21 results about "Local spectrum" patented technology

The invention relates to the field of image processing, in particular to an intelligent defect identification method and system for spunlace non-woven fabrics, and the method comprises the steps: dividing a standard defect-free spunlace non-woven fabric image into a plurality of local image blocks according to spatial positions; constructing local spectrum prior maps in one-to-one correspondence with the image space positions; the method comprises the following steps of: partitioning an image to be detected to obtain a plurality of original image blocks, and dynamically generating a personalized frequency domain mask for each original image block based on a local spectrum priori map to suppress a frequency band corresponding to normal texture; constructing a preliminary defect residual image and a spectrogram thereof; in response to the fact that the proportion of the energy of the low and medium frequency region in the full frequency domain energy in the spectrogram exceeds a preset threshold value, mask parameters are updated, the updated mask parameters are applied to the personalized frequency domain mask, and a final defect residual image is obtained; and performing threshold segmentation and / or connected domain analysis on the final defect residual image, and outputting a defect position. The defect detection method has the effect of improving the defect detection accuracy.

The invention discloses a cooperative spectrum prediction method based on minimum Bayesian risk, and relates to the technical field of cognitive radio and wireless communication. According to the method, each secondary user independently completes local spectrum sensing and power prediction; the fusion center performs system perception judgment by using a historical optimal fusion rule, calculates a channel state prior probability, and fits a predicted power condition probability density function of each user in two system states; then, according to a minimum Bayesian risk criterion, through alternative iteration optimization of each user judgment threshold and a system fusion rule, an optimal threshold and a fusion rule are obtained; and finally, obtaining a system-level cooperative spectrum prediction result through threshold comparison and fusion. The method does not depend on prior channel information, has good flexibility and expansibility when the number of users changes dynamically, improves prediction precision and robustness in a complex environment, and can balance main user protection and spectrum access efficiency through a cost factor.

This application proposes a method and system for monitoring the corona degradation of a generator stator based on partial discharge. The method includes: acquiring a multi-fault superimposed global partial discharge pulse (PRPD) map of the generator stator at various times within a preset time period, and marking multiple corona discharge local spectrum regions in the multi-fault superimposed global PRPD map; acquiring the number of discharges at each pulse amplitude and pulse phase within positive partial discharge pulses and the number of discharges at each pulse amplitude and pulse phase within negative partial discharge pulses at various times within the preset time period; determining the discharge pulse duty cycle, number of discharge pulses, and discharge pulse amplitude within each corona discharge local spectrum region at various times within the preset time period; and determining whether the corona performance of the generator stator has deteriorated based on the discharge pulse duty cycle, number of discharge pulses, and discharge pulse amplitude. The technical solution proposed in this application can accurately and effectively monitor the corona state of partial discharge in the original generator insulation.

This invention discloses a land cover classification method based on local spectral energy and adaptive contrast enhancement, belonging to the field of geographic information processing technology. The method first collects an image dataset of the land cover to be identified and divides it into a training set and a test set. The training set is used to train the land cover classification model, and the test set is used to test the model's performance, ultimately obtaining a recognition model that can be used for land cover classification. The improvement method involves introducing a new LFE-ACEMA module before stage 2 and stage 3 of the Swin Transformer. This module includes SEA branches, contrast branches, and spectral energy branches, and incorporates an adaptive contrast enhancement mechanism. By designing and training the improved land cover classification model, this invention achieves collaborative representation of global semantic context and local texture details, effectively suppressing inter-class confusion while accurately identifying land cover categories.

The application relates to the field of image processing, in particular to a spunlace non-woven fabric defect intelligent identification method and system, which comprises the following steps: dividing a standard non-defect spunlace non-woven fabric image into multiple local image blocks according to spatial positions; constructing a local spectrum prior atlas corresponding to the image spatial positions; performing block processing on a to-be-detected image to obtain multiple original image blocks, and dynamically generating a personalized frequency domain mask for each original image block based on the local spectrum prior atlas to suppress the frequency band corresponding to normal textures; constructing a preliminary defect residual graph and a spectrum graph thereof; in response to the proportion of the medium-low frequency region energy in the total frequency domain energy in the spectrum graph exceeding a preset threshold, updating the mask parameters, applying the updated mask parameters to the personalized frequency domain mask, and obtaining a final defect residual graph; performing threshold segmentation and / or connected domain analysis on the final defect residual graph, and outputting a defect position. The application has the effect of improving defect detection accuracy.

This application relates to the field of intelligent conditioning technology for grating filter sensing signals, and discloses a grating filter sensing signal conditioning method based on machine learning. The invention acquires observation response characterization data and conditional constraint state quantities, first performs spectral axis registration processing based on a preset reference frequency window to eliminate the influence of measurement coordinate offset, then performs residual frequency offset decoupling processing on the preset target frequency band to extract residual frequency offset characterization quantities, and constructs a residual dynamic state sequence by combining historical control states, further utilizes a preset time-series inference model to output voltage compensation quantity and conditioning reliability results, and finally determines the final control voltage through reliable gating conditioning and local spectrum scanning verification, thereby effectively distinguishing pseudo offset from the preset target frequency band true offset, and reducing the interference of original observation signal fluctuations, environmental changes and uneven internal electric field distribution on the control results.

The invention provides a self-adaptive frequency selection anti-interference method based on spectrum sensing in wireless communication, which comprises the following steps: acquiring network parameters and a time slot number, determining a time-frequency bit of a C1 channel in a frame period according to the time slot number, and judging whether to acquire the use right of the C1 channel, if so, generating a spectrum information broadcast message, and if not, sending the spectrum information broadcast message to a server; frequency spectrum information broadcast messages are repeatedly sent based on the time-frequency position in a frequency hopping manner; if not, local interference condition information, global sensing information, neighbor node local spectrum sensing information and C3 channel use frequency information are calculated; identifying whether the signal has unicast frequency point interference, if so, handshaking with a target node in a C3 channel on an MAC layer and replacing a high-quality frequency point; judging whether broadcast frequency point interference exists or not, and if yes, selecting a high-quality frequency point to generate broadcast channel frequency point information; judging the new and old conditions of the frequency information Fx used by the C3 channel, updating the local frequency if the frequency information Fx is new, and discarding the local frequency if the frequency information Fx is old; according to the invention, the frequency points with good channel quality are selected for communication according to the frequency spectrum, the anti-interference purpose is achieved, and reliable communication is realized.

The application discloses a multi-modal driver anger emotion regulation method based on hearing and olfaction, and comprises the following steps: performing global acoustic feature extraction on a driver voice signal to obtain global feature information; performing local spectrum feature extraction on the driver voice signal to obtain local feature information; fusing the global feature information and the local feature information, and performing emotion classification on the fused feature information to obtain an emotion classification result; preparing an audio and an odor; and regulating an angry emotion in the emotion classification result by playing the audio and releasing the odor. The application has high emotion recognition accuracy, wide application range and good emotion regulation effect.

The application belongs to the technical field of wireless communication, and discloses a regional autonomous security cooperation spectrum sensing method, system, device and terminal, determines the number of partitions k and initializes the divided regions; makes the sensing time slot n=1 and initializes the SU trust value T(n); makes regional decision and global decision by local spectrum sensing R(n), calculates Z(n) and D(n); updates the trust value T(n+1) and judges whether n≤N is true; if n≤N exists, makes n=n+1 and returns to the step of local spectrum sensing R(n); if n≤N does not exist, judges whether T i <thT exists; if T i <thT does not exist, the SU i is judged to be an honest node; if T i <thT exists, the SU i is judged to be a malicious node. The regional autonomous cooperation spectrum sensing model of the application relieves the sensing error influence caused by malicious attackers on the whole system, detects and positions to the MU, and realizes cooperation spectrum sensing with better security and accuracy. Through simulation experiments, the application has better global decision performance and good MU detection and positioning ability.

The invention discloses a turbulence self-adaptive steel casing intelligent leveling and hoisting multi-mode control method and system, and the method comprises the steps: arranging multi-source sensors in a steel casing hoisting operation area, collecting original signals of each sensor in real time, carrying out the normalization processing of the original signals, obtaining the observed quantity of each sensor, and carrying out the calculation of the observed quantity of each sensor; fusing the observed quantities of the sensors into a fused sensing field according to a preset multi-modal sensing fusion function; fourier transform is carried out on the fusion sensing field to obtain a corresponding frequency domain field, a local spectrum entropy is calculated according to the probability of frequency domain energy of the frequency domain field in each spectrum energy box, and a turbulence self-adaptive frequency spectrum is calculated according to the frequency domain field and the local spectrum entropy; and mapping the turbulence self-adaptive frequency spectrum into a current attitude error vector of the steel casing through a mapping operator, solving a control vector according to a target leveling attitude vector, a tracking weight matrix and an energy consumption balance penalty term, carrying out reverse normalization on the control vector, and generating a leveling instruction of each lifting point.

This invention belongs to the field of integrated sensing and communication technology, specifically relating to a high-precision ranging method for OFDM-ISAC based on adaptive CZT. First, the communication information of the received signal from the OFDM-ISAC system is extracted to obtain a two-dimensional matrix containing target distance and velocity information. Then, a two-dimensional fast Fourier transform is performed on the two-dimensional matrix to obtain a complex-valued periodic graph. The amplitude features of spectral peaks and their neighboring spectral lines are extracted from the distance slices. Next, the analysis window for CZT spectrum refinement is adaptively determined based on the amplitude difference relationship between adjacent spectral lines to the left and right of the spectral peak. CZT refinement is then performed on the local spectrum within the window to obtain high-resolution spectral information. Finally, based on the refined spectral features, an adaptive peak extraction strategy is used to accurately estimate the spectral peak position, and then the target distance is calculated through the mapping relationship between the peak index and the distance. This invention improves ranging accuracy without introducing significant computational overhead and ensures the robustness of the system.

This invention discloses a method and system for improving the stability of a coherent dispersive spectrometer based on cross-cancellation, belonging to the field of spectral imaging technology. It includes acquiring two interference spectra with a phase difference of π in an asymmetric coherent dispersive system; separating the local spectrum and interference spectrum through addition and subtraction; performing Fourier transform on both to obtain the local spectrum and interference spectrum in the frequency domain; determining the optical path difference based on the grating line spread function and signal-to-noise ratio condition; and dividing the interference spectrum into a first component region and a second component region; determining the weights using a phase residual optimization method within the frequency range from zero to the optical path difference; and performing a weighted summation of the spectral components of the local spectrum and the first component region to obtain the mixed spectrum after cross-cancellation. This invention further improves spectral resolution through high-frequency stitching and suppresses ringing effects through equalization processing. This invention significantly improves system stability and spectral resolution without increasing system size and complexity.

The application relates to the technical field of semiconductor manufacturing, and discloses a wafer defect detection method, a wafer defect detection device and a medium. The method comprises the following steps: performing image processing on a target wafer image of a wafer object to be detected to obtain a plurality of local spectrum regions; performing complex filtering processing on the local spectrum regions in a plurality of scales and a plurality of directions to obtain response information of each local spectrum region under different scales and different directions; determining a phase consistency residual graph, a frequency band leakage residual graph and a direction offset residual graph based on phase responses and energy responses in the response information, a pre-constructed spectrum phase reference field, a main frequency band and a main direction; performing multi-scale residual back projection processing on the target wafer image to obtain a multi-scale residual back projection graph; generating a defect saliency graph based on any one or several of the phase consistency residual graph, the frequency band leakage residual graph, the direction offset residual graph and the multi-scale residual back projection graph; and performing defect detection based on the defect saliency graph to obtain a target detection result.

This disclosure provides a dynamic spectrum allocation method, system, device, and medium for satellite-ground collaboration. The method includes: activating an SDR module in an idle time slot, scanning a designated frequency band broadcast by the satellite to generate a local spectrum fingerprint, and uploading it to the satellite after differential privacy encryption; receiving a federated learning global training timing instruction from the satellite, completing local iterative training of a lightweight spectrum prediction model based on local historical spectrum data according to the global training timing instruction, and encrypting and uploading the generated model gradient parameters to the satellite; receiving a spectrum allocation instruction from the satellite and determining the transmit and receive frequencies according to the spectrum allocation instruction; fusing GNSS and IMU data to calculate the velocity vector and satellite elevation angle of the mobile terminal relative to the satellite in real time. Embodiments of this disclosure can achieve two-way Doppler compensation between satellite and ground, improving frequency shift correction accuracy and uplink transmission reliability.

The application discloses a kind of adaptive guiding segmented reconstruction methods based on spectral encoding data, belong to spectral detection technical field.The method includes: obtaining main response and reference response vector, based on main response inversion global initial spectrum, based on reference response inversion self-reference spectrum;Utilize the structure intensity of global initial spectrum and self-reference spectrum, consistency and gradient information to construct wavelength-related confidence, realize flexible guidance;According to the fusion target spectrum in spectral valley region, adaptive segmentation with band overlap is carried out;In each segment, a multi-constraint optimization model containing data fidelity, smoothing, reference consistency, gradient consistency and anchoring term is established to solve local spectrum;Finally, the final spectrum is obtained by overlapping fusion and global refinement.The present application can adaptively recover complex peak shape and absorption valley without human intervention, significantly improve the accuracy and stability of underdetermined spectral reconstruction.

Techniques described herein can apply a game framework for dynamic spectrum allocation. A local spectrum management controller (SMC) associated with a geographic area can receive multiple spectrum requests from multiple network controllers in the geographic area. The SMC can allocate spectrum resources among the multiple network controllers by assigning a respective value to each respective spectrum request, wherein the respective value is adjusted based on a respective historic spectrum use efficiency of a respective network controller. The multiple spectrum requests can then be processed according to a spectrum allocation game which uses respective values to determine multiple spectrum allocations to the multiple network controllers.

The application discloses a credit mechanism-based anti-dynamic SSDF attack method and system, and the method comprises the following steps: allocating an initial reputation value to each user in a network; repeatedly executing the following steps until a preset termination condition is met; each user performs local spectrum sensing to determine whether a frequency band is occupied by observing the surrounding environment; calculating a comprehensive reputation value by using the current reputation value, the historical reputation value, a gain factor and a suppression factor; allocating a fusion weight to each node according to the calculated comprehensive reputation value; weighting and fusing the local spectrum sensing results of the nodes by using the allocated fusion weight to obtain a decision result; and comparing the global decision value with the local spectrum sensing value to update each reputation value. According to the historical and current sensing behaviors of secondary users in the users, the Beta reputation system is used to allocate reputation values to the secondary users, and the reputation values are combined into a comprehensive reputation value to measure the reliability of the secondary users in the centralized cooperative spectrum sensing.

The application discloses an optical frequency domain reflectometry demodulation method based on an adaptive local spectrum matching method, and relates to the technical field of optical frequency domain reflectometry demodulation methods. The method comprises the following steps: obtaining a reference signal and a measurement signal; converting the reference signal and the measurement signal into a wavelength domain respectively to obtain a global reference spectrum and a global measurement spectrum; adopting different intercept lengths to intercept the global reference spectrum respectively to obtain a plurality of local reference spectra with different wavelength ranges; adopting a plurality of intercept lengths identical to the global reference spectrum to intercept the global measurement spectrum respectively to obtain a plurality of local measurement spectra with different wavelength ranges; calculating an offset error value of each local reference spectrum according to each local reference spectrum and each local measurement spectrum with the same intercept length, and selecting a local reference spectrum with the minimum offset error value, so that the optimal offset amount of the local reference spectrum is taken as a wavelength offset amount between the reference signal and the measurement signal. The application discloses an optical frequency domain reflectometry demodulation device and a sensing system adopting the optical frequency domain reflectometry demodulation method.

The application discloses a kind of multi-user high energy efficiency hybrid resource allocation method suitable for IoV, for the case that multiple primary users and multiple secondary users exist simultaneously in cognitive vehicle networking, a reasonable and efficient resource allocation scheme is proposed.Each primary user enjoys a separate authorized frequency band, and the secondary user communicates using a hybrid spectrum access method without affecting the quality of service of the primary user, and shares the authorized spectrum with the primary user. Multiple secondary users report the sensing results to the fusion center by local spectrum sensing of all subchannels, while considering the presence of errors in the reporting channel, the false alarm probability and detection probability of the global decision are obtained;Under the constraint conditions of secondary user sending end average transmission power, primary user interference and system rate, the system model of maximum energy efficiency is determined, and the objective function is obtained;Then the objective function is converted into a convex function using distributed programming, the power allocation part uses the Lagrange function, and the optimal solution expression is obtained according to the KKT condition;Subgradient algorithm is used to calculate the Lagrange multiplier, and finally the optimal power allocation of the secondary user on each subchannel is obtained to achieve the purpose of maximizing the energy efficiency of the IoV communication system.

The invention discloses a fan blade fault detection method and a related device, and relates to the technical field of equipment detection.The fan blade fault detection method comprises the steps that after a wind sweeping sound signal generated by rotation of a fan blade is obtained, features of the wind sweeping sound signal are processed to obtain features of two dimensions, namely, the overall frequency spectrum feature of a preset voiceprint frequency spectrum frequency range; since the overall spectrum feature can represent the feature of the complete frequency band of the preset voiceprint spectrum frequency range, and the local spectrum feature can represent the cross-frequency-band fusion feature of each frequency band corresponding to the damage of different positions of the fan blade in the preset voiceprint spectrum frequency range, the local frequency spectrum feature can represent the cross-frequency-band fusion feature of each frequency band corresponding to the damage of different positions of the fan blade in the preset voiceprint spectrum frequency range. The features of the two dimensions not only cover the overall coarse-grained features, but also cover the fine-grained features of damage to different positions of the fan blade, so that the fan blade fault detection reliability can be improved by comprehensively utilizing the features of the two dimensions to perform fan blade fault detection.

The invention discloses a two-stage point cloud registration method based on angle consistency constraint, and belongs to the technical field of three-dimensional computer vision. The method comprises the following steps: firstly, extracting multi-level features of a point cloud and performing rough matching; then, constructing a first-order similarity matrix and a second-order similarity matrix by utilizing rotation invariance of a vector included angle, and accurately distinguishing an inner point and an outer point through global geometric compatibility; then, the optimal pose is solved by adopting seed selection, local spectrum matching and weighted singular value decomposition, and error overpoint pairs are eliminated; and finally, guiding fine registration by using the reserved high-quality overpoint pair. According to the method, the registration precision and robustness in low-overlap, weak-texture and repeated-structure scenes can be remarkably improved, and the registration problem in complex scenes is effectively solved.