210 results about "Denoising algorithm" patented technology

The invention relates to an electronic tag production quality control method in the technical field of information, and the method comprises the steps: carrying out the noise reduction of an image through a multi-scale denoising algorithm in a second image for vibration interference and dust interference, and obtaining a third image; the online detection system judges the printing quality of the electronic tag according to the OCR recognition result, and if abnormality is detected, an abnormal signal is generated and transmitted to the process control system; the process control system receives the abnormal signal, adjusts operation parameters of printing, fitting and cutting processes, ensures smooth process connection, and avoids unbalance of the production rhythm; comparing the adjusted process parameters with a preset product consistency standard, and if the parameter deviation exceeds a preset range, starting an automatic calibration module to ensure the product consistency; the operation state of the whole production process is monitored in real time through a system monitoring module, if the stability of the system is reduced, an early warning mechanism is triggered, and a maintenance signal is generated and transmitted to a maintenance system.

The invention discloses an improved filtering and local direction adaptive rust recognition method for a hull surface rust image, which comprises the following steps of: 1, performing gray processing on the hull surface rust image to obtain a gray image; 2, performing global denoising on the image after gray scale by adopting a wavelet transform and total variation fusion denoising algorithm to obtain a globally denoised image; 3, performing local denoising on the globally denoised image by adopting a self-adaptive Gaussian kernel denoising algorithm to obtain a final denoised image; 4, carrying out the edge extraction of the final denoised image through a direction self-adaptive edge detection algorithm, and obtaining a rust image; according to the method, the improved wavelet transform and total variation fusion denoising algorithm is combined, efficient noise removal is achieved, the size and the standard deviation of the filtering kernel are adjusted in a self-adaptive mode, noise of a local area is effectively processed, detail loss is avoided, and the image quality is improved.

The invention discloses an improved filtering and adaptive edge detection steel plate gap identification method for a hull surface image, and the method comprises the steps: 1, carrying out the denoising of a hull surface image containing a steel plate gap through employing a local weighted Gaussian blur and dynamic bilateral filtering fusion algorithm, and obtaining a denoised hull surface image; 2, carrying out the edge detection of a local region of the denoised hull surface image through employing a self-adaptive dynamic threshold Canny edge detection method, and obtaining a binary image; and step 3, carrying out edge extraction on the binary image to obtain a steel plate gap image. According to the method, through a local weighted Gaussian blur and dynamic bilateral filtering fusion denoising algorithm, the noise in the image is efficiently removed in a proportional fusion mode, meanwhile, through self-adaptive adjustment of filtering parameters, the noise influence of a local area is improved in a targeted mode, loss of detail information is avoided, and the image quality and detail presentation are remarkably improved.

The invention discloses a partial discharge on-line monitoring method of a switch cabinet and a storage medium, and relates to the technical field of power equipment monitoring. According to the method, multi-dimensional state parameters such as vibration amplitude and pulse density are obtained in real time by constructing a local, edge and cloud three-level processing architecture, and self-adaptive distribution of calculation tasks is achieved based on a dynamic unloading scoring formula. The local nodes execute time domain feature extraction, the edge nodes perform frequency domain enhancement and confidence evaluation, and the cloud completes time-space depth analysis. A multi-domain feature weighted fusion mechanism is innovatively adopted, the signal quality is improved in combination with a wavelet dual-threshold noise reduction algorithm, and quantization errors are corrected through a residual compensation module. When the edge node loads gt; and when 80%, dynamic resource scheduling is triggered, and resource allocation is optimized and calculated.

The invention relates to the field of image processing, and particularly discloses a rural highway pavement disease intelligent identification and positioning system comprising an image standardization module used for obtaining a standardized grayscale image; the wavelet decomposition module is used for obtaining a low-frequency sub-band and a plurality of high-frequency sub-bands; the high-frequency processing module is used for carrying out soft threshold processing on the high-frequency coefficient and retaining high-variance region features; the inverse transformation module is used for reconstructing the de-noised image; the edge enhancement module is used for highlighting crack and pit slot target contour information; the binarization module is used for segmenting a foreground disease candidate region; and the edge filling module is used for separating the disease from the background. According to the method, the core contradiction between background impurity removal and disease feature retention in rural highway tiny disease recognition is effectively solved, a traditional denoising algorithm either smooths tiny disease features or cannot thoroughly remove background impurities, and the system achieves the balance of the background impurity removal and the disease feature retention through cooperation of multiple modules.

The invention discloses a method and a system for correcting wrong words in minority language text image recognition, and the method comprises the steps: obtaining a minority language text image, removing the high-frequency noise of the minority language text image through employing a preset adaptive threshold denoising algorithm based on wavelet transformation, and generating a denoised image. The morphological contour features of at least one character are extracted from the denoised image, the morphological contour features comprise stroke thickness, stroke number and stroke order direction, the features are input into a support vector machine classification model, the language category of the denoised image is determined, and a language classification result is generated. And loading a corresponding character segmentation model and a corpus according to a language classification result, carrying out character-by-character segmentation and pattern matching identification on the denoised image, and generating a preliminary identification result and an identification confidence score of each character. According to the method for correcting the wrong words in the text image recognition of the minority language, the accuracy of character recognition of the minority language and the capability of correcting the wrong words are improved, and recognition challenges in complex scenes can be coped with.

The invention discloses a medical image semantic segmentation method based on attention mechanism optimization, and relates to the technical field of medical image processing, and the segmentation method comprises the specific steps: S100, data collection and label preprocessing: collecting medical image data from different medical institutions and a plurality of imaging devices, according to the method, the attention mechanism is introduced to carry out deep preprocessing on the medical image data, the precision and efficiency of semantic segmentation of the medical image are remarkably improved, the attention mechanism is utilized, key areas, such as diseased regions or tissue boundaries, in the image can be recognized and enhanced, meanwhile, noise and irrelevant information are effectively removed, and the accuracy of semantic segmentation of the medical image is improved. The refined preprocessing mode not only improves the quality of the image, but also provides a more accurate data basis for subsequent image detection and segmentation, and the method is also combined with a self-adaptive denoising algorithm, dynamic adjustment of contrast, brightness and color and a geometric transformation advanced preprocessing technology, so that the availability and diagnostic value of the image are enhanced.

The invention provides a Fresnel optical image target detection and identification method based on event stream data enhancement, and the method comprises the steps: carrying out the data preprocessing of collected Fresnel optical image event data through employing a denoising algorithm based on time-space continuity, and screening the original event stream data; converting the screened event stream data of the target area into pixel data under an image coordinate system by adopting a dual threshold limiting strategy of event quantity and accumulation time; existing image features are enhanced, and pixel-level fusion, feature-level fusion and decision-level fusion are carried out on event stream data of an event camera and RGB camera data; and the target detection module is used for performing target detection and recognition on the fused data by using a YOLO target detection model. According to the method, the detection of the Fresnel optical image target is realized, so that the detection and recognition capability of the Fresnel optical image target in a complex environment is improved, and the method is particularly suitable for scenes such as seaborne landing guidance and the like.

The invention relates to a switch cabinet partial discharge signal positioning method based on a time difference positioning method, and belongs to the technical field of electrical engineering and signal processing. Aiming at the problem that the ultrasonic partial discharge signal has white noise, an improved wavelet threshold algorithm is adopted to construct a threshold function, and a continuous self-adaptive wavelet threshold denoising algorithm is adopted to carry out denoising processing on the partial discharge signal, so that the problems of excessive denoising and incomplete denoising existing in a traditional denoising algorithm are solved, effective separation of the signal and the noise is realized, and the denoising efficiency is improved. And the performance of the denoising algorithm is improved. In order to obtain a time delay value between each path of signals, a generalized cross-correlation time delay estimation algorithm based on PHAT-SCOT joint weighting is used for substituting a time delay estimation value into a switch cabinet partial discharge positioning equation set, and an optimal equation is obtained. According to the partial discharge optimal solution algorithm based on DE-PSO, the optimization equation is solved, the spatial coordinate position generating the partial discharge signal is positioned, the defects of parameter setting and threshold selection of the optimization algorithm are overcome, and the positioning precision of the partial discharge signal is improved.

A deep learning fan fault diagnosis method based on double wavelet fusion and CEEMDAN decomposition comprises the following steps: S1, collecting vibration signals of a fan motor driving end, a fan driving end and a non-driving end, and constructing a data set; s2, performing improvement on the basis of wavelet packet noise reduction to form a double-wavelet fusion noise reduction algorithm; s3, carrying out noise reduction processing on the acquired signals; s4, carrying out sample division on the noise reduction signals according to a fixed time window, and carrying out classification according to measurement points; s5, randomly dividing the samples into a training set, a verification set and a test set; s6, decomposing all the samples by using CEEMDAN, and extracting IMFs; s7, sending the sample into the time-frequency domain joint feature extraction model of the corresponding measuring point for modeling; s8, training to obtain a diagnosis model and exporting a weight file; and S9, loading the model detection test set, and comprehensively judging the operation state of the fan. According to the invention, the signal processing quality and the fault identification accuracy are improved.

The invention provides a comprehensive noise reduction performance evaluation method for a nonlinear ultrasonic detection signal noise reduction algorithm, and the method comprises the steps: firstly constructing a multi-working-condition noise reduction performance pre-screening mechanism based on error band analysis based on early-stage experimental data, and then introducing a radar map as an evaluation tool for the comprehensive noise reduction effect of the signal noise reduction algorithm. The noise reduction effects of a moving average method (MA), a spectral subtraction method (SS), a short-time Fourier transform method (STFT), a wavelet transform method (WT) and an orthogonal matching pursuit algorithm (OMP) are compared, and finally a signal noise reduction algorithm with the optimal comprehensive efficiency is screened out. On the basis of algorithm optimization, quantitative mapping rules between microcrack three-dimensional geometric parameters and relative nonlinear coefficients are analyzed through regression modeling, and the significant level of the correlation degree of the relative nonlinear coefficients and microcrack size parameters is effectively improved; and a high-confidence theoretical support is provided for quantitative nondestructive detection of the microcracks in engineering practice.

The invention provides a Prony algorithm-based cable segment wave velocity acquisition method, device and system, and a medium, and the method comprises the steps: testing a to-be-tested cable, and obtaining a cable signal transfer function; on the basis of the cable signal transfer function, in combination with cable joint distribution, constructing a cable signal approximation function based on a Prony method; constructing a linear prediction model of the cable signal based on the cable signal approximation function; performing denoising processing on the linear prediction model based on a singular value decomposition method to obtain a denoised linear prediction model; solving the denoised linear prediction model to obtain an attenuation coefficient; and calculating the segmented wave velocity of the cable based on the attenuation coefficient. According to the method, the Prony estimation method is combined with the singular value decomposition noise reduction algorithm, high-precision extraction of attenuation constants and segmented wave velocity decoupling are achieved, and then the electrical distance positioning precision is improved.

The invention discloses a method for detecting tea saponin in a tea extract, and relates to the technical field of chemical analysis and computer science, and the method comprises the following steps: eliminating high-frequency noise of tea extract data by using a Gaussian filtering algorithm, eliminating baseline drift by using a baseline correction algorithm, and generating a tea extract sample data stream; the method comprises the following steps: performing dynamic gradient separation through a chromatographic signal simulation algorithm on the basis of a tea extract sample data stream to generate a virtual chromatographic peak sequence, calculating a peak area through a sliding window integration algorithm on the basis of the virtual chromatographic peak sequence, and generating chromatographic signal data by using a wavelet transform threshold denoising algorithm, the chromatographic signal data are input into the deep convolutional neural model, the feature pyramid network is obtained through the multi-scale feature fusion algorithm, the feature information of the chromatographic peaks is extracted from different scales by constructing the deep convolutional neural model and combining the multi-scale feature fusion algorithm, and therefore the recognition precision of the feature peaks is improved.

The invention belongs to the field of power equipment state monitoring, and relates to a generator partial discharge on-line monitoring method, system, equipment and medium, and the method comprises the following steps: obtaining four types of original signals of a generator; extracting a partial discharge feature significant signal layer from the original signal by using an adaptive hierarchical denoising algorithm; constructing a feature quantity library based on the partial discharge feature significant signal layer, and extracting the frequency spectrum gravity center and wavelet energy entropy of each type of original signals; performing fuzzy logic weighting on the frequency spectrum gravity center and the wavelet energy entropy of each type of original signals to obtain a primary fusion result of each type of original signals; performing deep fusion on the primary fusion result of each type of original signals by adopting an LSTM neural network to obtain a partial discharge primary judgment result; and obtaining the score of the partial discharge preliminary judgment result based on the abnormal degree score of the mahalanobis distance, and if the score is greater than a decision threshold, determining that partial discharge occurs. The partial discharge of the generator can be quickly and effectively judged, and field operation and maintenance personnel can respond to the partial discharge conveniently.

The invention provides a quick image recognition method for the water quality condition in a water supply pipeline, and the method is characterized in that a high-definition camera with a light source is used for obtaining a color image in the underground water supply pipeline, the color image is converted into a gray image through an OpenCV method, the gray image is converted into a high-resolution image through a super-resolution method, and the high-resolution image is used for recognizing the water quality condition in the underground water supply pipeline. Carrying out segmentation and feature enhancement on a distribution region of suspended particulate matters in the high-resolution image by applying an average signal-to-noise ratio, a local self-adaptive thresholding algorithm and a denoising algorithm, extracting a region related to a water quality turbidity parameter, comparing a feature region containing the water quality turbidity parameter with a standard water quality turbidity grade image database, and obtaining a water quality turbidity grade image; and judging the turbidity grade of water in the in-service water supply pipeline. According to the invention, rapid identification and real-time monitoring of the pipeline water quality turbidity grade in the water supply network are effectively realized, and a new method is provided for rapid identification and condition monitoring of the water quality of the water supply network.

The invention belongs to the field of image processing and computer vision in computer intelligent information processing, and discloses an industrial product unsupervised anomaly detection method and system based on a conditional control diffusion model, and the method comprises the steps: constructing a diffusion model based on conditional control and a multi-scale double-attention mechanism, reconstructing the to-be-detected sample image by using a diffusion model based on condition control and a multi-scale double-attention mechanism to obtain a reconstructed image with the same size as the to-be-detected sample image; and extracting feature maps of different scales from the to-be-detected sample image and the reconstructed image through the same pre-trained feature extraction model. Performing a two-stage background denoising algorithm on each scale feature map to obtain an initial abnormal feature map; calculating an average value of a plurality of initial abnormal feature maps obtained by using the same method to obtain a final abnormal feature map; and processing the final abnormal feature map by adopting a thermodynamic diagram generation algorithm to generate a final abnormal thermodynamic diagram, thereby realizing the anomaly detection of the to-be-detected sample image.

The invention discloses a method and a system for detecting internal defects of a bathroom glass door based on multispectral imaging. The method comprises the following steps: carrying out multi-view and multiband image acquisition on the bathroom glass door by adopting a combined spectrum light source; performing adaptive reflection correction on the acquired image to generate a multi-band basic image; optimizing the multi-band basic image through a hybrid denoising algorithm to generate a multi-band detection image; performing multi-modal feature extraction and fusion on the multi-band detection image to obtain a multi-modal fusion feature vector, inputting the multi-modal fusion feature vector into a multi-feature fusion model combining a convolutional neural network and a support vector machine, and identifying and grading defect types by adopting a dual-stage feature enhancement-classification architecture; and outputting a defect type and severity grading result to a terminal in real time. The method is used for solving the technical problems of visual angle and spectrum blind areas, insufficient interference suppression capability, wave band crosstalk, feature deficiency and poor small sample generalization in existing bathroom glass door defect detection.

The invention relates to the technical field of infrared imaging, in particular to an infrared image self-adaptive denoising algorithm and system based on an FPGA (Field Programmable Gate Array), and is characterized in that an adaptive blind pixel searching algorithm module captures data of a certain frame number at an FPGA end, performs multi-directional gradient detection on each pixel point, sets conditions to judge to obtain a blind pixel position, and marks the blind pixel position; the convolution kernel self-adaptive blind pixel replacement module performs self-adaptive blind pixel replacement multi-time module multiplexing at the PL end of the FPGA through a convolution kernel sliding block and gradient comparison to ensure complete coverage of a large blind pixel group, and the self-adaptive median filtering module intelligently selects and replaces pixels meeting requirements through automatic sorting of center pixels and surrounding pixels; according to the method, surface fuzzy filtering is carried out through a surface-blue algorithm module, so that a smooth area in an image is effectively smoothed, details of an edge area are reserved, blind pixels in a video stream can be effectively detected and compensated, and meanwhile traditional noise is suppressed and image details are reserved through multi-stage filtering.

The invention relates to a medical image tuning method, and belongs to the technical field of image processing. According to the invention, a medical image tuning device is adopted; a core board fusion algorithm module automatically identifies the number and pixels of connected cameras, automatically matches a built-in image processing algorithm module, and performs preprocessing through an exposure algorithm module, a denoising algorithm module and a contrast enhancement algorithm module to obtain a single or multiple images as a state 1; extracting image features of the state 1, automatically selecting a single-image algorithm module or a multi-image fusion algorithm module, and obtaining a state 2 through the fusion algorithm module; low-delay and high-pixel transmission is met, image information is completely reserved, a camera host can better complete image processing, and the final image quality is guaranteed; the problem of image information loss caused by image processing at the camera module end is reduced, and the problems of module temperature rise, component damage, influence on imaging quality and the like caused by image processing at the camera module end are solved.

The invention discloses a flammable and explosive gas remote monitoring method and system based on big data. The flammable and explosive gas remote monitoring method comprises the following steps: acquiring an original data set acquired by a multi-source sensor in real time; performing signal preprocessing on the original data set by adopting a denoising algorithm, and performing normalization processing to obtain a first multi-parameter data set; generating a second fusion feature set according to the first multi-parameter data set; if the parameter value of the second fusion feature set exceeds a preset threshold value, identifying a gas leakage event by adopting a classification model, and generating leakage probability and range data in combination with a diffusion model; performing compression coding on the leakage probability and range data, and dynamically adjusting the transmission frequency and the size of a data packet according to network state parameters to form a stable transmission stream; and decoding the stable transport stream at the monitoring center, generating a dynamic visual interface by adopting a visual engine, and judging whether the reliability of the system meets a preset standard or not according to interface parameters. According to the invention, real-time monitoring, intelligent identification and early warning of gas leakage are realized.

A computer implemented method of reconstructing magnetic resonance images (MRI) in Cartesian coordinates uses acquired magnetic resonance data and implements a Fourier transform to place the MRI data in k-space. The method allows for under-sampling the k-space and achieving an accurate output image by selecting an image model to map the sampled data and iteratively converge the model to an output that matches a region of interest subject to the MRI. The image model may be an alternating direction method of multipliers (ADMM) or an ADMM with non-convex low rank regularization algorithm. A de-noising algorithm may be at least one of a plug and play block matching and 3D filtering (PnP-BM3D), a plug and play weighted nuclear norm minimization (WNNM), or a plug and play denoising convolutional neural networks (PnP-DnCNN) algorithm. An iterative optimization of the variables of the model yields an output image.

The invention relates to the technical field of tunnel construction quality detection, in particular to a tunnel initial spraying concrete spraying quality detection method based on a visual technology, which comprises the following steps: S1, arranging an automatic cleaning device at the front end of equipment through multi-modal sensor fusion and a self-adaptive exposure denoising algorithm in combination with a multi-information joint correction technology, and performing multi-modal sensor fusion and self-adaptive exposure denoising; through original point positioning calibration, high-precision robust acquisition of three-dimensional coordinates and apparent vector data of the spraying and mixing surface in a complex environment is carried out. Through multi-modal sensor fusion, anti-interference processing and accurate calibration, high-precision robust acquisition of the spraying and mixing surface data in a complex tunnel environment is ensured, a reliable data basis is provided for subsequent detection and control, the spraying and mixing surface flatness control precision is effectively improved, the material loss rate is reduced, and the working efficiency is improved. And the strategy can be adaptively adjusted according to geological conditions and construction states, and the construction efficiency and safety are both considered.

The invention relates to the technical field of operator safety management, and discloses a non-contact physiological status rapid screening method and system and a medium, and the method comprises the steps: collecting an RGB three-channel space-time image sequence of a facial region of interest; carrying out illumination correction on the RGB three-channel space-time image sequence by adopting an adaptive gamma correction algorithm, carrying out end-to-end noise reduction processing on the illumination correction image sequence by utilizing a one-dimensional convolutional neural network auto-encoder noise reduction algorithm, and separating a remote photoelectric volume pulse wave tracing pulse signal; calculating physiological indexes of heart rate, heart rate variability and oxyhemoglobin saturation based on the remote photoplethysmography pulse signal; and performing grading judgment on the physiological indexes based on the physiological index grading standard, determining a final post suitability judgment grade in combination with the wooden barrel effect model, and generating a grading early warning signal. According to the method, accurate and rapid evaluation of the physiological state of the operator in a complex environment can be realized, the individual health degree can be comprehensively and objectively considered, and decision support is provided for reasonable distribution of posts and efficient configuration of personnel.

A power transmission component image noise reduction system based on an improved CLIP comprises a power transmission component image acquisition module, a preprocessing module, a feature extraction module, a feature quality evaluation module, a noise reduction module and an output module, the power transmission component image acquisition module is used for acquiring a power transmission component image, and the preprocessing module is used for preprocessing the acquired original image and outputting the preprocessed image to the output module. The feature extraction module is used for carrying out feature extraction on the preprocessed image, the feature quality evaluation module is used for evaluating feature quality, the noise reduction module is used for inputting the extracted features into a pre-trained noise reduction model for noise reduction processing, and the output module is used for outputting the denoised power transmission part image. According to the power transmission component image noise reduction system based on the improved CLIP, an image feature extraction algorithm based on an improved CLIP model is proposed to perform feature extraction on a power transmission component image, and an image noise reduction algorithm based on ViT-CNN collaborative coding and decoding is proposed to perform noise reduction processing on the power transmission component image.

The invention belongs to the field of metasurface electromagnetic signal processing, and particularly relates to a deep learning aided design-based metasurface two-dimensional high-resolution signal source positioning method. The super-surface two-dimensional high-resolution signal source positioning method based on deep learning aided design comprises a matched filtering algorithm and an image denoising algorithm. The method comprises the following steps: firstly, performing principle analysis and mathematical modeling on a metasurface two-dimensional signal source positioning function based on a ray tracing method and metasurface physical parameters to obtain a matrix equation; secondly, performing matrix inverse operation on the equality by adopting a matched filtering algorithm, preliminarily estimating two-dimensional position information of the signal source, and outputting the two-dimensional position information in an image form; and finally, inputting image information into the image denoising deep neural network by adopting an image denoising algorithm, and finally realizing two-dimensional high-resolution signal source positioning. According to the high-resolution signal source positioning method, multiple times of simulation and experiments are carried out on the basis of the two-dimensional gradient programmable metasurface platform, and a large number of data results prove that the method has the advantages of high resolution, low complexity and the like.

Methods and apparatus for temporally consistent video denoising directed at removing image noise. According to one example, a denoising algorithm uses a noise estimation block and an image denoising block operatively connected to one another. In various examples, the noise estimation block is implemented using a neural network or a filter arrangement including an entropy filter and is designed to analyze noisy frame sequences and generate a noise strength map. With this noise strength map, the image denoising block operates to perform the image denoising more efficiently, effectively reducing the image noise while preserving the texture of the original footage. In some examples, a joint loss objective is used to find configurations of both blocks that result in nearly optimal performance of the denoising algorithm.

The invention discloses an acoustic signal partial discharge detection system of a distribution network transformer, which is characterized by comprising a multi-sensor acquisition unit, a synchronous clock control unit, a signal preprocessing unit, an edge calculation processing unit and a communication and storage unit, the edge calculation processing unit integrates a cross-band denoising module, a multi-scale feature extraction module, a lightweight neural network diagnosis module and a diagnosis result fusion module, wherein the cross-band denoising module is used for performing joint denoising on audible sound and ultrasonic frequency band signals. According to the invention, a set of partial discharge detection scheme for a distribution network transformer scene is established, and the scheme flow comprises denoising, feature extraction, neural network training and diagnosis. According to the scheme, audible audio frequency band and ultrasonic frequency band data are covered, and interference generated by mechanical vibration of the transformer in the ultrasonic frequency band is removed through a soft threshold value of wavelet transformation and a pulse detection denoising algorithm.

The invention discloses a blasting data multi-dimensional analysis and anomaly detection processing system, and particularly relates to the technical field of blasting intelligent analysis processing, and the system comprises a distributed data collection module, a data preprocessing module, a multi-dimensional analysis engine, a hybrid anomaly detection module, a visual interaction interface, an early warning processing module, and an extensible interface module. Aiming at insufficient multi-source heterogeneous data fusion capability in the prior art, the invention innovatively adopts a WPT-EMD combined denoising algorithm and a dynamic standardization unit to work cooperatively, and effectively overcomes the problems of mode aliasing and noise residual in the traditional method through fine frequency band division of wavelet packet transform (WPT) and self-adaptive characteristics of empirical mode decomposition (EMD); in combination with a dynamic standardization strategy based on information entropy, feature space alignment of heterogeneous data such as vibration waveforms and stress fields is realized, the signal-to-noise ratio of original data is improved, the feature dimension consistency is improved, and a high-quality data foundation is laid for subsequent analysis.

The invention provides a compressed sensing nuclear magnetic resonance imaging reconstruction method based on an ADMM-CNN, and aims to solve the technical problems that under the limitation of an MRI imaging principle, the speed of an MRI imaging process is low, motion artifacts are generated, resolution is distorted, and MRI imaging cannot provide accurate pathological information. The method comprises the following steps: converting an image reconstruction process into an optimization problem according to a compressed sensing theory, and solving an iterative solution of the optimization problem by using an ADMM algorithm; enabling an iterative solution of the solved optimization problem to correspond to three parts of the CNN network; converting a sub-problem of an auxiliary variable of the optimization problem into a denoising model; solving the de-noising model through an existing trained CNN de-noising algorithm to obtain a solution of a sub-problem of an auxiliary variable of the optimization problem; and constructing a training sample set, and training parameters of the CNN network to obtain a reconstructed image. According to the method, a better recovery effect can be obtained under the condition that no obvious artifact is generated, the visual effect is better, and distortion is smaller.

The invention discloses a damage identification method and device for an in-service steel wire rope type horizontal lifeline, and belongs to the technical field of high-altitude operation safety facilities. The method comprises the following steps: synchronously acquiring a magnetic flux leakage signal and a surface image of an in-service steel wire rope through magnetic flux leakage detection equipment and a high-definition camera which are carried on a steel wire rope inspection robot; preprocessing the acquired magnetic flux leakage signal, wherein the preprocessing comprises singular value elimination and trend term removal processing; carrying out de-noising processing on the pre-processed signal by adopting an improved wavelet threshold de-noising algorithm fused with a Sigmoid function; extracting a characteristic value for representing the damage of the steel wire rope, and performing normalization processing to form a characteristic vector; and inputting into a BP neural network identification model optimized by a genetic algorithm for identification, and outputting an assessment result of the damage type and positioning of the steel wire rope. The method can realize automatic and quantitative detection and accurate identification of internal and external damages of the steel wire rope.