245 results about "Artificial neural network model" patented technology

The invention provides a composite material performance prediction and process optimization method based on a neural network, and the method comprises the steps: firstly collecting multi-source data in the preparation and test process of a composite material, carrying out the preprocessing of the data, screening key feature variables as input variables, constructing a feedforward artificial neural network model, and predicting and outputting the performance indexes of the composite material. And training the model, performing iterative optimization on model parameters, and optimizing composite material process parameters by using the optimized model based on a reverse optimization strategy of a genetic algorithm to obtain an optimal process parameter combination. The invention provides a scientific, efficient and reliable tool for design and optimization of composite materials, and particularly has wide application prospects in high-requirement industries such as aerospace and the like.

The invention discloses an intelligent rehabilitation training method, system and device based on a wearable hemiplegic patient, and relates to the technical field of rehabilitation training, and the method comprises the steps: collecting multi-dimensional data of the patient, carrying out the preprocessing, obtaining an evaluation value of the multi-dimensional data through an artificial neural network model, carrying out the splicing, generating an evaluation vector, and carrying out the recognition of the evaluation vector; performing optimization as an individual of a bald eagle search optimization algorithm to obtain an optimal evaluation value vector; defining an evaluation value in the optimal evaluation value vector as an input variable of fuzzy logic to perform fuzzy reasoning, forming a setting vector and a training target vector, constructing Bayesian prior distribution, a likelihood function and Bayesian posteriori distribution to perform maximization solution, obtaining an optimal training target vector to perform linear mapping, generating a specific value vector, and obtaining the optimal training target vector. A rehabilitation training task and dynamic adjustment and feedback of a specific value vector are executed; according to the invention, effective optimization and personalized customization of the rehabilitation training process are realized, and the efficiency and effect of rehabilitation training are improved.

The invention provides a bay water health status evaluation method, device and product, and the method comprises the following steps: S1, determining a sampling site and collecting a zooplankton eDNA sample to obtain amplicon sequence variant information; s2, annotating amplicon sequence variant information to obtain species composition information and abundance data; s3, obtaining a first evaluation candidate index according to the species composition information and the abundance data; constructing a co-occurrence network and determining a second evaluation candidate index according to the network topology parameters; combining the first evaluation candidate index with the second evaluation candidate index to obtain a third evaluation candidate index; s4, selecting a reference point and a damaged point, and obtaining key evaluation candidate indexes through an artificial neural network model; and S5, constructing a zooplankton integrity index based on the key evaluation candidate indexes, and evaluating the water quality health grade according to the zooplankton integrity index. By utilizing the technical scheme, the precision of evaluating the ecological health condition of the severely polluted bay water body can be improved, and meanwhile, the complexity of an evaluation index system is reduced.

The invention discloses a rapid progression type nasopharyngeal carcinoma risk prediction method based on an artificial neural network, and relates to the field of medical informatics crossing. The invention provides a rapid progressive nasopharyngeal carcinoma risk prediction method based on an artificial neural network, and aims to solve the problem that a rapid progressive nasopharyngeal carcinoma patient is difficult to recognize in time by depending on TNM staging and experience judgment in the prior art. According to the method, historical case data collection, missing value filling and standardization preprocessing, core feature determination through feature screening, class imbalance correction, feature coding and feature matrix construction are sequentially carried out, an artificial neural network model is trained and optimized under a cross validation framework, and performance and threshold values are determined on a validation set. During clinical application, patient features are input, and the model outputs a rapid progress risk probability and a risk level. Compared with a conventional staging or linear model, the method can improve the prediction accuracy, and achieves the early recognition and individualized treatment of a high-risk patient.

The invention discloses an asphalt aging degree nondestructive testing method and system, and belongs to the technical field of road material detection. The method comprises the following steps: firstly, preparing standard asphalt samples with different aging degrees, calibrating a real aging degree in combination with a destructive test, measuring broadband dielectric spectrums at a plurality of temperature points, and constructing a multi-temperature-range standard dielectric spectrum-aging degree database; comprehensive dielectric characteristic parameters containing fixed-point dielectric characteristics at a specific temperature point and evolution dielectric characteristics changing along with temperature are extracted from the database; training an artificial neural network model by using the characteristic parameters, establishing a nonlinear mapping relation with the aging degree, and forming an aging degree prediction model; and finally, performing lossless multi-temperature-range dielectric detection on a to-be-detected asphalt sample, extracting the same characteristics from the obtained dielectric data, inputting the same characteristics into the prediction model, and calculating the aging degree.

The disclosed exemplary embodiments include computer-implemented systems, apparatuses, and processes that perform homomorphic computations on encrypted third-party data within a distributed computing environment. For example, an apparatus receives a homomorphic public key and encrypted transaction data characterizing an exchange of data from a computing system, and encrypts modelling data associated with a first predictive model, such as a machine learning model or an artificial neural network model, using the homomorphic public key. The apparatus may perform homomorphic computations that apply the first predictive model to the encrypted transaction data in accordance with the encrypted first modelling data, and transmit an encrypted first output of the homomorphic computations to the computing system, which may decrypt the encrypted first output using a homomorphic private key and generate decrypted output data indicative of a predicted likelihood that the data exchange represents fraudulent activity.

There is provided a system and method for determining a three-dimensional model of an object using a projector and a light sensor arranged in a stereo configuration. The method including: receiving pixel intensity values captured by the light sensor for a plurality of images of a scene containing the object, wherein the images of the scene each capture different projection patterns emitted by the projector onto the scene; determining, using the pixel intensity values from a set of the plurality of images, correspondence between a projector plane and a camera plane to form a three-dimensional model of the object, the correspondence determined using a trained artificial neural network model that uses a combination of volume rendering and geometric representation; and outputting the three-dimensional model of the object.

The invention discloses a ship structure safety forecasting method based on an artificial neural network. The method comprises the following steps: collecting historical data; processing the collected historical data, performing time sequence alignment on the multi-source heterogeneous historical data, and normalizing the processed historical data; constructing an artificial neural network model; training an artificial neural network model by using historical data; and inputting wave, navigational speed and draught data collected in real time into the trained artificial neural network model, obtaining a stress result of the ship structure by the artificial neural network model, extracting stress circulation by using a rain flow counting method, performing fatigue accumulated damage calculation, and performing safety state evaluation according to the stress and the fatigue accumulated damage. According to the method, the real-time dynamic forecasting of the ship structure stress and the fatigue life is realized.

To provide a balance function management system for balance function state information generation and a balance function rehabilitation program.SOLUTION: A memory configured to store at least one processor and at least one artificial neural network model that stores instructions executable by the processor and is executed on a computing device, wherein the at least one processor is configured to input frame images of n (where n is a natural number) videos obtained by photographing a subject through n cameras to the at least one artificial neural network model, At least one of information related to the coordinates of the head, the coordinates of the center of the pupil, and the phase change of the eyeball of the subject according to the order of the frame images of the m-th (m is a natural number from 1 to n) moving image may be acquired, and information related to the movement of the head and the movement of the eyeball for generating the balance state information or performing the balance rehabilitation program may be generated using the acquired information.SELECTED DRAWING: Figure 1

The application relates to the technical field of marine hydrological monitoring, and discloses a seawater suspended sediment concentration low-altitude observation method, which uses a low-altitude observation platform to carry digital photography equipment, obtains seawater suspended sediment concentration images, collects water samples in the photographed sea area, positions the water samples through a global satellite positioning system, uses a filtering method to measure the suspended sediment content, pre-processes the obtained seawater suspended sediment concentration images, realizes automatic splicing of the images and automatic imaging of orthographic images, trains the processed image data and the measured suspended sediment concentration data based on an optimized artificial neural network algorithm, establishes a seawater suspended sediment concentration inversion model, and evaluates the model precision. The method uses the low-altitude observation platform to obtain high-resolution images, uses the optimized artificial neural network model to invert the suspended sediment concentration, has the advantages of high precision, low cost, flexibility, timeliness and the like, and can effectively make up for the deficiency of satellite remote sensing.

The invention relates to the technical field of coating preparation, in particular to a high-entropy alloy corrosion-resistant coating based on machine learning optimization and a preparation method thereof.The preparation method comprises the steps that a preparation data set of the high-entropy alloy coating is constructed, and coating element components, technological parameters and corrosion environment parameters in the preparation data set serve as input characteristics; taking the thickness, the hardness and the elastic modulus of the corrosion layer as output characteristics, training an artificial neural network model, and establishing a performance prediction model; a multi-objective particle swarm optimization algorithm is adopted, and magnetron sputtering process parameters are optimized and solved by taking the minimum thickness of a corrosion layer and the maximum hardness and elasticity modulus as optimization objectives; according to the optimal process parameter combination, preparing an AlCrFeMoTi high-entropy alloy coating on a ferrite / martensitic steel matrix by adopting a magnetron sputtering five-target co-deposition technology; and carrying out corrosion resistance test on the prepared coating, and comparing a test result with a predicted value of the model to verify the accuracy of the model.

The application discloses an intelligent real-time rock stratum inversion identification method based on a while-drilling parameter feature, which comprises the following steps: simulating drilling tests on different kinds of rock samples on an indoor drilling machine test platform, collecting while-drilling parameters in the drilling process of the rock mass by means of installing sensors on the drilling machine, performing time-frequency analysis and processing on the collected drill rod axial vibration signals and training, so as to obtain an artificial neural network model for identifying rock strata; inputting the collected while-drilling data into the trained artificial neural network model on a drilling operation site, and outputting identification results of the interface and lithology types of the drilled rock strata in real time by the artificial neural network model, so as to classify the stratum geology and rock-soil bodies, and the drilling parameters and path can be adjusted in time by the staff according to the while-drilling rock stratum identification results in the drilling process, which is favorable for guaranteeing the drilling quality, improving the drilling efficiency and saving a large amount of manpower and material resources.

The invention provides a stainless steel corrosion rate prediction method based on virtual sample generation and transfer learning, and relates to the technical field of data-driven prediction models, and the method comprises the steps: S1, obtaining target stainless steel material corrosion data and low alloy steel corrosion data, and carrying out the standardization processing; s2, determining the direction and range of virtual sample data generation based on an SMOTE virtual sample generation method, and generating a stainless steel material data synthesis sample; s3, constructing a cross-domain transfer learning model of a stainless steel material, training an artificial neural network model by using low alloy steel corrosion data, and transferring to a target domain model; and S4, constructing a corrosion performance prediction optimization model of the target stainless steel material, and performing optimization output to obtain a corrosion prediction result of the target stainless steel material. Cross-domain corrosion rule migration is realized through a virtual sample generation technology and migration learning, and an efficient and reliable solution is provided for stainless steel corrosion rate evaluation by increasing the basic data volume.

The present invention relates to a method for detecting and removing a motion artifact of a functional near-infrared spectroscopy signal. Disclosed are a method and a device for detecting and removing a motion artifact of a near-infrared spectroscopy signal in real time on the basis of an artificial neural network model, the method comprising: a conversion step of converting time series data of functional near-infrared spectroscopy signals measured through a plurality of channels from a target into image data; a detection step of detecting whether noise is present in the converted image data using a pretrained detection model; and a removal step of removing noise from image data where noise has been detected using a pretrained noise removal model.

This disclosure provides a message delivery method, etc., that is executed by at least one processor. [Solution] In one embodiment of the present disclosure, a message delivery method performed by at least one processor includes: identifying an event relating to a message request in an instant messaging application; using an artificial neural network model to obtain second information relating to at least one of a context relating to the time of event identification or a prompt relating to the event from first information relating to a user account connected to the instant messaging application; using an artificial neural network model to generate a first message based on the second information; and providing the first message through a first message room of the instant messaging application relating to the artificial neural network model.

The application discloses a design method and device of a metasurface optical neural network, and the design method comprises the following steps: obtaining an artificial neural network model and a metasurface optical neural network model, wherein the artificial neural network model is a trained model; selecting n intermediate layers in the artificial neural network model in the order from shallow to deep, one-to-one corresponding the n intermediate layers and n metasurface layers in sequence, and calculating an intermediate feature loss according to the similarity between the features output by each layer in the n intermediate layers and the features output by the corresponding metasurface layer; training the metasurface optical neural network model; and determining the process parameters of the metasurface according to the trained metasurface optical neural network model. The design method completely transplants the calculation capacity of the artificial neural network into the metasurface optical neural network, solves the problems of low design efficiency and low precision of the metasurface optical neural network, and is beneficial to the development of the metasurface optical neural network and the deployment in an environment with limited computing power and storage.

A method of determining deformation of a battery includes obtaining a first image by scanning a cross-section of a battery cell in one direction, inputting the first image into an artificial neural network model trained to distinguish a plurality of parts of the battery cell in the first image and obtaining coordinates corresponding to each of the parts, and generating a second image in which at least some of the coordinates are aligned according to a winding sequence related to the battery cell. Deformation of the battery cell is determined based on the second image.

The specification discloses a target detection method and device, a storage medium and an electronic device. In the embodiment of the specification, the activation function of each neuron in an original artificial neural network model is adjusted according to the required running time of a to-be-converted spiking neural network model, and an adjusted activation function is obtained. The trained model parameters obtained by training through the adjusted activation function are migrated to the spiking neural network model, and an initial spiking neural network model is obtained. In this method, since the adjusted activation function in the artificial neural network model is adjusted through the running time of the spiking neural network model, the model parameters obtained by training through the adjusted activation function are migrated to the spiking neural network model, and the spiking neural network model does not need to consume additional running time to achieve a performance close to that of the artificial neural network model, thereby reducing the calculation amount and power consumption of the spiking neural network model running.

The invention discloses a torque prediction method suitable for high-viscosity material mixing, and belongs to the field of hydraulic equipment. Rotation speed and pressure drop, temperature and flow in hydraulic power transmission are used as input, torque actually measured by a torque sensor is used as output, accurate prediction of the torque of the hydraulic kneading machine is achieved by training an artificial neural network model, the artificial neural network model is continuously optimized in a machine learning mode, and the prediction precision of the torque of the hydraulic kneading machine is improved. The prediction accuracy is improved, the artificial neural network model has good mobility, and an accurate torque prediction result is provided for the working condition that the torque sensor cannot be installed. The method is not limited by installation conditions and environmental factors of the torque sensor; high-precision torque prediction is realized through multi-working-condition testing, comprehensive data acquisition, redundant information removal, data normalization processing, and establishment and optimization of a feedforward neural network. The constructed artificial neural network model has good mobility and can adapt to different hydraulic mixing devices.

According to the artificial intelligence ammeter data visualization-based power distribution system fault position identification method provided by the invention, three basic learners, namely a random forest, a K-proximity algorithm and an artificial neural network, are combined, and an integration method of combining a plurality of different machine learning models into a prediction model can be adapted to different scenes, so that the accuracy of the prediction model is improved. For example, noise and data loss, fault resistance change, load or power distribution feeder structure change and the like can be realized. An integrated voting classifier is developed, and a random forest, kNN and an artificial neural network model are utilized to classify fault types and identify a limited number of data points of FL only using voltage measurement. According to the method, the key problem of insufficient multiple features is overcome, the fault position can be quickly identified, the method is not influenced by the initial angle of the fault and the resistance on the line, the anti-interference capability is high, the classification accuracy of multiple fault types is high, the detection precision is high, and the cost is not too high.

The application relates to the technical field of face prediction, and is a face prediction model construction method, a face prediction method and related devices.The method comprises the following steps: acquiring a training set, a test set and a verification set; each sample in the training set, the test set and the verification set comprises genomics features and real facial morphology features; a training number is set; a preset artificial neural network model is trained multiple times according to the training number and the training set; a plurality of face prediction standby models corresponding to the training are obtained; the verification set is predicted by using the obtained plurality of face prediction standby models; the optimal parameters are selected; and a face prediction model is output. Compared with the prior art, the application is cleaner in sample extraction, the sample quantity is improved, the model prediction obtained through training is more accurate, and furthermore, the application adopts neural network modeling, and the test set and the verification set are used for evaluating and analyzing the face prediction model, so that the prediction accuracy and precision of the model are improved.

The present invention is to provide a magnetic resonance image processing method. According to an embodiment of the present invention, a magnetic resonance image processing method by a magnetic resonance image processing apparatus comprises the steps of: obtaining a sub-sampled magnetic resonance signal; acquiring first k-space data from the sub-sampled magnetic resonance signal using a first parallel imaging technique; obtaining a first magnetic resonance image from the first k-space data by using an inverse Fourier operation; generating first input image data by preprocessing the first magnetic resonance image; and obtaining a first output magnetic resonance image from the first input image data using a first artificial neural network mode.

Techniques of nonlinear quantization of an artificial neural network model having first weights. For example, a predetermined number of unique, second weights having a nonlinear distribution in a weight space of the first weights can be identified to generate a quantized model based on replacing, in the artificial neural network model, the first weights with closest ones from the second weights. A linear mapping between the second weights and values of conductance of memristors of an accelerator configured to perform operations of multiplication and accumulation can be used to determine the same predetermined number of programming voltages. Conductance of the memristors can be programmed using the programming voltages in preparation of the accelerator to perform an operation of multiplication and accumulation in the quantized model. The nonlinear distribution and the linear mapping can be adjusted to increase or optimize the accuracy of the quantized model.

This invention discloses a method for accurate lithology identification and correction of logging data based on artificial neural networks, comprising the following steps: collecting logging data, laboratory test data, and regional geological background data of the target area from buried hills; preprocessing the collected logging data and laboratory test data to establish a correspondence between the logging data and laboratory test data, forming a dataset; constructing an artificial neural network model; training and optimizing the constructed artificial neural network model based on the dataset; inputting the logging data of the target well to be corrected into the trained artificial neural network model, outputting corrected mineral element content data; and, based on the corrected mineral element content data and combined with the regional geological background, achieving lithology classification and accurate identification of the target well. This invention uses the above method to convert logging data into laboratory-precision data, improving the accuracy of lithology identification under complex geological conditions such as Archean metamorphic buried hills.

The present disclosure relates to a thermal analysis system and method for a battery system. The thermal analysis system includes a learning data generation device configured to generate second thermal analysis data using first thermal analysis data and a first artificial neural network model. The first thermal analysis data is obtained through numerical thermal analysis of a battery system. The thermal analysis system also includes a model construction device configured to construct a thermal analysis model by using a second artificial neural network model with the first thermal analysis data and the second thermal analysis data as learning data.

The invention belongs to the field of environmental protection and micro-plastic identification, and discloses a micro-plastic infrared characteristic spectrum extraction and efficient and accurate identification method, which comprises the following steps of: 1, acquiring infrared spectrum data of a micro-plastic sample, and constructing a micro-plastic spectrum database; 2, extracting a characteristic spectrum from the infrared spectrum data in the step 1 by adopting a progressive two-step method combining equal-interval sampling and a competitive self-adaptive reweighted sampling algorithm; step 3, performing standard normal transformation on the transmittance of the characteristic spectrum in the step 2; and 4, constructing a feature training set and a feature test set by taking the transmittance of the infrared spectrum transformed in the step 3 as input and the micro-plastic type as output. Training the artificial neural network model by adopting the feature training set, and optimizing hyper-parameters in the model through the feature test set by applying a genetic algorithm to form a final micro-plastic identification artificial neural network model; and step 5, for micro-plastics to be identified, obtaining the characteristic transmittance of the micro-plastics to be identified through the spectral data obtained in the step 1 and the characteristic wave number extracted in the step 2, performing standard normal transformation on the micro-plastics to be identified through the step 3, inputting the transformed infrared spectral transmittance into the micro-plastic identification artificial neural network model, and giving the type of the micro-plastics. And identification of the micro-plastic is realized. According to the method, traversal sampling of the whole spectrum data set is effectively avoided, the extraction efficiency of the characteristic spectrum is improved, hyper-parameter optimization is performed on the artificial neural network model by adopting the genetic algorithm, overfitting of the model is effectively prevented, and the generalization ability of the model is improved.

The application discloses a turbine blade loss model construction method based on deep learning and loss weight analysis, in order to explore how to correct the existing turbine blade loss model, firstly, each loss in the blade loss needs to be split, and the prediction size of the existing model is compared, the coefficients and terms that need to be corrected and the correction terms that need to be added are analyzed, and then a loss prediction model with a correction form is formed. By comparing the turbine blade loss with different blade parameters, the blade parameter variables that need to be considered are found. The function relationship between the blade parameter variables that need to be considered and the coefficients that need to be corrected (or the terms and the correction terms that need to be added) is established by using an artificial neural network model, and is brought into the loss prediction model with the correction form, and then a turbine blade loss prediction model is constructed. The method can accurately predict the turbine blade loss in a large attack angle working range.

The present specification provides a training method of an artificial neural network model for correcting image color and an image correction method using a trained artificial neural network model. The training method of the artificial neural network model according to the present specification uses, as training data, a pair of images before and after color correction. In the training method, the image before color correction is input into the artificial neural network model, N parameter values (N is a natural number) are obtained from the artificial neural network model, and at least one color correction program is executed according to the obtained parameter values, whereby a converted image, the color of which has been converted from the image before color correction contained in the training data, may be obtained. In addition, the artificial neural network model may be trained such that a difference value between the image after color correction contained in the training data and the converted image is reduced.

Disclosed is a method training an artificial neural network model performed by a computing device according to an exemplary embodiment of the present disclosure. According to the present disclosure, the computing device identifies a first computing node and a second computing node for training an artificial neural network model, acquires information related to a first task for training the artificial neural network model, based on a user input, and dynamically allocates the first task to at least one of the first computing node or the second computing node, and the first computing node includes one or more local computing resources of the user and the second computing node includes one or more cloud computing resources.

The invention relates to the technical field of power device transient junction temperature evaluation, and discloses a neural network modeling method for IGBT transient junction temperature evaluation based on historical temperature rise characteristics, which comprises the following steps: collecting chip loss, thermal resistance aging grade, chip junction temperature and environment temperature data of an IGBT device under different working conditions, and preprocessing the collected data; constructing a transient junction temperature evaluation model by using an artificial neural network, introducing a Bayesian optimization algorithm to optimize hyper-parameters trained by a neural network model, training the model by using preprocessed data, and establishing a mapping relationship between historical temperature rise characteristic related data and future junction temperature; performing transient junction temperature evaluation on the IGBT device by using the trained artificial neural network model, evaluating the performance of the model, and performing optimization and improvement on the model according to an evaluation result; the technical problem that an existing transient junction temperature evaluation method cannot deal with a multi-chip packaging scene is solved.