120 results about "Neural network classifier" patented technology

The embodiment of the invention provides a charging service real-time monitoring method and device based on a service probe, and the method and device achieve the precise construction of a service event chain through constructing a multi-layer data analysis mechanism and integrating the data of an application layer, a session layer and a network layer. And designing an exception recognition strategy based on multi-dimensional feature fusion, and establishing a neural network classifier to perform exception event analysis in combination with hardware state features and scene features. A real-time monitoring engine is introduced, and dynamic early warning is carried out on business abnormity through hierarchical analysis and correlation analysis. According to the method, the defects of the traditional technology in the aspects of data analysis, feature analysis, real-time monitoring and the like are effectively overcome, and the intelligent level and the early warning effect of charging service monitoring are remarkably improved.

The invention discloses a layered energy management method of a fuel cell hybrid tramcar with self-adaptive working condition perception. In the recognition layer, a sliding window mechanism is adopted to extract time domain and frequency domain features of load conditions, feature data are clustered based on a spectral clustering algorithm driven by a deep auto-encoder, a data set with category labels is obtained, and a deep dynamic learning vector quantization neural network classifier is trained; in the strategy layer, a double-delay depth deterministic strategy gradient reinforcement learning algorithm is adopted, a reward function is constructed, and lithium battery SOC fluctuation penalty term limit parameters in the reward function are adaptively adjusted according to the real-time load working condition category output by the recognition layer; training the reinforcement learning agent to obtain an optimal power distribution scheme between the multi-stack fuel cell power generation system and the lithium battery; and according to the performance degradation degrees of different fuel cell stacks, a distributed cooperative control strategy considering performance difference is adopted to distribute the output power of each stack, so that the coordinated control of the running state of the multi-stack fuel cell power generation system is realized.

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 discloses an efficient intelligent network attack classification tool based on a federated learning framework, which comprises a central server and a plurality of end-side nodes, local models are deployed in each end-side node, a central model is deployed in the central server, and the models comprise multi-task logistic regression, random forest, XGBoost and long and short-term memory neural network classifiers; the local model collects local network flow data, performs model training and abnormal data detection and classification in combination with a D-S evidence theory optimization algorithm and a four-class complementary classifier, and transmits encrypted abnormal data and related key update parameters to the central server; the central model receives and aggregates the updated parameters, performs attack type classification based on abnormal data in combination with a D-S evidence theory optimization algorithm after updating the global model, and re-allocates a classification result and the updated global model to each end side node; the method has high robustness and high efficiency, the adaptability is enhanced, and the recognition capability of attack scenes is improved.

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.

According to various embodiments, a method for encrypting image data for a neural network are disclosed. The method includes mixing the image data with other datapoints to form mixed data; and applying a pixel-wise random mask to the mixed data to form encrypted data. According to various embodiments, a method for encrypting text data for a neural network for natural language processing is disclosed. The method includes encoding each text datapoint via a pretrained text encoder to form encoded datapoints; mixing the encoded datapoints with other encoded datapoints to form mixed data; applying a random mask to the mixed data to form encrypted data; and incorporating the encrypted data into training a classifier of the neural network and fine-tuning the text encoder.

Among other techniques, techniques for machine learning-based entity resolution are described. An example method includes receiving an entity resolution request, the entity resolution request indicating a first entity and a second entity; identifying a plurality of first attributes in a data model; identifying a plurality of second attributes in the data model; creating a first string based on the plurality of first attributes of the data model; creating a second string based on the plurality of second attributes of the data model; generating a first prompt based on the first string; generating a second prompt based on the second string; providing the first prompt to a domain-agnostic large language model; generating, by the domain-agnostic large language model using the first prompt, a first domain-agnostic large language model result; clipping the first domain-agnostic large language model result; providing the second prompt to the domain-agnostic large language model; generating, by the domain-agnostic large language model using the second prompt, a second domain-agnostic large language model result; clipping the second domain-agnostic large language model result; generating, by a downstream neural network classifier, a machine learning final result based on the clipped first and second domain-agnostic large language model result; and merging, based on the machine learning final result, the first entity and the second entity.

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.

A method and system of image hashing object detection for image processing are provided. The method comprises the following steps: obtaining image head class input data and image tail class input data differentiated from the head class input data and respectively of two images each of an object to be classified; respectively inputting the head and tail class input data into two separate parallel representation neural networks being trained to respectively generate head and tail features, wherein the representation neural networks share at least some representation weights used to form the head and tail features; inputting the head and tail features into at least one classifier neural network to generate class-related data; generating a class-balanced loss of at least one of the classes of the class-related data comprising factoring an effective number of samples of individual classes; and rebalancing an output sample distribution among the classes at the representation neural networks, classifier neural networks, or both by using the class-balanced loss.

The invention belongs to the technical field of mechanical fault diagnosis and signal processing, and particularly relates to a bearing fault diagnosis method, system and equipment based on a multi-channel analog filtering feature network and a medium. The method comprises the steps that a multi-channel analog filtering feature extraction network is constructed, and bearing vibration signal features are extracted through multiple band-pass filter channels arranged in parallel; and fault classification is carried out in combination with a simulated neural network classifier. The signal preprocessing comprises direct current component removal, amplitude normalization and segmentation processing; filter passband parameters and classifier parameters are jointly optimized through a particle swarm algorithm, and classification accuracy and decision confidence are used as fitness functions. The system comprises a signal acquisition module, a preprocessing module, a multi-channel filtering module, a feature extraction module, a simulation neural network classification module and a result display module, and realizes bearing fault diagnosis with low power consumption and high real-time performance. The method solves the problems that a traditional digital processing method is high in power consumption and large in delay, and is suitable for long-term monitoring and large-scale deployment of an industrial site.

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.

The invention discloses a personnel state detection system and method based on a wifi router, and the method comprises the following steps: S1, deploying the wifi router, collecting subcarrier CSI which changes due to the disturbance of the movement of personnel, and forming a CSI signal sequence; s2, configuring a neural network classifier and loading parameters; s3, preprocessing the CSI signal sequence to obtain a stable CSI time sequence signal; s4, constructing a graph structure taking the subcarriers as graph nodes, and generating a spectrogram; s5, performing Laplace feature transformation on the spectrogram, and extracting a frequency domain feature vector of the spectrogram; s6, constructing a multi-scale Sheng differential equation model, and generating a state trajectory under each time scale; and S7, splicing the state tracks to form a state evolution sequence, inputting the state evolution sequence into a neural network classifier, and generating a state recognition result. According to the method, CSI is used for modeling personnel disturbance, multi-scale dynamic representation and accurate recognition are achieved, and the method is suitable for a non-contact personnel state sensing scene.

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 an intelligent power grid power supply optimization method and device based on big data and a storage medium, and belongs to the field of power grid informationization. According to the method, original power consumption data of a target power grid within a period of time is collected, the original power consumption data is preprocessed, missing value compensation, error value correction, normalization and the like are included, and the original power consumption data are obtained; the method comprises the steps of obtaining high-quality power consumption data, improving a whale optimization algorithm through an ant lion rule, performing feature extraction on the high-quality power consumption data by adopting the improved whale optimization algorithm, identifying the power consumption data by adopting a neural network classifier to obtain power consumption abnormal information, and giving an alarm according to the power consumption abnormal information. The improved algorithm can effectively capture related information from power consumption data, the ability of the model to identify abnormal modes related to non-technical loss is enhanced, the whale optimization algorithm is improved through the ant lion rule, and the convergence speed, stability and global search ability of the whale optimization algorithm can be effectively enhanced.

The invention provides a voice processing method and system for mixed voice separation, noise reduction and voice recognition, and relates to the technical field of voice processing. The invention discloses the voice processing method and system for mixed voice separation, noise reduction and voice recognition. The system is mainly composed of a voiceprint database construction module, a mixed voice separation module, a self-adaptive noise suppression module and a voice recognition module. A CNN-RNN model is adopted to extract voiceprint features. Separating the mixed voice through a deep learning model based on an encoder-decoder structure and an attention mechanism; a noise estimation algorithm based on minimum statistics and a multi-modal noise reduction method are adopted to suppress noise; mFCC, LPCC and depth features are extracted from the noise-reduced voice and fused, and after optimization of an auto-encoder, an improved support vector machine (SVM) or a deep neural network classifier is used for recognition. According to the method and the device, the defects in the aspects of mixed voice separation, noise reduction, voiceprint recognition and processing efficiency in the prior art are overcome, the known voiceprint voice can be accurately separated, the noise is effectively reduced, high-precision voice recognition is realized, and the real-time processing requirement is met.

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.

The present invention relates to a neural network device having a synapse, the synapse having memory cells, each memory cell having a floating gate disposed above a first portion of a channel region between a source region and a drain region, a first gate disposed above a second portion of the channel region, and a second gate disposed above either the floating gate or the source region. A first line is electrically connected to the first gates in one of the rows of memory cells, a second line is electrically connected to the second gates in one of the rows of memory cells, a third line is electrically connected to the source regions in one of the columns of memory cells, and a fourth line is electrically connected to the drain regions in one of the columns of memory cells. The synapse receives a first plurality of inputs as voltages on the first line or the second line, and provides a first plurality of outputs as currents on the third line or the fourth line.

The invention provides a language information dynamic detection method based on a deep neural network, and relates to the technical field of voice processing, and the method comprises the steps: obtaining a to-be-detected mixed language voice stream, and extracting original acoustic features; generating an attention vector of the current time step based on an attention mechanism, and dynamically selecting a voice feature frame sequence window of the current time step in combination with unidirectional time selection and limitation of a specific time span; multiplying the dynamic window signal by the original acoustic feature signal to generate a local acoustic feature of the current time step detection language information; normalizing the local acoustic features of any length into fixed dimension features, inputting the fixed dimension features into a deep neural network classifier, and outputting a language probability value corresponding to the voice features of the current time step; based on the dynamic window information and the language probability value, the starting and ending time of each voice segment in the mixed voice stream and the corresponding language label are output, the time point of language switching is determined, and the method is particularly suitable for language switching recognition in the bilingual mixed voice stream.

The presently disclosed subject matter includes a novel computer-implemented method and computer system for the classification of tabular data using a new neural network classifier model (also referred to herein as “Tabular Neural Network Classifier” or TNNC). The disclosed method and system are characterized by improved accuracy and efficiency, as compared to other existing tabular data classification techniques such as Random Forests, XGBoost, etc. The inventor found that the TNNC exhibits in general a better TP to FP ratio in the classification output and a shorter processing time, as compared to existing tabular data classification techniques.