Beijing University of Posts and Telecommunications

Huadong Ma

Liang Liu

Huiyuan Fu

Wu Liu

Xiaolong Zheng

Dan Xia

Dawei Lu

Yu Cao

Haitao Zhang

Zheng Zhang

Cheng Zhang

Yifan Zhang

Zeyu Wang

Anfu Zhou

Pengrui Duan

Huanpu Yin

Dengkui Zhang

Chengling Jiang

Zihui Luo

Xinchen Liu

Aner Liu

The embodiments of the present invention provide a dynamic production scheduling method, apparatus and electronic device based on deep reinforcement learning, which relate to the technical field of Industrial Internet of Things, and can reduce the overall processing time of jobs on the basis of not exceeding the processing capacity of production device. The embodiments of the present invention includes: acquiring static characteristics, dynamic characteristics of each of jobs and system dynamic characteristics, inputting the static characteristics, dynamic characteristics of each of jobs to be scheduled and system dynamic characteristics into a scheduling model to obtain a job execution sequence or batch execution sequence of the jobs in each production stage, wherein, the static characteristics of the job include an amount of tasks and time required for completion, the dynamic characteristics of the job include reception moment, and the system dynamic characteristics include a remaining amount of tasks that can be performed by the device in each production stage. The scheduling model is a model obtained after training a first actor network based on static characteristics and dynamic characteristics of a sample job, system dynamic characteristics, and a first critic network.

Dynamic production scheduling method and apparatus based on deep reinforcement learning, and electronic device

The embodiments of the present invention provide a method, apparatus for cross-protocol opportunistic routing, an electronic device, and a storage medium, the method includes: when there is a first data packet in a low-power wireless network, simulating the first data packet to generate a second data packet including to-be-transmitted data in the first data packet; obtaining identification information of a destination node in the first data packet, and selecting a low-power node with the lowest delay to the destination node in the low-power wireless network, except the first low-power node, as a forwarding low-power node based on the identification information of the destination node; sending the generated second data packet to the forwarding low-power node, so that the forwarding low-power node forwards the to-be-transmitted data to the destination node. By using high-power nodes, when there is a data packet in the low-power node, the data packet can be sent in time without being transmitted in a reserved idle channel, thereby reducing the transmission delay of the data packet from the source node to the destination node in the low-power wireless network.

Method, apparatus for cross-protocol opportunistic routing, electronic device and storage medium

A routing method and apparatus for SDN-based LEO satellite network are disclosed. The LEO satellite network includes a control plane and a data plane. The control plane includes a central controller and a plurality of local controllers. The data plane includes a plurality of LEO satellite nodes and user terminals connecting to the LEO satellite nodes. The control plane may be located on the earth, and thus the centralized management and control of the data plane are placed on the earth. A local controllers monitors LEO satellite nodes in a subnet or subnets of the local controller. The distance between a local controller and a LEO satellite node is much smaller than the distance between a GEO satellite node and the LEO satellite node, and thus the time delay and the traffic loss of communication are reduced.

Routing method and apparatus for SDN based LEO satellite network

Embodiments of the present application provide an acoustic sensing-based text input method, comprising: obtaining audio information corresponding to text to be input; dividing the audio information to obtain an audio segment for each letter to be recognized in the text to be input; sending to the server, a type of the text to be input, the audio segments for letters to be recognized, and arrangement of the audio segment for the letter to be recognized in the audio information; receiving input result returned by the server, and displaying, based on the input result, text information corresponding to the text to be input on the display screen of the mobile terminal. The method allows effective text input without relying on a display screen.

Acoustic sensing-based text input method

Embodiments of the present invention provide an end-to-end lightweight method and apparatus for license plate recognition. The method comprises: obtaining an image to be recognized; obtaining a number of a license plate in the image to be recognized and position coordinates of the license plate in the image to be recognized on the basis of the image to be recognized and a pre-trained target license plate recognition model, wherein the target license plate recognition model comprises a target feature extraction network, a target region candidate localization network, a target super-resolution generation network and a target recurrent neural network. Because in this solution, once an image to be recognized is input into the target license plate recognition model, the target license plate recognition model can output the license plate number and position coordinates of the license plate in the image to be recognized, one realizes an end-to-end model. The model has relatively strong robustness, and it can detect and recognize pictures taken under different camera angles. Moreover, computation variables such as image features can be reused without repeated computations, the model takes up less RAM and the speed of license plate recognition is greatly improved.

End-to-end lightweight method and apparatus for license plate recognition

The present application discloses a vehicle searching method and device, which can perform the steps of: calculating an appearance similarity distance between a first image of a target vehicle and several second images containing the searched vehicle; selecting several images from the several second images as several third images; obtaining corresponding license plate features of license plate areas in the first image and each of the third images with a preset Siamese neural network model; calculating a license plate feature similarity distance between the first image and each of the third images according to license plate feature; calculating a visual similarity distance between the first image and each of the third images according to the appearance similarity distance and the license plate feature similarity distance; obtaining a the first search result of the target vehicle by arranging the several third images in an ascending order of the visual similarity distances. The solution provided by the present application is not limited by application scenes, and it also improves vehicle searching speed and accuracy while reducing requirements of hardware such as cameras that collect images of a vehicle and auxiliary devices.

Progressive vehicle searching method and device

The invention relates to a weather recognition method and device based on image information detection, including: obtaining an image extracting multiple first image features of the image with respect to each preset type of weather using a number of first preset algorithms preset correspondingly for different preset types of weather; inputting the multiple first image features to a preset multi-kernel classifier, the multi-kernel classifier performing classification according to the image features to identify the weather in which the image was taken. The multi-kernel classifier is realized by: selecting a first preset number of image samples for each of the preset types of weather; for the image samples of this type of weather, extracting the first image features of each image sample according to the first preset algorithm corresponding to this preset type of weather; and performing machine learning for the first image features according to a preset multi-kernel learning algorithm.

Weather recognition method and device based on image information detection

Methods and devices for determining scoring models of a three-dimensional animation scene frame are provided. A method can include obtaining a dataset of three-dimensional animation scene frames; obtaining a predetermined stereoscopic effect standard score and a predetermined visual comfort standard score corresponding to each three-dimensional animation scene frame; obtaining the disparity map of each three-dimensional animation scene frame, extracting disparity statistic features of each three-dimensional animation scene frame based on its disparity map, and combining the disparity statistic features into one feature vector; and, determining the stereoscopic effect scoring model and the visual comfort scoring model for a three-dimensional animation scene frame respectively based on the feature vector of each three-dimensional animation scene frame in conjunction with the corresponding stereoscopic effect standard score and visual comfort standard score, in order to reduce the influence of subjective factors from the producers on the scoring.

Method and device for determining scoring models of a three-dimensional animation scene frame

A method and a system for determining parameters of an off-axis virtual camera provided by embodiments of present invention can extract a scene depth map for each video frame from a depth buffer, determine the minimum value of edge depth values of the scene depth map as the closest scene edge depth of each video frame, determine the depth of a first object as the depth of an object of interest of each video frame, use the smaller value between the closest scene edge depth and the depth of an object of interest as the zero-parallax value and obtain a zero-parallax value sequence constituted by the zero-parallax value of each video frame. The present invention realizes automatic determination of the zero parallax of each video frame rather than manual setting thereof, and thus the determination will not be affected by factors such as lack of experience, and the amount of work for an technician is also reduced. Meanwhile, the present invention makes the zero parallax as close as possible to an object of interest without incurring the window occlusion problem, which can improve the stereo perception as much as possible while ensuring the comfortability, and can ensure the using effect of the determined zero parallax.

Method and system for determining parameters of an off-axis virtual camera

Disclose is a gait recognition method, firstly, extracting an initial gait feature of a gait video of a person to be recognized; secondly obtaining a corresponding optimized gait feature according to a trained sub neural network and the initial gait feature; then determining corresponding degrees of similarity according to the optimized gait feature of the person to be recognized and the optimized gait feature of each known person in a matching library, and determining information of the person to be recognized according to information of the known person in the matching library corresponding to the optimized gait feature which has the highest degree of similarity with the optimized gait feature of the person to be recognized.

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Beijing, China

Huadong Ma