57 results about "Iterative thresholding" patented technology

The invention discloses a cable hot-spot temperature identification method and device, terminal equipment and a storage medium. The method comprises the following steps: acquiring a steady-state temperature field reduced-order model, a cable surface temperature monitoring value and a ground surface temperature monitoring value of a power distribution network cable; obtaining an initial equivalent current based on a preset parameter space; taking the initial equivalent current as input, repeatedly executing an equivalent current iteration adjustment operation through a cable surface temperature monitoring value and a steady-state temperature field reduced-order model, stopping executing the equivalent current iteration adjustment operation when residual data is smaller than a residual threshold or the number of iterations is equal to an iteration threshold, and outputting a final iteration equivalent current; and inputting the iterative equivalent current and the surface temperature monitoring value into the steady-state temperature field reduced-order model to obtain a cable internal transient temperature field reconstruction result of the power distribution network cable. According to the invention, cable current changing in real time does not need to be monitored, the internal temperature field of the cable can be reconstructed only by monitoring the surface temperature of the cable, and the simulation accuracy of the cable temperature field is improved.

The invention discloses a mold quality detection method and system based on image processing, and relates to the technical field of mold quality detection.The method comprises the steps that a high-precision industrial camera is installed over a mold to obtain a mold surface gray level image, and graying and smoothing are conducted on the obtained image; constructing a distribution model according to the reference distance distribution condition of each pixel point in the smoothed image, calculating a period strength degree, preliminarily screening the pixel points according to the period strength degree, and distinguishing the possibility of texture pixel points and non-texture pixel points; by using the difference between normal texture periodicity and crack randomness, through calculating the periodicity intensity degree, screening texture pixel points and non-texture pixel points, screening suspected texture pixel points, calculating the periodicity similarity, screening out a texture region and then carrying out iterative threshold segmentation, the detection problem caused by mixing of cracks and normal textures is solved; and the detection accuracy is improved.

The invention relates to the technical field of image processing, in particular to a storage tank body defect detection method and system based on image processing, and the method comprises the steps: collecting a storage tank body surface image, and preprocessing the surface image to obtain a grey-scale map; dividing the grey-scale map into a plurality of defect suspected areas according to the gradient value of each pixel point in the grey-scale map, calculating the defect expression degree of each defect suspected area, and taking the defect suspected area of which the defect expression degree is greater than a preset threshold value as a defect area; iteratively adjusting the size of the defect region to obtain a to-be-segmented region corresponding to the defect region; and performing iterative threshold segmentation on each to-be-segmented region, and marking the position of the defect in the storage tank body according to the result of the iterative threshold segmentation. According to the invention, the segmentation areas are adaptively adjusted, so that defects of different sizes and shapes can be flexibly adapted, and the efficiency and accuracy of surface defect detection of the storage tank body are improved.

The invention discloses a morphological filtering iteration-based digital elevation model generation method. The method comprises the following steps of: 1) partitioning an image; 2) morphological grayscale filtering; (3) iterative binarization is carried out; 4) marking connected domains; 5) threshold filtering; 6) surface interpolation; and 7) merging processing. According to the method, morphological gray filtering and iterative threshold segmentation technologies are fused, a block optimization strategy and a dynamic connected domain screening mechanism are combined, and the problems that a traditional algorithm is sensitive in window size, poor in terrain adaptability, multiple in set parameters and the like are effectively solved. And the automatic processing efficiency of generating a digital elevation model (DEM) by a digital surface model (DSM) under a complex terrain and the terrain restoration precision are obviously improved.

The application provides a serial batch processing scheduling method based on a hybrid particle swarm algorithm in a fuzzy environment, and relates to the field of production scheduling.The method comprises the following steps: encoding and analyzing workpieces and machines based on machine indexes; in the case of parallel machine scheduling, initializing algorithm parameters based on the hybrid particle swarm algorithm, initializing a population by a heuristic algorithm, analyzing fitness, and updating the speed and position of particles; determining the minimization of the maximum completion time as an objective, executing a variable neighborhood descent local search strategy, and updating the local optimum and the global optimum; and in the case that an iteration index exceeds an iteration threshold, determining the end of the algorithm and outputting a target result to represent the allocation and scheduling information of the workpieces and machines.The application considers fuzzy processing time, learning effect and deterioration effect, and considers the scheduling problems of single machines and non-single machines in a fuzzy environment based on actual production conditions, which is helpful for iron and steel plants to formulate production strategies and reasonably arrange the allocation and scheduling of workpieces and machines.

A hyperspectral image super-resolution reconstruction method and device are proposed. For hyperspectral images, a fusion model based on an iterative threshold shrinkage method and non-local autoregression is established, the model is optimized, and then network design, network training, and network testing are performed. Therefore, the present invention effectively restores the missing high-frequency information by utilizing non-local self-similarity. At the same time, ISTA-Net is used to utilize information in the transform domain, making the network highly interpretable and improving the reconstruction effect.

The invention discloses an industrial big data processing method and system based on a cloud platform, and relates to the technical field of data processing.The method comprises the steps that edge nodes are deployed, a preset first machine model is loaded, reasoning training is executed on target industrial data, nonlinear damage factors are generated, and a first candidate set is screened out; uploading the first candidate set to a cloud platform, executing iterative training on the first candidate set by adopting a preset second machine model, and screening out a second candidate set; if the number of iterations reaches a preset iteration threshold value, a gradient distribution task is triggered, and the cloud platform is controlled to distribute gradient difference values according to the optimal gradient path; guiding the first machine model through the second machine model, training the first machine model through the first candidate set and the second candidate set to obtain an updated first machine model, and deploying the updated first machine model to each edge node for identifying nonlinear damage next time; according to the invention, the efficiency and accuracy of industrial big data processing are improved.

The present invention discloses a facial expression recognition method based on label distribution learning, belonging to the technical fields of deep learning and facial expression recognition. The present invention includes: preprocessing a face image to perform face detection and alignment; inputting an expression image and extracting expression image features; classifying the features and optimizing the feature extraction network with the goal of reducing the feature classification entropy; using a Gaussian function to generate a label distribution for the expression image and taking it as a kind of ground-truth; reconstructing a network model, inputting an expression image and extracting expression image features; classifying the image and optimizing the feature extraction network with the goal of reducing the cross-entropy loss and KL divergence loss; when the classification loss is less than a stop iteration threshold, outputting a classification result. The present invention converts the sample classification loss into a standard deviation, generates a label distribution for the sample through a Gaussian function, and the obtained label distribution not only represents the degree of various expression description samples, but also more represents the intensity of the expression, and can improve the recognition effect of the model.

The invention provides an optical tweezer generation method and device, electronic equipment and a storage medium, and the method comprises the steps: initializing an optical tweezer phase based on a designated optical tweezer phase and an optical tweezer position mask matrix, and initializing an optical tweezer amplitude based on a target optical tweezer amplitude diagram; and updating the phase of the spatial light modulator based on the phase of the optical tweezers and the amplitude of the optical tweezers, respectively updating the phase of the optical tweezers and the amplitude of the optical tweezers based on the phase of the spatial light modulator, and carrying out phase constraint on the updated phase of the optical tweezers based on the appointed phase of the optical tweezers and the position mask matrix of the optical tweezers. Updating the number of iterations and returning to update the phase of the spatial light modulator based on the new optical tweezer phase and the optical tweezer amplitude until the number of iterations reaches an iteration threshold value; and generating the optical tweezers based on the phase of the spatial light modulator. According to the method, the device, the electronic equipment and the storage medium provided by the invention, the global optimization capability is solved by utilizing the iterative algorithm, the phase of the optical tweezers is ensured to be controllable, and the risk of random jumping of the phase of the optical tweezers is avoided.

The invention provides a quantum classical hybrid calculation method and device. The method comprises the following steps: performing linearization operation on an objective function and constraint conditions in a QCBO model; setting a variable matrix and a linear mapping operator according to the objective function after linearization operation, the constraint condition and the corresponding co-orthogonality optimization problem; according to the current variable matrix and the linear mapping operator, calculating to obtain a residual vector, and calculating to obtain a secondary unconstrained binary optimization coefficient matrix; solving the current secondary unconstrained binary optimization problem to obtain a current solution vector; determining an iteration direction according to the current solution vector, updating the variable matrix by using the first step length, and taking the updated variable matrix as a current variable matrix; and when the number of iterations is greater than a preset iteration threshold value, performing rounding operation on the current variable matrix, and extracting a vector from the variable matrix after the rounding operation as a final solution. By applying the method, the problem of 0-1 binary optimization with nonlinear constraints can be effectively solved.

The invention provides a serial batch processing scheduling method based on a hybrid particle swarm algorithm in a fuzzy environment, and relates to the field of production scheduling, and the method comprises the steps: carrying out the coding analysis of a workpiece and a machine based on a machine index; under the condition that the scheduling type is parallel machine scheduling, based on a hybrid particle swarm algorithm, initializing algorithm parameters, initializing a population through a heuristic algorithm, analyzing fitness, and updating the speed and position of particles; determining the minimum maximum completion time as a target, executing a variable neighborhood descent local search strategy, and updating local optimum and global optimum; and under the condition that the iteration index exceeds an iteration threshold value, determining that the algorithm is ended, and outputting a target result to represent distribution scheduling information of the workpiece and the machine. According to the method, fuzzy processing time, a learning effect and a deterioration effect are considered, single-machine and non-single-machine scheduling problems in a fuzzy environment are considered based on actual production conditions, and an iron and steel plant is helped to formulate a production strategy and reasonably arrange distribution scheduling of workpieces and machines.

The application relates to an ISAR sparse imaging method, device and equipment based on a DA-ISTA network. An iterative threshold convergence algorithm (ISTA) is used to construct a deep neural network for ISAR sparse imaging. In the ISTA, the iteration process is tiled into a multi-layer network structure, and the linear sparse transformation in the ISTA algorithm is replaced by a nonlinear convolution operation in each layer of the network structure. In the DA-ISTA network, a large number of iterations are not required as in the ISTA algorithm. Only a small number of network structure layers are required to process ISAR sparse aperture echo data, and a high-quality ISAR imaging result can be obtained. The method improves the imaging quality and the calculation efficiency, and makes the DA-ISTA network interpretable.

The application discloses a kind of uniform missing linear frequency modulation signal fast iterative threshold deconvolution reconstruction method, solve the matrix singularity problem that sparse reconstruction algorithm appears in pseudo-inverse process in the reconstruction of uniform missing linear frequency modulation signal, the present application is based on fast iterative threshold algorithm, according to the pattern of uniform missing, deduce the point spread function due to signal time domain missing causes spectrum aliasing, then utilize point spread function for the spectrum of zero padding and dechirp processing after missing linear frequency modulation signal by fast iterative threshold algorithm carries out complex deconvolution, to recover the spectrum of signal in frequency domain, in turn reconstruct complete signal.The present application realizes complex deconvolution by fast iterative threshold algorithm, need not carry out matrix pseudo-inverse operation, to avoid matrix singularity problem.

This invention provides a test case generation method, apparatus, device, and medium based on a large language model, relating to the field of software analysis and testing technology. The method includes: generating prompt words based on the variable data flow graph corresponding to the function to be tested and the function itself, and inputting these prompt words into a fine-tuned large language model to obtain initial test cases; obtaining evaluation metrics corresponding to the initial test cases; if the evaluation metrics corresponding to the initial test cases do not meet the evaluation conditions, returning to the step of inputting prompt words into the fine-tuned large language model to obtain initial test cases corresponding to the function to be tested, until the number of iterations is greater than or equal to an iteration threshold; and determining the test cases corresponding to the function to be tested based on the evaluation metrics corresponding to historical test cases stored in a historical test case set, as well as the evaluation metrics corresponding to the initial test cases. The technical solution of this invention can improve the automation and accuracy of test case generation.

The present invention relates to a fast parallel imaging reconstruction method based on SIDWT and iterative self-consistency, and belongs to the field of magnetic resonance imaging technology. Parallel magnetic resonance imaging reconstruction has been a research hotspot in recent years. Based on the SIDWT transform, the present invention introduces the L1 norm regularization term of the transform coefficient into the iterative self-consistency parallel imaging reconstruction model in the K-space domain, and proposes an efficient reconstruction method named fSIDWT‑SPIRiT. This method aims at complex optimization problems containing data consistency terms, calibration consistency terms and L1 norm regularization terms. The data consistency terms and calibration consistency terms are first merged, and then the projected fast iterative thresholding method (projected Fast Iterative Shrinkage‑Thresholding Algorithm, pFISTA) is used to solve them. Experimental results show that the proposed fSIDWT‑SPIRiT method not only provides higher image reconstruction quality, but also effectively improves the convergence speed and reduces the image reconstruction time.

The present invention relates to the fields of deep learning and image clustering, and specifically to a complex image clustering method based on adaptive weights, which includes the following steps: First, initialize the clustering network using an existing classification network and a traditional clustering algorithm; Second, perform clustering on the images and calculate the network objective for this iteration in the direction of entropy reduction and update the network; Third, calculate the weights of each sample in the next iteration using the sample entropy value; Finally, when the clustering loss is less than the stop iteration threshold, output the clustering result. It solves the problem in the existing image clustering model that it is difficult to use the quality of graphical samples to determine the weight of a sample and use adaptive weights for model training.

The application belongs to the technical field of image compressive sensing reconstruction, and discloses an image compressive sensing reconstruction method and system based on optimization algorithm expansion. In the sampling stage, a convolution sampling method is used instead of a traditional random matrix sampling. In the reconstruction stage, a generalized iterative threshold shrinkage algorithm is expanded into a deep network, and a jump information connection structure is designed in a reconstruction submodule R. Residual modules are used to connect the feature information before and after the modules, so that the inherent information loss in the deep expansion network is avoided. Furthermore, a double-scale denoising module is designed at the back end of the reconstruction submodule R, and different scale features are combined to denoise the image. The application not only realizes the application of the algorithm expansion method in the image compressive sensing, but also improves the reconstruction effect by using the jump connection structure and the double-scale denoising module. The application has higher accuracy and better robustness.

The invention relates to a compressed sensing-based remote sensing image full-process adaptive coding and decoding method, and relates to the technical field of space remote sensing imaging. The method comprises the following steps of: calculating an image content complexity self-adaptive selection coding mode by utilizing a maximum between-class difference method; extracting an image feature factor, establishing a significance model of a corresponding mode, and guiding the coding tree block to carry out adaptive form partitioning; detecting the sparsity of the coding block through an information density function to mine the sparsity of the image, and realizing the adaptive sampling rate distribution of the image; and adaptively selecting a reconstruction matrix and a sparse base according to the category of the coding block, setting an iteration threshold according to the known sparseness, and reconstructing the coding block by using an orthogonal matching pursuit algorithm. According to the method provided by the invention, the compression ratio does not need to be manually preset, the sampling mode can be selected according to the image content, the compression efficiency is adaptively set by detecting the sparseness, and the requirements of on-satellite rapid compression and ground high-fidelity reconstruction are met.

The invention provides a target distribution method and device based on an aircraft, electronic equipment and a medium, and the method comprises the steps: generating a total situation value and a target discrete matrix corresponding to each particle based on a continuous matrix corresponding to each particle in a particle swarm matrix; determining a target total situation value and a target allocation scheme corresponding to the optimization target based on the total situation value corresponding to the at least one particle through a situation optimization algorithm; and through an aurora optimization algorithm and a lens reverse learning strategy, on the basis of the updated iteration parameters and the continuous matrix corresponding to at least one particle in the particle swarm matrix, updating the continuous matrix corresponding to each particle, and executing the step of generating the particle swarm matrix for the iteration parameters on the basis of the continuous matrix corresponding to each particle. And obtaining a target total situation value and a target distribution scheme corresponding to the optimization target until the iteration parameter is greater than the iteration threshold value, thereby continuously optimizing the optimization target through iteration, and realizing the maximization of the combat effectiveness.

The invention discloses a man-machine collaborative scheduling method and system based on an improved genetic algorithm, and the method comprises the steps: initializing an order scheduling environment, processing the imported order data, machine data and material data, and obtaining an initial order scheduling scheme; carrying out iterative optimization processing on the initial order scheduling scheme by adopting an improved genetic algorithm, circularly executing fitness calculation, selection, crossover and mutation operations when an iteration threshold value is not reached to obtain an updated scheduling strategy, and outputting a machine decision scheduling scheme when the iteration threshold value is reached; submitting the machine decision scheduling scheme to an artificial decision interface for determination processing, and receiving an artificial determination instruction generated based on analysis of the machine decision scheduling scheme; and processing the machine decision scheduling scheme according to the manual judgment instruction, and selecting to re-execute iterative optimization of the improved genetic algorithm or output the machine decision scheduling scheme as a final scheduling scheme. According to the invention, efficient optimization of order scheduling and improvement of decision accuracy can be realized.

The application discloses a kind of defect detection method and system of PHEMT, it is related to electronic device detection technical field, method includes: obtaining the surface image of PHEMT;The surface image is preprocessed, and target image is determined;The gradient response image of the target image is calculated;The gradient response image is iteratively thresholded, and final mask gradient image is determined;According to the target image, by Gaussian difference and fixed threshold segmentation, Gaussian segmentation image is determined;The Gaussian segmentation image and the final mask gradient image are merged, and defect region is determined;The defect feature of the defect region is extracted;According to the defect feature, by deep residual convolution network, defect classification is carried out, and the defect category of the PHEMT is output.The application can more accurately determine defect region, avoid the problem that defect region is not complete, improve the accuracy of defect region identification, ensure the integrity and accuracy of defect region.

The application discloses a high-voltage cable grounding loop resistance prediction method based on data driving, collects historical grounding loop resistance detection records of high-voltage cable sections with different static attribute labels under the same voltage level; adopts iterative threshold variational mode decomposition (ITVMD) to decouple irregular interval multi-loop resistance time series into a plurality of intrinsic mode components with different frequency characteristics; splices frequency domain characteristics of loop resistance mode components with static attributes to construct a comprehensive feature vector, and clusters the comprehensive feature vector through a K-means algorithm; combines a two-way delay embedding transformation (TDT) algorithm, and based on Tucker decomposition, adopts a block Hankel tensor autoregressive integrated moving average (BHT-ARIMA) multi-sequence prediction model to predict the high-voltage cable grounding loop resistance. Compared with the prior art, the application effectively solves the technical problems of low prediction accuracy caused by sparse grounding loop resistance data, complex coupling and missing values, and realizes accurate prediction of the loop resistance.

The invention relates to the technical field of petroleum and natural gas engineering, in particular to a permeability image data transmission method based on YUV conversion and iterative threshold segmentation, which comprises the following steps: S1, acquiring a first image which comprises permeability information, and converting the first image into a grayscale image; s2, determining a segmentation threshold value, performing image segmentation on the grayscale image by using an iteration method, and constructing a mapping relation between grayscale image information and color components; and S3, acquiring a second image according to the mapping relation, and acquiring permeability information according to the color component of the second image. According to the method, permeability data exchange among different simulation software can be realized, and the compatibility and availability of data are improved.

The present invention provides a radiographic image weld segmentation algorithm based on iterative thresholds. The threshold of the present invention is updated according to a fixed step size. The threshold of the current iteration increases upward according to the fixed step size based on the threshold of the previous iteration, gradually approaching the optimal segmentation threshold. The distance difference between two cycles is used as the judgment condition, which is more suitable for the situation where the weld is horizontally distributed in the image, and is conducive to the segmentation of the weld area.

This invention discloses an anomaly cleaning method for multibeam bathymetry point clouds, belonging to the field of data cleaning technology. It includes: acquiring multibeam bathymetry data to obtain a multibeam bathymetry point cloud dataset; performing data augmentation on the multibeam bathymetry point cloud dataset based on a depth-beam adaptive augmentation method to obtain a training dataset that conforms to the physical error laws of multibeam bathymetry; constructing a planar neighborhood local self-attention network to detect point cloud anomalies; training the planar neighborhood local self-attention network using the training dataset; and using the trained planar neighborhood local self-attention network, employing a hierarchical iterative threshold strategy to clean the original point cloud to be detected. The samples generated by this invention more closely approximate the real marine environment, thus enabling the trained model to possess strong generalization capabilities across water depths and incident angles. Simultaneously, modeling the Ping-Beam structural features of the point cloud effectively achieves automated, high-precision processing of multibeam data under complex sea conditions.

The present invention discloses a method for generating a digital elevation model based on iterative morphological filtering, comprising the following steps: 1) image segmentation; 2) morphological grayscale filtering; 3) iterative binarization; 4) connected domain labeling; 5) threshold filtering; 6) surface interpolation; and 7) merging. The present invention integrates morphological grayscale filtering with iterative threshold segmentation techniques, combines a segmentation optimization strategy with a dynamic connected domain screening mechanism, and effectively addresses the issues of traditional algorithms such as window size sensitivity, poor terrain adaptability, and the need for multiple parameter settings. This method significantly improves the automated processing efficiency and terrain restoration accuracy of generating a digital elevation model (DEM) from a digital surface model (DSM) in complex terrain.

The invention relates to a data cleaning method combining space-time clustering and an iterative threshold shrinkage algorithm, belongs to the field of data cleaning and preprocessing, and solves the problems of inaccurate anomaly detection, poor adaptability and low calculation efficiency in existing space-time power data cleaning. Comprising the following steps: S1, acquiring to-be-cleaned original power data in a low-voltage marketing and distribution fusion scene; the original power data comprises time layer data and space layer data; s2, subdividing the time layer data into abrupt change layer data and stationary layer data by using a clustering algorithm, and subdividing the space layer data into high-density layer data and low-density layer data; s3, performing data cleaning on the abrupt change layer data, the stationary layer data, the high-density layer data and the low-density layer data by using an iterative threshold shrinkage algorithm to obtain cleaned layered data; s4, calculating a layered residual error to obtain a final residual error; if the final residual error is smaller than a preset residual error threshold value, clean data are output; otherwise, optimizing the control parameters and returning to the step S2. And efficient and accurate power data cleaning is realized.

The invention discloses an all-sky imager aerosol optical parameter inversion algorithm constructed based on machine learning, and belongs to the field of meteorological observation. According to the method, an iterative threshold segmentation method and threshold self-optimization are used, a clear sky extraction quantitative standard is established for the first time, and the problems of manual dependence and poor consistency are solved; according to the method, an equal zenith angle scanning mode is simulated, multi-dimensional feature extraction is carried out from a sky area picture, aerosol scattering information reflecting multi-angle and multi-position information related to an aerosol scattering phase function and multi-angle information of total brightness is obtained, and key support is provided for SSA inversion; according to the method, an integrated regression model is constructed by taking the multi-dimensional features as input and AOD and SSA as output, a spatial difference and time difference matching sample is set, synchronous output is realized, a sample standard is defined, and efficiency and precision are greatly improved. The method is applied to the fields of atmosphere remote sensing, climate mode research, air quality evaluation and the like.

The present invention discloses a method for adaptively extracting digital holographic object image spectrum that eliminates stray spectrum. The holographic interference pattern of the object to be measured is collected and converted into a spectrum diagram, and the center coordinates of the object image spectrum are determined by phase information; iterative threshold segmentation processing is performed to obtain a spectrum binary segmentation diagram; the object image spectrum binary segmentation diagram is extracted and generated in the foreground area of the spectrum binary segmentation diagram according to the center coordinates of the object image spectrum; the object image spectrum binary segmentation diagram is subtracted from the spectrum binary segmentation diagram to obtain a stray spectrum area binary segmentation diagram, and a spectrum diagram after eliminating stray spectrum is obtained on the spectrum diagram according to the stray spectrum area binary segmentation diagram; the object image spectrum is filtered by an adaptively generated Butterworth filter, and a three-dimensional morphology diagram is reconstructed by unwrapping and distortion compensation. The present invention solves the contradiction between the amount of object detail information extracted and the amount of stray spectrum introduced, minimizes the amount of stray spectrum introduced while increasing the amount of object detail information extracted, thereby improving the quality of object three-dimensional morphology reconstruction.

The invention discloses a rapid sparse inverse convolution two-dimensional image restoration method. The method comprises the following steps: (1) reading an observation image and a convolution template; (2) carrying out linear transformation on a value range of an observation image pixel, and carrying out normalization processing on a convolution template signal; and (3) constructing a sparse inverse convolution two-dimensional image restoration optimization model, and performing hierarchical and staged rapid sparse inverse convolution restoration. In the first stage, when the L1 norm is minimized, a uniform adjusting parameter lambda is adopted to carry out threshold convergence, and in the second stage, different adjusting parameters are adopted to carry out L1 norm minimization in combination with the weighted L1 norm. According to the method, the L2 norm in the model is minimized through the gradient projection algorithm in combination with the two-dimensional finite discrete fast spatial convolution, then the L1 norm is minimized through the iterative threshold reduction algorithm, and the restored image is obtained through alternate iteration fast sparse solution of the two methods. According to the method provided by the invention, the spatial resolution of the image can be effectively improved, the processing speed is high, and the anti-noise capability is strong.