19 results about "Neural network classification" patented technology

The present application relates to a kind of generalization adversarial sample group generation method based on easily confused class feature injection, belong to the technical field of adversarial attack of computer vision field.It introduces the class disturbance update momentum in the direction of easily confused class feature vector in the process of generating class-oriented generalization disturbance sample group, the class disturbance update momentum makes the adjacent easily confused class of adversarial example faster update, to realize the generalization attack to class.The present application only integrates the features of easily confused class in the same class image, forms disturbance template, realizes class-oriented generalization disturbance sample group;Variable step size iterative attack means is used to explore easily confused class in the process of neural network classification, realize easily confused feature injection, while introducing class momentum parameter in the process of iterative attack, improve attack efficiency and alleviate the over disturbance shortcomings of generalization disturbance.

The application relates to the technical field of artificial intelligence, and provides a classification prediction method based on table data, which comprises the following steps: inputting a characteristic value, a characteristic type corresponding to the characteristic value and statistical information corresponding to the characteristic value into an embedding layer of a neural network classification model to obtain an embedding representation corresponding to the characteristic value; then determining an embedding representation sequence according to the embedding representation; inputting the embedding representation sequence into an encoding layer of the neural network classification model to obtain a semantic vector sequence; inputting the semantic vector sequence into an output layer of the neural network classification model to obtain a prediction result; determining a loss value according to the prediction result and a labeled label, and training the neural network classification model according to the loss value to obtain a target neural network classification model. Through the above scheme, the neural network model can understand the global statistical information of the characteristics like a tree model, the performance and accuracy of the neural network model in performing a classification prediction task based on table data are improved, and the satisfaction of users in the experience of financial products is improved.

The application discloses a power distribution system fault prediction method based on fractal theory prediction and BP neural network classification, collects current data of a line to be predicted in a power distribution system, and performs fractal prediction. The fractal prediction comprises the following steps: S1, establishing a current history sequence, obtaining an accumulated sum sequence and a measurement scale; S2, performing linear regression on the accumulated sum sequence curve in a double logarithmic coordinate system to obtain a fractal dimension; and S3, performing current value prediction of future time. After the fractal prediction, the actual current value is measured, the difference between the actual value and the predicted value is calculated, the BP neural network is inputted, and a fault prediction result is obtained. The power distribution system fault prediction is performed by adopting the method combining fractal theory prediction and BP neural network classification, the method has the characteristics of high accuracy, and is suitable for fault early warning and troubleshooting of a bidirectional power flow and a multi-party participated smart grid.

This application provides a method and system for adaptive process control in laser cutting, belonging to the field of laser cutting technology. The method involves: acquiring and preprocessing multimodal data of the material to be cut; extracting multidimensional feature vectors characterizing material properties; inputting these vectors into a deep neural network classification model; outputting a preliminary probability distribution of the material type; combining this distribution with electromagnetic and visual features as independent evidence; and using D-S evidence theory for fusion reasoning to determine the final material type. Then, based on a Kalman filter algorithm, spectral distance and geometric contour data are fused to obtain a final thickness estimate. Anomaly detection is performed using the final material type and final thickness estimate, triggering anomaly handling. Based on the final material type and thickness estimate, a preset database is queried to obtain a set of basic process parameters, which are then fine-tuned using a parameter optimization algorithm and configured into the CNC system of the laser cutting machine. This application achieves adaptive control of laser cutting through multimodal fusion recognition.

This invention relates to an image classification method, apparatus, and medium for transfer-based black-box adversarial defense. The method includes acquiring an input image to be classified; determining a predefined expansion factor, spatial phase offset, and gap-filling value; calculating signal pixel positions based on the expansion factor and spatial phase offset; mapping the pixel values ​​of each original pixel to a high-dimensional space according to their corresponding signal pixel positions to form an expanded representation; filling gaps in regions other than all signal pixel positions with gap-filling values; and training a target neural network classification model with the obtained expanded representation. During inference on the trained target neural network classification model, the same acquisition, determination, calculation, mapping, and filling operations are performed on the input image, and the obtained expanded representation is input into the target neural network classification model to obtain the classification result. This method achieves a synergistic improvement in clean sample accuracy and black-box transfer robustness without relying on adversarial training.

A deep learning-based method for monitoring anesthesia depth based on high time-frequency resolution bispectral density includes the following steps: acquiring single-channel EEG data, preprocessing it, and dividing it into time segments; extracting features from the EEG data, and calculating a high time-frequency resolution wavelet bispectral matrix by combining variational mode decomposition and synchronous squeeze wavelet transform; constructing and training a deep neural network classification model based on partially convolutional neural networks and gated recurrent units; for test data, inputting the extracted features into the model, and selecting the state with the highest output probability (awake, anesthesia maintenance, or awakening) as the final prediction result for the test data; this invention uses single-channel EEG signals as input, greatly reducing the difficulty of acquiring clinical signals, and the proposed high time-frequency resolution bispectral features combined with a dedicated deep learning network have a strong ability to capture complex nonlinear EEG dynamic features and cross-subject generalization adaptability; therefore, this invention can perform high-precision, multi-classification, and real-time automatic monitoring of anesthesia depth.

Diagnosis of prostate cancer often involves invasive measures, such as biopsies, in which tissue is removed and graded by a pathologist. Non-invasive measures, such as multi-parametric MRI, can be used to examine lesions and grade them using the PI-RADS scoring scale; however, achievable image resolution is limited by motion. A new paradigm, magnetic resonance histopathology (MRH), is disclosed, which aims to evaluate tissue texture at sub-millimeter resolution through a fast clinical acquisition process. The ability of MRH to identify cancerous tissue in the prostate through computer simulation analysis is demonstrated, reproducing the MRH measurement process on a set of high-resolution, histology slides annotated by pathologists (2, 4, 6, 8, 10, 12, 14). A dataset of spectral intensities (20) at sub-millimeter wavelengths is created, and a deep learning model (22) trained to classify the spectral data is based on normal or tumor tissue. A set of spatial frequencies is identified, which is used to optimize diagnostic capability under the constraint of limited acquisition time. In addition to single-region classification, the disclosed method and architecture integrate spatial context and local information, the inclusion of which improves model performance and denoises inference results. In addition to algorithms for estimating the physical length scale of lesions identified by the model, the trained model is demonstrated to be applied to unlabeled high-resolution 3D MRI data.

This invention discloses a quantitative discrimination method and application for the geological types of fracture-vuggy units in fracture-vuggy reservoirs, relating to the field of petroleum geological exploration technology. The quantitative discrimination method of this invention includes the following steps: using a clustering algorithm to perform cluster analysis on the static parameters of fracture-vuggy reservoirs, classifying fracture-vuggy units, analyzing the clustering results, and determining the number and characteristics of categories; using well-understood fracture-vuggy units as a dataset, with the static parameters described in step (1) as input and the geological type corresponding to each fracture-vuggy unit as output, constructing a neural network model; and optimizing the results of cluster analysis and neural network classification. This invention utilizes cluster analysis and neural network technology to achieve accurate classification of the geological types of fracture-vuggy units through quantitative discrimination using five selected key static parameters.

The present application belongs to the technical field of knowledge graph data classification, and discloses a knowledge graph data multi-level classification method, system and medium. The knowledge graph data multi-level classification method proposed by the present application firstly pre-processes the original data to obtain standard data, then establishes a multi-dimensional feature space containing entity dimension, relationship dimension and attribute dimension, constructs a feature matrix and performs hierarchical processing. Then, a deep neural network classification model is used for feature extraction and fusion, and the self-adaptive optimization of features is realized through a feature matrix optimization equation set, including feature importance evaluation, feature correlation calculation and feature weight optimization. Finally, the final classification model is obtained through model training and optimization, realizing multi-level classification of knowledge graph data and solving the technical problem of low classification accuracy caused by insufficient feature extraction of knowledge graph data in the prior art.

The application discloses an unknown threat identification method based on adaptive snapshot integration, which is applied to instant fine classification of network encrypted traffic and can identify unknown threats caused by concept drift. The method comprises an encrypted traffic preprocessing and feature extraction module, an encrypted traffic time sequence feature neural network classification module, a new category traffic discrimination module, a snapshot integration module and a transfer learning module. The method can cope with increasing unknown abnormal traffic, give unknown abnormal traffic clustering results for experts to mark, effectively solve the concept drift and category imbalance problem, and thus guarantee the reliability of abnormal traffic detection.

The application relates to the field of article classification and recognition, in particular to a medicine bottle classification method based on visual recognition. First, a vision acquisition device is used to acquire a medicine bottle image to be classified, the image is subjected to denoising, size normalization, brightness enhancement and the like, the processed image is input into a pre-trained neural network classification model to obtain a medicine bottle category confidence, and a category result meeting a preset threshold is output; the result can also be sent to a control module to control a sorting device. The application has the technical effect that medicine bottles can be efficiently and accurately classified and recognized, relevant devices can be controlled to perform corresponding operations, and the model can support the training and updating of new medicine bottle categories through transfer learning.

The present application relates to the field of intelligent terminal perception, and discloses a human body recognition method and device based on radar sensing and RGB camera fusion. The radar sensor analyzes the target micro-Doppler characteristic fingerprint vector and compares it with the pre-stored interference characteristic library, generates a wake-up signal after filtering the known interference source. The processor responds to the wake-up, calculates the predicted coordinates of the target at the exposure moment in combination with the system time delay, maps the predicted coordinates to the light measurement area parameters before starting the camera, converts the target speed into the upper limit parameters of the exposure time and the focus driving value and issues them. The camera collects images according to the parameters, and the processor classifies using a neural network: if it is not a human body, the interference suppression logic is triggered and the current characteristics are written into the interference library, and if it is a human body, the business response is executed. Through radar bottom filtering and camera parameter feedforward configuration, the present application solves the problems of high radar false alarm rate and poor cold start imaging quality of the camera, reduces the system power consumption and improves the recognition accuracy and response speed.

The application discloses a kind of hybrid classical-quantum extreme weather identification method, system and equipment for power system, and relates to power system technical field.The present application is aimed at solving the problems of existing extreme weather identification sample scarcity, data imbalance, insufficient feature expression, lack of power adaptability label, etc.The present application includes extreme weather label making on original meteorological data, obtaining labeled meteorological data set;Construct a hybrid classical-quantum generative adversarial network based on variational quantum circuit, and use extreme weather samples for adversarial training to generate expanded extreme weather sample data;Construct a hybrid classical-quantum deep neural network classification model containing a classical feature extraction layer, a variational quantum circuit feature mapping layer and a classification output layer, and use labeled data and expanded samples for training;Input the meteorological data to be identified into the trained classification model, and output the extreme weather identification result.This technical solution effectively improves the accuracy of extreme weather identification under small sample.

This invention discloses a non-destructive method for detecting strawberry freshness based on hyperspectral imaging and machine learning, belonging to the field of agricultural product quality testing technology. The method includes: acquiring hyperspectral images of strawberries in the 900-1700 nm wavelength range and extracting the average spectrum of the region of interest; performing multivariate scattering correction and Savitzky-Golay second-order derivative filtering preprocessing on the spectrum; using a genetic algorithm to screen a subset of characteristic wavelengths; and inputting the screened characteristic wavelengths into a pre-trained backpropagation neural network classification model to output the freshness grade determination result. This invention, through the technical path of "MSC and SG2 preprocessing—GA feature screening—BPNN classification," achieves rapid, non-destructive, and high-precision detection of strawberry freshness. The model's recognition accuracy exceeds 99% under storage conditions of 4℃ and 20℃, and can be widely applied to post-harvest grading, cold chain monitoring, and online sorting equipment for strawberries.

The application proposes a damage identification method based on composite material damage physical mechanism guidance using acoustic emission signals, and the acoustic emission data in the composite material is segmented; it is assumed that the acoustic emission data in the first stage is related to the matrix damage, so as to establish a semi-supervised guided deep neural network classification model in the first stage to predict the matrix damage in the second stage data; the clustering algorithm is applied to the data in the second stage which is not marked by the classification model; the data is marked by combining the classification and clustering results, and the semi-supervised guided deep neural network classification model in the second stage is updated; the cross-stage learning ability of the model is enhanced by iteratively adjusting the classification model, and the semi-supervised guided deep neural network classification model is formed. Update all acoustic emission data labels to provide comprehensive damage identification. The application proposes a classification+clustering acoustic emission signal identification method for the physical guidance of composite material damage, which realizes accurate identification of acoustic emission data of composite material damage.

Events may be classified in a staged manner using one or more neural networks and explicit classifiers. For example, a method may be performed which comprises classifying an event using a first explicit classifier, a neural network classifier, and a second explicit classifier. In such a method, classifying the event using the first explicit classifier may provide an initial classification for the event, and that initial classification may be a basis for classifying the event using the neural network classifier. Similarly, classifying the event using the neural network classifier may provide a neural network classification for the event, and the event may be classified using the second explicit classifier on the basis of the neural network classification. When the event is processed by the second explicit classifier, this may provide an output classification, and that output classification may be used as a basis for processing the event.

This application discloses a method and system for locating and verifying carbon emissions at the mesoscale level using a carbon dioxide lidar system. The method includes the following steps: obtaining CO2 concentration data within a preset mesoscale region using a CO2 lidar system; identifying carbon sources based on the CO2 concentration data; locating and tracking carbon sources based on the identification results; and verifying the carbon emissions of the carbon sources based on both the location and identification results. This application utilizes machine learning to train a neural network classification model to identify and classify carbon sources, track and locate them, accurately calculate the carbon emissions of each source, and verify their emissions. This enhances the management and supervision of carbon-emitting enterprises and improves the atmospheric environment.

This invention discloses a method and system for classifying and recognizing the color temperature of lighting lights based on data fusion. The method involves acquiring raw data, weighting and fusing it using a weighted collaborative bias confidence fusion algorithm to obtain a fused data source, which is then divided into training and test sets. A backpropagation (BP) neural network classification model is then constructed, and an elastic inertia weight dynamic adjustment mechanism and a multi-scale mutation mechanism are introduced into the particle swarm optimization algorithm for optimization. This optimization algorithm is used to optimize the initial weights and biases of the BP neural network classification model. The optimized parameters are then loaded into the BP neural network classification model. After training on the training set, the color temperature recognition result is obtained by inputting the model into the test set. This invention improves the initial parameters of the BP neural network by modifying the particle swarm optimization algorithm, effectively enhancing the model's global search capability and classification accuracy, thus ensuring recognition performance.

A kind of hierarchical feature interpretation classification network construction method based on human visual system, comprising the following steps: step 1), construct hierarchical natural language concept and hierarchical image block concept set;Step 2), shallow-middle-deep layer feature channel of convolution classification model is divided;Step 3), respectively map natural language concept to shared vector space of visual language model, extract respective hierarchical feature map sequence of shallow layer, middle layer and deep layer, and remap alignment hierarchical text concept embedding, fine-tune convolution classification model;Step 4), generate hierarchical multi-modal classification decision explanation;Step 5), through the decision error of convolution classification model and the corresponding error sample matching to natural language concept and image block concept and real class visual feature difference, detect whether it is subjected to feature level malicious attack.The present application can provide clear and intuitive feature consistency comparison, and bring safer, more transparent application basis for deep neural network classification model.