14 results about "Translation invariance" patented technology

The invention specifically relates to a method and a system for detecting a high-resistance grounding fault of a power distribution network, and belongs to the technical field of power distribution network fault detection. According to the method, the amplitude difference of the zero-sequence current is converted into the angle difference through the Grubby angle field coding, so that weak current distortion is amplified into a remarkable spatial stripe in the Grubby angle field image, and the abrupt change characteristic in the fault transient process can be effectively represented. Meanwhile, due to the symmetry and translation invariance of the Grubrum angle field matrix, the time domain dependency relationship of the zero-sequence current waveform of the high-resistance grounding fault is completely reserved, and the fault feature extraction capability can be effectively improved. According to the method, a traditional standard convolution kernel is improved according to polar coordinate characteristics of the Grubrum angle field image, time evolution characteristics are extracted by using radial branches of the multi-axis variable neural network, amplitude distortion characteristics are extracted by using angular branches of the multi-axis variable neural network, diagonal characteristics are extracted by using deformable convolution in a self-adaptive manner, and the expression ability of the extracted characteristics is effectively improved.

The invention discloses a DNAPLs inversion method based on Swin-Transform fusion multi-source data, and the method comprises the steps: generating a saturation field number and a permeability coefficient field array of DNAPLs, and forming an input tensor; preprocessing the input tensor to obtain a preprocessed tensor; the preprocessed tensor is input into a Swin Transform backbone network for feature extraction, and a feature tensor is obtained; re-parameterization is carried out to obtain assimilation hidden vectors; obtaining a first assimilation reconstruction tensor; judging the number of iterations to obtain a multi-source simulation observation vector of the first assimilation; and marking each channel of the finally obtained assimilation reconstruction tensor as a saturation field array and a permeability coefficient field array. According to the method, spatial position codes consistent with channel dimensions are introduced, the expression ability of a non-stationary space structure is improved, spatial position information is explicitly injected, responses of different spatial positions can be distinguished in the subsequent Swinin-Transform feature extraction process, and the limitation that only convolution translation invariance is relied on is avoided.

The application discloses a SAR polar coordinate imaging method and device based on flat flight equivalence, and the method comprises the following steps: constructing a flat flight equivalence slant range model of a large oblique SAR to obtain a baseband echo signal formed by target area scattering; performing distance direction matched filter processing on the baseband echo signal to obtain a signal after range pulse compression; sequentially performing Dechirp processing, two-dimensional resampling processing and IFFT transformation on the signal after range pulse compression to obtain a SAR polar coordinate primary imaging result; and performing geometric correction on the primary imaging result based on reverse projection to obtain an imaging result without geometric deformation and with good focusing. The SAR polar coordinate imaging method based on flat flight equivalence provided by the embodiment avoids the problem of azimuth translation invariance caused by diving through slant conversion, solves the problem that the traditional PFA is not applicable to diving SAR, and successfully extends the PFA imaging algorithm without deformation to diving SAR by correcting the geometric deformation problem caused by diving.

The application discloses a kind of mobile robot high-efficiency robust global positioning method based on laser radar.The application uses Radon transformation to convert rotation and translation change to the translation change of two axes of sinogram, uses translation isovariant feature extraction network to carry out feature extraction, guarantee rotation and translation isovariance, using Fourier transform to obtain the amplitude spectrum of frequency spectrum to realize translation invariance, by cross-correlation operation realizes rotation translation invariance similarity calculation, while supervising network extracts feature suitable for place identification task, to improve the representation ability of rotation translation invariant feature in laser point cloud.In addition, the application uses cross-correlation operation to estimate relative rotation and translation to provide good initial value for point cloud registration algorithm, and further solve accurate 6 degrees of freedom relative pose.

This invention discloses a channel estimation method for a flexible antenna array-assisted ISAC system. It constructs a multidimensional tensor channel model that integrates antenna rotation parameters. The received signals are then stacked into a tensor form with a Vandermonde structure. A two-stage parameter estimation framework is employed: the first stage utilizes the translation invariance of the tensor in the time delay dimension to achieve initial estimation of time delay and Doppler shift using the ESPRIT algorithm; the second stage performs tensor normalized multilinear decomposition within a Bayesian probabilistic framework, introduces hierarchical sparse priors to adaptively determine the model rank, and performs joint fine estimation of multidimensional channel parameters such as angle of arrival, departure angle, time delay, Doppler shift, and reflection coefficient. Simulation results show that this invention has higher estimation accuracy and stability under complex channel and low signal-to-noise ratio conditions, and is suitable for broadband large-scale ISAC systems assisted by flexible antenna arrays.

The application provides a kind of city rail vehicle autonomous obstacle detection method and system based on millimeter wave radar graph convolution, belongs to the technical field of rail transit obstacle detection, millimeter wave radar initialization, constantly input collected radar frame to queue;Read several frames of radar point cloud data from the queue, convert the processed point cloud data into a graph structure;The trained graph convolutional neural network is used to process the graph structure, and finally outputs the obstacle detection and classification result, and makes a warning for train travel.The high-dimensional embedding helps to realize information reorganization and high-dimensional aggregation of channel characteristics, and the rotation and translation invariance ensures the accuracy and robustness of obstacle detection;The jump connection structure effectively solves the over-smoothing problem of graph neural network;Through unique network structure and feature selection division, the feature relationship between different points is focused, and global information is obtained by combining attention mechanism, to realize more efficient and accurate detection of rail transit obstacle classification and detection.

The invention discloses an automatic adjusting method and system for an insulin pump, and relates to the technical field of automatic infusion control, and the method comprises the steps: collecting a signal output by a sensor in a motion monitoring unit, and preprocessing the signal to obtain a preprocessed signal frame; performing wavelet scattering transform processing on the preprocessed signal frame to generate a multi-order scattering coefficient set; calculating statistical characteristic parameters and constructing a multi-dimensional scattering characteristic vector; inputting the multi-dimensional scattering feature vector into a lightweight time sequence convolution class network, and outputting an event classification result; performing adaptive adjustment control on the infusion state of the insulin pump based on the event classification result; through translation invariance and multi-scale decomposition capability of wavelet scattering transformation and in combination with self-adaptive learning capability of a lightweight time sequence convolution network, high-precision real-time distinguishing of real mechanical shock and electromagnetic coupling interference is realized on an embedded platform, the false alarm rate in an electromagnetic environment is remarkably reduced, and the reliability of the system is improved. And infusion continuity and time sequence integrity are guaranteed.

The application discloses a point cloud registration method and system based on a rotation equivariant network, and the method comprises the following steps: obtaining point clouds to be registered; processing each point cloud to be registered by using a first network based on point rotation equivariance and translation invariance, obtaining a reference direction and a key point score of each point cloud; extracting key points according to the key point score, and extracting a rotation invariant and SO(2) equivariant feature for each key point according to the reference direction and a second network based on patches; based on a preset matching strategy, generating point correspondences with a high inlier rate according to the features; and solving a relative pose by using a corresponding grouping algorithm according to the point correspondences, thereby completing point cloud registration. The embodiment of the application constructs an efficient and universal feature learning framework, can generate point correspondences with a high inlier rate by using rotation invariant and SO(2) equivariant features, and can be widely applied to the technical field of computers.

The application discloses a pathological section tumor stroma ratio quantification method, and steps are as follows: (1) a weakly supervised neural network training process based on an image block: firstly, block a full section image scanned by a digital scanner, then assign a unique label, i.e., tumor or non-tumor, to each image block, and finally, train a neural network added with a two-dimensional random inactivation layer using cross-entropy loss and translation invariance loss; (2) a tumor stroma ratio quantification algorithm based on a neural network: generate a class activation mapping graph using a previously trained model to identify a section, calculate a stroma region using a morphological algorithm, and further calculate a tumor stroma ratio. The application is suitable for making an accurate quantitative judgment on the tumor state of a patient in a clinic, and compared with existing methods, the application has the characteristics of high repeatability, low execution cost and high calculation accuracy, can help a pathologist to quantize a prognosis factor difficult to manually calculate, and significantly reduces the workload of the pathologist.

The invention discloses an aero-engine sensor multi-fault self-diagnosis method based on a wavelet scattering network, and aims to solve the problem of insufficient diagnosis accuracy under the condition that multiple faults coexist and computing resources are limited. According to the method, firstly, sensor signals are collected, and a training data set covering normality, abrupt change, drift, bias and periodic disturbance is constructed; then, deep time-frequency features with translation invariance are extracted by using a wavelet scattering network, and signal energy distribution is effectively captured; dimensionality reduction is carried out on the high-dimensional features through principal component analysis, and redundant information is removed; and finally, a support vector machine multi-classification model is constructed based on the dimension reduction features, and rapid identification of various faults is realized. The method has high precision and high real-time performance under the condition of finite computing power, and the fault diagnosis capability of an aero-engine sensor system is remarkably improved.

A method for place re-recognition of a mobile robot based on a lidar estimable pose, said method comprising: using radon transform to convert rotation and translation changes into translation changes on two axes of a sinusoidal graph, and, on the basis of an amplitude spectrum of a spectrum, performing spectrum cross-correlation calculation on translation invariance and two images, so as to solve the translation property of the images. Translation invariance is used to generate a position descriptor and thereby perform candidate matching for place re-recognition; and cross-correlation calculation may be performed together with radon transform to solve relative rotation and translation. In the described method, a time-varying environment is considered, and by using a multi-channel feature BEV for representation, the capability of representing a local feature in a laser point cloud can be improved.

The application discloses a polar coordinate discretization-based inverted floating cone boundary optimization method and belongs to the technical field of open-pit mining. The method is characterized in that: the inverted floating cone model is constructed by means of angle difference in polar coordinates, the inverted floating model is subjected to discretization index processing, and the boundary optimization is carried out by using a block model; the slope angle interpolation processing based on polar coordinates is adopted, the step of setting the chamfer in a large amount of manual interaction mode required by traditional slope design is effectively avoided, and the slope can be automatically designed according to the principle of conservative value; the inverted floating cone moving speed is accelerated by using the sequence index-based mode, the index value is adopted according to the translation invariance characteristics of the inverted floating cone, and the calculation speed is effectively improved. Compared with the traditional inverted floating cone method, the application can not only calculate more reasonable economic value, but also significantly reduce the calculation complexity and greatly shorten the time for generating the final boundary with the optimal economic value. Meanwhile, when the coal price fluctuates, the final boundary can be quickly adjusted by using the method, so that the flexibility and economic benefits of the mining decision are improved.

The application discloses a power transformer short-circuit impact fault soundprint recognition method based on a translation-invariant CNN. Firstly, the audio signal of the transformer short-circuit impact fault collected is preprocessed to obtain time-frequency information features. Then, the input fault audio signal is subjected to feature dimension reduction by using a mel-frequency spectrum to reduce the translation offset of the impact soundprint response time-frequency diagram. Next, the CNN is improved, the local features of the input full connection layer are globally fused, and the translation invariance of the fault sample is enhanced. Finally, the test sample is used for test recognition according to the trained translation-invariant CNN model, and the soundprint recognition result of the test sample is obtained. The actual measurement data result shows that the method improves the short-circuit impact fault recognition rate on the basis of guaranteeing the accurate recognition of the remaining fault categories, effectively verifies the robustness of the translation-invariant CNN to the short-circuit impact fault soundprint recognition, and has high engineering application value.

This invention discloses a deep neural network feature enhancement method based on spatiotemporal metadata fingerprints, relating to the fields of artificial intelligence and spatiotemporal data processing. The method includes: parsing the absolute geographic location and observation time metadata of multidimensional spatiotemporal data; constructing a geospatial fingerprint tensor aligned with the spatial dimensions of the visual feature map; constructing a periodic and continuous temporal phase fingerprint vector using trigonometric function transformations; fusing the spatiotemporal fingerprint with visual features through cross-modal projection to generate a spatiotemporal attention mask; and dynamically calibrating the basic features using this mask. This invention effectively solves the problem of absolute spatiotemporal information loss caused by translation invariance in traditional convolutional neural networks, significantly improving the prediction accuracy of the model in non-stationary spatiotemporal scenarios.