26results about How to "Efficient training process" patented technology

This invention discloses a D2D user resource allocation method and storage medium based on a deep reinforcement learning algorithm, relating to the field of wireless communication technology. The method includes: constructing a wireless network model and discretizing the D2D transmit power; constructing a user signal-to-noise ratio calculation model with maximizing the communication system throughput as the optimization objective; setting a prediction policy network π, a prediction value network Q, a target policy network π′, and a target value network Q'; modeling the D2D communication environment as a Markov decision process, treating the D2D transmitter as an agent, iteratively loading the parameters of the target policy network to generate a policy that interacts with the environment, determining the state space, action space, and reward function; using the MAAC algorithm to optimize the policy for each D2D user; using a soft update method to iteratively update the parameters of the target policy network and the target value network until training is complete; and having the D2D user download the trained parameters of the target policy network and improve their policy.

A robustness measurement method for LeNet-5 networks based on adversarial boundary constraints is presented, relating to the field of deep learning model testing. The main steps include: for each training sample, dynamically generating adversarial examples based on the model's current state during training iterations; constructing a composite loss function based on standard cross-entropy loss and dynamic boundary constraint loss; performing end-to-end training on all parameters of the LeNet-5 network; and using the overall approximate robustness boundary as a measure of model robustness after training. This method effectively improves the resistance of the LeNet-5 model to fast gradient sign-based adversarial attacks without altering the basic structure of the LeNet-5 network by designing a new loss function.

This invention relates to the field of space mission planning technology, and provides a space multi-objective mission planning method that integrates reinforcement learning and curriculum learning. The method includes: constructing a space multi-objective mission simulation environment comprising an orbital dynamics model, an objective characteristic model, and a spacecraft constraint model; designing a progressive task sequence for agent training based on a curriculum learning method; and training the agent in the simulation environment using the progressive task sequence based on a deep reinforcement learning framework, updating network parameters by collecting experience data, and finally obtaining the optimal sequence decision-making strategy. This invention solves the technical problems of low decision quality, poor training efficiency, and weak policy adaptability caused by complex environments and sparse rewards in space multi-objective mission planning by guiding reinforcement learning through curriculum learning for stable and efficient training.

This invention provides a clustered federated multi-task learning method and apparatus for the Internet of Things (IoT). By clustering IoT terminal devices, the data distribution within the same cluster becomes more approximate. A federated multi-task learning algorithm is executed within each cluster, with global training and personalized training tasks performed on each IoT terminal device. This achieves data sharing within the cluster while fully utilizing local data from each IoT terminal device for training on personalized tasks, thus efficiently utilizing local data and improving training effectiveness. During local training on each IoT terminal device, the number of training rounds is adjusted based on computing power, fully utilizing the computing resources of each IoT terminal device and improving model training efficiency. Regularization constraints applied to personalized training tasks using the global model effectively prevent overfitting, control the degree of personalization, and improve model quality.

According to the method for training the target model based on reinforcement learning, the target model is used for executing tampering detection on an input image, and the method comprises the steps that a training sample is obtained, the training sample comprises a target image and boundary information of a plurality of truth value boxes, and the truth value boxes are borders of a part of images marked in the target image and comprising tampering content; and inputting the target image into the target model, and outputting boundary information of the plurality of prediction frames. And calculating the overall overlap ratio of the plurality of prediction frames and the plurality of true value frames. Determining a first reward score based on a preset piecewise function according to a numerical range of the overall overlap ratio; the function corresponding to each numerical range of the piecewise function is a constant function, and the value of the corresponding constant function is increased along with the improvement of the overall overlap ratio. And based on a reinforcement learning algorithm, according to the first reward score, updating the target model.

An alloy performance prediction method fusing vision-language-process multi-modal data comprises the steps that a multi-modal data set of an alloy material is constructed, and the multi-modal data set comprises three kinds of modal input including process parameters such as temperature and time of solid solution and aging treatment, SEM image visual information and SEM image description text information and corresponding alloy mechanical property true values; training a visual encoder ResNet50 model through comparative learning and training a language encoder BERT model through mask language modeling to obtain an SEM image and vector codes of language description of the SEM image; splicing and fusing process parameters such as temperature and time of solid solution and aging treatment and the vector codes obtained in the second step, and training a random forest regression device according to corresponding alloy mechanical properties; and the random forest regression device obtained through training is used for alloy performance prediction. According to the method, the structured process data, the unstructured SEM image data and the derived text description data are subjected to collaborative fusion and joint modeling for the first time, complementarity among different modal data is fully utilized, and more comprehensive and more three-dimensional digital representation of the alloy state is constructed; the multi-modal fusion framework and the feature learning mechanism are suitable for wide material systems. Meanwhile, the adopted'depth representation + random forest 'hybrid model has the advantage of high training efficiency while ensuring high prediction precision.

The present application relates to the technical field of artificial intelligence and federated machine learning, and particularly relates to an asynchronous federated learning aggregation method and system based on clustering cache. The present application clusters clients by intermediate features of the clients. In global aggregation, intra-cluster aggregation is firstly performed, and then global aggregation is performed based on client clusters. The client clusters are dynamically updated. In intra-cluster aggregation, an active set and a slow set are introduced to realize an asynchronous participation mechanism of intra-cluster aggregation. Weighted aggregation of the active set and the slow set solves the problem of system heterogeneity. The present application solves the defect of insufficient timeliness of existing federated learning methods in a data heterogeneous scene, and guarantees the timeliness and model training accuracy of federated learning in the data heterogeneous scene.

The invention discloses a long text recognition method and device based on a Transform architecture, a medium and a product, and relates to the field of text recognition, the method comprises the following steps: constructing a long text recognition model based on the improved Transform architecture; the improved Transform architecture is characterized in that an attention module is replaced by a linear attention module; the execution process of the linear attention module is as follows: performing linear transformation on the current input of the current position; determining an output vector based on the memory state of the previous location and the current input; determining an input gate and a forgetting gate based on the memory state of the previous position and the current input, and updating the memory state; using the long text data set to train a long text recognition model based on a lexical weighting method; and lexical element recognition is carried out based on the trained long text recognition model. According to the method, the limited storage space can be utilized more efficiently, and the performance on the long text task is more effectively improved.

The invention discloses a large model training and reasoning-oriented containerized operation method and computing equipment, and the method comprises the steps: receiving a training starting request or reasoning starting request for a large model, and determining a training parameter corresponding to the training starting request or a reasoning parameter corresponding to the reasoning starting request; for the training starting request, creating a trainer instance and introducing training parameters, starting a training container through the trainer instance based on the training parameters, and running a training program through the training container so as to execute a large model training task based on the training program; and for the inference starting request, creating an inference device instance and transmitting inference parameters, starting an inference container based on the inference parameters through the inference device instance, and running an inference program through the inference container so as to execute a large model inference task based on the inference program. On the basis, training and reasoning tasks of the large model can be efficiently and conveniently executed in a containerized environment, and an embeddable containerized operation capability can be provided for a model development platform, so that other platforms can conveniently and rapidly integrate the development capability of the large model.

The invention provides a work memory ability training method based on electroencephalogram neural feedback, a storage medium and equipment, and the method comprises the steps: obtaining electroencephalogram signal data of a tested object in a process of executing a training task, and obtaining task execution result data of the tested object, the training task at least comprising a digital memory breadth task and a spatial memory breadth task; extracting physiological features of the electroencephalogram signal data, and calculating a cognitive state index and a global cognitive ability index according to the physiological features and the task execution result data; the training task is adjusted according to the global cognitive ability index, and the tested object is trained again according to the adjusted training task; and when it is judged that at least one of the training times, the global cognitive ability index and the cognitive state index meets a preset training condition, generating a training report at least including the cognitive state index of the tested object during each training. The computing resource consumption, the training delay and the deployment cost in the multi-dimensional training of the working memory ability are reduced.

The invention discloses a cigarette carton missing detection method for a cigarette carton box. The method comprises the following steps: S1, collecting image data of the cigarette carton through a plurality of cameras when the carton is opened; s2, performing illumination adjustment on the acquired image data, and applying image enhancement processing to generate an enhanced image; s3, inputting the enhanced image into a target detection model, detecting whether the cigarette bar is missing, outputting a cigarette bar bounding box and confidence coefficient by the target detection model through extracting multi-scale features of the image and applying attention weight calculation, and judging a bar missing state according to a confidence coefficient threshold value; and S4, when it is detected that the cigarette bar is missing, triggering a data recording operation, recording related data of a missing event, including a timestamp, a box body number and an image fragment, and packaging, transmitting and storing the data. Through the steps of collecting images by multiple cameras, adjusting illumination, integrating a target detection model of an attention mechanism and automatically recording and transmitting data, the accuracy of detecting the carton lacking of the cigarette box of the cigarette piece is remarkably improved.

The invention relates to generating audio using autoregressive generative neural networks. Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a prediction of an audio signal. One of the methods includes: receiving a request to generate an audio signal; obtaining a semantic representation of the audio signal; generating an acoustic representation of the audio signal conditioned by at least the semantic representation using one or more generative neural networks; and processing at least the acoustic representation using a decoder neural network to generate a prediction of the audio signal.

This invention discloses a method for rapid prediction of data center temperature fields based on convolutional neural networks, comprising: acquiring the geometric information of the data center structure; performing geometric modeling and mesh generation on the data center structure to obtain the mesh structure of the data center; acquiring the operating parameters of the equipment within the data center; acquiring the temperature field data of the data center and dividing the temperature field data according to a height plane to generate the output dataset for training the convolutional neural network model; converting the mesh structure of the data center at different heights into a spatial feature map structure and recording the corresponding height information and operating parameter information to generate the input dataset for training the convolutional neural network model; and using the trained convolutional neural network model for temperature field prediction. This method can achieve accurate and reliable rapid prediction of the temperature field of data center rooms.

The invention discloses a data expansion and exoskeleton joint end-to-end torque estimation method based on a diffusion model, and the method comprises the steps: carrying out the normalization processing of time series data from a multi-source sensor, carrying out the fragmentation according to a fixed length, and constructing a training sample with a motion class label; then, training a classifier-free conditional diffusion model by adopting a sample, and simultaneously learning conditional and unconditional denoising mapping relationships in a manner of randomly inactivating category conditions; in the generation stage, based on a classifier-free condition guidance mechanism, multi-modal time series data with specified motion category features are gradually generated from random noise; and finally, fusing the generated synthetic data with real acquired data to train a joint torque end-to-end prediction network, thereby realizing joint torque estimation of input sensor time sequence data. The method improves the prediction precision, generalization ability and stability of the end-to-end torque estimation model in a multi-action and few-sample scene, and has a good engineering application value.

The invention provides a distributed edge machine room computing power integration method and system based on a DPU, and relates to the field of processors, and the method comprises the steps: transmitting parameter copy management data and consistency control function data to a DPU layer corresponding to a DPU card deployed in an edge machine room, and obtaining a target DPU layer; constructing a hardware hierarchical communication topology combining aggregation in the machine room and cross-machine room controlled transmission in the target DPU layer to obtain a target DPU, and executing bandwidth sensing, QoS scheduling, cross-machine room parameter coordination and traffic shaping control through the target DPU; the gradient processing and parameter synchronous control logic data corresponding to the edge machine room are transmitted to a target DPU, and hardware-level unloading and cross-machine-room intelligent scheduling are carried out through the target DPU; gPU resource virtualization and network virtualization control are carried out through the target DPU, so that the multiple edge machine rooms form a unified training resource pool logically, and a distributed edge machine room computing power integration result is obtained.

This application relates to a point cloud data completion method, apparatus, computer device, readable storage medium, and program product. The method includes: extracting features from the point cloud data to be completed at the current moment to obtain point cloud features of the point cloud data to be completed; temporally fusing the historical fused point cloud features from the previous moment with the point cloud features to obtain fused point cloud features at the current moment; identifying key points based on the fused point cloud features at the current moment to obtain key point identification results; and generating target point cloud data based on the key point identification results and the point cloud data to be completed. This method can improve the quality of point cloud completion.

The invention provides a transient voltage dynamic prediction system and method for a direct current sending end system based on an LSTM network, and belongs to the technical field of transient voltage prediction.The method comprises the steps that electrical quantity data of the direct current sending end system are collected in real time through a synchronous phasor measurement unit, and data preprocessing is executed; extracting time domain and frequency domain features, and screening out key feature subsets having significant influence on transient voltage prediction; constructing an LSTM deep learning model, and setting model hyper-parameters; training the model by using the training set, adjusting parameters to minimize a prediction error, and evaluating and optimizing the model by using the test set; and preprocessing electrical quantity data acquired in real time, inputting the preprocessed electrical quantity data into the trained LSTM deep learning model, predicting a transient voltage value at a future moment, and outputting the transient voltage value to an electrical control system. By adopting the LSTM network-based transient voltage dynamic prediction system and method of the direct current sending end system, the problems of low prediction precision and slow response speed in a traditional prediction method are solved.

This invention discloses a method, apparatus, computer device, and storage medium for multi-parameter identification of permanent magnet synchronous motors, relating to the field of motor control technology, and aimed at solving the problem of severe parameter coupling in traditional online motor parameter identification methods. The method includes: inputting the acquired dataset into an initial parameter identification model; in the initial parameter identification model, determining predicted motor parameters based on motor operating parameters, and determining the predicted voltages of the direct and quadrature axes corresponding to the predicted motor parameters; adjusting the parameters in the parameter identification model based on the loss value of the total loss function until preset conditions are met, thus obtaining a target parameter identification model; and inputting the acquired motor operating parameters of the target motor into the target parameter identification model to obtain the target motor parameters.

The invention discloses a power cable partial discharge positioning method and system based on Transform and end-to-end regression. The method comprises the following steps: acquiring and preprocessing a cable partial discharge original time domain signal; calculating multi-order cumulative sum features of the preprocessed signal, and splicing the preprocessed signal with each order of cumulative sum feature in a channel dimension to construct a multi-channel input tensor; inputting the multi-channel input tensor into a pre-trained multi-task deep learning model, and synchronously outputting partial discharge pulse and reflection pulse position probability distribution and a continuous scalar value representing the physical distance of a discharge point through one-time forward propagation based on a Transform architecture; and outputting the continuous scalar value as a positioning distance, and verifying a positioning result based on the probability distribution. According to the invention, end-to-end accurate mapping from the original waveform to the physical position is realized, the positioning precision is high, the anti-noise capability is strong, and the automation degree is high.

The application discloses a kind of based on global topology relation diagram's recommended system knowledge graph induction completion method, it is related to knowledge graph completion technical field.The method constructs global relation diagram by constructing user and article entity and its relation in e-commerce system.Constructs comprehensive relation embedding by adopting multi-head attention mechanism learning relation embedding and through adaptive weighting strategy.The comprehensive relation embedding is combined with original knowledge graph, and the attention score of target entity and adjacent entity is calculated using dynamic attention mechanism, and the user and article embedding are updated.Negative sample is generated by replacing user or article entity in recommended triple, and the positive and negative samples are scored using the scoring function, and the model is optimized.The recommendation task is converted into target article prediction, and the user preference value is returned through the trained model and the scoring function, and the highest scoring article is selected as the recommendation result.The method is suitable for cold start and long tail scenarios, can efficiently complete the knowledge graph of e-commerce recommendation system, and is suitable for offline or quasi-real-time deployment.

This invention provides a method, apparatus, device, and medium for detecting obstructive sleep apnea in children. The method includes: filtering an initial feature set using a recursive feature elimination method under nested cross-validation to obtain a target feature set; acquiring input data from a training sample set based on the names of the target features in the target feature set, and training N target machine learning models using the input data; extracting target feature data from the feature data of the object to be detected based on the names of the target features in the target feature set; inputting the target feature data into the N target machine learning models after training; and determining the detection result corresponding to the object to be detected based on the output results of the N target machine learning models. This invention can improve the sensitivity and specificity of detection, reduce the risk of missed or misdiagnosed cases, and lower data collection costs and clinical implementation barriers.

The invention provides a vehicle motion track prediction method and system based on large language model semantic guidance, and belongs to the field of automatic driving vehicle motion prediction. Comprising the following steps: collecting own vehicle data, roadside equipment data, participating vehicle data and map data, and carrying out multi-view space-time alignment and vectorization feature extraction to obtain position features, motion features and map features of each vehicle under the same coordinate system; inputting the features into GNN to generate a perception vector; inputting the perception vector into a lightweight neural network to obtain a motion prediction vector; generating a high-dimensional motion prediction vector of the predicted vehicle by using the LLM in combination with the position features of the region near the predicted vehicle; determining a final prediction vector according to the high-dimensional motion prediction vector and the motion prediction vector, and decoding the final prediction vector and the perception vector to obtain a final prediction track of the predicted vehicle; and after determining that the lightweight neural network has the LLM reasoning capability by using the loss adjustment parameter, stopping calling the LLM to predict the trajectory.

Embodiments of the present specification provide a generation model training method and device, wherein the generation model training method comprises: obtaining an original image; performing diffusion processing on the original image, and performing attenuation processing on an image component of the original image to obtain a set of noisy images; determining a noisy image according to the original image and the set of noisy images, and inputting the noisy image into an initial generation model for processing to obtain a restored image; and adjusting the initial generation model based on the restored image and the original image until a target generation model that satisfies a training stop condition is obtained. During the diffusion and inverse diffusion processing, the attenuation processing of the image component is combined, so that the generation model can learn the image change process in different dimensions, thereby effectively improving the model training precision.

The application provides a kind of multi-agent risk perception security computing method based on federal reinforcement learning, belong to wireless communication and information security field.This method provides a kind of multi-agent joint framework based on federal reinforcement learning, let vehicle according to the dynamic characteristics of internet of vehicles environment, autonomous selection computing mode.In "safety first" mode, federal server as agent observes current channel quality, vehicle's historical federal participation rate and last time participation state and other parameters, and uses safety reinforcement learning algorithm to select vehicle participating in this round of federal training.In "efficiency first" mode, vehicle as agent, does not participate in federal training, observes current channel quality and edge node available resources and historical service quality, and uses multi-agent safety reinforcement learning algorithm to select edge node for computing task offloading.This method can take into account the data security and computing efficiency of vehicle, and reduce vehicle energy consumption and task computing delay.

The invention provides a model training method and device and related equipment, and is applied to a first client, the method comprises the steps that model parameters of a global model are received, the model parameters of the global model are obtained based on aggregation of model parameters of K local models of K clients, the K clients comprise the first client, and the K local models are selected from the first client; k is a positive integer greater than 1; updating the local model of the first client based on a weight parameter, a hierarchical parameter, a model parameter of the global model and a model parameter of the local model of the first client; the weight parameter is used for aggregating the model parameter of the global model and the model parameter of the local model of the first client, and the hierarchy parameter is used for indicating a hierarchy range for aggregating the global model and the local model of the first client; and training the updated local model of the first client based on the data of the data center corresponding to the first client.

A multi-class grasping detection incremental learning method emphasizing task relationship, in the training stage, a teacher network for grasping detection is constructed and trained using grasping detection dataset in turn, a student network for semantic segmentation and grasping detection is constructed and trained using small semantic segmentation dataset, and the grasping detection function of the student network is supervised and protected by the teacher network based on a task relationship function; in the detection stage, the real-time collected RGB image is input into the trained student network to obtain the grasping detection frame with category information and the semantic segmentation mask corresponding to the RGB image. The present application can efficiently train a multi-class grasping detection network to realize multi-class grasping detection in engineering.