4results about How to "Improve perceived efficiency" patented technology

The application discloses a kind of based on multi-source fusion and reinforcement learning's composite material additive manufacturing adaptive control method, first in printing process Synchronous acquisition space position, infrared temperature, tension and compression force and multi-source sensor data such as visible light image;Through the lightweight perception module constructed to each road data is parallelly processed and feature extraction, to force data adopts second-order difference to carry out state discrimination, to temperature data adopts multistage threshold segmentation and extracts forming area statistical feature, and reward feature is generated in combination with image recognition and position mutation detection;The multi-dimensional state features after fusion are combined with reward function, and the agent is trained using reinforcement learning algorithm to learn the optimal printing parameter decision strategy;The trained agent is used for real-time control, and the best printing speed and layer thickness instruction are dynamically output to the mechanical arm according to the current state. The application realizes accurate description and real-time adaptive control of complex multi-physical field coupling process, effectively improves printing quality and process stability.

The application discloses a kind of vehicle-road cooperative perception method and device based on vehicle-mounted point cloud, belong to the field of automatic driving, the method includes: using each vehicle in current time slot in driving environment complexity and communication link data rate of cooperation perception, construct the optimization problem of maximum total perception quality, solve it to obtain the point cloud processing task priority and optimal configuration corresponding to each vehicle;Each vehicle uses corresponding optimal configuration to sample compression and back to roadside server, so that roadside server uses point cloud processing task priority to carry out target detection and issue to each vehicle to carry out environmental perception.The optimization problem in the application considers the problem of communication resources, processing priority and the cooperative perception accuracy of multiple vehicles and roadside server, thereby solving the technical problems of poor perception quality caused by large delay, low accuracy and limited resources of existing automatic driving vehicles.

The application provides a robot visual operation control method and system based on eye tracking, and belongs to the field of robot vision and artificial intelligence. The method comprises the following steps: a head-mounted device with eye tracking function is used to synchronously collect data streams when an operator performs an operation task, wherein the collected data streams comprise eye movement data streams and scene visual data streams when the operator performs the operation task; for the preprocessed data streams, a dynamic Bayesian network is used to extract gaze-intention coupling features; the preprocessed data streams and the gaze-intention coupling features are input into a robot operation model with a double-path attention fusion that has been constructed, so as to obtain online attention results, and the robot performs corresponding operations according to the online attention results. The method of the application can quickly focus on key areas in complex scenes such as occlusion and interference, and improves the task success rate.

The application relates to the technical field of intelligent application of automobiles, in particular to an automatic driving perception method and device of a vehicle, a vehicle and a storage medium, wherein the method comprises the following steps: acquiring initial picture data of the surrounding environment of the vehicle; inferring at least one target task according to the initial picture data, and detecting whether preset adversarial samples exist in the initial picture data in the inference process; if the initial picture data does not have the preset adversarial samples, obtaining a perception result of the initial picture data after the inference is completed; otherwise, the preset adversarial samples of the initial picture data are denoised by using a preset denoising strategy, first picture data after denoising is obtained, and the perception result of the initial picture data is obtained again based on the first picture data. Therefore, the problems that all data are usually denoised in the related art, the workload of data processing is large, the perception efficiency is low, and noise of input data of a perception task cannot be effectively avoided are solved.