157 results about "Training performance" patented technology

The invention provides a training method and device for a semiconductor device performance prediction model and related equipment, and the method comprises the steps: determining key parameters and performance parameters of a semiconductor device, the key parameters comprise key size parameters and / or process parameters, and the performance parameters comprise electrical characteristic parameters of the semiconductor device; based on the multiple groups of key parameter values and the corresponding performance parameter values, establishing a sample set; the key parameter values are disturbed, and the correlation degree of the key parameters and the performance parameters is determined; wherein after the key parameter value is disturbed, the change condition of the corresponding performance parameter value is used for representing the correlation degree of the key parameter and the performance parameter; initializing the weight of a preset neural network model based on the performance prediction target and the correlation degree of the key parameter and the performance parameter; and inputting at least part of samples in the sample set into a weight-initialized preset neural network model for training to obtain a semiconductor device performance prediction model.

The invention provides a federated learning differential privacy method based on Rayleigh divergence and adaptive noise adjustment, and aims to balance data privacy protection and model training performance and improve model accuracy and convergence speed of federated learning on the premise of protecting user data privacy. And the contradiction between privacy protection and model performance in the existing federated learning is solved. The method comprises the following steps: step 1, constructing a privacy loss quantification model based on Rayleigh divergence; 2, deducing a tight upper bound of a Gaussian noise standard deviation; 3, initializing noise parameters of the federated learning system; 4, the client side executes local model training and noise adding; 5, updating the aggregation model of the central server and evaluating the performance; step 6, implementing a self-adaptive noise adjustment decision based on model performance; and step 7, iterating federal learning training until convergence or completion.

This application provides a method for sending training data of an AI model, which is applied to a scenario in which a reporting network element of the training data sends the training data to a training network element of the AI model. After obtaining the training data, the reporting network element classifies the training data into reference data and non-reference data, and indicates the training data by sending first-type information and second-type information to the training network element. The first-type information includes full information of the reference data, and the second-type information includes incremental information of the non-reference data relative to reference data corresponding to the non-reference data. Thus to help reduce overheads of sending the training data, thereby helping reduce air interface overheads in an AI model training or updating process, reduce a transmission delay, and improve model training performance.

The invention relates to the technical field of unmanned aerial vehicle training, and discloses an unmanned aerial vehicle training system and method based on multi-modal analysis and a dynamic scene, and the method comprises the steps: obtaining multi-source data, carrying out the preprocessing, obtaining multi-modal data, and inputting the multi-modal data into an intelligent analysis module; receiving the multi-modal data, analyzing the multi-modal data to obtain scene modeling information, and unifying the format of the scene modeling information; carrying out scene coverage simulation based on the scene modeling information, calculating a scene coverage rate, carrying out dynamic adjustment on the scene modeling information based on the scene coverage rate, updating the scene coverage simulation, calculating a training adaptation degree by fusing the hardware performance of the unmanned aerial vehicle after updating, and judging whether training is allowed or not; if yes, obtaining training performance data, calculating training performance values and sorting; training data of the trainees are obtained based on scene coverage simulation, the training proficiency is calculated, the training levels of the trainees are dynamically adjusted, training demand values of the trainees are calculated according to historical training data and sorted for analysis, and the training scene and the unmanned aerial vehicle are matched.

According to the mechanical arm control method and system based on the self-adaptive force field, the motion state of a patient is monitored, the motion state is compared with a demonstration space trajectory, and training performance information of the patient is obtained. Based on the training performance information and the constructed adaptive force field model, auxiliary force applied to the patient is obtained through calculation, wherein the auxiliary force comprises normal force, tangential force and viscous force. And the applied auxiliary torque is calculated based on the tail end posture of the mechanical arm and the target posture, the joint control torque is obtained according to the auxiliary force and the auxiliary torque, and the dynamic torque and the null space control torque of the robot dynamic model are obtained. And the joint control torque, the dynamic torque and the null space control torque are combined to obtain the comprehensive control torque for controlling the mechanical arm. Therefore, the self-adaptive adjustment of the auxiliary force is realized based on the position, speed and direction of the patient, and the time degree of freedom can be obtained in the motion of the self-adaptive force field, so that the patient obtains more freedom of active motion under the assistance of the space force field.

The invention discloses an iterative supervision fine tuning data generation method and system, and relates to artificial intelligence and data processing technologies, and the method comprises the steps: generating a basic training data pair based on a pre-training language model, and the basic training data pair comprises an instruction-response pair; performing model training based on the basic training data, and determining training performance feedback data according to the performance of the model on the verification set and the task index; analyzing a model performance index according to the determined training performance feedback data; predicting and optimizing an optimization strategy of training data and training parameters based on the model performance index and the basic training data pair; and performing model training according to the optimized training data and the training parameters so as to execute AI model training platform data generation by using a strategy output by the model after training. Model performance feedback, automatic task mining and sample optimization mechanisms are introduced, training data are dynamically adjusted and expanded, and closed-loop iteration and performance-oriented optimization of data generation are achieved.

The invention discloses a DIKWP-driven individualized brain-map interactive feedback system, which is used for neural rehabilitation and brain-computer interface training. The system obtains brain activity data of a patient through electroencephalogram acquisition equipment, constructs a personal brain-semantic map in combination with cognitive evaluation, and establishes a mapping relation between semantic units and brain region responses. In the training process, the DIKWP semantic analysis module performs multi-layer analysis on indexes such as reaction time, accuracy and intention achievement, generates multi-mode instant feedback such as visual sense, auditory sense or tactile sense, and performs directional reinforcement on a weak semantic domain. The system has a dynamic target optimization capability, and the difficulty can be automatically adjusted according to training performance; when attention distraction, emotion abnormity or semantic deviation is detected, a safety intervention mechanism is automatically triggered, and training effectiveness and safety are guaranteed. According to the method, closed-loop individualized rehabilitation interaction is realized, the adaptation degree and efficiency of brain-computer interface training are remarkably improved, and the method is suitable for various rehabilitation scenes such as languages, movement and cognition and has a good industrial application prospect.

The invention discloses an intelligent computing cluster parallel training performance optimization method oriented to large model training, and relates to the technical field of artificial intelligence computing. Hardware feature indexes of heterogeneous computing nodes in an intelligent computing cluster are collected in real time, a hardware topological graph is constructed, and an optimal computing power combination suitable for the hardware topological graph is calculated; the method comprises the following steps: selecting an optimal communication path by adopting a self-adaptive routing algorithm, coding and decoding transmission data in combination with a mixed precision compression technology to realize efficient transmission of the transmission data, constructing a fault prediction model based on a deep learning algorithm, and inputting real-time data in a hardware monitoring log into the fault prediction model to obtain a prediction result. Prediction results are classified and stored according to a preset result classification standard, check points are trained based on the prediction results to achieve rapid recovery, the super-node computing power utilization rate is improved, meanwhile, a communication path can be dynamically adjusted according to the network congestion state, and the fault recovery speed is improved.

The invention discloses a method for establishing a rough-arrangement search model based on LTR, and the method comprises the following steps: S1, collecting candidate total data, and completing the preprocessing operation; s2, the sorting score of each piece of data is calculated and arranged in an ascending order, and the data are divided into equivalent data buckets; s3, randomly extracting data to form a training sample pair, generating a Pair-wise data set, and marking a sorting relationship; s4, executing rough-arrangement search model training and calculating a prediction score; s5, constructing a loss function by adopting a Lambda gradient signal, feeding back a sorting error and iteratively updating model parameters; s6, monitoring a training performance index, stopping training after a stopping condition is met, and deploying the model to an online environment; and S7, storing the trained model file, and defining an input format, a sorting structure and an output interface. According to the method, the sorting accuracy and fine sorting consistency of the rough sorting search model are improved, and the generalization ability of the model is remarkably enhanced.

The invention discloses a rail transit intelligent operation and maintenance practical training system based on cloud computing, which relates to the technical field of operation and maintenance practical training teaching and comprises a practical training integration unit, a data acquisition unit, a practical training analysis unit, a priority analysis unit, a task issuing unit and a period updating unit. According to the invention, the training performance index is utilized to accurately quantify the ability weakness of the trainee, the qualified training items and the unqualified training items are divided in combination with the threshold, and the computing resources are focused on the unqualified items, so that the cloud computing load is reduced; secondly, through dynamic sorting of practical training project priority indexes, it is ensured that weak practical training projects with high fault risk and high cost are preferentially trained, and the practical training resource allocation accuracy is improved; and meanwhile, the maximum release times of a single training item are forcibly constrained through a preset release times threshold value, and each training item in the unqualified set is trained at least once, so that the training trainees are prevented from being overtrained on a single item, and other weak items are prevented from being omitted.

The present application describes methods of using the pre-configured AI / ML (artificial intelligence / machine learning) based model training modes in wireless mobile communication system including base station (e.g., gNB, TN, NTN) and mobile station (e.g., UE). In AI / ML model is applied to radio access network, model training performance can be degraded depending on UE ML condition or capability changes. Therefore, model operation (e.g., model training / inferencing / monitoring / updating) can be set up between network and UE by configuring model training capability elements.

The invention discloses a pulse neural network lightweight method based on nonlinear pruning, and relates to the technical field of artificial intelligence and neural networks. The method comprises the following steps: S1, constructing an NDI-LIF spiking neural network model, and introducing a bilinear product term into an input current in a dynamic updating process of a neuron membrane potential; s2, constructing a nonlinear synaptic pruning mechanism, representing a connection weight as a re-parameterization function composed of a re-parameterization weight and a conversion gain coefficient, and pruning the connection weight based on the re-parameterization function; and S3, training the NSPDI-SNN model to obtain a lightweight pulse neural network. By introducing a nonlinear dendritic integration mechanism and a state-adjustable synaptic pruning mechanism, balance between network sparsity and high performance is realized while the expression ability of the model is enhanced, and a lightweight spiking neural network model with high spatial-temporal expression ability, highly sparse structure and reasonable biological mechanism is constructed. And the generalization ability and the efficient training performance under various tasks are ensured.

This application provides a method for sending training data of an AI model, which is applied to a scenario in which a reporting network element of the training data sends the training data to a training network element of the AI model. After obtaining the training data, the reporting network element classifies the training data into reference data and non-reference data, and indicates the training data by sending first-type information and second-type information to the training network element. The first-type information includes full information of the reference data, and the second-type information includes incremental information of the non-reference data relative to reference data corresponding to the non-reference data. The training data is indicated by using the full information of the reference data and the incremental information corresponding to the non-reference data, so as to help reduce overheads of sending the training data, thereby helping reduce air interface overheads in an AI model training or updating process, reduce a transmission delay, and improve model training performance.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology, medical health and the like, and discloses a federal training method, device, equipment and medium based on segmented sparse coding. Performing sparse processing in combination with historical residual errors, generating residual errors and sparse segments, and encoding and uploading the residual errors and sparse segments to a server; the server performs weighted aggregation on the segments with the same identifier according to the sample size and issues the segments; the client fuses global and local segments, updates low-rank adaptive parameters, stores residual errors and completes training; and after a preset round is completed, integrating the updated parameters and the pre-training model to form a fine tuning model, and processing task input data to generate a result. According to the method, the communication burden is reduced through segmented sparse compression and weighted aggregation, the adaptation effect is improved through exponential decay fusion, and the training performance and efficiency are both considered in a communication limited and privacy sensitive environment.

The invention relates to the technical field of intelligent robot training, and provides an intelligent robot cerebellum training system and method based on simulation and reality fusion, and the system comprises a simulation training terminal, a reality training terminal, a cerebellum learning simulation terminal, a data fusion and modeling terminal, a control execution terminal, and a central monitoring and evaluation terminal. The simulation training terminal is used for performing action training and generating an initial control strategy and simulation interaction data; the reality training terminal is used for executing an action task and collecting execution data; the data fusion and modeling terminal is used for receiving simulation interaction data and execution data and carrying out distribution alignment; the cerebellum learning simulation terminal is used for calculating a prediction error and outputting a correction signal; the control execution terminal is used for generating real-time state feedback; and the central monitoring and evaluation terminal is used for generating a global scheduling instruction according to the training data and the real-time state feedback. The method has the effects of reducing the training cost of the cerebellum of the intelligent robot with the body and improving the training performance.

The invention discloses an efficient data parallel training method in a high-delay low-bandwidth scene, and the method comprises the steps: constructing a communication-calculation tradeoff model based on a real-time network state through dynamic combined optimization of a gradient compression ratio and a delay step length, and carrying out the self-adaptive adjustment of parameters, so as to balance the precision and efficiency; an error feedback mechanism is used for compensating compression loss, and precision attenuation under the high compression rate is reduced; the synchronization frequency is controlled in combination with a delay gradient aggregation strategy, and calculation and communication parallelization is achieved; network conditions are periodically monitored, parameters are updated, and the cooperative defect of a traditional independent optimization strategy in a dynamic environment is overcome. Experiments show that under the conditions of 32 nodes, 100 Mbps bandwidth and 500 ms delay, compared with a D-SGD method and a DD-EF-SGD method, the training efficiency of the method is improved by 5.07 times and 1.24 times respectively, and the distributed training performance of communication limited scenes such as a cross-wide area network is remarkably optimized.

The present application relates to the technical field of intelligent training, in particular to a kind of intelligent shooting examination training system and method, including the following contents: posture recognition module, trajectory monitoring module, hit analysis module, rhythm comparison module, label output module.In the present application, based on the motion image of the process of training personnel holding gun to firing, the synchronous angle trajectory of shoulder and elbow is identified in real time, compared with the combination of standard action, the matching difference of each period is accurately screened and quantified, the change of laser trajectory coordinate point and direction during aiming is analyzed, the action control stability is dynamically evaluated, the hit point sequence is used, the structured score is formed by combining path extensibility and hit area density, the start and end time of action unit and rhythm arrangement of training process are compared one by one, the phenomenon of advance and delay is identified, the performance score is flexibly adjusted according to the weight of multi-dimensional index, and the accurate matching between training performance evaluation and actual ability requirement is realized.

The invention discloses an end-side collaborative multi-task scheduling method, which comprises the following steps of: training performance predictors of end equipment and an edge server on the end equipment and the edge server for different deep neural network categories respectively; when the end equipment receives a deep neural network task request, utilizing the trained performance predictor to dynamically generate an optimal partition point of a task by taking minimization of estimation delay as a target according to a task division strategy based on resource awareness, and cooperatively finishing the task by the end equipment and the edge server; when the deep neural network tasks are packaged in batches, performing optimization arrangement of the tasks according to the residual service quality of the tasks; a task batch processing strategy is adopted, irregular partition tasks in one batch are sorted and distinguished according to a starting layer, the partition tasks are combined to execute batch processing, and end-side collaborative multi-task processing is achieved. The invention provides a high-throughput end-side collaborative multi-task scheduling optimization method, and the processing capability of a system in a multi-task scene is improved.

The invention discloses a visible light-infrared multi-mode target detection method based on transfer learning, and relates to the technical field of target detection. The objective of the invention is to solve the technical problem of poor model training effect caused by a small number of partial category samples in existing visible light-infrared multi-modal target detection. The method comprises the following steps: firstly, supplementing pictures for categories with insufficient sample quantity in a data set so as to expand small sample category data quantity; then, a weight obtained by pre-training on the single-mode data is used as an initial weight of the YOLOv11 model; and finally, the initial weight is loaded to a visible light-infrared multi-mode target detection training process, and model training is completed. According to the method, the performance of the YOLOv11 model in a multi-modal target detection task is effectively improved by multiplexing the single-modal pre-training weight through transfer learning and combining a small sample data supplement strategy, compared with a non-transfer learning detection scheme, the detection precision and stability are remarkably improved, and the method can be widely applied to scenes needing multi-modal target detection.

This invention belongs to the field of artificial intelligence hardware architecture design and discloses a method, system, device, and medium for cross-layer optimization of multi-core packaging and optical interconnect clusters for large language model training. The method includes: constructing a design space, including a core hardware architecture layer, an optical interconnect network layer, and a training parallel strategy layer; performing an outer-layer search to search for the architecture parameters of the multi-core modules within the core hardware architecture layer; for each outer-layer search sampling point, performing an inner-layer search to collaboratively optimize the optical interconnect network topology and training parallel strategy within the optical interconnect network layer and the training parallel strategy layer; and outputting the Pareto optimal design point on the training performance and cluster cost plane. The technical solution described in this invention can guide the design of related training clusters, thereby fully leveraging the advantages and potential of core technology and optical interconnect technology in large language model training.

The present disclosure relates to communication methods, communication devices, a communication system, a storage medium and a program product, and belongs to the technical field of communications. A method comprises: a first device receives second data corresponding to first data transmitted by a second device, wherein the first data and the second data form training data pairs, and the training data pairs are used for training a first model. In the method provided in the present disclosure, while transmitted data of a transmitting end is unknown, the transmitted data of the transmitting end and received data of a receiving end are combined as training data pairs on the basis of the received data, so as to train a model, thereby improving the model training performance and accuracy.

We provide the system. [Solution] A means for generating an individual training plan using a trainer generated based on a virtualized character selected by the user, A means of analyzing the user's exercise in real time and providing immediate feedback according to the training performance, A means of recording training results and managing rewards based on points awarded to users, A means of providing community features to share results among users and promote competition, A system that includes this.

The invention relates to the technical field of virtual experience, and discloses an immersive simulation training evaluation system and method based on virtual reality, and the system comprises an equipment interaction perception module which is used for capturing the sight line, gesture data and feature data of a user; the environment scene simulation module is used for reconstructing and rendering a three-dimensional model of a target scene and performing digital simulation; the task training and logic control module is used for establishing task process management, analyzing a user instruction and adjusting the difficulty degree of a task according to real-time training performance after the user instruction is executed; the evaluation analysis module is used for quantitatively scoring the training performance of the user and generating a personalized training scheme according to a scoring result; and the data management platform is used for uploading the data to the cloud for storage and sharing. According to the method, a whole-process closed loop from scene construction, task control to evaluation optimization is realized, and the system response efficiency is improved; and a personalized training scheme can be generated according to a scoring result, so that targeted training of the user is facilitated, and the training efficiency is improved.

The application provides a mechanical arm control method and system based on an adaptive force field, wherein the motion state of a patient is monitored, the motion state is compared with a demonstration space trajectory, and training performance information of the patient is obtained. Based on the training performance information and an adaptive force field model constructed, an auxiliary force, including a normal force, a tangential force and a viscous force, applied to the patient is calculated. Based on the end posture of the mechanical arm and a target posture, an auxiliary torque applied is calculated. According to the auxiliary force and the auxiliary torque, a joint control torque is obtained, and a dynamic torque of a robot dynamics model and a null space control torque are obtained. In combination with the joint control torque, the dynamic torque and the null space control torque, a comprehensive control torque for controlling the mechanical arm is obtained. In this way, adaptive adjustment of the auxiliary force is realized based on the position, speed and direction of the patient, time freedom is obtained in the motion of the adaptive force field, and the patient can obtain more active motion freedom under the assistance of the space force field.

The invention provides a sample set updating method and device and electronic equipment, and the method comprises the steps: carrying out the feature extraction of a training sample in a first sample set, obtaining a first feature of the training sample, and carrying out the space mapping of the first feature, and obtaining a second feature of the training sample; clustering the training samples to obtain a plurality of first training sample groups; for each training sample group, based on the second features of the training samples in the first training sample group, performing sample screening on the first training sample group to obtain a second training sample group corresponding to the first training sample group; and replacing each first training sample group in the first sample set with a corresponding second training sample group to obtain a second sample set. According to the invention, the training performance of the sample set for training the machine learning model can be effectively improved.

According to one exemplary embodiment, an apparatus, system, method and / or computer program product may provide an athletic training performance analysis apparatus including at least one athletic training performance analysis device including at least one user interface coupled to the at least one athletic training performance analysis device configured to interact with a user to receive a user selection of at least one training performance analysis routine, the at least one user interface may include: at least one electronic computer processor; at least one input device coupled to the at least one electronic computer processor to receive the user selection; at least one output device coupled to the at least one electronic computer processor to provide output to the user; at least one electronic memory coupled to the at least one electronic computer processor; and at least one of: wherein the at least one electronic computer processor is configured to save or retrieve the at least one training performance analysis routine from the at least one memory; where the at least one electronic computer processor is configured to electronically monitor and electronically analyze the at least one training performance analysis routine; wherein the at least one electronic computer processor is configured to randomize at least one challenge by the at least one athletic training performance analysis device; or wherein the electronic computer processor is configured to at least one of: combine a plurality of previously saved of the at least one training performance analysis routine, or shuffle a plurality of previously saved of the at least one training performance analysis routine.

The invention discloses a reinforced training method for improving the middle-section text analysis capability of a large language model, and relates to the field of natural language processing and generative large language models.The method comprises the steps that middle-section double-window construction is conducted according to a dynamic scale factor, extraction and splicing are conducted from original long text corpora based on window positions, and the middle-section text corpora are obtained; obtaining a preliminary training sample; performing middle text information density evaluation on the preliminary training sample, and verifying and optimizing the preliminary training sample to obtain an optimized sample; and inputting the optimized sample and the updated position identification sequence into a large language model, and realizing iterative training of the large language model through a position-aware middle loss weighting mechanism to obtain an optimized large language model. According to the method, the retrieval and understanding accuracy of the model in the process of processing the middle position information of the sequence can be remarkably improved in a balanced manner, the U-shaped performance curve which is sunken originally is successfully leveled, and the training performance bottleneck is overcome.

Provided is a load control unit with which a user of a training instrument can immediately ascertain his / her performance and optimize training. A load control unit (10) is installed in a training instrument (1) and controls the load of the training instrument (1), the load control unit (10) executing first display processing for causing a display device (30) to display the target speed of at least a part of one stroke relative to the stroke amount of a user who uses the training instrument (1), detection processing for detecting the speed of the stroke of the user and the stroke amount of the user for each prescribed time, and second display processing for causing the display device (30) to sequentially display the speed with respect to the stroke amount for each prescribed time in accordance with the target speed.

The communication method and device provided by the embodiment of the invention are used for improving the training performance. The method comprises the following steps: receiving first capability information from first management equipment, wherein the first capability information indicates training capability supported by the first management equipment and / or network element equipment; and sending a training request to the first management device according to the first capability information, wherein the training request is used for requesting to execute a first reasoning function by adopting a first training technology. According to the mode, the second management equipment can obtain the training technology supported by the first management equipment and / or the network element equipment, so that the second management equipment can select the appropriate training technology for the specific reasoning function according to the requirement to trigger the first management equipment and / or the network element equipment to execute training.

The invention provides an agent communication strategy optimization method based on reinforcement learning, and the method enables a system to adaptively select an optimal beam forming and interference strategy according to a real-time channel state through introducing a reinforcement learning technology and combining the performance evaluation of a pre-training model, thereby achieving the optimization of an intelligent agent communication strategy. The balance between the maximization of the communication quality of the target user and the most intensified interference effect of the non-target user is realized, and the reliability and safety of the system are guaranteed in a complex dynamic environment. By constructing the pre-training performance mapping table and guiding the reinforcement learning algorithm to adaptively select the optimal model strategy according to the channel state, the receiving capability of the unauthorized user is effectively interfered while the communication quality of the target user is guaranteed, and the system security is improved. According to the method, efficient, self-adaptive and intelligent communication interference cooperative control can be realized, and the robustness, the anti-interference capability and the transmission quality of the system are effectively improved.