45 results about "Process training" patented technology

The invention discloses a polyester knitted fabric surface antistatic treatment method and system, and relates to the technical field of polyester knitted fabrics, the method comprises the following steps: determining a treatment process scheme according to a target application scene, calling a sample process record, and constructing a standard process training set; constructing and training an antistatic performance prediction model based on the standard process training set in combination with a knowledge distillation mechanism; a cost evaluation factor is constructed, a process evaluation function is defined in combination with the antistatic performance prediction model and the cost evaluation factor, and the process evaluation function is associated with a penalty space; and by taking the process evaluation function as an optimization target, carrying out adaptive capacity expansion and shrinkage iterative optimization on the treatment process parameters, determining a target process parameter combination for the polyester knitted fabric according to an iterative optimization result, and carrying out surface antistatic treatment. Therefore, the technical effects of reducing the parameter adjustment cost, improving the adjustment efficiency and enhancing the anti-static treatment flexibility are achieved.

A computer implemented method of managing a first model that was trained using a first machine learning process and is deployed and used to label medical data. The method comprises determining (202) a performance measure for the first model, and if the performance measure is below a threshold performance level, triggering (204) an upgrade process wherein the upgrade process comprises performing further training on the first model to produce an updated first model, wherein the further training is performed using an active learning process wherein training data for the further training is selected from a pool of unlabeled data samples, according to the active learning process, and sent to a labeler to obtain ground truth labels for use in the further training.

The invention discloses a bearing fault detection method based on knowledge distillation. The method comprises the steps of obtaining a bearing operation process training data set; preprocessing the acquired data to obtain a target data set; pre-training the offline CNN model by using the target data set to obtain a teacher model, extracting a soft label of the teacher model and constructing a student model; training a teacher model and a student model by using the target data set, and extracting a soft label corresponding to the teacher model and a soft output probability corresponding to the student model; knowledge of the teacher model is distilled to guide student model training. According to the method, continuous wavelet transform is adopted to convert an image from a one-dimensional vibration signal to a two-dimensional time-frequency signal, data information contained in an original signal is better represented, a knowledge distillation method is utilized to process a teacher model, knowledge of the teacher model is transferred to a small-scale student model, the scale of the model is well reduced, and the teaching efficiency is improved. And the model complexity is reduced. And meanwhile, the student model obtains knowledge from the soft labels of different teachers, so that overfitting can be effectively reduced.

Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. In an example method, a first plurality of weights for a base model and a second plurality of weights for an adapter model associated with the base model are accessed. A quantized plurality of weights is generated based on the first plurality of weights, a first quantization scale for the first plurality of weights, and the second plurality of weights. A loss is generated based on processing training data using the quantized plurality of weights. An updated second plurality of weights is generated based on updating the second plurality of weights based on the loss. A machine learning model comprising quantized versions of the first plurality of weights and the updated second plurality of weights is deployed.

The embodiment of the invention discloses a training data processing method for a large model, which comprises the following steps of: obtaining an initial data set containing a reasoning process of the large model, determining a condition required by the large model to deduce an output result in model input of the initial data set in the reasoning process, and outputting the output result according to the condition. And updating conditions determined in the model input, so that the model input does not have conditions required by the output result of the large model any more, obtaining a training data set which is marked as incapable of outputting the result, and performing fine adjustment on the large model to be adjusted based on the training data set. According to the method, conditions necessary for outputting results are determined through the reasoning process of a large model, and then corresponding conditions in model input are updated based on the conditions, so that a training sample set does not have the conditions for reasoning a real output result any more, and the ability of training the large model to recognize the model input without the output result is achieved; and the illusion condition of a large model output result is avoided.

1. Name of the Design Product: Sulfonation Process Training and Examination Device. 2. Use of the Design Product: For sulfonation process training operations. 3. Design Key Points of the Design Product: Lies in the shape. 4. Picture or Photograph Most Indicating the Design Key Points: Stereogram.

The method performed by an electronic device may include: outputting monitoring data of a target scenario among one or more predetermined scenarios related to simulation errors, in a production mode of a semiconductor process simulation based on test data input by a user; switching from the production mode of the semiconductor process simulation to a maintenance mode; and terminating the target scenario when a user input related to simulation maintenance satisfies a maintenance condition of the target scenario while in the maintenance mode.

The invention relates to the technical field of network security, and discloses a rebound shell detection response system and method, a medium and a product, and the method obtains tetrad information, a process parameter set and a process behavior chain in real time through Netlink, avoids missing detection caused by short process injection time, and reduces system resource occupation; a probability distribution table is constructed in the process training module to serve as a dynamic baseline, so that false alarms caused by baseline stiffness are reduced; in the engine analysis module, the characteristics and behaviors of the host layer process are analyzed in combination with a probability distribution table, and whether the host layer process is a rebound shell process or not is judged, so that the problems that traditional rule detection is easy to bypass and encrypted traffic cannot be identified are solved, and false alarms and missing alarms are reduced; the multi-dimensional depth defense principle is combined in the dynamic response module, the rebound shell process can be thoroughly eradicated, the problems that traditional detection response is delayed and blocking is not thorough are solved, and the real-time performance, the accuracy and the rapid blocking performance of rebound shell detection are improved.

1. The name of this design product: Phosgene and phosgenation process training and operation examination device. 2. Purpose of this design product: Used as a training device for chemical industry practitioners on phosgene and phosgenation process operation. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: three-dimensional picture.

1. Name of the design product: Chlorination process training and examination device. 2. Use of the design product: For the training and assessment combining simulation and practical operation of chlorination process. 3. Design key points of the design product: Lies in the shape. 4. Picture or photograph that best shows the design key points: Perspective view.

This invention discloses a development and training method and system based on artificial intelligence in the logistics field, including the following steps: receiving input of logistics professional training requirements, preprocessing and standardizing the input content, completing the training environment configuration and resource initialization, and obtaining clear training objectives and resource configuration parameters; based on the determined training objectives, collecting relevant data of logistics scenarios. This invention deploys full-process logistics professional training by constructing an edge-cloud collaborative training architecture, breaking the limitations of traditional training equipment and scenarios, improving the flexibility and practicality of training, processing training data through a combination of automated annotation and manual verification to improve data annotation efficiency and accuracy, quickly constructing high-quality standardized logistics training datasets, and building AI models for logistics scenarios through hyperparameter optimization and incremental training mechanisms to improve model adaptability and recognition capabilities, and lower the model development threshold.

The invention relates to artificial intelligence, and provides a model training method, a text processing method and related equipment. The model training method comprises the following steps: calling a large language model to process a training sample, and determining the probability that lexical elements in the training sample are pre-allocated to a plurality of expert networks of the large language model for processing; determining loads of a plurality of expert networks based on a plurality of probabilities corresponding to the lexical elements; determining a first expert network and a second expert network from the plurality of expert networks according to the loads and the load threshold values of the plurality of expert networks; determining a third expert network for processing the lexical elements according to the probability that the lexical elements are allocated to the second expert network; and training a large language model based on an output result of the lexical elements by the first expert network and an output result of the lexical elements by the third expert network. According to the method, the problem of overload of each expert network in the training process of the large language model can be solved while the training effect of the large language model is ensured.

The present disclosure provides a method for training a machine learning process, the method comprising: (a) obtaining identified features in a first computerized process, the identified feature is identified in a second computerization process based on an overlap between a first highest match of an unlabeled and randomly obtained feature and a second highest match of an unlabeled and correctly or wrongly indicative detected feature of the reference classification; and (b) training, by the first computerized process, a machine learning process using a training dataset of features and further based on the identified features to provide a determination of the reference classification with respect to the automatic driving application.

The invention provides a model training method and device, a task processing method and device, equipment, a medium and a product, and the model training method comprises the steps: processing training data according to a text generation model of a current training round to obtain a prediction text set and a sample label set corresponding to the training data, determining reward values corresponding to the prediction text set in a plurality of preset optimization dimensions; according to the reward value corresponding to each preset optimization dimension, determining an advantage value of each preset optimization dimension; according to the preset dimension weight of each preset optimization dimension and the advantage value of each preset optimization dimension, determining a dimension loss value of each preset optimization dimension; and training a text generation model according to the dimension loss values of the plurality of preset optimization dimensions to obtain a text generation model of the next training round. According to the embodiment of the invention, the training precision of the text generation model can be improved.

The invention discloses a self-adaptive promotion robot driving training method based on capability grading and task decomposition, and aims to overcome the defects that in the prior art, test taking is emphasized, the capability is light, feedback is extensive, and a path is rigid. According to the method, a pyramid type three-layer teaching framework is adopted, and basic vehicle control training, atomic operation fragment strengthening and comprehensive process training are sequentially carried out. The system collects operation data in real time, constructs a dynamic ability portrait based on indexes such as vehicle speed standard deviation, calculates a comprehensive score through a weighting formula, realizes self-adaptive promotion in combination with a threshold value, and supports personalized point location marking and flexible splitting training. According to the invention, transformation from test taking to ability is realized, training precision and efficiency are improved, individual demands of trainees are adapted, teaching resource configuration is optimized, and the method is suitable for intelligent driving training scenes.

The present application relates to the field of computer technology, and in particular to a method and apparatus for processing training data, which obtains an original training data set, wherein each training sample contained in the original training data set corresponds to at least two candidate labels; determines the labeling confidence of each candidate label corresponding to each training sample, and filters each candidate label corresponding to each training sample according to the labeling confidence of each candidate label; samples each training sample corresponding to each filtered candidate label so that the number of training samples corresponding to different categories of candidate labels meets a preset quantity difference condition, thereby obtaining a target training data set; and uses the target training data set to perform model training, thereby improving the reliability and balance of the training data and thereby improving the accuracy of the model training.

The utility model provides an automatic rifle live ammunition simulation process training device, and relates to the field of simulated live ammunition training, four corners inside an installation assembly are respectively provided with an installation hole, the upper side and the lower side of the installation hole are communicated in two directions, and the rear side of a guide mechanism is fixedly connected with a shooting assembly. The problems that in the prior art, although some simulation training devices exist, the simulation training devices have obvious defects in the aspects of simulation of real spot shooting feeling, operation feedback, correction guidance and the like, and the high-precision and high-efficiency spot shooting training requirements cannot be met are solved. The rack assembly is arranged at the bottom end of the servo motor A and is in meshing transmission with an out-stroke gear arranged on an output shaft at the bottom end of the servo motor A, so that when the recoil force feedback training device is used, real recoil force feedback can be simulated through meshing transmission of the driving gear and the rack assembly to be used by trainees, and then the purpose of precise training is achieved.

A method for training of machine learning processes, the method includes (a) obtaining, at a first computerized process, an identified signature, the identified signature identified at a second computerized process based on an overlap between a first top matching of signatures that are untagged and randomly obtained and a second top matching of signatures that are untagged and are correctly or erroneously indicative of a detection of a reference classification; and (b) training, by the first computerized process, a machine learning process using a training dataset of signatures and further based on the identified signature, to provide determinations for the reference classification with respect to an automated driving application.

1. The name of this design product: Amination process training and examination device. 2. Purpose of this product design: This product design is used for the training and assessment of industrial personnel on the amination process operation by combining simulation and actual operation. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: three-dimensional picture.

This application discloses a model training method and a cloud management platform based on a cloud management platform, which can improve model training efficiency. The method includes: a tenant sending a model training request for a specific model to the cloud management platform, whereby the tenant sets the number of queries, keys, and values ​​for the model. Based on this request, the cloud management platform determines multiple computing nodes. If the number of computing nodes exceeds the number of keys, the cloud management platform decomposes the model into multiple sub-models and deploys them on these computing nodes. These computing nodes can use these sub-models to process training data, obtaining the queries, subkeys, and subvalues. Then, these computing nodes can fuse the subkeys and subvalues ​​to obtain the keys and values. Subsequently, these computing nodes can use the queries, keys, and values ​​to complete the subsequent training of the sub-models.

The invention discloses a heterogeneous edge calculation multi-teacher federated distillation method based on course learning, and aims to reduce the influence of statistical heterogeneity on model performance on the premise of protecting the heterogeneity of an edge device model. The method specifically comprises the following steps: S1, a client performs local training by using a private data set and global knowledge, and then transmits output obtained by reasoning on a public data set to a server; s2, the server evaluates the quality of the output uploaded by each client by taking a sample as a unit, and then performs weighted aggregation to generate comprehensive knowledge; and S3, the server follows the thought of course learning, dynamically trains a global model by using comprehensive knowledge, and then sends global knowledge output by the global model to the client. And repeating the above process, and training until the global model converges. Compared with a previous federated learning method, the method has the advantages that the performance on four data sets is obviously improved, and the method is generally suitable for various training tasks.

1. The name of this design product: Fluorination process training and examination device. 2. Purpose of this product design: This product design is used for the training and assessment of industrial personnel on fluorination process operations by combining simulation and actual operation. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: three-dimensional picture.

This invention discloses a modular VR / AR surgical teaching system and method based on a local large-scale model. The local large-scale model training module is deployed in a local computing environment, storing structured medical knowledge and dynamically generating teaching scripts and assessment question banks. A lightweight VR / AR hardware group collects multi-dimensional user operation data and provides multi-sensory feedback. A full-process training control module, with a built-in physical simulation engine and evaluation logic, constructs a virtual surgical scene, simulates tissue mechanical changes in real time, compares user operations with standard surgical procedure models and generates error correction guidance, and generates a quantitative ability assessment report based on the entire process data. A multi-scene data mapping interface adopts a modular architecture based on a standardized intermediate communication protocol, achieving low-coupling logical isolation and dynamic integration between functional modules through a unified data exchange format and abstract interface specifications. This application adopts an edge-side deployment strategy to protect patient privacy and ensure real-time force feedback and tissue deformation rendering in the VR / AR scene.

The present application relates to a computing system, a model training method and apparatus, and a product. The computing system relates to a computing unit, and the computing unit comprises: a main board, which is configured with a central processing unit (CPU); and a base board, which is connected to the main board by means of a first communication link, wherein the base board is configured with a plurality of accelerator cards, and the plurality of accelerator cards are connected to each other by means of a second communication link. The main board is used for splitting a training task of a target model into a plurality of concurrent model training tasks and releasing same to the plurality of accelerator cards, and processing training results of the plurality of accelerator cards, so as to obtain a trained target model. The plurality of accelerator cards are used for concurrently executing the respective model training tasks thereof, so as to obtain the training results. The computing system forms an elastically scalable computing system architecture by means of modular base-board design and interconnection, such that the computing power and bandwidth of the computing system can match model training tasks at different parameter scales.

An XR-based semiconductor manufacturing process training device according to an embodiment of the present invention is configured to determine set values for parameters based on user input data, identify the spec data to which the set values belong among at least one spec data corresponding to the parameters defined in each manufacturing process, assign weights corresponding to the spec data to the set values, and calculate and display final data based on the first motion information and the weighted set values.

Embodiments of the present application disclose a task processing method, device and equipment, a computer storage medium and a computer program product, which comprise: performing grouping processing on a to-be-processed training task for model training to obtain multiple groups of training subtasks, and assigning each group of training subtasks a corresponding service node; executing the multiple groups of training subtasks in a parallel processing manner through the multiple service nodes corresponding to each group of training subtasks; stopping execution of a target group of training subtasks in which a target service node is located when a fault occurs in the target service node during execution of the multiple groups of training subtasks; determining a backup service node, and executing unexecuted training subtasks in the target group of training subtasks through the backup service node and other service nodes; the other service nodes are service nodes corresponding to the target group of training subtasks except the target service node, and the problem of a large amount of waste of processor time is solved.

The invention provides a model training method of a text processing model and a text processing method.The model training method of the text processing model comprises the steps that a current training sample set is obtained, and the current training sample set comprises sample texts and text labels; inputting each sample text into a text processing model, obtaining a prediction result corresponding to each training sample output by the text processing model, and training the text processing model according to the text label and the prediction result; determining a training sample with a prediction result different from the text label as a to-be-processed training sample, and obtaining an error type and sample scene information corresponding to the to-be-processed training sample; generating an expanded training sample according to the training sample to be processed, the error type and the sample scene information, and adding the expanded training sample to a next training sample set; and continuing to train the text processing model based on the next training sample set. Through an iterative training mode, the model training efficiency and the model processing capability of the text processing model are improved.

The invention provides a training set processing method, device and system for model training, and relates to the field of artificial intelligence, and the method comprises the steps: obtaining a to-be-processed training set for training an artificial intelligence model, carrying out the sample optimization operation of a plurality of samples according to the distribution characteristics of the plurality of samples in the to-be-processed training set, and obtaining a first processed training set. Wherein the distribution characteristics are used for indicating the relationship between the quality scores of the plurality of samples and the sample number corresponding to the quality scores of the plurality of samples, and the quality scores are used for indicating evaluation of the influence of using the samples to train the artificial intelligence model on the model reasoning effect. Therefore, the low-value samples in the training set can be screened and removed, and the distribution condition of the samples with different values in the training set can be adjusted. The model is trained based on the processed training set provided by the invention, so that the computing power consumed by model training can be reduced, the model training speed is improved, and the model precision is guaranteed or improved.

The invention provides a source code-oriented fine-grained vulnerability detection method and system. The method comprises the following steps of: acquiring existing program source codes and vulnerability sensitive elements as training data; compiling a program source code in the training data into an intermediate representation, performing inter-process slicing on the code value flow graph by taking a central node containing vulnerability sensitive elements as a reference, and extracting information related to vulnerabilities in codes; performing vector representation on the generated slice sub-graph, and converting information of a graph structure into a format capable of being input into a vulnerability detection model; training a vulnerability detection model by using the processed training data; and extracting a slice sub-graph of the source code of the target program, generating node embedding, inputting the node embedding into the trained vulnerability detection model for detection, and predicting whether the slices of the target program contain vulnerabilities or not and the specific vulnerability positions. According to the method, high accuracy of vulnerability detection can be realized, vulnerabilities can be detected on fine grit, and vulnerability positions can be accurately positioned.

The invention discloses a machining process practical training simulation method and related equipment, and the method comprises the steps: obtaining the engineering data and process data of a target workpiece; performing feature extraction on the engineering data, and constructing a case resource library in combination with process data; performing semantic association analysis on the processing cases in the preset knowledge resource library and the case resource library to generate a process knowledge network; constructing a virtual practical training environment according to the process knowledge network; collecting interaction data of the learner in the virtual practical training environment, and evaluating the interaction data to obtain an evaluation result; and generating a guidance animation based on the evaluation result and the process knowledge network. According to the invention, the learning efficiency of simulation training can be improved.