9 results about "Morphological filtering" patented technology

The application discloses a kind of floating platform reciprocating compressor fault diagnosis method and device, including for the vibration signal to be treated decomposition obtains IMF component;By index, some orders of IMF containing obvious impact characteristics are screened out;The screened IMF component is filtered respectively, and non-impact component is filtered out;The IMF component after adaptive variable scale morphological filtering is reconstructed to obtain noise reduction signal;Amplify the mutation component in noise reduction signal;Solve the envelope of mutation component, and utilize filter to carry out smooth filtering to envelope line;Peak value detection is carried out to extract the phase feature of impact component in signal;The phase feature of impact is acquired.Diagnosis device includes: acquisition module, processing module, extraction module and diagnosis module, establishes reciprocating compressor fault feature knowledge base, and carries out fault diagnosis according to the impact feature of vibration signal captured by extraction module.And support and guarantee are provided for the predictive maintenance of future offshore platform reciprocating compressor.

The application provides a vesicle segmentation method and system based on a cryo-EM tomography image, and is applied to the technical field of intelligent processing and analysis of biomedical images.The application carries out preprocessing on the cryo-EM tomography image through deconvolution and missing wedge recovery, combines parallel inference of a double-path pre-training segmentation model, and then carries out multi-stage optimization processing such as fusion, ellipsoid fitting and morphological filtering, so that the accuracy and morphological expression ability of vesicle segmentation are effectively improved, and the technical problems of insufficient vesicle segmentation precision and missing morphological parameters of the cryo-EM tomography image are solved.

This invention provides a thyristor-controlled surge arrester and its control method. By constructing a dual-channel voltage processing algorithm based on morphological filtering and adaptive Kalman filtering, it achieves rapid and reliable judgment of power frequency overvoltage. The controllable surge arrester provided by this invention is installed at the phase-to-phase position of the protected transmission line and includes a nonlinear voltage limiting branch, a main protection circuit, an energy extraction and voltage division module, and a control and detection module. The nonlinear voltage limiting branch includes a first varistor and a first parallel capacitor connected in parallel with it. The main protection circuit consists of a positive thyristor conduction branch and a negative thyristor conduction branch. The two conduction branches are connected in antiparallel and in series with the nonlinear voltage limiting branch to achieve bidirectional controlled conduction of the positive and negative half-cycles of the power frequency overvoltage at both ends of the controllable part of the thyristor-controlled surge arrester. Electrically isolated thyristor gate triggering control is achieved through a pulse transformer and an isolation triggering circuit.

The invention relates to the technical field of artificial intelligence, in particular to a method for dynamically monitoring the construction process of a floor support plate, which comprises the following steps of: acquiring stress data of the whole construction period of the floor support plate, and constructing a training data set with a construction stage category label; performing adaptive multi-scale morphological filtering processing on the stress data in the training data set; on the basis of a predefined discrete structural element scale set, processing the stress data by adopting two composite operations of performing closing operation after morphological opening operation and performing opening operation after the closing operation; constructing a construction monitoring model and finishing model training by using the training data set so as to dynamically monitor the construction process; wherein the construction monitoring model takes a deep convolutional neural network with adaptive deep convolution and cross-layer feature bridging as a trunk feature extractor, and determines the classification probability of a construction stage through an attention classification layer based on external feature guidance and a weighted loss function of mutation perception.

The application provides a morphological filtering image loss optimization method and device of an optoelectronic tracking system, and the method comprises the following steps: acquiring a basic morphological operation sequence formed by a morphological filtering algorithm adopted by the optoelectronic tracking system; determining a first pixel number required to be added and a second pixel number required to be deleted corresponding to each basic morphological operation based on the number of structure elements corresponding to each basic morphological operation; and performing optimization processing on each basic morphological operation, so that the optimized basic morphological operation performs the following operations: adding a corresponding first pixel number of pixels before and after a to-be-processed image sequence to obtain a reconstructed image sequence; performing the basic morphological operation on the reconstructed image sequence to obtain an operation result sequence; and deleting a corresponding second pixel number of pixels before and after the operation result sequence to obtain an output image sequence. According to the scheme, pixel loss can be effectively avoided, and the performance of the optoelectronic tracking system is improved.

The invention provides a morphological method and device for removing noise of a ship and a medium, and belongs to the technical field of seismic exploration. The method comprises the following steps: step 100, inputting seismic shot gather data; step 200, determining a parallel minimum granularity unit by adopting an SPARK parallel framework, and forming a target set of morphological filtering calculation; step 300, constructing structural elements of morphological filtering, and then predicting noise; step 400, performing boundary noise process treatment; and step 500, separating interference noise of the large ship to obtain de-noised seismic shot gather data. According to the method, two-dimensional mathematical morphology filtering and boundary processing of bidirectional structure elements are combined for denoising calculation, interference noise can be accurately predicted, denoising is accurate, parallel calculation is adopted, the calculation amount can be greatly reduced, the calculation efficiency is greatly improved, and the calculation cost is saved.

The invention provides a rotor three-dimensional morphological filtering method based on unequal interval sampling, and belongs to the technical field of rotor measurement. In order to solve the problems that traditional three-dimensional morphological filtering can only process data in one direction once, and data fusion easily causes model error expansion, the method comprises the following steps: carrying out identification analysis on a geometric structure of a rotor; the number of target points and adjacent sampling points is set for area division, and the weight of the sampling points of each area is distributed; carrying out multi-scale unequal interval sampling on the rotor; carrying out superposition processing on the sampled data, and fitting a model; performing shape detection on the surface of the rotor; constructing a multi-scale morphological probe according to the structural characteristics of each region of the shaft diameter; constructing a morphological pyramid of the whole rotor, fusing and processing the collected data, and completing axial-radial bidirectional filtering at one time; and sequentially performing morphological operation on a subsequent layer of the morphological pyramid until all data are processed by the multi-scale morphological probe to obtain a rotor three-dimensional model.

The application aims to provide a rolling bearing fault diagnosis method based on morphological product envelope spectrum, and belongs to the field of bearing fault diagnosis. Different order median filters are used to carry out noise reduction processing on the vibration signal; then, the minimum amplitude of the waveform between adjacent local minimum points in the noise-reduced signal is set to zero to construct an adaptive enhanced time-varying structure element. A morphological gradient operator is used to carry out morphological filtering on the original signal in combination with the constructed adaptive enhanced time-varying structure element, and the envelope spectrum of the filtered signal is calculated. Meanwhile, an improved characteristic frequency intensity coefficient is proposed to adaptively select the optimal order of the median filter, and then the envelope spectrum corresponding to the morphological operator is obtained. The application can effectively suppress the interference of background noise by constructing the morphological product envelope spectrum, and the improved characteristic frequency intensity coefficient can effectively represent the information richness of bearing faults.

This invention discloses a method for outlier detection in multi-beam data based on adaptive progressive morphological filtering. The method employs a closed-open-open combined average morphological operator for filtering, extracting a local terrain slope tolerance parameter. Based on this parameter, the elevation threshold is adaptively and dynamically adjusted during iterative morphological filtering. Through multi-level filtering windows and iterative calculations, the morphological reference plane gradually approximates the actual seabed until the iteration termination condition is met. Then, the terrain slope is extracted and a global judgment threshold is calculated. The depth deviation between the actual measured depth and the reference elevation is compared based on spatial location, thereby achieving accurate removal of outliers from the original discrete point cloud. By establishing a local dynamic adjustment and global decision mechanism for the filtering threshold, the method effectively avoids the filtering and under-filtering defects caused by fixed parameters, improving denoising accuracy while ensuring high fidelity of the actual seabed micro-topography.