42 results about "High dimensionality" patented technology

The invention discloses a hyperspectral image classification method based on an S2CFM-spatial spectrum convolution fusion Mama network model, and the method employs a parallel double-branch structure to extract the spatial context information and spectral sequence features of a hyperspectral image, and finally integrates the features through a dynamic convolution fusion module, thereby achieving the classification of the hyperspectral image. The method solves the problem of unbalanced utilization of space-spectrum information in a traditional method, introduces a space spectrum convolution fusion Mama network, and fuses a multi-scale convolution block (MCB), a dynamic convolution block (DCB) and a space spectrum Mama block (S2MB); the method solves the problems that in the prior art, the capacity of CNN for capturing spectral information is relatively limited, and a complex spectrum-space characteristic relation in hyperspectral data is difficult to fully represent; the problems that in HSI data, due to the fact that the dimensionality is high and the sample size is limited, an over-fitting problem is prone to occurring, and the generalization performance of the HSI data is limited are solved through a Transformers-based architecture.

A method for enhancing detection of morphed biometric modality data is provided that includes the steps of receiving, by an electronic device, biometric modality data of a person and extracting, using a trained machine learning model operated by the electronic device, high dimensionality feature vectors from the received biometric modality data. Moreover, the method includes the steps of generating a distribution from the high-dimensionality feature vectors, calculating a deviation between the generated distribution and a corresponding record high-dimensionality feature vector distribution of the person, and comparing the calculated deviation against a threshold deviation value. In response to determining the deviation satisfies the threshold deviation value, the method includes the step of determining the received biometric modality data was morphed.

The invention discloses a brain decoding system based on a functional magnetic resonance image and a potential diffusion model. The brain decoding system comprises a functional magnetic resonance image feature extraction module, an image generation module and an image output module, the functional magnetic resonance image feature extraction module combines spherical convolution and a mask auto-encoder, neural activity features related to visual stimulation are extracted from functional magnetic resonance imaging fMRI data, and the image generation module generates an image with high semantic information by using the neural activity features as conditions. And the output module processes and outputs the generated image. The method solves the major challenges of reconstructing visual stimulation from fMRI data, including the problems of low signal-to-noise ratio and high dimension, effectively reduces the dimension of fMRI data, accurately captures the distance between brain regions, and learns local and global brain activation modes by introducing a feature learning algorithm based on surface fMRI data; besides, the design of an anti-noise potential diffusion model (NR-LDM) further improves the quality of a reconstructed image by reducing the influence of fMRI noise, an image with more accurate semantic information can be generated, and improvement of the accuracy of brain decoding based on fMRI data is facilitated.

The invention provides a process parameter optimization method and equipment in an injection molding process and a medium, and belongs to the technical field of plastic product production. The process parameter optimization method in the injection molding process comprises the following steps: collecting production data of the injection molding process, wherein the production data comprises a production sample and a plurality of process parameters; extracting a principal component corresponding to the production sample by using a PCA method, and projecting the production data to the principal component for dimensionality reduction to obtain a principal component matrix; calculating the comprehensive contribution score of each process parameter in the multiple process parameters on the principal component by using the principal component matrix, and screening the process parameters according to the comprehensive contribution scores to obtain a contribution process parameter matrix; inputting the contribution process parameter matrix into a quality prediction model constructed based on XGBoost, and predicting and outputting a corresponding prediction quality index; the process parameters are optimized using the predicted quality indicators. According to the method and the device, the problem that the XGBoost modeling effect is influenced by information redundancy and variable collinearity caused by over-high dimensionality of process parameters in the prior art can be solved.

The invention belongs to but not limited to the technical field of spectral analysis, and discloses a spectral analysis transfer learning method and system based on central symmetry, and the method comprises the steps: collecting spectral data of a source domain and a target domain, and making a component information table; preprocessing the spectral data of the source domain and the target domain, and performing feature extraction and enhancement on the spectral data; constructing a source domain deep learning model, pre-training the deep learning model by using the preprocessed source domain spectral data set, and optimizing the performance of the deep learning model on source domain data by adjusting model parameters; in the transfer learning process, the feature center of the source domain data is calculated, and the feature center of the target data is adjusted when the source domain data is transferred to the target data; adding a new layer in the migration network, wherein the new layer comprises an additional convolutional layer or a full connection layer; and retraining and optimizing the migrated deep learning model based on the target domain spectral data set. According to the method, the problems of high dimension and sparsity of the spectral data are solved, and the efficiency and effect of transfer learning are remarkably improved.

The invention provides a planned trajectory generation method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a state space and an action space of a vehicle; according to the potential collision risk and the action space, classifying the predicted trajectories of the side vehicle to obtain a risk trajectory set and a safety trajectory set; and through a partially observable Markov decision process, modeling is performed on a state space, an action space, a risk trajectory set and a safety trajectory set to obtain a reference trajectory. The decision planning process under the uncertainty of the side vehicle behavior is modeled as POMDP, so that the uncertain behavior of the side vehicle is fully predicted, and the driving safety of the vehicle is ensured. Besides, in consideration of too high dimensionality of a multi-traffic-participant prediction trajectory cluster in a complex scene and easy falling into dimensionality disaster of the POMDP model, the prediction trajectory of the side vehicle is classified into a risk trajectory set and a safe trajectory set through a potential collision risk, so that state space dimensionality reduction is realized, and decision planning efficiency and decision stability are effectively improved.

The present application discloses a method, system, and computer system for processing queries to sparsely populated datasets. The method includes (i) receiving a query from a client system, (ii) determining, based at least in part on the query, a plurality of partitions of a hypercube that are expected to comprise information used in generating a response for the query, (iii) communicating a set of requests to a data source storing the hypercube, the set of requests comprising one or more requests corresponding to the plurality of partitions of the hypercube, (iv) obtaining, from the data source, a set of results to the set of requests, the set of results comprising data stored in the plurality of partitions of the hypercube, (v) determining a plurality of virtual objects for the set of results, (vi) combining the plurality of virtual objects into a response to the query, and (vii) providing, to the client system, the response to the query.

A power load prediction method, system and device based on GHOA-CNN-iTransform-LSTM, and the method comprises the steps: firstly obtaining the multi-source data of a power load and analyzing the causal relationship, then constructing an analysis model based on scene injection, constructing an improved hiking optimization algorithm GHOA based on a hierarchical strategy evolution framework, optimizing the hyper-parameters of the analysis model, and finally obtaining the multi-source data of the power load. And finally, after the analysis model is trained, power load prediction of the smart power grid is realized. By acquiring the multi-source data, analyzing the causal relationship and adaptively transforming the kernel of the analysis model, the analysis model can integrate the time and the causal logic for dynamic allocation, accurate perception of external events is realized, a stable deduction result is obtained from complex data, and the accuracy of the deduction result is improved. According to the method, the self-adaptive optimization of the optimal parameters is realized through the hierarchical strategy evolution framework, the search strategy is ensured to dynamically adapt to the high dimension and complexity of the parameter space, and the precision and adaptability of power load prediction are ensured.

Systems and methods are provided for reducing a number of performance metrics generated by network functions to a number of reduced dimension metrics, which can be used to detect anomalous behavior and generate a warning signal of the detected anomalous behavior. The disclosed systems and methods transform raw performance metrics in a high dimensionality space to a reduced number of metrics in a lower dimensionality space through dimensionality reduction techniques. Anomalous behavior in network performance is detected in the high dimensionality space using the reduced dimension metrics. The systems and methods disclosed herein convert the reduced dimension metrics back to the high dimensionality space, such that the performance metrics from network functions can be utilized to understand and address potential problems in the network.

A computer device for automatic feature detection comprises a processor, a communication device, and a memory configured to hold instructions executable by the processor to instantiate a dynamic convolution neural network, receive input data via the communication network, and execute the dynamic convolution neural network to automatically detect features in the input data. The dynamic convolution neural network compresses the input data from an input space having a dimensionality equal to a predetermined number of channels into an intermediate space having a dimensionality less than the number of channels. The dynamic convolution neural network dynamically fuses the channels into an intermediate representation within the intermediate space and expands the intermediate representation from the intermediate space to an expanded representation in an output space having a higher dimensionality than the dimensionality of the intermediate space. The features in the input data are automatically detected based on the expanded representation.

The invention discloses a breast ultrasound image end-to-end segmentation method based on image weight reduction, which comprises the following steps: firstly, processing a breast ultrasound image by using UNet to obtain a high-dimensional feature image of the image; performing tumor area rough prediction according to the feature map, and performing map data construction to obtain sparse map data of the breast ultrasound image; and through a data loading method of splitting the sparse graph into sub-graphs and sparse features, the data is transferred from the CPU to the GPU, so that the amount of data occupied and transmitted by the memory of the CPU is reduced, the GPU computing resources can be fully utilized, and the training process is accelerated. According to the method, the graph neural network can be utilized to assist in improving the segmentation precision, end-to-end training acceleration is provided, the model precision and the training efficiency are considered, and the problem that the high dimension and the large data volume of the medical image enable a graph structure to consume a large amount of resources during calculation, and consequently the calculation cost is high is solved.

The present invention provides a multi-RIS-assisted semi-blind joint channel estimation and symbol detection method and system. The solution includes establishing a multi-RIS-assisted MIMO communication system model; establishing a signal received by a base station as a parallel factor tensor model; converting the channel estimation and symbol detection problem into an iterative optimization problem of a cost function based on a tensor expansion; and solving the cost function through ALS. The solution solves the problem of excessively high dimensionality of received signals due to the multiplication of the number of RIS reflection elements in multi-RIS-assisted communication scenarios, making it difficult to accurately and quickly obtain channel state information. Compared with the pilot-assisted ALS algorithm and the KAKF semi-blind receiver in single-RIS-assisted communication scenarios, the method can effectively improve estimation accuracy and reduce bit error rate and pilot overhead.

The invention relates to the technical field of computer vision and image processing, and discloses an image interpolation method based on high dimensionality. The method comprises the following steps: establishing a video decoder module, a timestamp analysis module, a feature extraction module, a time sequence correlation modeling module, a time sequence correlation modeling module, a convolutional neural network module, an edge smoothing module, an edge detection module, an edge enhancement module, an image output module and a quality evaluation module; the time stamp analysis module records time points and positions; the feature extraction module extracts image edge feature data; the time sequence correlation modeling module captures time and spatial correlation data; the convolutional neural network module performs pixel-level interpolation on image frames; the edge enhancement module is used for enhancing edge pixel points, the image output module is used for generating high-quality interpolation image frames, and the quality evaluation module is used for carrying out quality inspection on the generated image.

The present disclosure relates to methods, systems, and apparatuses for featurizing time-series data to enhance machine learning model training. Time-series data, such as transaction records, is preprocessed to identify fields including descriptive and categorical information. Categories are assigned using a first machine learning model, and tags are applied based on domain-specific patterns or large language models. The processed data is organized into a star schema data structure comprising a fact structure and associated dimension structures. Features are generated from the data structure based on time windows, incorporating statistical metrics and identified patterns. These features are provided to a machine learning module to train a second machine learning model, improving accuracy and adaptability for applications such as customer behavior prediction and financial analysis. The disclosed approach addresses challenges of high dimensionality, noise, and temporal dependencies in time-series data, enabling robust and contextually relevant feature generation.

The application discloses a hyperspectral image classification method and system based on a quaternion deep network, mainly strengthens the learning ability of the network and improves the training efficiency of the network through quaternion algebra theory and deep learning theory, and solves the problems of few hyperspectral image data samples, spectral space variability and the like.The implementation scheme is as follows: firstly, considering the high dimensionality of data, dimensionality reduction is carried out through principal component analysis.Next, a deep attention module is constructed, important features are strengthened and noise interference is suppressed, so that the quality of data is improved.Then, a multi-branch network is constructed, the strengthened features are grouped, and a quaternion generator is designed to map the hyperspectral image data to a quaternion space for processing.Meanwhile, a multi-scale quaternion attention module is embedded to further strengthen the feature expression capability.Finally, the features of each branch are fused, and high-precision classification of the hyperspectral image is realized.

The invention discloses a transaction fraud detection method, system and device and a storage medium, belongs to the field of financial fraud detection, and solves the technical problem that in the prior art, when transaction data with high dimensionality, small samples and unbalanced extreme categories are adopted, only shallow combination of homogeneous integration and simple migration is adopted, and performance improvement in a cross-domain scene is limited. Obtaining a public credit card fraud data set, pre-training the deep neural network structure to obtain a feature extractor, and extracting migration features; splicing the migration features and fraudulent transaction detection data features to construct a combined data set; designing a base model layer composed of a plurality of heterogeneous base models to predict the combined data set, and training the meta-model layer based on a prediction result to obtain a TEL integrated model; and performing fraud detection on the transaction data by using the TEL integration model to obtain a fraud detection result. The method is used for realizing high-accuracy and high-stability transaction fraud detection.

Mechanisms for training a distributed neural network are provided, the mechanisms including: for each of a plurality of sub-networks: performing, using a hardware processor, a transform on data in a data structure having at least two dimensions to provide training data having a higher dimensionality than the at least two dimensions; and training the sub-network using the training data independently of other of the plurality of subnetworks. In some of these embodiments, the at least two dimensions is two dimensions. In some of these embodiments, the training data is stored in a three-dimensional structure. In some of these embodiments, the transform is a discrete Fourier transform. In some of these embodiments, the transform is a discrete cosine transform.

A method and system(s) for processing queries to sparsely populated datasets. The method includes (i) receiving a query from a client system, (ii) determining, based at least in part on the query, a plurality of partitions of a hypercube that are expected to comprise information used in generating a response for the query, (iii) communicating a set of requests to a data source storing the hypercube, the set of requests comprising one or more requests corresponding to the plurality of partitions of the hypercube, (iv) obtaining, from the data source, a set of results to the set of requests, the set of results comprising data stored in the plurality of partitions of the hypercube, (v) determining a plurality of virtual objects for the set of results, (vi) combining the plurality of virtual objects into a response to the query, and (vii) providing the response to the query.

This invention discloses an early fault diagnosis method for high-speed train traction transformers with missing data, comprising: (1) optimizing the individual position iteration equation and initial population position of the slime mold algorithm using Lévy flight and optimal point set distribution, and optimizing the running structure of the slime mold algorithm using a teaching optimization algorithm; (2) combining the improved slime mold algorithm with a CatBoost classifier to improve the F1 score of multi-class classification and finding the optimal CatBoost parameter configuration for classification performance; (3) training and classifying transformer fault data using a model combining the slime mold algorithm and the CatBoost classifier. This invention overcomes the influence of the large scale, high dimensionality and nonlinearity of transformer data on multi-class classification and the imbalance of the number of each category. Addressing the problem of mutual influence between different CatBoost parameter settings, it uses optimization algorithms to find the optimal parameter settings for classification performance. Finally, starting from the structural characteristics of the slime mold algorithm, it improves the problem of slow convergence speed and easy getting trapped in local optima in the traditional slime mold algorithm.

The application relates to the technical field of computer vision and image processing, and discloses an image interpolation method based on high dimensionality, which comprises a video decoder module, a timestamp analysis module, a feature extraction module, a time sequence correlation modeling module, a time sequence correlation modeling module, a convolutional neural network module, an edge smoothing processing module, an edge detection module, an edge enhancement module, an image output module and a quality evaluation module; the video decoder module extracts images; the timestamp analysis module records time points and positions; the feature extraction module extracts image edge feature data; the time sequence correlation modeling module captures time and space correlation data; the convolutional neural network module performs pixel-level interpolation on image frames; the edge smoothing processing module optimizes the interpolation results; the edge detection module identifies pixel points; the edge enhancement module is applied to perform enhancement processing on edge pixel points; the image output module generates high-quality interpolated image frames; and the quality evaluation module checks the quality of the generated images.

This application discloses a grounding grid corrosion diagnosis method, device, equipment, and medium based on sensitivity analysis and dimensionality reduction, relating to the field of power equipment fault diagnosis technology. The method equates the grounding grid to a resistive network. With the goal of minimizing a nonlinear diagnostic model, an objective function is constructed based on the initial resistance amplification factor of each branch and the sum of squared residuals between the measured and calculated port voltage values. An improved Hippo optimization algorithm is used to minimize the objective function, obtaining the resistance amplification factor of each branch. The sensitivity of each branch resistance to the objective function is calculated, and it is determined whether the sensitivity value is less than a preset threshold. If it is less, the branch is marked as a healthy branch. The improved Hippo optimization algorithm is used iteratively to solve for the other branches except for the healthy branches until the convergence condition is met, obtaining the target resistance amplification factor. The grounding grid corrosion state is diagnosed based on the target resistance amplification factor. This method is suitable for grounding grid systems with high dimensionality and sparse corrosion branches, accurately locating corrosion branches and their degree of corrosion.

This invention relates to a physical layer authentication scheme based on sparse autoencoders and knowledge distillation. To enhance the model's learning ability, the scheme first proposes SensSpec-GAN, a generative adversarial network data augmentation model based on Sensitivity-Specificity Loss. This model enhances data diversity and the model's ability to identify negative samples by introducing sensitivity and specificity constraints into the generative adversarial network. Subsequently, addressing the computational burden caused by the high dimensionality of CSI data, this invention proposes SABES, a sparse autoencoder model based on the vulture optimization algorithm, which can enhance the model's feature representation ability by automatically adjusting the model's hyperparameters. Finally, this invention proposes AMTDF, a multi-teacher knowledge distillation model with adaptive weights. This model improves the model's performance on the target task by adjusting the contributions of different teacher models to the student model through an adaptive weight mechanism. Experimental results show that the authentication model proposed in this scheme achieves an accuracy of 96.32%, and the training speed is 2.46 times faster than the benchmark algorithm.

A method for enhancing detection of morphed biometric modality data is provided that includes the steps of receiving, by an electronic device, biometric modality data of a person and extracting, using a trained machine learning model operated by the electronic device, high dimensionality feature vectors from the received biometric modality data. Moreover, the method includes the steps of generating a distribution from the high-dimensionality feature vectors, calculating a deviation between the generated distribution and a corresponding record high-dimensionality feature vector distribution of the person, and comparing the calculated deviation against a threshold deviation value. In response to determining the deviation satisfies the threshold deviation value, the method includes the step of determining the received biometric modality data was morphed.

The invention discloses a voltage prediction method and system suitable for a power distribution network under distributed aggregation resource access, and the method comprises the steps: carrying out the partitioning of the power distribution network based on the dynamic response of a distributed power supply; classifying multi-source data, including time series data and multi-dimensional heterogeneous data, for the divided regions; applying an improved CSO-OIF Elman model to the time series data to predict the voltage fluctuation in the region in real time; and a residual error threshold filtering and switching mechanism is established, and when the prediction residual error of the improved CSO-OIF Elman model exceeds the standard, the voltage fluctuation prediction result of the XGBoost model is driven based on the multi-dimensional heterogeneous data. According to the method, the complementarity of the multi-source data is fully utilized, the time sequence dynamic state of the voltage under DG access is captured, the nonlinear correlation of the multi-dimensional characteristics is mined, compared with a single model, the voltage prediction precision can be remarkably improved, and the method is particularly suitable for a power grid scene with large distributed power supply output fluctuation and high multi-source data dimensionality.

The application provides a proxy-assisted collaborative particle swarm optimization algorithm for expensive large-scale constrained optimization problems, and an urgent need for efficient processing of expensive large-scale constrained optimization problems in the field of intelligent manufacturing and the like. The existing methods often perform poorly when facing high dimensionality, high calculation cost and complex constraints at the same time. The application decomposes a complex optimization problem into a main-aid problem for collaborative solution by splitting large-scale decision variables, so as to overcome the curse of dimensionality. A hybrid proxy model management mechanism of the main-aid problem is designed, the model precision and the calculation resource allocation are dynamically optimized, and the calculation cost is reduced. A multi-population collaborative evolution optimization algorithm of the main-aid problem is given, and the solving efficiency is significantly improved by introducing an information migration and feedback mechanism. The method is applied to X benchmark test problems and an aero compressor blade aerodynamic optimization design problem, and is compared with x kinds of popular algorithms, and the effectiveness of the method is verified by experiments.

The application provides a hyperspectral band selection method and system based on hypergraph learning, and belongs to the field of image information processing. In order to solve the problems that the existing high-dimensional data calculation is complex, and the redundant information existing between adjacent bands may cause information overlap and noise interference in subsequent problem analysis. The application comprises self-representation learning on hyperspectral data; then hypergraph learning is carried out, the hyperspectral image is segmented, hypergraph definition is carried out, and the node-node relationship in the hypergraph is described by using a hypergraph Laplace matrix; a unified framework of the joint self-representation model and hypergraph learning is designed, and the hyperparameters are optimized through a hyperopt library; finally, the update of the selection matrix P is carried out, and the hyperspectral image bands are selected according to the update result. The application considers the high dimensionality of the hyperspectral image, the insufficient training sample and the data structure and matrix information entropy problem, has the characteristics of good reliability and relatively high precision, and is suitable for popularization and use.

A system, method and computer program product for analyzing data of high dimensionality (e.g., sequence reads of nucleic acid samples in connection with a disease condition) are provided.

This invention, entitled "A Visual Guidance Robot Unordered Grasping Planning Method Based on Deep Reinforcement Learning," belongs to the field of robot grasping planning technology. Addressing the problems of existing methods relying on local optimum scoring in complex scenarios, which easily leads to frequent misgrasping, and the high dimensionality of end-to-end learning exploration, this invention proposes: acquiring sensor data to extract foreground point clouds, generating a set of grasping candidates filtered by physical feasibility; constructing reinforcement learning state vectors and inputting them into a policy network, with discrete branches outputting target candidate indices and continuous branches outputting pose and execution parameter residuals; obtaining refined pose parameters after action calculation, and then executing the grasping operation. This invention compresses the decision space to the candidate neighborhood, balancing exploration efficiency and long-term continuous box clearing benefits, and is mainly used for grasping unordered scattered objects in industrial warehousing and logistics sorting.

The invention relates to a high-dimensional space visualization method based on a data board, and the method comprises the steps: carrying out the dimension analysis and space dimension reduction of a high-dimensional experiment space, and obtaining a low-dimensional experiment space and a two-dimensional experiment space; data processing is conducted on the high-dimensional experiment space, the high-dimensional experiment space comprises text type data and list data, and the text type data and the list data are mapped into a single numerical value type and visually displayed in a data board; the data board comprises five panels: an evolution progress visualization panel, an experiment point evolution visualization panel, a factor evolution visualization panel, a score distribution visualization panel and a score trend visualization panel. According to the method, the problem that the high-dimensional experimental space is difficult to visualize due to high dimension and dynamic evolution is solved, the high-dimensional experimental space data deduced by simulation can be simply and intuitively presented to the user, and the user can more easily understand the sampling condition and the evolution direction of the experimental space by means of the assistance of an intelligent algorithm.