84 results about "Neural network classifier" patented technology

The invention discloses a high-strength steel welding defect nondestructive detection and identification method based on multi-modal data fusion. The method comprises the following steps: welding data acquisition: acquiring a two-dimensional image and three-dimensional point cloud data of a high-strength steel welding part to form a data pair; performing data space alignment: generating a space-aligned image-depth map data pair; feature extraction and fusion: obtaining fusion features with spatial geometric information and two-dimensional visual information through feature extraction and fusion; defect identification and classification: identifying defect pixels, decoding and recovering space information of a defect area, calculating the three-dimensional size of the defect, taking the feature information associated with a connected domain of each pixel with the defect and the three-dimensional size as input, and automatically classifying defect categories through a pre-trained full-connection neural network classifier. According to the method, the welding defects of the high-strength steel can be accurately identified and accurately and quantitatively analyzed.

The invention relates to a risk identification method and system. The method comprises the following steps: acquiring communication behaviors, equipment fingerprints and service interaction data in real time through a privacy compliance interface; carrying out parallel preprocessing and safe desensitization on the data; inputting a feature construction engine to extract a communication mode, an equipment behavior and an interactive semantic feature vector in parallel, constructing a dynamic weighted hypergraph communication map, and outputting a social risk feature vector by using a time sequence hypergraph neural network; inputting the four types of features into a privacy perception multi-mode gating attention fusion module to output fusion features; generating a risk score through a deep neural network classifier; and monitoring an abnormal event based on Apache Flink, adaptively adjusting a decision boundary in combination with the dynamic risk entropy, and triggering secondary verification. According to the method, the problems of multi-modal data conflict, privacy disclosure and decision stiffness are solved, and the recognition precision and the real-time performance are improved.

The invention discloses an expandable category guide anomaly detection method and device for multiple categories of targets, and relates to the field of computer vision. The method comprises the following steps: carrying out abnormal region guided adaptive enhancement preprocessing on an industrial image to obtain a target image; judging the category of the target image according to a pre-constructed lightweight neural network classifier; activating at least one anomaly detection model according to the category of the target image; and performing anomaly prediction on the target image by using the activated anomaly detection model, and fusing with the confidence corresponding to the anomaly detection model to realize adaptive anomaly discrimination of the industrial image. According to the method, the sample category is quickly judged through the lightweight classifier, the pre-screening and path guidance of the anomaly detection model are realized, the category guidance weight is generated by using a confidence coefficient mechanism, the subsequent model fusion strategy is endowed with higher adaptability, and the structural clarity, the model selection accuracy and the overall calculation efficiency of the system are effectively improved.

The present invention belongs to the technical field of mechanical fault diagnosis and signal processing, and specifically relates to a bearing fault diagnosis method, system, equipment and medium based on a multi-channel analog filter feature network. The method constructs a multi-channel analog filter feature extraction network, uses multiple parallel bandpass filter channels to extract bearing vibration signal characteristics, and combines an analog neural network classifier to perform fault classification. Signal preprocessing includes removing DC components, amplitude normalization and segmented processing; the filter passband parameters and classifier parameters are jointly optimized through a particle swarm algorithm, with classification accuracy and decision confidence as fitness functions. The system includes signal acquisition, preprocessing, multi-channel filtering, feature extraction, analog neural network classification and result display modules, achieving low-power, high-real-time bearing fault diagnosis. The present invention solves the problems of high power consumption and large delay of traditional digital processing methods, and is suitable for long-term monitoring and large-scale deployment in industrial sites.

The invention relates to the technical field of power equipment monitoring, and discloses an abnormal discharge electric field feature extraction method. The method comprises the following steps: standardizing and normalizing data acquired from a multi-channel electric field sensor array of a transformer substation to generate a standardized data stream; performing wavelet decomposition, adaptive threshold noise reduction and reconstruction on the basis of the stream to obtain a denoised signal; time domain statistics and frequency domain energy spectrum analysis are carried out on the signals, features are fused, and a joint feature vector is constructed; training a convolutional neural network classifier based on the vector; and updating a dynamic threshold rule according to the classifier output probability, and finally realizing real-time early warning and model feedback through GPU parallel computing. According to the method, the signal-to-noise ratio and the feature expression capability of the abnormal discharge signal can be effectively improved, and the detection accuracy, the real-time performance and the self-adaptive capability of the system are enhanced.

The invention provides a physical constraint and adversarial network fused equipment fault prediction method, and relates to the field of equipment fault prediction. According to the method, firstly, multi-source heterogeneous modal information of target equipment is fused, equipment operation parameters and operation and maintenance logs are obtained, and an equipment fault reasoning text corresponding to the operation and maintenance logs is generated through a large language model; secondly, introducing physical constraint information into time sequence modeling; thirdly, based on a multi-modal fusion type generator structure of residual fusion and a multi-head potential attention mechanism, multi-modal feature fusion and a fault sample generated through confrontation are obtained; and finally, predicting a target equipment fault risk of a future time window by using a convolutional neural network (CNN) classifier. According to the method, multi-modal information is fused, a physical constraint and sample generation mechanism is introduced, the problems of fault sample scarcity and multi-factor coupling modeling are effectively relieved, and the precision and robustness of equipment fault prediction are improved.

There is provided a computer-implemented method for verifying the robustness of a neural network classifier with respect to one or more parameterised transformations applied to an input, the classifier comprising one or more convolutional layers, the method comprising: encoding each layer of the classifier as one or more algebraic classifier constraints; encoding each transformation as one or more algebraic transformation constraints; encoding a change in an output classifier label from the classifier as an algebraic output constraint; determining whether a solution exists which satisfies the classifier constraints, transformation constraints and output constraints, and determining the classifier as robust to the local transformations if no such solution exists. A perception system and a computer readable medium are also provided.

The invention discloses a fan starting condition intelligent diagnosis method and system based on multi-modal data fusion. The fan starting condition intelligent diagnosis method comprises the steps of S1, collecting fan starting data through multi-modal sensing data to construct time sequence original data; s2, performing pre-optimization processing on the time sequence original data by using a sound and vibration alignment mechanism, and respectively extracting a first time sequence feature and a second time sequence feature through a double-flow lightweight FNet branch; s3, dynamically aligning the time-frequency physical association between the first time sequence feature and the second time sequence feature by adopting cross attention, and outputting a deep joint feature; and S4, collecting an abnormal feature sample, splicing the combined depth combined feature and the abnormal feature sample, inputting the spliced deep combined feature and the abnormal feature sample into a lightweight neural network classifier, outputting a fan fault confidence coefficient, and judging whether the fan is started or not according to the fan fault confidence coefficient and a preset confidence coefficient threshold value.

Disclosed is a neural network enabled interface server and blockchain interface establishing a blockchain network implementing event detection, tracking and management for rule based compliance, with significant implications for anomaly detection, resolution and safety and compliance reporting.

The invention relates to the technical field of gesture recognition interaction, and discloses an intelligent interaction system and method based on gesture recognition. The method comprises the following steps: when an environment meets a condition, synchronously acquiring an image sequence of a gesture and depth distance field data; a hand joint point two-dimensional motion track is extracted from an image, surface deformation fluctuation information is separated from depth data, the two-dimensional motion track and the surface deformation fluctuation information are subjected to space-time registration fusion, a continuous motion track curved surface in a three-dimensional space is constructed, and then geometric topology features and dynamic change features of the continuous motion track curved surface are extracted. And inputting the fusion features into a neural network classifier subjected to incremental learning training, outputting corresponding semantic tags and confidence evaluation values, and mapping to generate a control instruction after verification. According to the method, an accurate three-dimensional dynamic model is constructed through deep fusion of multi-source data, and an incremental learning mechanism is utilized to enable the system to have online adaptive capability, so that the recognition precision of complex gestures and the long-term applicability of the system are improved.

Embodiments of the present application provide a quantum neural network classifier training method and device, electronic equipment and medium. The scheme is as follows: obtaining a training data set and a to-be-trained classifier; for each training sample data, classifying the training sample data by using the to-be-trained classifier to obtain a first predicted label; calculating a first loss value of the to-be-trained classifier according to a sample label corresponding to each training sample data and the first predicted label; when the to-be-trained classifier has not converged, adjusting the classifier parameters based on the first loss value, and returning to execute the step of classifying each training sample data by using the to-be-trained classifier to obtain the first predicted label corresponding to the training sample data until the to-be-trained classifier converges at the current time. Through the technical scheme provided by the embodiments of the present application, the optimization of the quantum neural network classifier is realized, and the classification accuracy and attack resistance of the quantum neural network classifier are improved.

A system and method utilizes masked language models in order to provide data-type detection, such as (but not limited to) prediction of columnar headings. Two masked language models are pre-trained on example columnar text. One model predicts missing data at the entity level (e.g., masked entity names that may be made up of whole words), while the other predicts missing data at the character level (e.g., masked individual characters). The table with missing column headings is fed into both models, and the output is contextual word embeddings and contextual character embeddings. These results are merged, and then fed into a neural network classifier to then predict the column names.

The invention discloses a vehicle collector shoe detection method, device and equipment and a storage medium, is applied to vehicle collector shoe detection equipment comprising a collector shoe acquisition unit, a collector shoe image storage and recognition unit and data transmission equipment, and relates to the technical field of automatic detection. Comprising the following steps: generating a pulse signal by using magnetic steel in a collector shoe acquisition unit when a vehicle passes, and sending the pulse signal to an area-array camera in the collector shoe acquisition unit to acquire an image of a collector shoe to obtain a target collector shoe image; transmitting and storing the target collector shoe image to a collector shoe image storage and identification unit by using data transmission equipment; a deep neural network classifier in the collector shoe image storage and recognition unit is used for carrying out component positioning on the target collector shoe image to obtain a component positioning result, geometric dimension, inclination angle and surface defect detection is carried out on the component positioning result in sequence, and an abrasion value detection result, a crack detection result and a notch anomaly detection result are obtained; the efficiency of detecting the collector shoe is improved.

To provide a real-time task distribution method and device for disassembly work by human and robot cooperation.SOLUTION: The present invention obtains an image of a fastening part to be disassembled, then constructs a two stage neural network classifier to detect the quality state of the fastening part to be disassembled, and finally uses a genetic algorithm to solve a disassembly human and robot task distribution plan, thereby realizing the detection and state classification of the fastening part to be disassembled. The real-time human-robot collaborative task distribution can make full use of the advantages of the accuracy and durability of the robotic arm, and at the same time, use the intelligent resources of the operator to greatly reduce the fatigue of the operator. In addition, the flexibility of the disassembly work is improved, the problem of uncertain failure of the product is solved, and the quality, safety and efficiency of the disassembly work are improved by making the best use of the flexible response ability of the human and the accurate repetitive operation ability of the robotic arm.SELECTED DRAWING: Figure 1

The invention provides a dual confidence calibration method and system for a neural network classifier, equipment and a medium, and effectively solves the problems that an existing single-stage calibration method is difficult to give consideration to excessive confidence, under confidence, class imbalance sensitivity and the like. A mixed loss function fusing bifocus loss and difference between multi-class confidence and accuracy is introduced in a training stage, so that a neural network classifier is promoted to generate well-calibrated prediction distribution; a class-by-class multi-partition temperature scaling model optimized based on a coupling simulated annealing method is adopted in the reasoning stage, and calibration requirements of different classes and different confidence intervals are more accurately met compared with a traditional temperature scaling technology of a single temperature coefficient; in the training process, classifiers of different rounds are stored, multi-model calibration results are averaged class by class in the test stage, the calibration stability is effectively improved, prediction confidence errors are reduced, and the method is particularly suitable for the safety key fields such as medical diagnosis and automatic driving which have extremely high requirements for prediction reliability.

Provided are a method for regulating a wind-photovoltaic-storage power station based on electricity, green certificate, and carbon price prediction, a device, a medium, and a product. The method includes: inputting acquired historical price data into a price prediction model, and outputting a predicted price; determining a deviation vector of price data based on the historical price data and the predicted price, and generating an uncertainty set of the predicted price by using a multi-kernel-based one-class support vector machine algorithm; classifying the uncertainty set of the predicted price by using a neural network classifier, to obtain multiple types of price scenarios; solving, based on predicted prices under the multiple types of price scenarios, a joint clearing model by using a Pied Kingfisher Optimization (PKO) algorithm, to obtain an operation strategy for the wind-photovoltaic-storage power station; and regulating the wind-photovoltaic-storage power station based on the operation strategy for the wind-photovoltaic-storage power station.

Implementations of a fully configurable, modularized home robot are described that implement trained neural network classifiers to solve problems of providing home health care and monitoring of the elderly and / or infirm, providing entertainment for the family, providing environmental and safety monitoring coupled with mechanisms to clean the air and remedy indoor climates such as humidity and temperature, and provide a mechanism for caring for pets left at home when the owner is away based upon sensory input.

A system and method for measuring and assessing cognitive abilities of a user are disclosed. The method includes generating an individualized assessment protocol comprising a plurality of tasks each configured to evaluate cognitive mental specification. The method also includes the user selecting at least one task tasks using a semi-random process. The method further includes displaying a task-specific interface to present the selected task to user. The method includes receiving user responses to the task along with associated parameters. The method includes transmitting an individualized performance score table to a server. The method also includes processing the individualized performance scores using a population database, a neural network classifier, and regression models, which adjust their parameters based on the received scores. The method further includes computing individualized assessment scores to generate a profile of cognitive mental abilities. The method also includes transmitting profile of cognitive mental abilities to user device.

The application discloses a weakly supervised positioning method based on prompt learning, comprising the following steps: encoding image description containing image categories to obtain category discriminative features corresponding to different categories of images; using the category discriminative features to initialize to obtain learnable features, and optimizing the learnable features by using a denoising diffusion model to obtain category representative features; optimizing the denoising diffusion model with the purpose of removing noise in the images; setting a neural network classifier to classify the images to be positioned to obtain the categories of the images; obtaining the corresponding category discriminative features and category representative features according to the categories of the images, fusing the category discriminative features and the category representative features to obtain combined features, inputting the combined features into the optimized denoising diffusion model to obtain multiple cross-attention maps, fusing the multiple cross-attention maps into one activation map, and then obtaining the positioning result of the target. The weakly supervised positioning method based on prompt learning disclosed by the application has significantly improved positioning accuracy.

The embodiment of the application provides a kind of based on the real-time monitoring method and device of charging service of service probe, through the construction of multilayer data analysis mechanism, through the data of application layer, session layer and network layer, the accurate construction of service event chain is realized.Design based on the abnormality identification strategy of multidimensional feature fusion, combine hardware state feature and scene feature, establish neural network classifier to carry out abnormal event analysis.Introduce real-time monitoring engine, through hierarchical analysis and correlation analysis, dynamically early warning is carried out to service anomaly.The method effectively solves the deficiency of traditional technology in data analysis, feature analysis and real-time monitoring etc., significantly improves the intelligent level and early warning effect of charging service monitoring.

The present invention discloses a voice wake-up method and system based on a time-domain binary neural network. The method comprises the following steps: obtaining an audio file to be recognized, thereby obtaining a voice signal to be processed; extracting acoustic features from the voice signal to be processed, and performing dimensionality conversion processing on the voice signal to obtain acoustic features after dimensionality conversion processing; inputting the acoustic features after dimensionality conversion processing into a pre-trained time-domain binary neural network (TBNN) model to obtain probability outputs of keywords and non-keywords; and determining whether to wake up the system based on whether the highest probability among the probability outputs of keywords and non-keywords is a keyword. The present invention greatly reduces the number of parameters and the amount of computation of the neural network classifier, while significantly improving the wake-up speed and reducing the power consumption of the voice wake-up system.

The present invention pertains to the computer implemented method for identifying a microorganism in a blood comprising: receiving (101) a microscopic image of a blood smear; detecting (102) plurality of microorganism cells in the microscopic image using a deep learning cellular segmentation model; extracting (103) from the segmented microscopic image a patch of size HxW centred around a centroid of the detected microorganism cell for each detected microorganism cell; encoding (104) of each patch by a deep neural network encoding model into a patch representation vector u n of size 1×D; aggregating (105) the patch representation vectors un using a pooling method to generate a sample representation vector u of size 1×D; and indicating (106) a species of microorganism present in the microscopic image by classifying the sample representation vector u by a neural network classifier to class representing specific species. In another aspect the invention relates to the data processing system for carrying out the steps of the method according to the invention.

A method of scanning N×N pixels using a vector-by-matrix multiplication array by (a) associating a filter of M×M pixels adjacent first vertical and horizontal edges, (b) providing values for the pixels associated with different respective rows of the filter to input lines of different respective N input line groups, (c) shifting the filter horizontally by X pixels, (d) providing values for the pixels associated with different respective rows of the horizontally shifted filter to input lines, of different respective N input line groups, which are shifted by X input lines, (e) repeating steps (c) and (d) until a second vertical edge is reached, (f) shifting the filter horizontally to be adjacent the first vertical edge, and shifting the filter vertically by X pixels, (g) repeating steps (b) through (e) for the vertically shifted filter, and (h) repeating steps (f) and (g) until a second horizontal edge is reached.

The invention discloses a medical pathology image adaptive probability calibration classification method and learning based on label noise, and the method comprises the steps: firstly, obtaining a data set, and dividing the data set into a training set and a verification set according to a certain proportion; the training set is used for training a deep neural network classifier, and the verification set is used for calibrating the model. Secondly, probability distribution under a noise label is calculated, and a noise transfer matrix is introduced to adjust prediction distribution of the model; and thirdly, constructing a consistency calibrator by optimizing an optimal temperature scaling factor T and combining a noise transfer matrix, so that the consistency calibrator can be theoretically converged to a calibrator trained on clean data. Finally, the superiority of the method in different data sets and noise environments is verified through experiments, and the improvement of the existing built-in calibration method and post calibration method is realized. According to the method, the noise transfer matrix is introduced, self-adaptive adjustment is carried out on calibration errors in different noise environments, and finally robust probability calibration is achieved.

The invention discloses a webpage classification method and system based on a Delaunay triangulation double-graph neural network, and belongs to the technical field of webpage classification. The method comprises the steps that original graphs and node features of webpage classification are acquired; the original graph comprises a plurality of nodes and directed edges among the nodes, the nodes are webpages, the directed edges are hyperlinks among the webpages, and the node features are webpage contents; constructing a shadow graph by adopting a Delaunay triangulation method; respectively carrying out graph convolution operation on the original graph and the shadow graph to obtain local structure information and global relation information of nodes; and the local structure information and the global relation information are fused to generate final node representation, and all nodes are classified by adopting a neural network classifier based on the final node representation. According to the method, the graph expression capability and the information spreading efficiency are effectively enhanced and the webpage classification effect is improved on the premise of not destroying the original graph structure semantics.

The invention discloses a method and a system for intelligently identifying damage of a hydrogen conveying pipeline based on multi-sensor feature fusion. The method comprises the following steps: firstly, acquiring multi-source sensor detection signals including electromagnetic and ultrasonic; performing time domain, frequency domain and time-frequency domain feature extraction on each sensor signal, and constructing an original feature set; then, a restricted Boltzmann machine is adopted to carry out unsupervised deep feature learning and dimension reduction processing on the high-dimensional features, feature-level fusion of multi-sensor data is achieved, and a fusion feature vector with higher characterization capacity is obtained; and finally, inputting the fusion feature vector into a pre-trained BP neural network classifier to realize intelligent identification and classification of different damage types and damage degrees of the hydrogen delivery pipeline. The method has the advantages of being high in detection efficiency and accurate in recognition result, and the accuracy and reliability of hydrogen conveying pipeline damage recognition are effectively improved.

This invention discloses a method for rapid training of neural network models in a small-sample environment, comprising the following steps: S1, acquiring a small-sample dataset containing several categories and preprocessing it to obtain a standardized training sample set, wherein the number of samples in each category of the small-sample dataset satisfies the small-sample distribution characteristics; S2, training an initial neural network classifier based on the training sample set, and constructing prototype point-boundary corridor topology maps for each category in the feature latent space. This invention relates to the field of neural network model training technology. This method for rapid training of neural network models in a small-sample environment, by constructing prototype point-boundary corridor topology maps, can clearly understand the relative positions of each category in the feature latent space and the distribution characteristics of inter-class classification boundaries, thereby accurately identifying boundary gap areas with insufficient sample coverage and prone to misclassification, providing a clear target area for subsequent supplementary sampling operations.

This invention provides a device fault prediction method that integrates physical constraints and adversarial networks, relating to the field of device fault prediction. First, this invention integrates multi-source heterogeneous modal information of the target device to obtain device operating parameters and operation and maintenance logs, and generates device fault inference text corresponding to the operation and maintenance logs through a large language model. Second, physical constraint information is introduced into the temporal modeling. Third, based on a multi-modal fusion generator structure using residual fusion and multi-head latent attention mechanisms, multi-modal feature fusion and adversarially generated fault samples are obtained. Finally, a convolutional neural network (CNN) classifier is used to predict the target device fault risk in future time windows. This invention integrates multi-modal information and introduces physical constraints and sample generation mechanisms, effectively alleviating the problems of fault sample scarcity and multi-factor coupled modeling, thus improving the accuracy and robustness of device fault prediction.

A millimeter-wave radar voice reconstruction and recognition method based on a physical guide network comprises the steps that a millimeter-wave radar is used for transmitting a radio-frequency signal to a to-be-detected target and receiving an echo signal, and meanwhile a reference audio signal is collected; extracting a steady-phase signal Mel spectrum according to the echo signal; generating a simulated radar Mel spectrum by performing an audio signal simulation on the reference audio signal and the common speech data set; synchronizing and standardizing the stable-phase signal Mel spectrum and the simulated radar Mel spectrum, and constructing a voice signal data set; constructing a multi-mode voice reconstruction network model; training a multi-modal voice reconstruction network model according to the voice signal data set; inputting a newly collected real millimeter wave radar signal into the trained multi-mode voice reconstruction network model for voice reconstruction, and outputting a non-contact voice Mel-frequency spectrogram; and inputting the voice Mel spectrogram into the constructed lightweight convolutional neural network classifier, and outputting an identity category label of the speaker.

The invention provides a switched reluctance motor speed regulation system fault diagnosis method based on an adaptive sliding window integration algorithm, and relates to the technical field of motor speed regulation system fault diagnosis. The method comprises the following steps: firstly, collecting current of each phase under different working conditions to construct fault data, extracting features through normalization and harmonic analysis, obtaining fault features through feature selection, and training a plurality of integrated learning classifiers and neural network classifiers; collaborative decision making is sequentially carried out on data to be diagnosed in the graded sliding window, rapid and accurate online identification of single-tube short circuit and winding turn-to-turn short circuit faults is achieved, and the stability and reliability of a speed regulation system are improved.