351 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 a design method of a special-shaped underwater pressure-resistant shell, which comprises the following steps: S1, according to the use environment of a to-be-designed special-shaped underwater pressure-resistant shell, determining various types of structure parameters which are required by the to-be-designed special-shaped underwater pressure-resistant shell and have association relationship and the initial value range of the structure parameters; s2, collecting a plurality of groups of data samples of the structure parameters conforming to the incidence relation in an initial value range; s3, m evaluation indexes used for evaluating the performance of the to-be-designed special-shaped underwater pressure-resistant shell are determined according to the use environment of the to-be-designed special-shaped underwater pressure-resistant shell, original evaluation values corresponding to the m evaluation indexes of each group of data samples are obtained, and the multiple data samples and the corresponding m original evaluation values form a data set; and S4, training an artificial neural network model by using the data set to obtain an initial prediction model for predicting an original evaluation value of the to-be-designed special-shaped underwater pressure-resistant shell. According to the method, multi-dimensional design of the special-shaped underwater pressure-resistant shell can be achieved, the multi-performance requirement is met, the design process is simple, and the design efficiency is high.

The invention discloses a steel rail abrasion intelligent prediction method and system considering railway space line shape, and the method comprises the steps: S1, building a vehicle-track coupling dynamic model based on vehicle structure characteristic parameters and track structure characteristic parameters; s2, based on the vehicle-track coupling dynamic model, establishing a steel rail abrasion calculation model, and calculating inner and outer side steel rail abrasion values under different track space linear parameters to construct a steel rail abrasion sample data set; s3, constructing a steel rail abrasion prediction artificial neural network model, and training the steel rail abrasion prediction artificial neural network model by using the steel rail abrasion sample data set; s4, optimizing the trained steel rail abrasion prediction artificial neural network model to obtain a final prediction model; and S5, predicting the abrasion of the railway steel rail by using the final prediction model. According to the method, the railway space linear parameter-steel rail abrasion prediction model is established, so that the steel rail abrasion under different plane and vertical section linear parameters and combinations is efficiently and accurately obtained.

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.

An object of initial unknown position on a map may be determined by traversing through moving and turning to establish motion trajectory to reduce its spatial uncertainty to a single location that would fit only to a certain map trajectory. An artificial neural network model learns from object motion on different map topologies may establish the object's end-to-end positioning from embedding map topologies and object motion. The proposed method includes learning potential motion patterns from the map and perform trajectory classification in the map's edge-space. Two different trajectory representations, namely angle representation and augmented angle representation (incorporates distance traversed) are considered and both a Graph Neural Network and an RNN are trained from the map for each representation to compare their performances. The results from the actual visual-inertial odometry have shown that the proposed approach is able to learn the map and localize the object based on its motion trajectories.

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 present invention relates to proteomics, and techniques for predicting de novo sequencing of chains of amino acids, such as peptides, proteins, or combinations thereof. Particularly, aspects of the present invention are directed to a computer implemented method that includes obtaining a digital representation of a mass spectrum, the digital representation including a plurality of container elements, encoding, using an encoder portion of a bidirectional recurrent neural network of long short term memory cells and gated recurrent unit cells, each container element as an encoded vector, decoding, using a decoder portion of the bidirectional recurrent neural network, each of the encoded vectors into a sequence of amino acids; and recording the sequence of amino acids as a multi-dimensional data set of amino acids types and a probability of each of the amino acid types in each position of the complete amino acid sequence.

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 a solar heat utilization optimization method of a heat collector, heat collector equipment and a computer device. The method comprises the following steps: predicting radiation characteristics of suspended nanoparticles based on an artificial neural network model by utilizing a boundary element method; parameters of the heat collector are optimized through a genetic algorithm, and the radiation characteristics of the suspended nanoparticles are evaluated; constructing a heat collector structure optimized by the genetic algorithm in the finite volume method simulation framework, and verifying the rationality of parameters of the heat collector structure; preparing a ternary mixed nano suspension, and verifying the stability of the ternary mixed nano suspension; a heat collector device is prepared based on the ternary mixed nano suspension, a sensor network is arranged in the heat collector device to continuously collect data, the solar heat utilization rate of the heat collector device is determined according to the data, and the data is uploaded to an upper computer to be managed and analyzed. Optimization and performance improvement of the solar heat collector equipment are achieved, and the thermal performance of the heat collector equipment is improved.

The invention provides a power grid dispatching anti-error method based on voice recognition and artificial intelligence, which comprises the following steps of: firstly, converting dispatching voice content into a text record through a voice recognition technology, and generating a standardized dispatching log; secondly, preprocessing and feature extraction are carried out on the text data, noise data are removed, and key features are extracted; then, a scheduling operation knowledge base is constructed based on historical data and expert experience, and rapid retrieval and updating are supported; then, an Elman artificial neural network model is adopted for text recognition, and error instructions are recognized and marked; and finally, the Bayesian network is utilized to carry out anti-error prediction, and scheduling errors are reduced, so that the efficiency and reliability of the power grid scheduling system are improved. According to the method, the safety and reliability of power grid dispatching operation can be comprehensively improved.

The invention discloses a biomass gasification product distribution prediction method based on a hard constraint physical information neural network. The method comprises the following steps: collecting and preprocessing gasification experiment data; constructing a multi-layer artificial neural network model, and converting prior monotonicity knowledge into an inequality constraint combination only related to network parameters by adopting a hard constraint learning mode; model training: aiming at regression loss and regularization loss of experimental data, adopting a constrained particle swarm optimization algorithm to perform network parameter optimization on the model under an inequality constraint combination to obtain a hard constraint physical information neural network model; and predicting biomass gasification input data by adopting a hard constraint physical information neural network model to obtain a biomass gasification product distribution condition. According to the method, the monotonicity knowledge between the biomass gasification products and the key input parameters is embedded into the neural network model, the problem that biomass gasification experiment samples are insufficient can be effectively solved, and a more accurate and more interpretable biomass gasification product distribution result is obtained.

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.

One example relates to a method for providing an immersive virtual golf service using an eXtended Reality (XR). The method may comprise: capturing an image of an actual golf ball placed on a ground through a camera of an XR device; reproducing and displaying the image within a virtual golf environment displayed on a display unit of the XR device; capturing, in real-time, a golf swing of a user through the camera of the XR device; analyzing a golf swing motion of the user in real-time using an artificial neural network model; and reproducing and displaying the analyzed golf swing motion within the virtual golf environment displayed on the display unit of the XR device.

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 relates to the technical field of operation time prediction, in particular to an operation time real-time prediction model based on preoperative and intraoperative information and an artificial neural network, which comprises the following steps: a data acquisition module, construction of an artificial neural network model, a data preprocessing module and real-time deployment. The data acquisition module comprises the following steps of collecting a large amount of performed operation data in advance, including preoperative information, intraoperative information and actual operation time, classifying operation types and difficulty levels, classifying physical states of operation doctors, and performing preoperative acquisition; data of age, gender, weight, health condition, basic disease and previous operation records of a patient are subjected to collection model selection, a neural network is fed forward, and a simple multi-layer sensor is adopted; according to the scheme, the preoperative physical condition of the patient is compared with the previous operation record, the experience of an operation doctor is collected, and the sudden unexpected condition in the operation is predicted, so that the operation time is predicted and evaluated.

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 following disclosure relates to a pipeline system specialized for a drone and an operating method thereof. The pipeline system may: receive an input of a user related to a target artificial neural network model; detect a list of drones connected to the pipeline system specialized for the drone; receive movement paths and sensor data of the drones on the basis of the list of the drones; analyze the movement paths and the sensor data of the drones; train the target artificial neural network model on the basis of the input of the user, the movement paths, and the sensor data; and select valid target drones from the list of the drones to transmit the trained target artificial neural network model to the valid target drones.

According to an embodiment of the present invention, there is provided a magnetic resonance image processing method, the magnetic resonance image processing method being performed by a magnetic resonance image processing apparatus, the magnetic resonance image processing method including: inputting an input image having a slice resolution higher than 0 and lower than 1 to an artificial neural network model; and outputting an output image having a slice resolution of 1 from the artificial neural network model.

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 invention discloses a moderate aquatic product processing method based on a neural network and a genetic algorithm. The method comprises the following steps: obtaining aquatic product processing data through a Box-Behnken design experiment; constructing a back-propagation artificial neural network model, and training the back-propagation artificial neural network model through the aquatic product processing data to obtain a trained back-propagation artificial neural network model; predicting key quality indexes in the aquatic product processing process through the trained back propagation artificial neural network model; global optimization is carried out on the key quality indexes through a genetic algorithm, and an optimal parameter combination is obtained; and properly processing the aquatic products according to the optimal parameter combination. According to the method, multiple key quality indexes can be predicted and optimized at the same time, the technological parameters are globally optimized through the genetic algorithm, and moderate processing of aquatic products is achieved.

The invention relates to a land utilization spatial distribution prediction method considering ground feature spatial distribution characteristics, and the method comprises the steps: obtaining key factors and land utilization data of target land class spatial distribution, inputting the key factors and the land utilization data into an artificial neural network model, and obtaining a suitability distribution probability, utilizing a Markov model to predict the future grid number of each land utilization type; the method comprises the following steps: acquiring a land type area variation ratio, determining a neighborhood weight parameter based on the land type area variation ratio, acquiring a land utilization type transfer matrix, setting a cost matrix through the land utilization type transfer matrix, further acquiring spatial distribution of each land utilization type, and calculating a shape control parameter according to the spatial distribution; and inputting the suitability distribution probability, the future grid number, the neighborhood weight parameter, the cost matrix and the shape control parameter into a CA model to obtain a prediction result of target land class spatial distribution. The method can better adapt to and cope with complex scenes in practical application.

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.