46 results about "Neural network design" patented technology

An adaptive impedance-based multi-mobile-robot collaborative transportation control method. Each mobile robot estimates the actual pose and ideal pose of a reference point and the first and second derivatives of the ideal pose by means of two finite-time fully-distributed observers, respectively; and then, on the basis of the estimated poses of the reference point, the pose of an end-effector of a mechanical arm, and closed-chain constraints for collaborative transportation, an ideal trajectory of the end-effector of the mobile robot, and an estimated value of a pose deviation between the end-effector of the mobile robot and the reference point are obtained. An adaptive impedance system of each mobile robot is interconnected with a virtual energy tank, and the energy tank is used to guide the updating of impedance parameters, thereby ensuring the passivity of the entire collaborative adaptive impedance system. To process unknown system dynamics of mobile robots, an asymptotic tracking adaptive neural network controller is designed using a neural network, thereby asymptotically achieving an ideal adaptive impedance relationship. The operational accuracy of multi-robot collaborative transportation systems is improved while ensuring safe collaboration.

The invention belongs to the technical field of robot control, and discloses a visual servo intelligent robust control method for a mobile mechanical arm for a complex operation task, which comprises the following steps of: 1, acquiring a working scene image, and calculating a space coordinate of a target feature point in real time through a projection transformation model; step 2, dynamically inhibiting visual measurement noise by adopting adaptive Kalman filtering, generating a feedforward compensation signal through an integral sliding mode observer, and decoupling chassis slippage disturbance; step 3, inputting the pose signal into a radial RBF neural network, designing an RBF gain scheduler, and adjusting an output proportion-integral gain in real time to suppress external time-varying disturbance; 4, adopting a hybrid visual servo mode switching mechanism, and generating a mobile platform control instruction through an integral sliding mode surface in a position servo mode; and 5, designing a three-order composite controller to drive the mechanical arm so as to realize accurate grabbing. According to the method, the grabbing deviation caused by kinematics uncertainty of the mobile platform and dynamic environment disturbance is eliminated.

The invention discloses a piezoelectric micro-positioning platform preset performance control method based on an extended state observer, and the method mainly comprises the steps: firstly, building a piezoelectric micro-positioning platform system model considering input hysteresis and unknown disturbance; secondly, designing an extended state observer based on a radial basis function neural network to estimate an unmeasurable state of the system and lumped disturbance containing input hysteresis; then, a preset performance function is used, so that the overshoot performance of the system is effectively improved; thirdly, in combination with a preset performance function, a first-order sliding mode differentiator and an expansion state observer, providing a virtual control law and an adaptive control law; and finally, a preset performance controller is utilized, a Lyapunov stability theory is combined, proper parameters are selected to ensure that the closed-loop system is kept stable within preset time, and tracking errors are controlled within a set error range.

The invention discloses a watermarking method and device suitable for any resolution image input, and the method comprises the steps: generating a fixed low-resolution watermark residual error through a watermark encoder according to an input carrier image and a watermark message, restoring the watermark residual error to the size of the carrier image, and superposing the watermark residual error with the carrier image to obtain a watermark-containing image; designing a watermark discriminator based on the residual neural network, wherein the watermark discriminator is used for distinguishing the difference between the watermark-containing image and the carrier image; a watermark noise layer is used for simulating various distortion operations suffered by an image under a digital channel, and disturbance is applied to the watermark-containing image processed by a watermark discriminator; and transmitting the watermark-containing image to which the disturbance is applied in a real channel, inputting the watermark-containing image into a robust watermark decoder, and decoding and extracting a watermark message. According to the method and the device, image input of any resolution can be processed, good invisibility and robustness can be kept, and copyright protection and leakage traceability of the image of any resolution can be realized.

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.

The application relates to a personalized privacy risk dynamic measurement method based on a deep neural network, and specifically comprises the following steps: S1, collecting personalized privacy demands of a user, and establishing a personalized user privacy demand vector; S2, according to the personalized user privacy demand vector, extracting a feature matrix in a user picture by using a convolutional neural network, calculating a privacy feature vector from the feature matrix by using the convolutional neural network, multiplying the privacy feature vector with the personalized user privacy demand vector, obtaining a personalized privacy feature vector of the user in the picture, and identifying sensitive privacy information in the picture; and S3, predicting a leakage risk of the personalized privacy feature vector. The application is designed based on a deep neural network, solves the problem of privacy risk assessment from the perspective of user shared privacy image historical behavior characteristics, measures the risk size of user privacy information leakage, reduces the user privacy information leakage risk from the source, and enhances the user privacy consciousness.

The invention is suitable for the technical field of optical computing, and provides a knowledge distillation-based on-chip optical neural network design method, which comprises the following steps of: constructing an initial population of an optimization algorithm based on a preset process constraint condition; the plurality of individuals in the initial population are in one-to-one correspondence with the plurality of groups of optical neural network parameters; performing iterative optimization on the initial population by using an optimization algorithm to obtain an optimal optical neural network parameter meeting a preset process constraint condition; constructing an optical neural network based on the optimal optical neural network parameters, and taking the constructed optical neural network as an on-chip optical neural network; the calculation process of the fitness value of the individual in the optimization algorithm comprises the following steps: training the optical neural network corresponding to the individual by using the knowledge distillation framework, and determining the fitness value based on the training loss value and the physical parameter of the optical neural network. According to the invention, the area of the on-chip optical neural network is reduced, the complexity of the on-chip optical neural network is reduced, and the performance of the on-chip optical neural network in processing complex tasks is improved.

The application discloses a five-phase permanent magnet synchronous motor harmonic suppression control method based on machine learning, which comprises the following steps: S1, constructing a mathematical model of a five-phase permanent magnet synchronous motor driving system in a rotating coordinate system, and designing a five-phase permanent magnet synchronous motor control system based on a deep neural network; S2, deeply integrating motor physical prior information into a training process to enhance the generalization ability of the model, and proposing a hybrid optimization strategy to improve the fitting precision of a predicted network voltage and an actual working voltage, suppress fluctuations in the prediction process, and make the predicted value more suitable for actual operation requirements. The application overcomes the limitations of the existing double-plane current control method, and fully utilizes the technical advantages.

The invention relates to a method for extracting the power consumption of an individual device within a set of devices (E1, E2, E3,..., En), the method comprising transforming a stream of measurements of an overall power consumption into a time series, and dividing (S1) the time series into a set of sequences, and then, for each sequence, the steps of: - generating (S4) a positional encoding matrix (MEP); - extracting (S5) features from the sequence to form a feature matrix (MF); - projecting (S6) statistical metrics into a statistics vector (MS); - concatenating (S7) these various elements (MEP, MF, MS) to form an input matrix (ME) provided to a transformation neural network designed to infer (S8) a time sequence corresponding to the individual device.

The application discloses a kind of based on deep reinforcement learning's decoy penetration aircraft cooperative trajectory planning method, it is related to aircraft path planning technical field, including: the aircraft motion trajectory model of penetration aircraft and decoy aircraft is constructed;Penetration aircraft process Markov decision model is constructed, including state space model, action space model and reward function, and reward function is designed based on threat area;Using deep deterministic policy gradient (DDPG) algorithm, penetration framework based on actor-critic (Actor-Critic), by improving deep Q network, the Actor network and Critic network of penetration aircraft and decoy aircraft are designed using deep neural network, according to aircraft penetration process Markov decision model, heading angle change is obtained, the position of aircraft is updated after being substituted into trajectory model, and motion trajectory is obtained.The application has excellent exploration ability, and can solve the aircraft trajectory planning problem of higher real-time requirement in dynamic environment.

The invention discloses a circRNA (Ribonucleic Acid) and miRNA (Micro Ribonucleic Acid) interaction prediction system and a circRNA and miRNA interaction prediction method of a graph Fourier pulse neural network. The method comprises the following steps: on the basis of high-throughput sequencing omics data of complex diseases, constructing a heterogeneous biological information network containing drugs, diseases, proteins, circRNA, miRNA and lncRNA; converting the topological features of the entities into a unified feature space by using a graph convolutional network; designing a pulse graph neural network in combination with Fourier coding and a pulse neural network, and extracting a topological structure and high-order semantic features in the network; fusing sequences, topologies and semantic features of circRNA and miRNA through a gate multilayer perceptron to obtain embedding features of circRNA and miRNA; and finally, the interaction of circRNA and miRNA is predicted by adopting a Bayesian classifier. According to the method, heterogeneous biological information is modeled from the perspective of network science, Fourier coding, spiking neurons and graph embedding learning are utilized, the action mechanism of circRNA and miRNA in complex diseases can be disclosed, and the method has good practicability and application prospects in the fields of artificial intelligence, life science, clinical medicine and the like.

A method for extracting the power consumption of an individual piece of equipment within a set of equipment (E1, E2, E3, …, En), comprising transforming a stream of measurements of overall power consumption into a time series, and dividing this series (S1) into a set of sequences. Then, for each sequence, the following steps are performed: generation (S4) of a positional encoding matrix (PEM); extraction (S5) of features from the sequence to form a feature matrix (FM); projection (S6) of statistical metrics into a statistics vector (SM); and concatenation (S7) of these different elements (PEM, FM, SM) to form an input matrix (IM) provided to a transformation neural network designed to infer (S8) a time sequence corresponding to the individual piece of equipment. Figure for the abstract: Fig. 1

The invention discloses a celestial body physical parameter prediction method and system, and relates to the technical field of astronomical data processing and analysis, and the method comprises the steps: obtaining a photometric image and observation spectrum data of a to-be-measured celestial body; inputting the photometric image and the spectral data into a lightweight multi-modal parameter prediction model, performing coarse-grained feature extraction on the photometric image through two layers of large-kernel inverted bottleneck 2D convolution blocks, and performing coarse-grained feature extraction on the spectral data through a 1D convolution operator; aligning the granularity of the extracted coarse-grained features through a dimension transformation module, and performing multi-modal feature fusion on the two groups of aligned coarse-grained features to obtain fine-grained depth semantic features; the fine-grained depth semantic features pass through a parameter regression module, and a parameter prediction result is output; according to the method, the calculation consumption in the reasoning process is reduced by adopting a lightweight neural network design, and the accuracy of a prediction result is improved through feature information complementation among different modalities.

This invention discloses a pre-time formation switching control method for heterogeneous UUVs in confined environments based on mobility perception. The method includes: establishing a heterogeneous UUV formation switching system model; modeling the confined area as a time-varying curve virtual pipeline and defining a mobility index as the trigger for formation switching; constructing a data-driven neural network to approximate uncertainties and disturbances; designing a pre-defined time restraint controller for the actual root leader; generating a reference trajectory from the virtual root leader; and determining the leader formation's position and velocity based on the actual root leader information; designing a pre-defined time affine switching observer to observe the desired state of followers; and designing a full-state pre-time controller by combining a unified obstacle function and a neural network, ultimately achieving formation switching control. This invention integrates core technologies such as mobility perception, pre-time control, a unified obstacle function, and data-driven neural networks to construct a complete heterogeneous UUV formation switching control scheme, achieving comprehensive improvements in environmental adaptability, control accuracy, convergence performance, operational safety, and formation cooperative robustness.

The invention provides a finite time cross-domain aircraft cluster formation control method based on a neural network, and relates to the field of aircraft control, and the method specifically comprises the steps: building a unified nonlinear non-strict feedback system for the dynamic characteristics of cross-domain aircrafts in different domains; collecting real-time flight states and position information of a plurality of followers; designing an improved Tan type nonlinear mapping function, and identifying an initial output constraint type of the system to obtain an unconstrained new variable; constructing a dual neural network combining a shared network and an exclusive network, designing a backstepping method, combining a switching function and a non-singular fast finite time control strategy, and controlling the output of a dual neural network structure; and a switching threshold event triggering mechanism is designed, so that the unmanned aerial vehicle group has different responses in different environments. According to the technical scheme, the problem that mode switching, dynamic coupling and cooperation consistency in cross-domain cooperation are not considered in a formation control method in the prior art is solved.

The present application relates to a kind of machine learning-based groundwater funnel area monitoring data processing method, belong to artificial intelligence technical field.It includes the following steps: groundwater level and space-time auxiliary feature data are collected to construct historical data set, then through the strategy of fusion time-space correlation and periodic priori, data interpolation denoising is completed, and regular sequence is obtained;Subsequently, multi-scale periodic-trend decomposition and time-frequency analysis are realized by adaptive sliding window, and multi-dimensional feature matrix is formed by splicing;Then, the spatiotemporal graph neural network of multi-scale spatiotemporal graph convolutional encoder and physically guided attention decoder is constructed, and the joint loss function of mean square error and physical regularization term fusion is designed;After obtaining the optimal model by Adam optimizer training, it is deployed to monitoring system to realize water level real-time prediction, and the development risk of funnel area is analyzed in combination with historical data and warning threshold, and early warning is issued.The present application can improve prediction accuracy and robustness, and provide accurate decision-making basis for groundwater funnel area management.

Disclosed are systems and methods for accelerated reconstruction of dynamic, undersampled MRI data using a temporally-aware neural network system. An exemplary method includes receiving, for each of a plurality of time frames, undersampled k-space data; for one of the plurality of time frames, generating a reconstructed image by processing the undersampled k-space data for the time frame together with prior information derived from at least one previous time frame of the plurality of time frames, wherein the prior information comprises at least one of: (i) a k-space memory representing previously estimated or acquired k-space data, or (ii) an image-domain memory representing previously reconstructed images.

Techniques for a BLE-CS processing architecture using a parametric, data-driven neural network design for phase-based distance (PBR) measurement are described. The neural network can integrate feature transformation and distance estimation to simultaneously generate a clean spectrum and distance estimate. The neural network can receive PBR measurement data from constant-tone signals across a frequency range exchanged between two devices. From the PBR measurement data, the neural network can extract features representative of non-integer frequencies across this frequency range. These non-integer frequencies can be sampled at non-fixed positions and in ascending order. Based on the extracted features, the neural network can estimate the distance between the two devices.In a single embodiment, the neural network can combine scene identification, denoising, feature transformation, and distance estimation steps into a single model. The neural network can adapt to different indoor or outdoor scenes without requiring an explicit scene identification step.

The application discloses a kind of high supersonic interceptor multi-constraint midcourse guidance method systems considering information loss, belong to interceptor technical field.The method is first established under the ground trajectory system Intercept missile mathematical model, then the end attack angle constraint is converted into terminal line-of-sight constraint tracking problem, simplify the problem difficulty;Information is estimated using a filter, then a trajectory classifier is designed based on long short memory neural network and a trajectory prediction database is trained, the database is used for trajectory prediction as the support of guidance rate design, finally the expected shooting angle is designed by the estimated target aircraft information, and the guidance rate is derived, which improves the accuracy of guidance, and the effectiveness of the method is verified by MATLAB simulation software.

This invention relates to the field of weld defect type identification technology. Existing weld defect detection methods fail to achieve intelligent and efficient detection due to the diverse types of weld defects. This invention provides a low-cost self-learning neural network design method for ultrasonic detection of weld defects. It performs high-dimensional spatial domain feature representation on the initial one-dimensional ultrasonic signal, enriches the feature expression of weld defect data, selects the feature domain with better performance, constructs an adaptive scaling network for weld defect detection, and uses a multi-objective, training-free network search and evaluation method to iteratively search and evaluate candidate networks for weld defect detection, obtaining a balanced solution on both network classification accuracy and parameter quantity. Ultimately, it achieves the selection of a weld defect type detection network with superior overall performance without any training.

This invention relates to the field of missile maneuvering, and provides a method for predicting the maneuvering intentions of hypersonic vehicles (HGVs) based on a remote sensing constellation. A model is established for the maneuvering intention sequence of HGVs; based on the model, the types of maneuvers of the target in the longitudinal and lateral directions are described, and the maneuver amplitude and frequency are quantified; based on the model, a parallel stacked long short-term memory (PSLSTM) neural network is designed; based on the quantified maneuver amplitude and frequency and the parallel stacked PSLSTM, a process for identifying and predicting the maneuvering intentions of HGVs is designed; based on the identification and prediction process of different maneuvering parameters, a sensor switching strategy is designed to improve the adaptability of remote sensing satellites to the uncertainty of target intentions and increase the overall tracking benefit. This invention addresses the uncertainty of target maneuvering intentions during continuous tracking and observation of HGVs by a constellation tracking observation system, improving the adaptability of remote sensing satellites to this problem.

The invention relates to a workshop-adaptive intelligent fusion terminal defect detection method and a workshop-adaptive intelligent fusion terminal defect detection system in the technical field of electric power detection. The intelligent fusion terminal defect detection method comprises the following steps: collecting appearance image data, electrical parameter data and structure size data of a target, converting the data into corresponding channel characteristics, and inputting the channel characteristics into a defect identification neural network to obtain a result whether a defect exists or not and a defect type. Through the full-process design of multi-source data acquisition, lightweight intelligent detection and workshop equipment adaptation, the improved defect recognition neural network and the embedded attention module are adopted, so that the detection efficiency is remarkably improved, the workshop production line takt is completely matched, meanwhile, the detection precision and the coverage range are greatly expanded, the product reject ratio is reduced, and the product quality is improved. Moreover, the adaptability and robustness are enhanced through the lightweight neural network design, and the multi-scene production requirements are met.

The invention belongs to the technical field of ship and ocean engineering, and discloses a short-peak wave nonlinear characteristic enhancement forecasting method, which comprises the following steps of: firstly, constructing a weak nonlinear fluctuation characteristic propagation parameter matrix extraction and forecasting method based on an ICWM wave theory to provide a weak nonlinear wave component forecasting result meeting a wave propagation physical rule; then designing and constructing a nonlinear feature mapping operator by adopting a KAN neural network, and performing feature fitting on a nonlinear residual term between a weak nonlinear wave forecasting result and an actual wave by utilizing the operator to realize effective extraction of a strong nonlinear wave component; and finally, high-precision forecasting of complete wave information is realized through superposition of forecasting results of the two wave components. According to the method, the defects that the precision of a current hydrodynamic forecasting method is insufficient when the current hydrodynamic forecasting method faces strong nonlinear feature forecasting and an abnormal forecasting result is generated due to the lack of physical constraints of a neural network method are effectively overcome, and the stability and the accuracy of strong nonlinear short peak wave forecasting are effectively improved.

The invention relates to the technical field of radar foresight imaging, and discloses a terahertz single-photon radar foresight three-dimensional imaging method under low detection times, which comprises the following steps of: performing pulse accumulation through a single-channel terahertz single-photon detector to obtain a photon statistical time histogram, and performing normalization processing on the photon statistical time histogram; a neural network architecture suitable for a low-detection-frequency scene is constructed, a simple denoising module is embedded in the neural network architecture, and the neural network is trained; and inputting the photon statistical time histogram of the scene to be detected into the trained neural network model, and outputting a depth vector. According to the method, special network optimization design is carried out for a low-detection-frequency scene of the single-photon detector, different detection frequencies within the range of Count = 2000-5000 are effectively dealt with through a network architecture based on end-to-end neural network design, the imaging quality is improved, noise propagation can be effectively inhibited under the condition of low detection frequencies, and the imaging effect is remarkably improved.

The application provides a limited time cross-domain aircraft cluster formation control method based on a neural network, relates to the field of aircraft control, and specifically comprises the following steps: a unified nonlinear non-strict feedback system is established according to the dynamic characteristics of cross-domain aircraft in different domains; real-time flight states and position information of a plurality of followers are collected; an improved Tan-type nonlinear mapping function is designed, the initial output constraint type of the system is identified, and new variables without constraints are obtained; a double neural network combining a shared network and a dedicated network is constructed, a backstepping method combined with a switching function and a non-singular fast limited time control strategy are designed, and the output of the double neural network structure is controlled; and a switching threshold event triggering mechanism is designed, so that the UAV cluster has different responses in different environments. The technical scheme of the application overcomes the problem that the formation control method in the prior art does not consider mode switching, dynamic coupling and collaborative consistency in cross-domain cooperation.

A computer-implemented method for training a neural network (60), wherein the neural network is designed to accept an input (x) characterizing at least one sensor measurement (S) and to provide an output (x) characterizing a classification and / or regression result of the at least one sensor measurement (S) and / or a probability that the sensor measurement (S) occurs among a set of sensor measurements, or wherein the neural network (60) is designed to provide an output (x) characterizing a prediction of a sensor measurement for generating a training and / or test data set for training another machine learning system, wherein the training method comprises the following steps: • Performing a singular value decomposition of a weight matrix or weight tensor of the neural network (60); • Grouping singular values ​​and corresponding singular vectors into a plurality of groups based on the magnitudes of the singular values; • Training the neural network by adjusting each group according to a distinct non-zero learning rate and / or distinct optimizers.

The application discloses a real-time prediction method for electromagnetic thermal fields of a transformer based on physical constraint embedding neural networks, and belongs to the technical field of transformer monitoring. The method comprises the following steps: selecting a load rate, an environmental temperature and a shell convection heat transfer coefficient as key parameters, generating samples by optimal Latin hypercube sampling, establishing a three-dimensional electromagnetic-thermal-fluid coupling finite element model to construct a training / test database; constructing a deep fully connected neural network with five easy-to-measure parameters as input and winding temperature cloud maps as output, designing a total loss function containing data / physical loss for training; after verification, deploying an online monitoring system, collecting parameters in real time and outputting winding temperature cloud maps. The application has the characteristics of high precision, strong generalization and easy deployment, and provides support for intelligent operation and maintenance of transformers and digital twins.

The invention discloses a road network traffic state unified representation learning method. Comprising the following steps: acquiring multi-source traffic state data of a target area from a traffic perception system, and constructing a graph structure containing road nodes and traffic state attributes; designing a traffic network encoder based on the graph neural network, and extracting a semantic representation vector of each road node; designing a traffic environment encoder based on the full-connection neural network, and extracting a representation vector of each traffic environment combination; and constructing a positive sample pair and a negative sample pair according to a structural relationship between roads and traffic state similarity, defining and minimizing a comparison loss function, and performing comparison training on a traffic network encoder and a traffic environment encoder to obtain a unified traffic state semantic representation space. The method has remarkable advantages in the aspects of multi-source heterogeneous data fusion, traffic state space consistency modeling and semantic interpretability, and is widely applied to intelligent traffic application scenes such as traffic prediction, congestion management and data completion.

This invention discloses a self-developing neural network design method and storage medium inspired by DNA damage repair mechanisms. The method includes selecting a neural network model and defining its hyperparameters; acquiring an image dataset and dividing it into training, validation, and test sets to form a sequence of tasks; training the neural network model based on the first task data in the training set; testing the trained neural network model based on the first task data in the validation set; performing self-developing growth on the neural network model in both width and depth directions to increase its size; repeating the training and testing process and determining whether further increasing the neural network model size is necessary; if growth stops, obtaining the model's classification accuracy based on the task data in the test set; repeating the process until all task data in the test set has been tested. This invention promotes the intelligent development of neural networks in image classification tasks and their practical applications by dynamically adjusting the size of the neural network model and growing new neurons during training to cope with constantly changing tasks and environments.

The invention belongs to the field of spectrum sensing, particularly relates to a communication signal modulation pattern recognition method based on a transformation neural network, and aims to provide a communication signal modulation pattern recognition method with higher robustness. The method comprises the steps of training set data enhancement, modulation recognition neural network design, recognition model training, reasoning recognition and the like. Firstly, sample enhancement in the aspects of frequency offset, phase offset, sampling, channel and the like is carried out on training set sample data, then a modulation pattern recognition neural network composed of a signal conversion processing part and a feature extraction recognition part is designed, and finally, the network is trained by utilizing the enhanced samples to obtain a modulation pattern recognition model. And the model is used to complete modulation style identification. The brand new modulation recognition method is designed, the modulation pattern feature extraction is more accurate, the robustness of the recognition model is higher, and the accuracy of signal modulation pattern recognition in a complex electromagnetic environment can be improved.