12results about How to "Remove noise" patented technology

The invention provides a target identification method and system based on multi-source information fusion, and relates to the technical field of low-altitude target detection. The method comprises the following steps: acquiring target radar track data, interception equipment track data and position area information data; based on target radar track data, extracting a first feature in an RCS form dimension, and extracting a second feature in a motion dimension; based on the target radar track data and the track data of the monitoring equipment, determining a frequency spectrum monitoring correlation factor and regional position information features; performing feature fusion on the first feature, the second feature, the spectrum interception correlation factor and the regional position information feature to obtain a target feature, and identifying a target type; and identifying a target threat level based on the target type and the target radar track data. The method and the device are used in a target identification process based on multi-source information fusion, and the technical problem that the target threat degree cannot be accurately identified in a complex environment in the prior art is solved.

This application provides a method, device, equipment, and medium for detecting vascular stenosis. The method includes: acquiring a binary image of a blood vessel diameter curve; determining a convex hull and multiple initial straight lines forming the convex hull in the binary image; determining multiple target straight lines from the initial straight lines, and performing line fitting on each target straight line to obtain multiple fitted straight lines and a fitted line equation corresponding to each fitted line; determining reference diameter information for each target center point in the binary image based on the fitted line equation corresponding to each fitted line; and determining at least one stenotic segment in the blood vessel and the stenosis rate corresponding to each stenotic segment using the equivalent diameter information of each target center point and the reference diameter information of each target center point. The detection method and device improve the accuracy and robustness of detecting vascular stenosis segments and stenosis rates.

The invention discloses a method and system for testing the capacitance value of an integrated capacitor, and relates to the field of electrical measurement, and the method comprises the steps: obtaining the rated information of a to-be-tested integrated capacitor, and generating a control parameter based on the rated information; iteratively calculating the capacitance value of each phase of the to-be-tested integrated capacitor based on a preset adaptive optimization algorithm and the control parameters; and obtaining a target capacitance value of each phase until a preset convergence condition is met, and completing the test. In each iteration process, acquiring a three-phase voltage signal and a three-phase current signal of the to-be-tested integrated capacitor based on the current control parameter; performing feature extraction on the three-phase voltage signal and the three-phase current signal to obtain spectrum feature information; constructing a coupling matrix of the to-be-tested integrated capacitor based on the spectrum feature information, and separately calculating an independent capacitance value of each phase based on the coupling matrix and a coupling residual error to obtain a first calculation result; and optimizing the control parameters based on the first calculation result to obtain the control parameters of the next iteration process.

A method and system for EEG emotion recognition based on multi-task and attention mechanisms is disclosed. The method comprises: S1. Acquiring corresponding EEG signal data according to the locations of emotion-related EEG channels in the international standard 10-20 electrode placement system; S2. Preprocessing the acquired EEG signals using bandpass filtering, power frequency notch filtering, downsampling, baseline correction, artifact removal, and signal segmentation, dividing the signal into windows with a duration of 0.5 seconds; S3. Extracting power spectral density and differential entropy features from the EEG data of each time slice, representing the feature vectors using a two-dimensional brain map to preserve spatial information, and stacking brain maps of all bands and features in three-dimensional space, with each map serving as input to the model; S4. Integrating a convolutional module, a long short-term memory network variant module, and an attention module, employing a multi-task learning strategy to obtain an optimal model capable of accurately identifying both arousal and valence of emotions in EEG signals simultaneously. This invention solves the problem of previous models focusing only on a single feature and a single recognition task, constructing a highly accurate multi-task model in the field of emotion recognition.

The invention discloses a fruit tree branch sample disease detection image identification and detection method and system, and the method comprises the steps: synchronously collecting a high-resolution RGB image, a near-infrared image and a depth image of the surface of a target fruit tree branch, and carrying out the preprocessing of the high-resolution RGB image, and obtaining a to-be-detected image; carrying out graying filtering on pixel points of the to-be-detected image to obtain a grayscale image; selecting a local window for the grayscale image to carry out window sliding to calculate a curvature characteristic value of a central pixel point; extracting a fusion coding vector by using the three-dimensional initial feature vector set, the near-infrared image and the depth image; and constructing a historical fusion coding vector database, and matching the fusion coding vector with the historical fusion coding vector database to obtain a fruit tree branch and trunk disease detection result. Different image analyses are performed by combining a high-resolution RGB image, a near-infrared image and a depth image, and fused features are extracted to understand different disease detection results.

The application discloses an infrared image non-uniformity prediction and correction method based on wavelet transform, which comprises the following steps: adopting double-density dual-tree complex wavelet transform to perform multi-scale decomposition on an input infrared image; in a high-frequency subband, detecting a blind element position based on a local variance statistical method; in a low-frequency subband, constructing an autoregressive model, predicting non-uniformity of a background region, and performing accurate processing on a prediction result; combining the prediction results of the high-frequency subband and the low-frequency subband to generate a pre-correction coefficient; and adopting the pre-correction coefficient to perform correction processing on an original infrared image to obtain a corrected image. Through the combination of an adaptive fusion strategy, space-time consistency constraint and dynamic gain and bias updating technology, the problems of non-uniformity noise, balance between details and background, device drift compensation and consistency in a dynamic scene in infrared image processing are solved, and the accuracy and stability of infrared image processing are significantly improved.

The invention relates to a self-adaptive quantum random number generation method based on real-time entropy estimation, and belongs to the technical field of quantum random number generation. According to an existing integrated quantum random number generator, due to the non-stationary characteristic of a physical entropy source, the entropy rate dynamically drifts, and after-processing parameters are fixed, randomness safety and generation efficiency are difficult to guarantee at the same time. According to the method, the state transition probability of the original random bit stream is counted in real time, the confidence interval correction is introduced to calculate the minimum entropy, the input bit width is dynamically adjusted accordingly, the Toeplitz matrix is reconstructed for random extraction, and self-adaptive adjustment of the compression ratio is achieved. According to the method, the compression ratio is increased to ensure the security of the output random number when the entropy source quality is reduced, and the compression ratio is reduced to improve the generation efficiency when the entropy source quality is improved, so that the dynamic balance between the randomness security and the generation efficiency is realized on the premise of keeping integration and low cost.

The invention discloses a photovoltaic power generation prediction method and device, relates to the technical field of intelligent power generation, and solves the problems that data errors exist in data acquisition and power generation processes, and a certain deviation exists between a predicted value and an actual value due to the fact that an output result is directly used as the predicted value. The method comprises the following steps: acquiring historical power generation amount, real-time weather data, historical weather data and corresponding historical total power generation amount of a plurality of photovoltaic panels; dividing the photovoltaic power generation area into a plurality of power generation areas based on the historical power generation amount of the plurality of photovoltaic panels; analyzing a power generation threshold value based on the historical weather data and the corresponding historical total power generation amount; predicting the generating capacity of the plurality of areas based on the real-time weather data and the generating threshold to obtain predicted generating capacity; integrating the predicted generating capacity of the plurality of areas to obtain photovoltaic predicted generating capacity; the generating capacity is accurately predicted, and prediction errors caused by uncertain factors are reduced.

The application discloses a glacier change prediction method and system, and belongs to the technical field of glacier monitoring; the application is based on a data-driven glacier mass balance model, considers the feedback mechanism of glacier morphological evolution on glacier mass balance and dynamic process, reduces the uncertainty of glacier change prediction results through iterative processing, and further improves the accuracy of future glacier change prediction. On this basis, the glacier mass balance model used by the application comprises a dimension reduction module, an attention mechanism module and a mapping module; the nonlinear mapping relationship between the glacier meteorological parameter sequence, the glacier morphological parameter and the glacier mass balance data is learned through the joint dimension reduction module and the attention mechanism module, the multiscale characteristics of the time series data can be fully mined, and the error of the glacier mass balance estimation is further reduced.

The invention discloses a plane target adapting to a mobile scanning environment and a bull's-eye positioning method and system. Relates to the field of three-dimensional laser scanning and the technical field of engineering measurement. The method comprises the following steps: acquiring point cloud data of a complete front pattern of each planar target and preprocessing the point cloud data to obtain a target point cloud data set; processing the target point cloud data set to obtain a centralized point cloud, and performing singular value decomposition on the centralized point cloud; constructing a point cloud data plane according to the decomposition result of the singular value, calculating the distance from the point cloud data to the point cloud data plane, and determining an optimal plane; a cross wire of each plane target is decomposed, original data of straight lines are extracted and optimized, the optimized original data are fitted, and two straight lines are obtained; and projecting the two straight lines to an optimal plane to calculate an intersection point, thereby completing target center identification. The method can effectively remove the influence of noise and outliers, enables the data to be more concentrated and accurate, and remarkably improves the precision and reliability of subsequent calculation.

This invention relates to the field of fault diagnosis technology for high-voltage, high-capacity DC-DC converters, and provides an online diagnostic method and system for open-circuit faults in IGBTs of CETDC submodules. The method includes: constructing a fusion representation of multi-channel capacitor voltages; establishing a VMD adaptive decomposition optimized by the INFO algorithm; constructing a composite fitness function and constraining the optimization process; establishing a sensitive mode screening based on envelope entropy statistical characteristics; constructing multi-domain feature vectors of sensitive modes and establishing a fault identification model; and establishing interpretability analysis and key feature compression. This invention maintains high-precision diagnosis under complex operating conditions and noise disturbances, and improves the real-time performance, stability, and engineering usability of online deployment through a closed-loop process of "parameter adaptive decomposition - sensitive mode screening - key feature compression - attention time-series identification - interpretability verification".

The invention provides a multi-channel intelligent acoustic actuation control system and method based on improved LMS filtering, and belongs to the technical field of underwater noise test equipment, and the system comprises an actuation unit, a control unit and a signal processing unit. The actuating unit comprises acceleration sensors and vibration exciters, the multiple vibration exciters are installed in the model and used for simulating mechanical vibration, and the multiple acceleration sensors are arranged nearby the vibration exciters and used for measuring vibration response of the model; the control unit comprises a control computer, a linear power supply module and a signal collector, the control computer is connected with a digital-to-analog converter, and the digital-to-analog converter is connected with a signal conditioner; the signal processing unit comprises an alternating current purification power supply and a power amplifier, and the signal processing unit transmits a collected vibration acceleration signal to the control computer through a signal collector. According to the invention, accurate control and real-time state feedback of the vibration exciter are realized, so that the reliability and accuracy of underwater noise testing are improved.