659 results about "Nonlinear transformation" patented technology

The invention relates to the technical field of voice processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a voice feature processing method, device, equipment and medium. Performing time resolution analysis based on the fused Mel band energy to generate a multi-scale Mel spectrum amplitude value, and performing nonlinear transformation on the multi-scale Mel spectrum amplitude value according to the noise intensity parameter to generate a noise suppression Mel component; and generating a perception weighting coefficient according to an auditory perception model, and executing frequency domain energy adjustment on the noise suppression Mel component to generate Mel spectrum representation. On the basis of frequency resolution self-adaption, time resolution dynamic adjustment and auditory perception modeling, nonlinear transformation and perception weighting processing are applied to the multi-scale Mel spectrum amplitude value, the influence of noise interference on voice features can be effectively reduced, and the key information retention capacity of voice signals is enhanced.

The invention discloses a multi-scale semantic guidance image compression method and system and a storage medium, and the method comprises the following steps: obtaining input image data, carrying out the preprocessing of an input image, and obtaining standardized image data; inputting the standardized image data into a pre-trained semantic segmentation network to generate a multi-scale semantic feature map and a semantic weight map corresponding to the multi-scale semantic feature map; a three-stage pyramid encoder is constructed, and the standardized image data is subjected to the following steps of: sampling under depth separable convolution to generate multi-scale features; the reversible neural network carries out nonlinear transformation on the multi-scale features; the multi-scale feature subjected to nonlinear transformation is decomposed into a low-frequency sub-band and a high-frequency sub-band through adaptive discrete wavelet transformation, dynamic selective state space modeling is executed on the high-frequency sub-band based on a semantic weight map, and a compressed code stream is generated; and inputting the compressed code stream into a decoder, decoding based on a lightweight Mama module, and reconstructing an image in combination with inverse wavelet transform and a semantic weight map.

The invention discloses a coal mine industry multi-dimensional data real-time monitoring and abnormity positioning method and system, and the method comprises the steps: collecting real-time data in coal mine operation, and carrying out the preprocessing; and carrying out dimension construction on the preprocessed data, and fusing the data of different dimensions to form multi-dimensional data. And inputting the multi-dimensional data into the anomaly detection model, and carrying out backtracking analysis on the abnormal data. And performing exception handling and response according to the detected exception and backtracking analysis. According to the coal mining industry multi-dimensional data real-time monitoring and anomaly positioning method and system provided by the invention, correlation analysis and fusion are performed on different-dimensional data, key features are extracted by using principal component analysis, and efficient fusion of the multi-dimensional data is ensured in combination with nonlinear transformation. The main information is reserved, and the accuracy of anomaly detection is improved by enhancing the representativeness of the features. The abnormal response time is shortened, and the emergency processing accuracy is improved.

The invention discloses a vehicle owner behavior data driving type vehicle insurance risk assessment method based on a large language model, and relates to the technical field of risk assessment. Comprehensive vehicle owner and vehicle behavior data are collected from multiple channels, and deep analysis is performed on the data through the large language model; driving behavior features, historical accident record features and vehicle use condition features highly related to the vehicle insurance risk are extracted, and the influence of various factors on the risk is accurately reflected in combination with a nonlinear conversion function; key features are converted into numerical vectors and stored in the form of a multi-dimensional feature matrix, and high-quality input data are provided for a deep learning model; a risk assessment model constructed by adopting a PyTorch framework is combined with loss function optimization and a gradient descent algorithm, so that efficient model training and prediction are realized; and the risk assessment result is adjusted in real time through dynamic data updating, so that the accuracy of vehicle insurance risk assessment and the response speed to the behavior change of the vehicle owner are remarkably improved.

The invention discloses a low-carbon city subjective and objective mixed planning interval entropy weighting evaluation method, and relates to the technical field of city management and low-carbon evaluation, and the method comprises the steps: collecting and preprocessing index data in real time, building a user behavior prediction model according to the index data, and predicting the index data of a user; dynamically adjusting weight distribution of subjective and objective evaluation indexes based on the predicted index data, and constructing a comprehensive evaluation model to calculate a comprehensive score of each region in the city; and formulating optimization measures of each region according to the comprehensive score. A prediction model is established by using a support vector machine, an evaluation result is highly matched with real-time data through dynamic weight adjustment, the hysteresis problem of traditional static evaluation is avoided, and more accurate and flexible low-carbon city evaluation is realized through dynamic adjustment of weight distribution and construction of a comprehensive evaluation model. And the comprehensive score of the urban area is calculated by adopting a nonlinear transformation function, so that the marginal effect change of the evaluation index under different load conditions can be better reflected.

The invention discloses a word embedding vector extraction method and system based on a large language model, and belongs to the field of natural language processing, and the word embedding vector extraction method based on the large language model comprises the following steps: S1, embedding a global context attention module, generating a Q / K / V vector through linear transformation, and calculating a global attention weight in a cross-position manner; s2, splicing coding features of adjacent layers, and performing gating fusion to generate a global context dynamic semantic state; s3, multi-head attention joint coding is carried out on the dynamic semantic state of the current layer and the global state; s4, performing average pooling to generate a global semantic vector, and calculating a dynamic attention weight; s5, performing weighted fusion on the dynamic weight and the global vector to generate an enhanced code; s6, outputting a final word vector by a GELU nonlinear transformation full-connection layer; the method has the beneficial effects that the global context semantic capture capability is enhanced, and the expression effect of the word embedding vector is improved.

The invention discloses a multi-scale and mixed attention high-robustness motor imagery recognition method, which comprises the following steps: S1, acquiring motor imagery electroencephalogram signals, preprocessing the motor imagery electroencephalogram signals, dividing a training set and a test set, segmenting the training set, recombining the training set and expanding a training data set; s2, multi-scale feature extraction is conducted on the motor imagery electroencephalogram signals through a multi-scale convolution embedding module, and time dynamic and space cooperation features of different frequency bands are captured; s3, inputting the multi-scale features into LG-KAT, and respectively modeling a local fine-grained feature and a global time sequence dependency relationship through a local attention branch and a global attention branch; s4, features output by LG-KAT and low-layer embedded features are fused and flattened, a classification layer based on GR-KAN is input for nonlinear transformation and category mapping, model parameters are trained and optimized, and motor imagery task classification is achieved. The invention further discloses a multi-scale and mixed attention high-robustness motor imagery recognition system.

The invention provides a photovoltaic prediction method based on a data double decomposition and deep learning optimization model, and the method comprises the steps: collecting and preprocessing historical photovoltaic power data and meteorological associated data, carrying out the double decomposition of the preprocessed historical photovoltaic power data, and obtaining photovoltaic power component data; combining the photovoltaic power component data with meteorological associated data to construct a plurality of groups of photovoltaic-meteorological component data sets; an iTransform-KAN photovoltaic power prediction model is constructed, the photovoltaic-meteorological component data set is used to train and test the iTransform-KAN photovoltaic power prediction model, and the trained iTransform-KAN photovoltaic power prediction model is obtained; and determining a final predicted value through a linear superposition strategy based on the photovoltaic power component predicted value. According to the method, precise stripping of multi-scale features of photovoltaic power and adaptive learning of nonlinear transformation are realized, so that the capability of modeling a complex dynamic relationship is improved.

The invention discloses a coronary heart disease preliminary screening method and device based on facial feature analysis and a medium, and relates to the technical field of image understanding, and the method comprises the steps: extracting each part sub-region of a human face, and obtaining a tensor image of each sample data through the stacking of the sub-regions; obtaining the flattened feature maps of the tensor images, and integrating the feature maps to obtain a feature matrix of the corresponding sample data; calculating a feature mean value in the image dimension direction according to the feature matrix, and taking the feature mean value as a global feature tensor of a corresponding sample; carrying out dimension reduction processing and nonlinear transformation on the global feature tensor, and carrying out binary classification probability distribution output of relevance between the subregions and the coronary heart disease; performing updating iteration of the classification weight of the sub-region through cross entropy loss function constraint and a stochastic gradient descent optimizer; and performing primary screening of the coronary heart disease through the iterated primary screening model of the coronary heart disease. According to the method, the key feature capturing capability of the model is improved, and then the judgment capability on the coronary heart disease risk is enhanced.

The invention discloses an acute pancreatitis complication assessment method based on artificial intelligence, and belongs to the technical field of medical information, and the method specifically comprises the steps: firstly, obtaining a clinical data set containing real-time clinical indexes and basic disease historical records; performing time sequence analysis on the historical records of the basic diseases, extracting long-term influence characteristics of the historical records of the basic diseases and generating basic disease influence factors; carrying out standardization processing on the real-time clinical indexes and the basic disease influence factors, and generating a comprehensive feature vector after calculating association weights among features; inputting the vector into a trained artificial intelligence evaluation model, and outputting a complication risk probability through layer-by-layer nonlinear transformation; and finally, mapping the risk probability value into a risk level, and integrating patient information to generate a structured risk assessment report. And an acute pancreatitis complication evaluation scheme integrating the acute stage index and the chronic basic disease influence is established.

The invention provides a full-text retrieval method and system fusing various types of documents, and relates to the technical field of information retrieval, and the method comprises the following steps: obtaining document representation through document content extraction and structure recognition, generating a cross-modal semantic vector by using word embedding and nonlinear transformation, constructing a hierarchical index and a cross-document association graph, and obtaining a full-text retrieval result; the basic correlation score is calculated after the query request is received, and the comprehensive score of the candidate content segments is calculated based on the association graph to determine the optimal retrieval result, so that unified representation and retrieval of heterogeneous documents are realized, the cross-document retrieval precision and relevance are improved, and the processing capability of a retrieval system on complex queries is enhanced.

The invention discloses a PCB defect real-time detection method based on multi-scale feature fusion, and relates to the technical field of PCB defect real-time detection methods, and the method comprises the steps: obtaining a to-be-detected PCB image, carrying out the size normalization and pixel value standardization processing of the image, and obtaining a standardized image meeting the input requirements of a model; inputting the standardized image into a backbone network of a teacher detection model, and extracting a multi-scale primary feature map containing texture information in different directions through a grouping convolution structure; transmitting the multi-scale primary feature map to a neck network of a teacher detection model, and performing weighted fusion on feature maps of different scales by using a learnable weight to generate a multi-scale fusion feature map; and in an up-sampling path of the neck network, generating channel description information after global pooling is performed on the deep fusion feature map, generating a channel attention weight through nonlinear transformation, acting the weight on a primary feature map of a corresponding level, and outputting an enhanced feature map.

The invention discloses a hyperspectral fusion imaging method and device based on deep nonlinear transformation tensor low-rank representation, and the method comprises the following steps: collecting a priori multispectral image while collecting hyperspectral compression measurement in an observation scene, and taking the two data as input at the same time; based on tensor representation and deep learning theories, novel deep nonlinear transformation tensor low-rank regularization for the hyperspectral image is established, and global high-dimensional low-rank correlation of the hyperspectral image in a deep nonlinear transformation domain is fully described; taking the regularization item as a target function, modeling compression imaging and a spectrum degradation process into two data fidelity items, and constructing a fusion imaging model and a loss function; and minimizing a loss function through a learning algorithm to optimize the model, and fusing and reconstructing a complete hyperspectral image. According to the novel method and the novel device provided by the invention, high-precision fusion imaging of the observation scene can be realized without manual intervention and explicit intermediate steps.

The invention provides an infrared small target detection method and system based on depth-guided low-rank sparse decomposition, and relates to the technical field of remote sensing image processing, and the method comprises the steps: obtaining all original infrared images shot by remote sensing equipment, and sequentially stacking the original infrared images according to an obtaining time sequence, and obtaining an infrared original tensor; performing low-rank background and sparse target decomposition processing based on the infrared original tensor to obtain a low-rank sparse tensor decomposition model; a low-rank background tensor containing nonlinear transformation is obtained through processing of a constructed hierarchical nonlinear tensor ring background module; processing through a sparse target module fused with an attention mechanism to obtain a sparse feature tensor of the infrared small target area; and reconstructing a low-rank sparse tensor decomposition model guided by the deep neural network, and carrying out solving processing to obtain a final infrared small target detection result. According to the invention, accurate, robust and rapid detection can be carried out on a small target under a complex background.

The invention belongs to the technical field of load prediction in a low-voltage distribution area, and particularly relates to a time sequence prediction method for attention mixed multi-scale decomposition, which comprises the following steps: S1, preprocessing original time sequence data to obtain a standardized sequence X; s2, inputting X into MJDA, and outputting uniform high-dimensional representation U after feature enhancement; s3, inputting U into TCDA, and carrying out cross-dimension dependence modeling and deep nonlinear transformation to obtain a final enhanced feature U1; s4, inputting U1 output by the TCDA into a hybrid expert predictor group; the predictor group is composed of K parallel expert predictors, and a corresponding expert prediction result is obtained; meanwhile, U output by the MJDA is processed through a noise perception gating network, and weight distribution U used for expert predictor fusion is generated; and according to the U, carrying out weighted summation on the output of the K expert predictors to obtain a prediction result. According to the method, high-precision and high-stability load prediction can be realized in a low-voltage distribution area environment with limited resources.

The invention relates to a ginkgo leaf canopy near-earth remote sensing image super-resolution reconstruction method, which comprises the following steps: (1) collecting ginkgo multispectral remote sensing images at different heights, and establishing a data set by aligning real LR images and HR images; (2) recovering a high-resolution image from the low-resolution remote sensing image by adopting a super-resolution reconstruction model; the super-resolution reconstruction model RTGAN takes an SRGAN model as a baseline model; the RTGAN is composed of a generator network and a discriminator network, an MDRB composed of a plurality of continuous residual blocks is integrated in the generator network, and a U-Trans discriminator is used as a discrimination network; the generator network maps the LR image into an SR image through multilayer nonlinear transformation and feature extraction; the discriminator network evaluates the quality and authenticity of the generated image, and distinguishes a real HR image and a generated SR image; finally, the discriminator network cannot distinguish the two images, and a high-quality SR image is obtained.

The invention provides a miniaturized spatial macro-energy ultraviolet single-frequency laser. The miniaturized spatial macro-energy ultraviolet single-frequency laser comprises a single-frequency pulse seed laser module, a three-stage amplifier and a nonlinear transformation module, the single-frequency pulse seed laser is a passively Q-switched single non-planar annular cavity and can output single-frequency 1064nm pulse laser of ten microfocus magnitude. The three-stage amplifier adopts a compact side pumping batten crystal structure, so that 1064nm single-frequency laser output with energy of 350mJ can be realized; by improving the output energy of the first amplifier, high-gain amplification of small-signal incident laser pulses is realized, and the pressure of subsequent amplification is reduced; the nonlinear transformation module can realize long-life and high-reliability ultraviolet single-frequency pulse output of hundred millijoule and hundred hertz by controlling the power density; the method can be used for satellite-borne hyperspectral laser radars, satellite-borne wind measurement laser radars and the like to obtain global meteorological information. The invention mainly solves the problems of miniaturization, high frequency stability and high reliability of the large-energy space ultraviolet single-frequency laser.

The invention provides a deep steganography image secret information blind extraction method based on self-supervised learning. The deep steganography image secret information blind extraction method comprises a coding stage, a self-supervised learning task generation supervision signal, attention coupling through an attention coupling module and a decoding stage. The coding stage comprises the following steps of: respectively taking two secret-containing images as input, performing Haar transformation method processing, splitting input features generated by the processing of the Haar transformation method of the secret-containing images into two paths through a bidirectional coupling mechanism of a coupling layer, realizing feature decoupling and cross-path interaction by utilizing nonlinear transformation, different paths are enabled to focus different components of secret-containing images respectively, and meanwhile, information lossless in the feature transformation process is ensured through reversible design. According to the method, the self-supervised learning technology is innovatively utilized, the model can deeply mine the characteristics and rules of the secret-containing image, and therefore secret information blind extraction independent of a secret key is achieved.

The invention provides a flash encryption method for a security chip, the security chip and electronic equipment, and belongs to the field of digital security, the security chip comprises two flash encryption modules which are connected in parallel, the first flash encryption module carries out encryption and decryption processing on target data of which the values are all '1', and the second flash encryption module carries out encryption and decryption processing on other target data; each flash encryption module is used for encrypting and decrypting target data by using a Feistel structure; dividing the target data into a preset number of groups, encrypting each group by using different round functions, and performing nonlinear transformation on each round function by using different S boxes; and each flash encryption module scrambles the address based on a preset scrambling formula, and stores the encrypted ciphertext based on a scrambling result. The encryption delay can be reduced, the encryption strength is enhanced, and the software and hardware communication cost is reduced.

The invention discloses an infrared and visible light image data association method based on multi-view clustering, graph construction is completed by aiming at obtained data of different data sources, nodes represent data entities, and edges represent similarities or relationships among the entities. By means of a graph convolution network, input multi-view data features and a structure graph are mapped step by step through a series of nonlinear transformation, and features in a feature graph are extracted. After feature representation of data is independently learned from each view angle, the features are fused to obtain global feature representation, and meanwhile, features of similar samples in other different view angles are integrated to learn a common space. In addition, the multi-view clustering is guided and compared by using the pseudo labels, so that the multi-view data is effectively processed, and the accuracy and efficiency of association are improved.

The invention discloses a communication anti-interference method and device of a navigation system and electronic equipment. The method relates to the communication field of the navigation system, and comprises the following steps: acquiring a first signal received by a satellite communication antenna of the navigation system and second signals received by a plurality of sampling antenna units in a preset antenna array; performing band-pass filtering on the first signal and the plurality of second signals to obtain a filtered first signal and a plurality of filtered second signals; performing nonlinear transformation processing on the plurality of filtered second signals to obtain interference cancellation signals; the filtered first signal and the interference cancellation signal are subjected to signal synthesis, a cancellation output signal is obtained, and the average power of the cancellation output signal is smaller than or equal to preset power. According to the invention, the technical problem of poor communication anti-interference capability of a navigation system in the prior art is solved.

The invention relates to the technical field of image processing, and provides a high-precision image splicing and correcting method based on a multi-view camera, and the method comprises the steps: recognizing a potential distortion region in an image according to a plurality of view images, and outputting the distortion region; detail features of the distortion areas are extracted, the distortion type of each distortion area is derived by combining geometric consistency of adjacent areas, and local correction parameters are generated; performing accurate correction on each pixel by adopting self-adaptive nonlinear transformation and combining a mapping relation between a pixel position and a distortion degree, and obtaining an image after distortion correction; and after image splicing is carried out on the images after distortion correction, the transition effect of the edge is optimized by combining a multi-scale region fusion technology. According to the invention, the visual unnatural phenomenon at the seam can be eliminated, and the precision and visual experience of the whole image splicing are improved.

The invention discloses a security industry system application change level quantitative evaluation method, which comprises the steps of reading original change application data, historical change records and market transaction data, and respectively generating a change feature vector, a historical influence vector and a market feature vector; performing time window segmentation and weight calculation on the market feature vector to generate a time sequence feature matrix; carrying out nonlinear transformation on the change feature vector and carrying out dependency analysis on the change feature vector and a historical influence vector to generate a coupling feature tensor; decomposing the coupling feature tensor and performing feature mapping with the time sequence feature matrix to generate an evaluation score; and performing clustering analysis based on the evaluation score and the historical influence vector, generating a level threshold vector, and further obtaining a level change result. According to the method, through dynamic fusion and adaptive optimization of the multi-dimensional data, accurate quantitative evaluation of the system change risk is realized, and the accuracy and reliability of evaluation are improved.

The invention belongs to the technical field of polymer additive manufacturing, and relates to a melt electrostatic direct writing preparation method of a polymer fiber scaffold with a continuous gradual change structure, which comprises the following steps of: designing a basic homogeneous pattern according to the transition interface characteristics of a to-be-repaired tissue, and discretizing the basic homogeneous pattern into a dense ordered point array; designing nonlinear transformation functions f and g according to structural change characteristics at a transition interface, mapping points one by one by using the nonlinear transformation functions f and g so as to obtain a point column, performing equidistant sampling on the point column to obtain an expected printing path, and finally performing melt electrostatic direct writing based on the expected printing path so as to obtain the melt electrostatic direct writing. And preparing the polymer fiber scaffold with the continuous gradually-changed structure. According to the method, a curve printing process is adopted to simulate a smooth transition interface between tissues, so that the structure and mechanical bionic degree of the polymer fiber scaffold are improved.

A method for successively executing first and subsequent block cryptographic computations. These cryptographic computations include transforming a respective input block (38, 64) composed of a plurality of elements (107k), via a plurality of linear transformations (109, 115, 116, SR) and non-linear transformations (112), into a corresponding output block. The method includes:implementing a functional correspondence between input and output of the non-linear transformations (112) applied on the elements of the first input block (38, 64) during the first block cryptographic computation, andapplying dynamical obfuscation (124) by re-encoding the functional correspondence into a modified functional correspondence between the input and the output of the non-linear transformations applied on elements of the next input block during the next block cryptographic computation.

The invention relates to the technical field of electric energy metering information transmission, and discloses an electric energy metering information transmission system and method. Through data acquisition and preprocessing, voltage, current and power signals are acquired, a normalized time sequence matrix is generated, the feature expression capability is enhanced through nonlinear transformation, and the problem of signal complexity is solved. And feature extraction and compression are combined with sparse processing, key feature information is extracted, the data dimension and redundancy are reduced, the storage and transmission pressure is reduced, and the transmission efficiency is improved. The dynamic transmission strategy adaptively adjusts the frequency and priority based on the network state, and enhances the transmission reliability in combination with a verification mechanism. And in decoding and reconstruction, signal features are recovered by using a reverse reconstruction algorithm, so that the precision and data availability under a high compression ratio are ensured. Data analysis and anomaly detection are combined with high-quality features and a dynamic algorithm, abnormal values and abnormal fluctuations are recognized in real time, and the adaptive capacity of the system in a complex environment is improved.

The invention belongs to the technical field of image data processing, and particularly discloses a single low-illumination image enhancement method for simulating multi-exposure image fusion. The method comprises the following steps: carrying out nonlinear transformation on a single low-illumination input image to generate a virtual exposure sequence containing a reference frame and a plurality of non-reference frames; inputting a virtual exposure sequence into a VMEIF-Net network, processing a reference frame and a non-reference frame through a multi-branch feature coding structure, introducing a space attention module to a non-reference frame branch to screen features consistent with the reference frame, iteratively optimizing texture details by adopting a feature feedback unit, and inhibiting artifacts through a global feature analysis unit, so as to obtain a VMEIF-Net network; and generating an enhanced image through feature fusion and decoding operation. According to the invention, through multi-branch feature extraction and feedback connection, high-level feature information is transmitted back to a feature fusion stage, so that the information transmission efficiency is improved; by introducing the global context sensing module, the receptive field range is expanded, generation of artifacts can be inhibited, and rich image detail information is kept.

The embodiment of the invention provides a polypropylene film defect detection method, equipment and a medium. The method comprises the following steps: screening normal samples in a polypropylene film image to construct an image data set; extracting multi-scale features of the polypropylene film image; performing channel attention and space attention calculation on the multi-scale feature to obtain a first feature; performing channel conversion on the first features of different scales to obtain second features of the same channel; performing nonlinear transformation and local spatial feature extraction on the second feature to obtain a third feature; performing defect detection and defect positioning according to the third feature to obtain a detection result; the prior that a large number of defect samples are needed in traditional detection is eliminated, and efficient defect detection can be achieved after learning of a large number of normal samples.

The invention provides a facial skin flaw enhancement method based on a Lab color space. The method comprises the following steps: firstly, acquiring an RGB face image and converting the RGB face image into a CIE Lab color space with uniform perception; then, performing differentiation treatment according to the manually selected skin flaw type: for the vascular flaw, extracting statistical characteristics of a component and driving adaptive nonlinear transformation, and generating a grey-scale map which highlights the red flaw; for pigment flaws, nonlinear transformation is carried out on the component L, and then collaborative linear weighting and feature amplification are carried out on the component L, the component a and the component b, so that a grey-scale map with highlighted pigment spots is generated. And finally, coloring the grey-scale map in the Lab color space through adjustable parameters to generate a high-contrast color enhanced image. The method overcomes the dependence on hardware and training data in the prior art, can clearly and adaptively enhance various flaws such as acnes, couperose streaks and color spots, shows robustness under different illumination, and can be widely applied to clinical beauty, later photography and real-time video processing.

Embodiments of this application provide a method method for transforming data and a related device. The method includes: obtaining an input image, wherein the input image includes N pixels, Nis a positive integer; performing a nonlinear transformation on values of the N pixels to obtain N first pixel values; obtaining, according to a quantized model and the N first pixel values, M second pixel values, wherein M is a positive integer; performing a reverse transformation corresponding to the nonlinear transformation on the M second pixel values to obtain M third pixel values; determining, according to the M third pixels values, an output image.