7results about How to "Hard to get" patented technology

This invention discloses a method for restoring turbulent degradation images based on complex amplitude detection, belonging to the field of computational optics imaging technology. This method constructs a computational optics imaging system without a hardware correction unit. It utilizes a wavefront sensor and an imaging sensor to simultaneously acquire Hartmann images and target degradation images. A computational unit performs wavefront complex amplitude reconstruction and aberration correction. Based on this, a physically constrained end-to-end neural network architecture is designed. By reconstructing the wavefront complex amplitude, it achieves accurate mapping and inference from the Hartmann image to the point spread function of the imaging system. Finally, a multi-scale deconvolutional network is combined to complete high-quality restoration of the turbulent degradation image. This invention eliminates the need for a hardware corrector, overcomes the physical limitations of traditional adaptive optics, effectively solves the problem of insufficient point spread function accuracy, and enables time-delay-free and highly efficient turbulent image restoration under low-cost conditions.

The application discloses a ship radiation noise line spectrum estimation method based on multi-snapshot sparse Bayesian learning, and belongs to the field of underwater acoustic signal processing; the method utilizes the sparse characteristics of the ship radiation noise line spectrum, that is, the line spectrum is not uniformly distributed in the whole frequency range, but is discrete and sparse, converts the line spectrum estimation problem into a sparse signal recovery problem, so as to solve the problems in the background technology, under the condition that the data sample length is limited and the sparsity is unknown, the Bayesian framework utilizes probability modeling to adaptively adjust the hyperparameters from the global optimization angle, can effectively reduce the sidelobe, realizes high resolution, reduces the background noise fluctuation variance through multiple observation snapshots statistical average, enhances the signal-to-noise ratio of the line spectrum output, and improves the line spectrum feature detection performance.

This invention belongs to the field of wireless communication, specifically relating to a method for calculating the spatial polarization domain information of received signals based on a three-element circular array receiver. This invention utilizes the similarities and differences between known steering vector configurations to solve the problem of rapidly obtaining parameters when the spatial polarization domain parameters of the received signal are unknown. Using the received signals at each point of the three-element circular array receiver, a model is constructed in pairs. Using the rotation-invariant subspace algorithm and the least squares method, the pairwise differences of the received signals at each point of the three-element circular array receiver are calculated. Given the known steering vector configuration, the elevation and azimuth angles are solved by solving equations. Using the calculated elevation and azimuth angles, and the known steering vector configuration, the phase angle and phase difference in the polarization domain are directly calculated by solving the matrix correlation using the least squares method. Finally, all state estimation results are obtained, and the root mean square error of each angle at different snapshot numbers and signal-to-noise ratios is calculated.

This application provides a training method, apparatus, electronic device, and storage medium for a lesion detection model, belonging to the field of image processing technology. It involves acquiring a first medical image and a second medical image, where the first image is unlabeled and the second image is labeled. A preset teacher model is used to detect lesions in the first medical image, and a preset student model is used to detect lesions in both the first and second medical images. Based on the lesion detection results, a loss calculation is performed to obtain target loss data. The first network parameters of the preset student model are adjusted based on the target loss data, and the second network parameters of the preset teacher model are adjusted based on the first network parameters to train the preset student model, thus obtaining a lesion detection model. This lesion detection model is then used to detect lesions in the target medical image, obtaining the location and type of the target lesion, thereby improving the accuracy of lesion detection.

The application discloses a kind of based on preset performance constraint's main steam temperature model predictive control method and system, it is related to thermal automatic control technical field.The method includes: offline pre-training neural network prediction model using historical operation data, to approximate the dynamic and output mapping of controlled object;Online at each sampling time based on the neural network model is constructed and solved input constraint model predictive control optimization problem, wherein the cost function introduces the barrier term formed by preset performance function, limit the error envelope that converges with time in predicted tracking error;According to the deviation and error margin of actual output and model output, construct compensation control amount, and with predicted control amount superposition form final control rate;Through online parameter update, correct model error to adapt to working condition change.The application does not need accurate mechanism model, can realize main steam temperature fast, small deviation tracking, effectively suppresses overtemperature and frequent manual intervention.

The application discloses a time-delay-based IPv6 address credit risk detection method and device, which comprises the following steps: aggregating an IPv6 address set to obtain a candidate first prefix set; performing time-delay measurement and aggregation based on a live address first prefix set obtained by detecting the candidate first prefix set, and extracting time-delay set features after the time-delay measurement and aggregation; inputting the time-delay set features into a trained IPv6 mobile network address identification model for feature identification and classification, so as to obtain a first prefix set of IPv6 mobile network addresses according to an IPv6 mobile network identification and classification result; calculating the score of each first prefix in the first prefix set based on a risk index of the credit of the first prefix of the IPv6 mobile network address, so as to obtain different credit scores, and obtaining a risk detection result of the credit of the first prefix of the IPv6 mobile network according to the credit scores. The application solves the problems of data deviation and difficulty in obtaining input data, and reduces the cost of active detection.

The invention belongs to the technical field of helicopter fault diagnosis, and relates to an intelligent diagnosis method and system for potential faults of a helicopter rotor based on sound signals. The method comprises the steps that typical fault features are implanted into feature parameters, and rotor models in various fault states are generated; flow field parameters are solved, and the dynamic pressure load on the surface of the rotor wing blade is obtained; a sequence signal of sound pressure changing along with time is collected through the virtual acoustic sensor array; extracting fault features; constructing a fault classification and discrimination model based on machine learning; and outputting the fault state and the fault type of the rotor wing. According to the method, the influence of the rotor wing fault on the sound radiation characteristic can be comprehensively reflected, and the problem that a helicopter rotor wing fault sample is difficult to obtain is effectively solved through high-fidelity geometric modeling, fault implantation and a CFD and CSD coupling strategy; the multi-dimensional fault features are extracted, the fault diagnosis model is constructed in combination with the machine learning algorithm, the fault diagnosis accuracy is high, and automatic recognition and type positioning of faults can be achieved.