimization target corresponding to each target reconstructed ground based on at least one set of associated feature items in the image pairs corresponding to each target reconstructed ground; optimizing the basic point cloud reconstruction model corresponding to the ground to be reconstructed according to the local optimization target corresponding to each target reconstructed ground, and obtaining the target point cloud reconstruction model of the ground to be reconstructed. The present application achieves the effect of fast and accurate point cloud reconstruction of a large range of map elements.

The invention relates to an electromagnetic wave while-drilling multipath two-way transmission system, method, device, equipment and medium, the system comprises an underground transceiver system, a ground transceiver system, underground measuring equipment and a PC, the underground transceiver system comprises an underground transceiver and an underground antenna, and the ground transceiver system comprises a ground transceiver and a ground antenna; the underground transceiver comprises a first digital signal processing module, and the first digital signal processing module comprises a first sending processing module and a first receiving processing module; the ground transceiver comprises a second digital signal processing module, and the second digital signal processing module comprises a second sending processing module and a second receiving processing module. According to the underground transceiving system and the ground transceiving system, the electromagnetic wave channels are multiplexed in a time division mode, and the influence of conflicts and interference in the data transmission process is reduced. Source coding processing, convolutional coding processing and code element repeated processing are carried out on initial data, so that wireless electromagnetic waves can be transmitted while drilling more efficiently, stably and reliably.

The invention discloses a wheel track roughness separation method. The method comprises the following steps: resampling an original time domain ABA signal and interpolating the original time domain ABA signal into an angular domain signal with equal angle intervals; according to a wheel rotation total period and a synchronous average window, an angular domain signal of the ABA signal is segmented into N segments by using a synchronous average thought; carrying out downsampling on the segmented segments by using a measurement matrix based on an independent identically distributed Gaussian matrix; adding data points corresponding to each random down-sampling signal and calculating an average value to obtain a sequence of sparse angular domain synchronous average SADSA; reconstructing a sequence based on an orthogonal matching pursuit (OMP) algorithm in a compressed sensing basic theory (CS) to obtain a periodic component extracted from the ABA signal; the invention also discloses an application of the wheel-rail roughness separation method in quantitative detection of rail corrugation, and the invention proposes that ABA signals are subjected to random downsampling and accurate reconstruction by using a compressed sensing principle under the condition of not changing original data attributes.

This invention discloses a method, system, medium, and product for detecting terminal crimping imaging quality, relating to the field of imaging quality detection. In this method, deep learning feature extraction is combined with image processing technology. Through the synergistic effect of a dynamic threshold adjustment strategy library and an edge detection morphological processing rule library, a complete adaptive detection process is formed. This invention can automatically adjust detection parameters based on real-time image brightness conditions and terminal specification information, ensuring stable detection accuracy under different lighting environments and diverse terminal types. This reduces the problems of unstable detection accuracy and poor versatility caused by fixed parameters in existing technologies, improving the reliability and practicality of terminal crimping quality detection.

This application provides a method and system for detecting the location of welds in coated pipelines based on magnetic field attenuation characteristics. The method includes: applying pulsed eddy current excitation to the coated pipeline, wherein the pulsed eddy current excitation uses a square wave or step wave signal; arranging multiple magnetic field sensors along the circumference of the pipeline and receiving voltage signals induced in the pipeline by the pulsed eddy currents, which include secondary magnetic field decay signals; measuring the magnetic field decay rate of the voltage signal received by each magnetic field sensor, wherein the magnetic field decay rate is characterized by the attenuation slope of the secondary magnetic field decay signal; superimposing the attenuation rate slope values ​​of the multiple magnetic field sensors in the later stage of magnetic field decay to obtain a superimposed attenuation rate slope value; and determining the presence and location of the weld based on a comparison between the superimposed attenuation rate slope value and a preset threshold, wherein the preset threshold is set based on the attenuation rate slope value of the weldless region.

The present application relates to the field of optical fiber sensing, in particular to a kind of optical cable icing monitoring method and device.Mainly include: obtaining the first sensing data sequence in each sensing period, according to the first sensing data sequence, obtain the phase data sequence in each period;According to the phase data sequence, obtain the frequency spectrum data of the corresponding position phase change, according to the average value of the frequency spectrum data in multiple sensing periods, obtain the characteristic frequency, and estimate the icing thickness of the optical cable according to the change of the characteristic frequency.The present application reduces the interference fading of signal and environmental random noise, improves the single-span distance of icing monitoring, and can improve the icing monitoring accuracy.

The invention discloses a false fluorescence suppression method and system for a fluorescence endoscope camera system, and the method comprises the steps: carrying out the denoising, binaryzation, threshold processing and nonlinear mapping pseudo-color processing of a fluorescence image, obtaining a false fluorescence threshold range, and enhancing the fluorescence image; obtaining a highlight region binary image of the visible light image according to the visible light image; obtaining a fusion weight map of the visible light image and the fluorescence image according to the false fluorescence threshold range and the highlight region binary image of the visible light image; and fusing the visible light image and the enhanced fluorescence image according to the fusion weight map to obtain a final enhanced fluorescence image. According to the method, false fluorescence interference can be inhibited, and the accuracy and distinction degree of pathological tissue development are enhanced.

The present application relates to the technical field of intelligent management, and in particular to an intelligent management method, system and medium for engineering measurement based on the Internet of Things. By constructing an Internet of Things system containing monitoring nodes, gateways and cloud platforms, the present application can obtain the structural deformation data of each node in the engineering project area in real time, analyze the deformation data on the cloud platform, extract the change trend of the deformation parameters, compare the deformation parameters with the pre-set thresholds and historical data, so as to determine whether the deformation exceeds the limit or is abnormal, and then generate a monitoring report to achieve intelligent evaluation and early warning of the structural safety status.

The present invention discloses an automated management method and system for electromagnetic compatibility test data. The prior art lacks an effective data verification function, and cannot automatically detect and correct abnormal values ​​in the data, resulting in difficulty in ensuring the integrity and accuracy of the data. The method adopted by the present invention includes: constructing an automated data acquisition system, acquiring test data in real time, performing multi-level preprocessing on the test data, extracting features from the fused data, and obtaining nonlinear spatiotemporal features; using nonlinear spatiotemporal features to perform time series anomaly detection, detecting abnormal data points, using an adaptive interpolation correction method to perform correction, and introducing a nonlinear optimization method based on Lagrange multipliers to further optimize the correction effect of the data. The present invention improves the accuracy of anomaly detection by extracting complex patterns and potential abnormal information from the data, and ensures that the corrected data has good smoothness and consistency.

The invention discloses an infrared image detail enhancement method based on a video stream, and the method comprises the steps: superposing an infrared original image in the video stream to obtain an original image data matrix, carrying out the bilateral filtering and extreme value extraction of the original image data matrix, carrying out the smoothing processing through combining with a historical frame average parameter, and obtaining a parameter enhanced image; and inputting the original image data matrix and the original image data matrix after bilateral filtering into a detail enhancement module for enhancement, carrying out smoothing processing in combination with a historical frame average parameter after extreme value extraction to obtain a parameter enhanced image, and finally, carrying out linear superposition on the parameter enhanced image to obtain an enhanced infrared original image. According to the method, optimization design is carried out on the basis of a single-frame infrared image detail enhancement algorithm, and compared with a single-frame infrared image enhancement algorithm, the enhancement algorithm based on the video stream needs to pay attention to the input image difference between frames and the influence of the difference on the enhanced video stream under the algorithm framework of the enhancement algorithm based on the video stream.

The present application belongs to the field of airborne fire control radar, and particularly relates to a method for angle measurement of a two-dimensional multi-baseline phase interferometer with a special-shaped array. The method comprises: obtaining array elements involved in angle measurement, and calibrating the channels of each array element; determining three-dimensional coordinate axes based on the radar array surface, wherein the horizontal axis is the X axis, the vertical axis is the Y axis, and the vertical radar array surface is the Z axis, and according to the spacing between each array element on the Y axis, a multi-baseline phase interferometer is used to successively infer the angle deambiguation algorithm to solve the first spatial incoming wave angle; converting the array element phase difference not on the X axis to the mapping array element on the X axis, and according to the spacing between each array element on the X axis and the mapping array element, a multi-baseline phase interferometer is used to successively infer the angle deambiguation algorithm to solve the second spatial incoming wave angle; calculating the azimuth and elevation angle of the radar system of the incoming wave; performing N samplings, and looping through steps 2 to 4, and calculating the average value of N groups of azimuth and elevation angles as the final incoming wave direction.

The present invention relates to an autocollimator device based on an orthogonal cross multi-slit group. A light source is located at the focus of a focusing lens, and a divergent light beam of the light source is converged by the focusing lens to uniformly illuminate the orthogonal cross multi-slit group grating plate to form a positioning light spot, and the orthogonal cross multi-slit group grating plate is located at the focal plane of the collimating objective lens; the positioning light spot light is reflected by a right-angle prism and a beam splitter prism, and then emitted from the collimating objective lens to irradiate the reflector to be measured; the light emitted by the collimating objective lens is reflected by the reflector to be measured and then returned, and is focused on the photosensitive surface of a planar array image detector after passing through the collimating objective lens and the beam splitter prism in sequence; the planar array image detector is used to image the positioning light spot, and realize high-precision monitoring of the position change of the positioning light spot image, and further calculate the deflection angles of the reflector to be measured in the x and y axis directions. The present invention improves the resolution of the autocollimator by averaging the image position twice.

The application discloses a multi-sampling rate seismic data reconstruction method and device, and belongs to the technical field of petroleum geophysical exploration processing. The multi-sampling rate seismic data reconstruction method comprises the following steps: establishing a regular observation system based on an irregular observation system; establishing a three-dimensional array based on the regular observation system; obtaining the numerical value of an element in the three-dimensional array; obtaining a to-be-reconstructed receiving point set in the regular observation system based on the numerical value of the element in the three-dimensional array; performing two-dimensional curved wave transformation on each receiving line in the regular observation system to obtain a three-dimensional array after first reconstruction; and performing three-dimensional curved wave transformation on the numerical value of an element in the three-dimensional array after first reconstruction to obtain a three-dimensional array after second reconstruction. The application adopts two-dimensional curved wave transformation to reconstruct three-dimensional data, reduces the generation of random noise in the reconstruction process, reduces the iteration number of three-dimensional curved wave transformation, improves the calculation efficiency, and has good reliability and practicability.

The invention provides a quantum efficiency obtaining method and system and electronic equipment, and belongs to the technical field of image sensor testing and data analysis, and the quantum efficiency obtaining method comprises the steps: collecting multi-frame original image data; averaging each pixel value of each pixel point in the multi-frame original image data to obtain average frame image data; averaging pixel values of all pixel points in the test area in the average frame image data to obtain an average pixel value of an average frame; the ratio of the first difference value of the average pixel values of the average frames under the two test exposure times to the second difference value of the two test exposure times is used as the responsivity of the image sensor; and acquiring the actual photoelectric responsivity and quantum efficiency of the image sensor according to the responsivity of the image sensor, the irradiance of the incident light and the conversion gain of the image sensor. According to the quantum efficiency acquisition method, the quantum efficiency acquisition system and the electronic equipment provided by the invention, the test precision of the quantum efficiency can be improved.

The invention relates to the technical field of laser gyroscopes, and in particular to a temperature-adaptive laser gyroscope correction system, comprising a gyroscope body and a correction unit, wherein the correction unit comprises: a temperature acquisition module, which is arranged on the gyroscope body and acquires current working temperature data of the gyroscope body; a data operation and processing module, which acquires the acquired temperature data, performs operation and processing through an established functional relationship model between angular velocity and temperature data, and outputs expected angular velocity data; an error correction module, which acquires angular velocity data actually output by the gyroscope body, performs operation and processing on the angular velocity data compared with the expected angular velocity data, obtains current angular velocity error parameters, and calculates angular velocity compensation parameters based on an error evolution model; and a feedback adjustment module, which outputs the angular velocity compensation parameters to a control circuit of the gyroscope body based on the angular velocity compensation parameters, so as to dynamically correct the angular velocity output of the gyroscope body and realize continuous stability and precision of the angular velocity output of the laser gyroscope.