127 results about "Frequency map" patented technology

The invention relates to the technical field of risk prediction, in particular to a collision risk prediction method of a low-altitude aircraft in high-density flight in a complex environment, which comprises the following steps: acquiring a point cloud through a three-dimensional laser radar, clustering to generate an obstacle trajectory, matching the trajectory to identify a disturbance characteristic segment, calculating a deviation angle by combining a path vector to generate a disturbance frequency graph, and calculating the collision risk of the low-altitude aircraft. And extracting a parameter modeling dynamic safety interval, and fusing multiple factors to evaluate a collision risk level. According to the method, obstacle trajectory topology is constructed through combination of three-dimensional laser radar point cloud time window division and density clustering, sudden change features are identified through trajectory similarity matching, a Gaussian kernel dynamic safety envelope is generated through combination of course offset statistics and included angle operation, and a self-matching threshold value is established through normalization parameters and radial basis weighting. The method improves the high-density flight collision prediction precision, enhances the weather and obstacle heterogeneity matching capability, reduces the misjudgment early warning delay, and achieves the multi-variable flight situation collaborative analysis.

The invention relates to the technical field of power equipment monitoring, in particular to a transformer fault diagnosis method, system and device based on multi-modal deep learning and a storage medium. Obtaining the volume fraction of gas dissolved in oil of the transformer, the local discharge capacity, the sleeve dielectric loss factor, the vibration data and the infrared image data; continuous wavelet transform processing based on integrated gradient is carried out on the vibration data, and key fault frequency bands are dynamically screened to generate a time-frequency map; inputting the numeric data into a stack-type denoising auto-encoder network to extract depth features; respectively inputting the infrared image and the radio frequency map into a double-branch convolution encoder for early feature fusion, and extracting map depth features through a stack type convolution auto-encoder network; and based on the Dempster-Shafer evidence theory, carrying out conflict resolution and evidence synthesis on the fault probability distribution output by the two types of modes, and outputting a diagnosis result.

The invention discloses a multi-source partial discharge diagnosis system and method for power equipment. The system comprises a signal acquisition unit and a signal processing unit, and the signal processing unit comprises a signal preprocessing module, an atlas generation module and an image generation module; in the method, a signal acquisition unit acquires an optical signal generated by partial discharge of multiple discharge sources of power equipment, converts the optical signal into a current signal and sends the current signal to a signal processing unit, and a signal preprocessing module in the signal processing unit obtains a time domain waveform signal and a PRPD statistical sequence based on the optical signal; the map generation module obtains a two-dimensional time-frequency map based on the time-frequency waveform signals and the PRPD statistical sequence, the image generation module generates an IFCNN network, and fusion feature maps of the optical signals generated by the discharge sources are output in a classified mode based on the time-frequency waveform signals and the two-dimensional time-frequency map through the IFCNN network. The partial discharge of different discharge sources of the power equipment can be effectively distinguished.

The invention relates to a tooth surface analysis technology, and discloses a tooth surface three-dimensional ripple extraction method based on two-dimensional Fourier transform, which comprises the following steps of: acquiring tooth surface deviation data, mapping the tooth surface deviation data to a two-dimensional plane, removing bulging components in a deviation distribution diagram, and performing two-dimensional Fourier transform to obtain a two-dimensional amplitude-frequency diagram; performing amplitude conversion processing on two-dimensional amplitude-frequency data in the two-dimensional amplitude-frequency graph to obtain a two-dimensional bright spot amplitude-frequency graph; extracting bright spot information in the two-dimensional bright spot amplitude frequency graph; extracting the maximum amplitude ripple in the two-dimensional amplitude-frequency data according to the corresponding bright spot position; and extracting amplitude ripples reaching a preset amplitude height, and obtaining three-dimensional data of the amplitude ripples based on two-dimensional Fourier inversion to draw tooth surface three-dimensional ripples. The invention further provides a tooth surface three-dimensional ripple extraction device based on two-dimensional Fourier transform, electronic equipment and a storage medium. According to the method, waviness components causing abnormal noise in the machining process can be accurately recognized, and the accuracy of finding noise sources in the gear machining process is improved.

The invention discloses a feature fusion processing method for anesthesia depth multi-modal data, and belongs to the technical field of graphic data processing and pattern recognition, and the method comprises the steps: obtaining a multi-modal physiological signal and an electromyographic signal of a patient; performing time axis calibration on the physiological signal to generate an alignment signal; extracting a multi-modal feature vector and calculating an anesthesia depth index; performing deviation analysis on the basis of the electromyographic signal and the index to obtain an electromyographic response deviation index; performing graphical feature mapping on the real-time electroencephalogram signal to generate a real-time time-frequency map; when the deviation index exceeds a safety threshold value, performing graph pattern matching with a pattern template library to calculate a similarity score; and outputting the current anesthesia state mode in a classified manner. According to the method, a multi-modal signal graphical feature fusion technology is adopted, and a dynamic map generation and pattern matching mechanism is combined, so that the problem of complex pattern recognition of physiological signal graphic data can be solved, and the accuracy and timeliness of anesthesia state classification are improved.

The invention belongs to the technical field of gear box wear state evaluation. The invention provides a vibration and oil fused wind power gear box wear state evaluation method and system, and the method comprises the steps: converting a vibration signal of a wind power gear box into a time-frequency domain image through employing a Markov transfer field, carrying out the resampling of the time-frequency domain image, and obtaining a vibration two-dimensional time-frequency graph with the same pixel as a two-dimensional image of an abrasive particle ring; performing down-sampling on the two-dimensional image of the abrasive particle ring and the two-dimensional time-frequency image of the vibration, fusing the two-dimensional image of the abrasive particle ring after down-sampling and the two-dimensional time-frequency image after down-sampling, and performing up-sampling on a fusion result; and obtaining a wear state evaluation result according to an up-sampling result. According to the method, feature selection deviation caused by subjective factors is avoided, and the accuracy and robustness of wear state recognition are improved.

The invention provides a steel shell concrete interface void detection system and method, and relates to the technical field of civil engineering structure health detection, and the system comprises a mobile scanning unit, an excitation unit, an infrared imaging unit, a signal processing and acquisition unit and a comprehensive analysis terminal. The method comprises the following steps: controlling a mobile scanning unit to carry out step-by-step impact excitation and signal acquisition so as to obtain a frequency diagram, and synchronously sampling by using an infrared thermal imager so as to obtain a thermal analysis diagram; and then binarizing the two images respectively, fusing the two images by adopting a noise suppression algorithm to generate a comprehensive image, and judging that the interface is void according to the fused image. According to the method, the high sensitivity of the impact echo method to the layering defect and the accurate description capability of the infrared thermal imaging method to the defect shape are fused, so that nondestructive, efficient and high-precision detection of the steel shell concrete interface void is realized, and the misjudgment and leak detection risks of a single method are effectively reduced.

The invention discloses an LFM frequency hopping signal long-distance target echo detection method, terminal equipment and a storage medium, and the method comprises the steps: constructing the two-dimensional time-frequency features and one-dimensional matched filtering features of a target echo, extracting the two-dimensional time-frequency features through employing a CNN network, and extracting one-dimensional time sequence features through an SE-CNN-GRU network; carrying out weighted fusion on the multi-dimensional features through a self-adaptive weighted fusion module; a multi-head attention mechanism is introduced to enhance the fused features; and outputting a classification result through the full connection layer and the Softmax layer to obtain a detection result of the target echo. According to the method, one-dimensional matched filtering and a two-dimensional time-frequency graph are selected as input features of the artificial intelligence detection method, the advantages of different artificial intelligence networks are fully utilized to perform multi-dimensional feature mining, adaptive weighted fusion is performed through complementarity and correlation of the multi-dimensional features, and the fused features are further enhanced through an attention mechanism, so that the detection accuracy of the artificial intelligence detection method is improved. Therefore, the target echo detection capability is improved, and high detection accuracy and robustness can be realized in a low signal-to-noise ratio environment.

The invention discloses a radar target template signal establishment method based on depth model adaptive segmentation, mainly relates to the technical field of template signals, and is used for solving the problems that a target contour is easy to fracture or false detection by clutters is easy to cause when a time-frequency map is subjected to binary segmentation by a simple threshold slice in the prior art. And the threshold parameter is very sensitive to the signal-to-clutter ratio and the environmental change. Comprising the following steps: reading a one-dimensional radar echo sequence, and mapping the one-dimensional radar echo sequence to a two-dimensional time-frequency domain to obtain a time-frequency image; multi-scale features are obtained, and a matrix is prompted; obtaining a binary mask corresponding to the mask feature through the multi-scale feature and the prompt matrix; determining the binary mask corresponding to the highest confidence score as a final mask; screening time-frequency transformation data corresponding to the radar echo sequence by using the final mask to obtain output data; and recovering the output data into a time domain signal by using inverse short-time Fourier transform, and taking the time domain signal as a target template signal.

The invention relates to the technical field of signal processing, and provides a hidden weak signal feature recognition method and system, and the method comprises the steps: carrying out the time-frequency transformation of a to-be-recognized signal, and generating a time-frequency diagram; inputting the time-frequency graph into the feature recognition model, and extracting multi-scale features from the time-frequency graph through a backbone network; sending a feature map with the highest semantic hierarchy in the multi-scale features into a convolution attention module, and sequentially executing channel attention weighting and space attention weighting in the convolution attention module to obtain an enhanced feature map; fusing the enhanced feature map and other scale features in a feature fusion layer to obtain a fused feature map; and based on the fused feature map, identifying a weak signal through a detection head. Compared with the prior art, the method has the advantage that the recognition accuracy of weak signals in communication signals is greatly improved.

The invention relates to the field of wind power generation system fault diagnosis, in particular to a wind power generation system fault diagnosis method and system based on multi-source data fusion. The method comprises the following steps: acquiring an original vibration signal in a wind power generation transmission system, and preprocessing the original vibration signal to obtain a two-dimensional frequency diagram of an original domain data sample; constructing a residual network based on separation attention mechanism optimization as a classifier, and obtaining a migration diagnosis teacher model for a target domain through a conditional domain adversarial migration method; a mask information entropy knowledge distillation strategy is adopted, an information entropy graph output by the teacher model is calculated and multiplied by a target area mask, and refined knowledge is migrated to a lightweight student model; and inputting the to-be-tested data sample of the target domain into the lightweight student model to obtain a diagnosis result. The invention aims to provide a wind power generation system fault diagnosis method and system based on multi-source data fusion, and solves the problems that fault data of a wind power generation transmission system is scarce and model calculation resources are limited.

The invention relates to the technical field of wireless communication, and particularly discloses a frequency hopping communication method fusing a self-attention mechanism and lightweight convolution, which comprises the following steps: S1, a frequency hopping signal detection and parameter estimation step: S1-1, a data preprocessing step: performing short-time Fourier transform (STFT) on a received communication signal, and generating a time-frequency graph; for a given observation signal x (t), the short-time Fourier transform is # imgabs0 #, w (t) is a window function, and e-j2piftau is in a complex conjugate form; and after sampling at equal intervals, the discrete form is # imgabs1 # imgabs2 # frequency dimension k = 1, 2,..., K, and time dimension n = 1, 2,..., N. And S1-2, a feature extraction step: inputting the generated time-frequency graph into a lightweight convolutional layer, and extracting the time-frequency feature of the frequency hopping signal. And S1-3, a multi-task processing step: sharing the features extracted by the convolutional network to a frequency hopping signal detection branch and a parameter estimation branch. By adopting the technical scheme of the invention, the requirements of efficient and accurate processing of frequency hopping signals in a complex electromagnetic environment can be met.

The invention belongs to the field of electroencephalogram signal anomaly detection, and relates to an electroencephalogram anomaly detection method for a multi-feature map convolutional adaptive brain network, which comprises the following steps: acquiring an original electroencephalogram of a user, and performing short-time Fourier transform on the original electroencephalogram to obtain a time-frequency map; using bandwidth integral to apply power spectral density to represent spectrum characteristics of the time-frequency graph; extracting a difference entropy feature and a Shannon entropy feature of the time-frequency graph; respectively inputting the frequency spectrum features, the difference entropy features and the Shannon entropy features into a trained adaptive graph learning GNN model to obtain a detection result; wherein the adaptive graph learning GNN model is composed of an adaptive brain network learning module and a hybrid-jump GNN module; according to the algorithm, function connection corresponding to electroencephalogram signal fragments does not need to be calculated in advance before model learning, corresponding hyper-parameters are dynamically adjusted in the model fitting process, and therefore the brain network structure of each sample is completely learned in a data-driven mode.

The invention discloses an unmanned aerial vehicle radio frequency signal identification method based on a deep prototype network, and the method comprises the following steps: 1, data preparation: collecting radio frequency signals between different types of unmanned aerial vehicles and a remote controller, generating and preprocessing a time-frequency graph, and dividing the time-frequency graph into a training set and a test set; 2, a model construction and training step: constructing a deep prototype network GLF-SAProNet fusing multi-scale cavity convolution and adaptive attention feature enhancement as a feature encoder, and training and testing the model by using the training set and the test set to obtain a trained recognition model; and 3, an identification application step: preprocessing a to-be-identified unmanned aerial vehicle radio frequency signal through the data preparation step, inputting the to-be-identified unmanned aerial vehicle radio frequency signal into the trained identification model, and outputting a model classification result of the to-be-identified unmanned aerial vehicle radio frequency signal. According to the method, the multi-scale cavity convolution and the adaptive attention feature enhancement are fused, and the small sample learning normal form is utilized, so that high-precision and robust recognition of the model of the unmanned aerial vehicle under the condition of limited samples is realized.

The invention relates to an information processing apparatus, a method of controlling the information processing apparatus, a program product, and a storage medium. The information processing apparatus includes: an obtaining unit that obtains an image in which a work area and an object are captured; a first detection unit that detects a working area from the image; a second detection unit that detects an object region from the image; a frequency map generation unit that generates a frequency map on the basis of the number of times of detection of the object region of each grid in the working region; a heat map generation unit that generates a heat map in a case where a frequency map is caused to correspond to the working area detected by the first detection unit; and a display control unit that superimposes and displays the heat map at a position based on the working area on the display device.

This invention relates to the field of target recognition technology, and in particular to a radar target recognition method and system based on micro-Doppler sensing attention. First, a micro-Doppler time-frequency map is generated based on the radar echo signal. Second, the micro-Doppler time-frequency map is input into a lightweight backbone network for feature extraction to obtain an initial spatiotemporal feature map. Third, a dual attention mechanism is used for the first residual compression feature extraction to obtain a first-stage feature map. Then, the first-stage feature map is input into a max-pooling layer for feature downsampling to obtain a downsampled feature map. Next, a dual attention mechanism is used for the second residual compression feature extraction to obtain a second-stage feature map. Finally, the second-stage feature map is input into a classification output module for classification calculation to obtain the radar target recognition result. This invention achieves high-precision target classification under strong background noise by introducing a time-axis integral pooling mechanism and a channel attention mechanism that conform to the physical laws of incoherent accumulation of radar signals.

This application relates to a terahertz video synthetic aperture radar moving target imaging method based on time-frequency analysis. The method includes: constructing a radar echo model of the moving target based on instantaneous range and radar echo; processing the radar echo model by performing a short-time Fourier transform on the compressed echo signal; obtaining the Doppler center frequency of the scene echo from the obtained time-frequency distribution map; performing Doppler frequency zero-padding, range migration correction, and azimuth processing on the compressed echo signal according to a pre-set function; observing the range cell position of the moving target from the obtained imaging results; and extracting and performing time-frequency analysis on the moving target signal. After determining the Doppler center frequency of the moving target through time-frequency analysis and the time-frequency map, velocity estimation is performed; and the image of the moving target is refocused based on the characteristics of the time-frequency distribution line of the moving target echo. This method can quickly achieve velocity estimation and image refocusing of moving targets.

The invention discloses a fault detection method and device for a variable-speed rotating part and terminal equipment, and relates to the technical field of fault diagnosis. The method comprises the following steps: firstly, acquiring a vibration signal of a variable-speed rotating part in a preset time period; inputting the vibration signal into a feature enhancement layer of a pre-trained fault detection model for fault feature extraction, obtaining fault features of the vibration signal at different time points, and determining a one-dimensional feature map corresponding to a preset time period; inputting the one-dimensional feature map into a wavelet convolution layer of a fault detection model, and performing multi-frequency scale feature extraction on the one-dimensional feature map through a plurality of channels to obtain a plurality of one-dimensional sub-feature maps of different frequency scales; and splicing the plurality of one-dimensional sub-feature maps into a two-dimensional time-frequency map according to the frequency scale, inputting the two-dimensional time-frequency map into a two-dimensional convolutional neural network of the fault detection model, and determining a fault detection result of the variable-speed rotating member based on the two-dimensional time-frequency map. According to the embodiment of the invention, the fault features of different frequency scales can be associated, and the fault detection accuracy of the variable-speed rotating part is improved.

This invention discloses a signal detection method based on an improved YOLOv5. The method includes: 1. Performing a short-time Fourier transform on the received signal to obtain its time-frequency map, and then converting the time-frequency map to grayscale to construct a signal detection time-frequency map dataset; 2. Introducing the CBAM module into the classic YOLOv5 to improve the feature extraction capability of the deep learning network; 3. Replacing the NMS algorithm in YOLOv5 with the WBF algorithm to improve the accuracy of the final predicted bounding box; 4. Using an improved Focal-EIoU loss function to enhance the influence of high-quality prediction results during the training process; 5. Training the improved YOLOv5 network model using the Adam optimizer and the signal time-frequency map dataset; 6. Inputting the time-frequency map of the signal to be detected into the trained network model to obtain the signal detection result. This invention is the first to propose using an improved YOLOv5 network model to detect target signals present in received broadband data. This method is simple and practical, achieving high signal detection performance with low complexity, and has pioneering significance for the application of deep learning networks in signal target detection.

The invention specifically relates to a radar target identification method based on attitude angle division, and the method comprises the steps: obtaining the actual measurement data of a plurality of types of targets, each target sample comprising a time-frequency map, the real-time distance, azimuth angle and pitch angle of a target relative to a radar, and the distance, azimuth angle and pitch angle of the target in a previous time slice; calculating a course angle based on measured data; the method comprises the following steps of: preliminarily setting and dividing angle areas by utilizing a known course angle and a pitch angle on the basis of target micro-Doppler characteristics embodied by a time-frequency spectrum, and constructing a time-frequency spectrum data set of various types of targets in different angle areas; constructing a data set by using convolutional neural network training, then verifying classification precision, and judging whether a neural network model needs to be retrained by continuing an iterative division method or not according to an identification result; and determining a division boundary, training a neural network model, and finally inputting a time-frequency spectrum of a target to finish accurate classification. According to the method, the classification precision of radar target recognition is improved.

The invention discloses a bridge health state detection method and system based on a double-branch Mangbar network. The method comprises the steps of collecting and processing bridge vibration field data, generating a vibration field time profile map and an instantaneous frequency map, and obtaining a data set and a training set; taking the data set as input, and constructing a double-path lightweight network model for bridge health state recognition; carrying out model training and optimization on the dual-path lightweight network model by adopting the training set; and inputting a test sample into the trained double-path lightweight network model, outputting a bridge health state classification result, and evaluating the performance of the model. The system comprises a data acquisition module, a model construction module, a training module and a performance evaluation module. The distributed sensors are arranged on the two sides of the bridge road surface, vehicle driving serves as an excitation source, and bridge health condition detection can be achieved under the condition that normal operation of traffic is not affected.

The invention relates to the technical field of high-voltage circuit breaker fault identification, and discloses a high-voltage circuit breaker fault identification method, system and device and a medium, and the method comprises the steps: converting a multi-state vibration signal of a high-voltage circuit breaker into a multi-state signal time-frequency diagram; performing data expansion on the multi-state signal time-frequency diagram, and dividing the multi-state signal time-frequency diagram into a training set and a test set; extracting a time-frequency graph feature vector of the training set, and performing parameter optimization on the fault classifier according to the time-frequency graph feature vector to obtain an optimized classification model; calculating verification model parameters according to the optimization classification model; and constructing a target fault prediction model according to the verification model parameters, performing fault prediction on the to-be-diagnosed high-voltage circuit breaker signal by using a target fault diagnosis model, and calculating a corresponding feature importance thermodynamic diagram and a target fault evaluation factor. According to the method, the accuracy of target fault category prediction can be improved, the fault reason is directly positioned through the feature importance thermodynamic diagram, and accurate identification of high-voltage circuit breaker fault detection is realized.

This invention proposes a soft measurement method for the internal state of a ball mill based on DEM simulation and acoustic signals. The method includes: constructing a DEM simulation model based on the physical parameters of a laboratory ball mill and performing iterative calibration; under different operating parameters, acquiring acoustic signals from the laboratory ball mill experiment and obtaining corresponding internal state data from the calibrated simulation model to collaboratively construct a soft measurement training dataset; generating paired noise reduction module training data through continuous wavelet transform, time-domain superposition, and pairing processing; and training the noise reduction module and the soft measurement module based on this data to achieve noise reduction processing of noisy time-frequency maps and accurate prediction of the internal state variables of the ball mill. This invention uses internal state variables such as filling rate, collision energy distribution, and material particle size distribution extracted from DEM simulation as training labels, fundamentally solving the problem of limited soft measurement accuracy caused by insufficient label accuracy in existing methods.

This invention discloses a method for identifying unipolar and bipolar depression based on deep learning-based multi-state EEG fusion, comprising: Step 1, performing continuous wavelet transform on EEG signals in open and closed states respectively to obtain open-eye time-frequency maps and closed-eye time-frequency maps; Step 2, using a deep learning model to extract features from the open-eye and closed-eye time-frequency maps obtained in Step 1 to obtain feature vectors for open-eye and closed-eye states; then, fusing the feature vectors in open-eye and closed-eye states to obtain a multi-state fused feature vector; finally, using a deep learning classification network to classify and identify the multi-state fused feature vector to obtain the identification result, thus completing the identification. This invention uses a deep learning model based on EEG signals to perform three-class classification identification of unipolar depression, bipolar disorder, and healthy individuals, improving classification performance.

The invention relates to the technical field of signal modulation recognition, provides a measurement and control signal modulation recognition method based on dynamic convolution and a time-frequency attention mechanism, and constructs and trains a composite measurement and control signal modulation recognition model based on the dynamic convolution and the time-frequency attention mechanism. And when a to-be-identified composite measurement and control signal is received, according to a preset signal time-frequency conversion function, obtaining a to-be-identified time-frequency graph corresponding to the to-be-identified composite measurement and control signal. And inputting the to-be-identified time-frequency graph into the trained composite measurement and control signal modulation identification model, and obtaining a target modulation type of the to-be-identified composite measurement and control signal. As the model is constructed based on the dynamic convolution and the time-frequency attention mechanism, the dynamic convolution can be utilized to improve the feature learning ability, obtain richer attention convolution features and improve the robustness of the model in the convolution feature extraction process, and meanwhile, the time-frequency attention mechanism is utilized to obtain richer time-frequency feature information, so that the robustness of the model is improved. And the identification accuracy of the to-be-identified composite measurement and control signal is improved.

The invention relates to the technical field of smart power grids, and discloses a multi-node harmonic coupling feature recognition method based on a graph convolutional neural network, comprising the following steps: step S101, calculating to obtain an amplitude type edge weight spectrum surface; step S102, constructing a sharp point geometric evidence; step S103, calculating to obtain a gating adjacency matrix; step S104, calculating to obtain a continuous score; step S105, calculating to obtain a smooth score; and step S106, using the global maximum value of the smooth score as an identification result, and forming a positioning triple. According to the method, through joint convolution of line graph gating and a frequency graph, time-frequency positioning of a strongest harmonic influence path in a power distribution network is realized, and a single quantization result and corresponding position and time are given; and converting a double-peak-to-single-peak merging event into a learnable edge-level signal by using a sharp point geometric evidence, and ensuring that the output is unique by using a global extreme value.

The invention provides an intelligent diagnosis device and method for internal defects of concrete. The intelligent diagnosis device comprises an electromagnetic exciter, an accelerometer, a multi-sensor array, a synchronous trigger circuit and a data processing unit, the electromagnetic exciter drives the firing pin with the specific mass, so that the impact energy and frequency are adjustable, the electromagnetic exciter adapts to different concrete strengths and thicknesses, and transient impact on the surface of the concrete is achieved. The accelerometer is used for directly measuring stress wave signals generated and returned by impact; the multi-sensor array comprises an acoustic sensor, a thermal infrared imager, a positioning and distance measuring module and a control and data acquisition unit, and the thermal infrared imager records the full-field temperature change of an impact point and a surrounding area to form a thermal image series; the control and data acquisition unit is internally provided with an embedded system and is responsible for controlling triggering of the electromagnetic exciter and synchronously receiving data of the accelerometer and the thermal infrared imager; and the data processing unit converts the acoustic signal into an acoustic time-frequency graph and converts the thermal image sequence into a time sequence thermal characteristic graph.

The invention discloses a corrosion damage quantitative evaluation method based on an optical microscope, and the method comprises the steps: carrying out a corrosion test after carrying out the pretreatment of a to-be-tested sample; obtaining the pitting depth and the corrosion rate of the corrosion area of the to-be-tested sample; an optical microscope is used for collecting the surface damage morphology of the sample to be tested after the corrosion test, a sequence splicing three-dimensional contour image is obtained, and height distribution matrix data is extracted; processing data of the height distribution matrix to obtain a height frequency diagram of the to-be-tested sample after corrosion, comparing the height frequency diagram with a height frequency diagram of an uncorroded standard sample, determining a corrosion damage threshold value, and counting point, line, surface and body characteristic parameters of a corrosion damage part; and determining a subitem damage weight, calculating a weighted damage value, and quantitatively evaluating the corrosion resistance of the to-be-tested sample. According to the invention, the optical microscope is adopted to accurately acquire the three-dimensional morphology parameters, and the three-dimensional morphology parameters are compared and analyzed, so that the method has the advantage of intuitively and accurately reflecting the corrosion resistance, and the accurate quantitative evaluation of the corrosion resistance of the material can be realized.

This invention relates to a method and system for identifying individual open-set radiation sources based on space-frequency domain fusion and Hybrid-EVT, belonging to the field of radiation source identification technology. It addresses the problems of frequent occurrences of unknown emission sources and the failure of the closed-set hypothesis in radiation source identification under complex electromagnetic environments. The method includes: acquiring raw IQ signals, preprocessing them to obtain a time-frequency map; extracting features from the time-frequency map based on the SFFNet model to obtain a discriminant activation vector; training the SFFNet model and a classification head based on samples of known radiation source categories, and calculating the mean activation vector for each category; constructing a Hybrid-EVT hybrid extremum model based on the training set activation vectors; inputting test samples into the SFFNet model with frozen parameters to obtain the activation vector of the test samples; evaluating the tail probability of the distance between the test samples and candidate categories, introducing unknown channel probabilities; if the unknown confidence level meets preset conditions, it is determined to be an unknown radiation source; otherwise, the corresponding known category is output. This invention is applicable to scenarios involving enhanced security for wireless device authentication.

The invention discloses an underwater wavelength scale sound scattering object classification and identification method based on continuous wavelet transform and Botnet, and the method comprises the steps: extracting the time-frequency joint features of a sound scattering signal through the continuous wavelet transform, and processing an obtained time-frequency graph through the feature extraction capability based on a convolutional neural network method, thereby recognizing and classifying an object. According to the method, a framework of ultrasonic scattering-time frequency analysis-deep learning is provided, the gradient disappearance problem in deep network training is effectively relieved through cross-layer jump connection, and it is ensured that the model can capture multi-level detailed information in a time frequency feature map; and global structure information of a time-frequency domain can be mined through a self-attention mechanism, so that the precision of a multi-category classification task is improved.