78 results about "Batch training" patented technology

The invention provides a digital twin model incremental learning method and system and an equipment state prediction method.The digital twin model incremental learning method introduces an incremental learning mechanism based on elastic weight merging to train an industrial equipment state parameter prediction model; the current working condition samples are stored in an experience playback buffer area, the experience playback buffer area adopts a circular queue structure, the samples stored in the buffer area are used in a mixed mode for training when a new working condition is learned, and mixed loss optimization is executed on a current data batch; after batch training is completed, Fisher information matrix diagonal terms, about historical data, of current parameter configuration are calculated firstly, then global parameter importance is updated until Neural ODE incremental training converges, and finally a complete model state tuple fusing multi-working-condition knowledge is output; the incremental Neural ODE can continue to learn knowledge of new working conditions on the premise that knowledge of old working conditions is not forgotten, and the sustainable evolution ability of the digital twin under the limited resource condition is achieved.

The invention discloses a satellite orbit forecasting method based on deep learning physical constraint loss, and the method comprises the following steps: 1, carrying out the normalization preprocessing of input data, forming a training data set and a test data set, and constructing batch processing training data; and 2, performing dimension expansion on sample data points in each window in the batch processing data formed in the step 1, constructing a multi-dimensional feature space of the sample points, and forming a batch processing input data format capable of being introduced into the model. And 3, performing forward reasoning on the batch data formed in the step 2 by using a model, and obtaining a batch processing orbit prediction value output by the model at the next moment through a CNN lightweight spatial-temporal feature extraction module and a BiLSTM bidirectional time sequence neural network module. And 4, taking the track prediction value obtained in the step 3 and the truth value label in the training set obtained in the step 1 as input, calculating to obtain a loss value of a current training iteration batch through a multi-random learning loss module fusing physical constraints, and performing reverse updating of model parameters to complete model training. And step five, through the steps two to four, performing reasoning verification on the model by using the test set formed in the step one, and comparing with a truth value in the test set to obtain a model test result.

The invention specifically relates to a federated learning-based distributed dyeing machine anomaly detection method, and relates to the technical field of industrial equipment anomaly detection and federated learning, and the method comprises the steps: compressing an obtained weight difference value, encrypting the weight difference value, and transmitting the weight difference value to a federated server; according to the method, a CNN-LSTM mixed structure is adopted for model training, space and time sequence characteristics are considered, and dyeing machine multi-sensor data association and abnormal hysteresis characteristics are adapted; incremental training and small-batch training strategies are adopted to solve the problem of computing power memory limitation of edge equipment, so that the model can be locally and stably trained in the dyeing machine, and network dependence is avoided; a compression transmission link, a dynamic sparse adaptation process stage and hierarchical quantification distinguishing of information levels are performed, so that the data volume is compressed, the transmission efficiency is guaranteed, key information is reserved, the federal learning process is stably operated in an industrial environment, and a dyeing machine production scene is comprehensively adapted from data processing to model training deployment; and a solid support is provided for abnormal detection landing.

A method includes identifying at least one point in a set of image frames within a temporal window. The set of image frames within the temporal window forms video content. The method also includes extracting temporal information including movement of the at least one point through the set of image frames within the temporal window based on estimation of a local motion vector and / or a supervised optical flow represented in the set of image frames. The method further includes generating a video portion based on association of the temporal information with the set of image frames within the temporal window. In addition, the method includes inputting the video portion as at least part of batch training data for one or more generative machine learning models, where the one or more generative machine learning models that are configured by being trained with the video portion generate temporally stable video content.

This invention provides a method and system for predicting the state of power equipment based on online testing adaptation, comprising: collecting power equipment state data in real time through sensors and forming test samples; filtering a set of adapted historical samples from a historical sample memory bank that meet preset conditions in terms of similarity to the test samples in the latent space through a transferable historical sample selection module, wherein the historical sample memory bank stores historical power equipment state data; performing time-frequency domain hybrid data augmentation on the test samples and the adapted historical sample set through a transferable online augmentation module to generate an augmented sample set; inputting the augmented sample set into a pre-trained power equipment state prediction model for batch training, dynamically adjusting the model parameters to adapt to the distribution shift; and fusing the output of the dual-stream predictor of the power equipment state prediction model to generate the power equipment state prediction result for the next time period. This invention can perform power equipment state prediction.

The invention discloses a semi-supervised domain self-adaptive deep forgery detection method, relates to the technical field of forgery detection, and solves the technical problem that a model cannot fully adapt to data distribution of a new domain due to the fact that a small amount of annotated data and a large amount of unannotated data in a target domain are generally difficult to use at the same time in an existing method. The method comprises the following steps: performing multi-batch training on a deep neural network model by adopting samples, wherein each batch of samples comprise a source domain labeled sample, a target domain labeled sample and a target domain unlabeled sample; in the training process, a joint loss function is optimized by adopting stochastic gradient descent, network parameters are iteratively trained, any input image is detected by using a trained Xception model, and the probability that the image is real or forged is output; according to the method, a semi-supervised field adaptive framework is introduced, so that the feature distribution difference between a source domain and a target domain is effectively reduced, and the cross-domain generalization ability is remarkably enhanced.

The application provides a behavior recognition model training method based on time-frequency fusion enhancement, comprising: A1, obtaining a training set, wherein original samples are sensing data, and labels indicate the behavior categories of human bodies when the corresponding sensing data is collected; A2, dividing the training set into multiple batches, and iteratively training a feature extractor of a behavior recognition model in batches, wherein each batch training comprises: A21, respectively performing time domain enhancement and frequency domain enhancement on each original sample and then fusing, A22, based on each original sample and the corresponding time-frequency enhanced sample, training the feature extractor to extract sample features according to the input sample through a contrast learning manner, and narrowing the distance between the sample features of the original sample and the corresponding time-frequency enhanced sample and widening the distance between the sample features of the original sample and other samples; A3, obtaining a behavior recognition model comprising the feature extractor trained in step A2 and a classifier, and performing classification training on the behavior recognition model by using the training set.

The application discloses a model training data construction method and device, which can be applied to various scenes such as cloud technology, artificial intelligence, intelligent transportation and Internet of Vehicles. The method comprises the following steps: obtaining a set of original object images and a set of object wearing images corresponding to a virtual wearing object in a virtual object display platform; extracting object key point information corresponding to each object wearing image in the set of object wearing images and a wearing object category corresponding to each object wearing image; determining an object wearing image with a matching result representing a successful matching result in the set of object wearing images as a preliminary screening wearing image; screening an image meeting a preset condition from the preliminary screening object wearing image to obtain a target wearing image; and constructing a training data set of the virtual wearing object based on the set of original object images and the target wearing image. The application realizes rapid and accurate construction of a batch training data set of the virtual wearing object.

This paper presents a graph neural network node classification method that integrates meta-learning and mini-batch training, belonging to the field of information technology. First, the method utilizes the METIS algorithm to divide the original large-scale graph data into multiple non-overlapping connected subgraphs. Then, by constructing a hybrid selection mechanism based on label coverage and label entropy, subgraphs with high information content and strong representativeness are selected from the subgraph pool as the meta-learning task. Subsequently, iterative training is performed on the selected subgraphs using the meta-learning framework to capture the general prior features of the graph structure, thereby obtaining a set of initial parameters for the model with rapid adaptability. Finally, these optimized initial parameters are transferred to the mini-batch training stage on the full dataset, guiding the model to achieve rapid convergence through high-quality initialization. On large-scale benchmark datasets, this method significantly reduces the number of iterations required by the model while maintaining the same classification accuracy as current mainstream graph neural network models, thus greatly shortening the overall training time.

The invention provides a model training method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence system performance optimization and deep learning framework scheduling. The method comprises the following steps: in a current batch training process of a target model, acquiring execution information of a corresponding bottom kernel when a processor executes a plurality of computational operators of the target model; according to the execution information, determining a global key path, related to the total calculation duration, of the target model in the current batch training process, and identifying target operators forming training iteration delay in calculation operators contained in the global key path; and generating a delay optimization instruction for the target operator, and in response to the delay optimization instruction, adjusting training configuration parameters corresponding to the target operator in an upper-layer training framework of the processor so as to apply the adjusted training configuration parameters in a subsequent batch training process of the target model. According to the scheme, the training performance of model training can be improved.

Systems, methods, and computer-readable media provide a graph processing system that incorporates a graph neural network (GNN) based recommender system (RS), as well as a method for training a GNN based RS to address feature leakage that leads to overfitting of the trained GNN based RS. A message correction algorithm is used to modify a user node embedding and a positive item node embedding generated by the graph neural network when generating mini batches of training triples used to train the GNN based RS. The GNN message passing operations are performed on one graph only, in contrast to existing approaches which typically run GNN message passing operations on multiple adjusted input graphs constructed for multiple training triples.

The invention discloses a long-tail radiation source individual identification method and device based on field generalization and a storage medium. The core of the method is that a plurality of types of source domain data sets distributed in a long-tail mode are obtained; sampling to obtain batch training data; after extracting feature representation by using a feature extractor, inputting the feature representation into a classifier and a domain discriminator at the same time; based on a classification prediction result, combining a field prediction result of a gradient inversion mechanism and class balance loss to construct an integrated overall objective function; all model parameters are jointly optimized by minimizing the target function, and an optimized recognition model is obtained and is finally used for performing high-precision individual recognition on target domain radiation source signals. Through collaborative optimization of classification precision, category balance and domain generalization ability, the problem of insufficient recognition performance of the model on an unknown target domain in a complex scene with training data long tail distribution and domain offset is effectively solved.

This invention relates to the field of fluid mechanics and transient process analysis of hydraulic systems, and discloses a method for determining the occurrence boundary and risk level of liquid column separation. The method utilizes a visual test bench to conduct multiple experiments under different combinations of test parameters, collecting data and simultaneously recording the liquid column separation state. Based on Buckingham's π theorem, dimensional analysis is used to construct multiple independent dimensionless parameter sets from the test parameter combinations. These are used as input features, with the separation state as the target variable. A machine learning algorithm is introduced for multiple batch training to calculate importance scores, selecting the dominant dimensionless parameter with the highest and most stable score. Subsequently, regression analysis is used to fit the data boundary, solving for the intersection point and outputting the critical equilibrium point as a quantitative benchmark for the occurrence boundary. After confirming separation, high-scoring dimensionless parameters are selected to construct composite judgment parameters, which are compared with preset thresholds to classify the risk level. This invention can accurately define the critical equilibrium point of cavitation occurrence and quantitatively assess its risk severity.

The invention relates to a multi-teacher mixed distillation method and system and computing equipment, and the method comprises the following steps: evaluating and screening teacher models, and obtaining pre-training models with cross-domain migration potential to form a teacher set; selecting a lightweight neural network as a student model, and configuring an independent MLP mapping layer for each teacher model in the teacher set to adapt to an output dimension; sampling is carried out in a pre-training data set of each teacher model, and mixed batch training data containing multi-field samples is constructed; and inputting the mixed batch training data into the student model, outputting a prediction result through an MLP mapping layer, and constructing a loss function in combination with teacher model output to complete distillation training. The student model obtained by the invention is no longer limited to a certain specific scientific field or a certain data feature, but becomes a relatively general scientific time sequence signal analysis model, and shows excellent performance in multiple fields.

The application provides a kind of twin-screw oil pump on-line monitoring mechanical fault real-time diagnosis method, comprising: obtaining the fault data of twin-screw oil pump to be trained;Data preprocessing is carried out for each parameter attribute related to the fault;Based on the corresponding input attribute of the corresponding node position of the neural network, the fault related data to be processed is displayed;Get the initial network model trained;Align the fault data node parameters in the target optimization parameters;After the weight is optimized using L-BFGS algorithm, supervised training is carried out, and the BP neural network model trained is obtained;The completed BP neural network model is used to train the fault related data in real-time database in batches, and finally the result of accurate prediction is achieved.The twin-screw oil pump on-line monitoring mechanical fault real-time diagnosis method can greatly reduce the production accident rate of twin-screw oil pump, improve the economic efficiency of enterprise production, and protect the life safety of enterprise workers.

The invention provides a virtual training method, system and device, electronic equipment and a storage medium. According to the method, the acquired training instruction is responded, the virtual scene corresponding to the training instruction is loaded, the operation instruction is acquired through the input device, the input device can be a mouse or a keyboard or other devices, VR equipment or other high-cost devices are not needed in the training process, and batch training can be achieved; obtaining an operation instruction for any second entity virtual object; wherein the operation instruction comprises an operation position relation between the second entity virtual object and the first entity virtual object; and generating a training report according to the position relationship corresponding to each second entity virtual object and the sequence of obtaining the operation instruction of each second entity virtual object. According to the virtual training party disclosed by the invention, not only is the result monitored, but also the assembly process is finely monitored, so that the training efficiency is improved to a certain extent.

The application discloses a differential privacy model training method based on a buffer mechanism, a medium and a system, wherein the method comprises the following steps: sampling a training data set to obtain small-batch training data; calculating a gradient value and processing the gradient value to generate a private gradient, which is applied to a current model parameter to obtain a preselected weight; sampling a verification data set to obtain small-batch verification data; calculating a loss change value, and performing clipping and noise adding processing on the loss change value to generate a noisy loss change value; judging whether the noisy loss change value is less than a preset rejection threshold; if yes, adding the candidate weight to the buffer; when the number of candidate weights is equal to a preset number threshold, determining the optimal weight according to the relative noisy loss change value, and updating the current model parameter based on the optimal weight; the method can effectively protect privacy, improve the updating quality of the model in the training process, and improve the accuracy of the final prediction result.

The application discloses a general domain self-adaptive image classification model implementation method and system, wherein the method comprises the following steps: setting a source domain image dataset with classification labels and a target domain image dataset different from the source domain data distribution and without classification labels as image data for classification model training; training closed set and open set classifiers in the source domain classification model, outputting classification probabilities belonging to each category of the source domain through the closed set classifier, outputting in-class and out-of-class classification probabilities and maximum open set entropy through the open set classifier; determining the most confused categories of the target domain from the maximum open set entropy and setting a transition zone, separating simple samples outside the transition zone with a self-confidence higher than a preset first threshold value and difficult samples inside the transition zone with a self-confidence lower than a preset second threshold value; and performing batch training on the classification model by combining a cross-entropy loss, a selection optimization strategy loss and a neighbor aggregation strategy loss function, so as to obtain a final general domain self-adaptive image classification model.

The invention relates to the technical field of artificial intelligence, and particularly provides a forklift tray tracking method, system and device based on a lightweight model and a medium, and the method comprises the steps: constructing a rotating target data set of a standard tray, and carrying out the targeted data enhancement and preprocessing; secondly, performing three-point improvement on a YOLOv12 algorithm: introducing a brand new StarNet backbone network, constructing a high-dimensional implicit feature space through star operation, and improving feature expression capability while reducing parameter quantity; a dynamic hybrid convolution module is designed in the neck network, multi-scale features are adaptively extracted by using multi-branch deep convolution and a dynamic weight fusion mechanism, and the flexibility of the model is enhanced; and a lightweight rotation detection head is provided, and the rotation angle of the tray is efficiently predicted while the parameter quantity is reduced and the small-batch training stability is improved through application group normalization and a shared convolution structure. And finally, combining the improved detection model with a ByteTrack tracking algorithm of the optimized adaptive rotating frame to form a complete identification tracking system.

The application provides a pipeline parallel distributed training method, device and system for a deep neural network. The method comprises: sequentially transmitting each group of small batch training data constituting a current batch of training data to an edge server via an optical network, so that the edge server and the cloud server cooperatively perform asynchronous parallel cooperative training on different sub-task models constituting the deep neural network in a communication and training decoupling manner, and the edge server sequentially outputs the gradients corresponding to each group of small batch training data; and sequentially receiving the gradients of each group of small batch training data. The application can ensure correct transmission of data during model training, reduce communication overhead during training, achieve load balancing between devices, improve model training efficiency, effectiveness and resource utilization of devices participating in training.

This invention belongs to the field of domain adaptation technology for ultra-high-definition (UHD) images. It proposes a multi-granularity sensing and prototype-driven UHD image domain adaptive recognition method, comprising the following steps: First, acquiring a cross-image domain conventional image dataset and an UHD image dataset, and preprocessing both datasets to obtain a preprocessed batch training image dataset; second, constructing and initializing a multi-granularity feature extractor and a feature classifier; then, training an unsupervised domain adaptive recognition model for UHD images based on the batch training image dataset and combining the multi-granularity feature extractor and the feature classifier; finally, inputting the UHD image to be recognized into the unsupervised domain adaptive recognition model for classification to obtain the recognition result. This invention solves the problems of neglecting fine-grained features in UHD images and the difficulties, limited quantity, and high computational resource consumption of UHD image domain annotation.

The invention relates to the technical field of financial risk management, and discloses a financial risk intelligent management method based on deep learning. The method comprises the following steps: acquiring multi-dimensional financial data such as market transaction, user credit, asset mobility and macroeconomic indicators, and optimizing a data acquisition process; extracting features by using a deep belief network to obtain optimized feature data; and dividing batches according to the optimized feature data, analyzing a risk trend through small-batch training of a deep neural network, and obtaining a risk assessment index. And based on the index, adjusting parameters and weights of the graph neural network by using a Bayesian optimization algorithm, and generating an optimization model. Receiving a real-time financial data flow and an optimization model, searching and deducing a risk path through a Monte Carlo tree, and outputting a risk control decision signal; a near-end gradient method and a branch and bound method are combined to optimize asset configuration to realize risk hedging; and finally, tracking the state after risk control by using particle filtering, and feeding back to a relevant link adjustment strategy.

The invention discloses a reinforcement learning training method and device of a model, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence such as machine learning. According to the specific implementation scheme, a batch of training data sets are obtained, wherein the training data sets comprise multiple pieces of training data; based on a pre-configured hyper-parameter, segmenting the training data set to obtain a plurality of micro-batch training data groups; carrying out asynchronous parallel processing on the generation tasks and the reward calculation tasks of the plurality of training data groups; and performing parameter adjustment on the model based on task processing results of the plurality of training data groups.

Systems and methods are disclosed that perform efficient training of a graph neural network (GNN) using graph structure-aware randomized mini-batching. For example, nodes from a graph may be obtained. Subsequently, the nodes of the graph may be grouped into communities and then the order of the communities as well as the nodes within each of the communities may be shuffled. Based on shuffling the order of the communities and the nodes within the communities, mini-batches for training the GNN may be determined. Following, based on a sampling bias, a sub-graph may be constructed for each of the mini-batches to obtain a plurality of sub-graphs. The sampling bias may indicate a bias for sampling intra-connections instead of inter-connections. After, the GNN may be trained based on the constructed sub-graphs.

The application discloses a kind of multimodal data distributed training method and system, it is related to robot field, including: each configuration data pool is equally divided into N shards, and the shard with same serial number in different configuration data pool is distributed to the same computing node;Each computing node loads corresponding shard data subset according to the shard serial number distributed;Each computing node independently executes modal decision;According to modal decision result, corresponding data loader is selected to load homogeneous batch training data of corresponding mode from the data subset of the node;Each computing node sequentially executes forward propagation and back propagation to homogeneous batch training data, and obtains the gradient of each node;Global gradient fusion is carried out to the gradient of all computing nodes, and global gradient is obtained;All computing nodes update model parameters using global gradient.The application is physically isolated from the forward interference and Loss scale conflict between different modal data by shard scheduling and isolated training.

The present disclosure provides a training method of a deep learning model, relates to the technical field of artificial intelligence, in particular to deep learning and computer vision technology, and can be applied in the smart city scenario. The specific implementation scheme is as follows: determining at least one image block containing a target object from each original image in a plurality of original images for a batch training, obtaining an image block set; for each original image, generating an enhanced image of the original image according to the image block set; for each original image, adding interference to the original image according to an output result of the deep learning model for the original image, obtaining an adversarial image of the original image; and training the deep learning model using the enhanced image and the adversarial image of each of the plurality of original images, obtaining a trained deep learning model. The present disclosure also provides an image processing method and device, an electronic device and a storage medium.

The application relates to the technical fields of machine learning, artificial intelligence and medical health, and proposes a pre-training classification model training method, device, equipment and medium, which comprises the following steps: acquiring a batch training sample set; determining a target label category contained in the batch training sample set; expanding the batch training sample set according to the target label category and an initial label embedding layer corresponding to the target label category, to obtain an expanded batch training sample set; and training a pre-training classification model and the label embedding layer by using a contrast learning loss function according to the expanded batch training sample set, to obtain a trained target classification model and a target label embedding layer. Through the technical scheme, the model training process is simplified, overfitting is avoided, and the generalization effect is improved.

The invention relates to a color difference recovery model training method and device. The method comprises the steps of performing multi-batch training on a color difference recovery model, and training the color difference recovery model through joint training data in the training process of each batch to obtain a model output image; obtaining an updated color difference recovery model corresponding to the current training batch based on the joint loss function, the model output image and the joint training data; under the condition that a preset training completion condition is not met currently, processing the joint loss function based on a loss function weight corresponding to the current training batch to obtain a processed joint loss function; and a trained color difference recovery model is obtained. Through adoption of the method, training is performed under the condition of considering physical priori knowledge, and a light-weight chromatic aberration recovery model with relatively high generalization ability, relatively high chromatic aberration recovery accuracy and relatively good image quality can be obtained.

The invention discloses a training method of a knock signal defect recognition model, relates to the technical field of intelligent nondestructive testing and signal mode recognition, and can at least partially solve the problem of weak minority class recognition capability caused by strong subjectivity of manual interpretation, insufficient robustness of manual features and unbalanced class samples in the prior art. The method comprises the steps that knocking signal samples are acquired and subjected to category labeling, and a sample set is constructed; event detection, peak value alignment, fixed-length segmentation and preprocessing are carried out on the knocking signal sample; dividing a training verification test set and constructing small-batch training data streams; inputting the preprocessed sample into a one-dimensional deep convolutional neural network for multi-layer feature extraction to obtain a feature vector; inputting the feature vectors into a classification head and outputting probability distribution through a Softmax classifier; a cross entropy loss function is combined with an optimization algorithm for training until convergence; and outputting the deployable model. According to the invention, end-to-end automatic feature learning and multi-class defect discrimination are realized.

The application discloses a kind of training methods of knock signal defect identification model, it is related to intelligent nondestructive testing and signal mode identification field, it can at least partially solve the problems that the subjectivity of artificial interpretation is strong, manual feature robustness is insufficient in prior art, class sample imbalance leads to the weak recognition ability of minority class.The present application comprises: obtaining knock signal sample and carrying out class labeling, construct sample set;Knock signal sample is detected, peak alignment, fixed-length segmentation and pre-processing are carried out;Divide training verification test set and construct small batch training data stream;The pre-processed sample is input into one-dimensional deep convolutional neural network to extract multi-layer features, and a feature vector is obtained;The feature vector is input into the classification head and the probability distribution is output by the Softmax classifier;Cross-entropy loss function is used in combination with optimization algorithm for training until convergence;Output deployable model.The present application realizes end-to-end automatic feature learning and multi-class defect discrimination.