Hon Hai Precision Industry Co., Ltd.

Tsinghua University

Beijing Funate Innovation Technology Co., Ltd.

International Business Machines Corporation

China Mobile Communications Group Co., Ltd.

China Mobile Communication Co., Ltd Research Institute

Beijing University of Posts and Telecommunications

Utopus Insights, Inc.

Huawei Technologies Co., Limited

Zte Corporation

Byd Company Limited

Globalfoundries Inc.

Nokia Technologies Oy

Kyndryl, Inc.

Beijing Sensetime Technology Development Co., Ltd.

Liang Liu

Shou-Shan Fan

Kai-Li Jiang

Li Qian

Chen Feng

Yang Wei

Peng Liu

Yu-Quan Wang

Pi-Jin Chen

Li Li

Jie Tang

Lin Xiao

Jia-Ping Wang

Wei Zhuang

Junmei Qu

Zhao-Fu Hu

Li Fan

Cai-Lin Guo

Chang-Hong Liu

Zhuo Chen

Qun-Qing Li

Kai Liu

Bing-Chu Du

Ga-Lane Chen

Lei-Mei Sheng

Wen Jun Yin

Wen-Mei Zhao

Jia-Shyong Cheng

Yuan-Chao Yang

Jun Mei Qu

Yi Ge

Qi Cai

Jeah-Sheng Wu

Ying Li

Jin Dong

Caesar Chen

Xiao Guang Rui

Feng Drake Zhu

Hai-Yan Hao

Hao Wang

Hai Feng Wang

Yuan Yao

Nan Hu

Xin Xin Bai

Yang Liu

Bo Yang

Qi Yu

Qian Ma

Ju Wei Shi

Yu Fen Yuan

Chao Zhu

Qing-Yu Zhao

Rui Xiong Tian

Jerry Wu

Krishna Chaitanya Ratakonda

Xing Zhang

Nan Li

Yong Sen Zheng

Feng-Yuen Dai

Qingbo Wang

Li-Na Zhang

Ying Zhou

Fang Quan Xie

Qi Ming Tian

Rui Hou

Chi-Chuang Ho

Wei Liu

Lei Chen

Wei Wei

Chao Zhang

Xiaodong Xu

Guang Yang

Gang Li

Hua Yin Huang

Fang Liu

Wen Jie Wang

Sai Zeng

Yang Wu

Na Li

Duan-Liang Zhou

Hongbin Luo

Yi Ren

Wei Xue

Qing Bo Wang

Manish Mahesh Modh

Shuai Liu

Ke Wei Sun

Nan Liu

Zhe Zhang

Chuanfu Wang

Xiaohua Tang

Feng Yang

Ningyu Chen

Hui Ma

Ying-Hui Sun

Xuguang Zhou

Lingyun Wang

Xiaoran Zhang

Xiao Zhong

A data transmission method includes: a terminal in an RRC inactive state transmits a data packet and an RRC resume request to a first base station or the CU of the first base station or the DU of the first base station; receive an RRC release message transmitted by the first base station or the CU of the first base station or the DU of the first base station.

Provide data transmission to avoid large transmission delay

A phase shifter, an electrically regulated antenna, a network device, and a phase shifter manufacturing method are provided. The phase shifter includes: a shielding member, and an adjustable dielectric layer provided in a shielding space formed by the shielding member; the adjustable dielectric layer includes a line layer and a dielectric member. The line layer includes a feed circuit and an empty slot arranged corresponding to the feed circuit and used to form an air strip line. The dielectric member is inserted into the empty slot, and a dielectric constant of the feed circuit is changed by controlling a depth of the dielectric member inserted into the empty slot.

Phase shifter, electrically regulated antenna, network device, and phase shifter manufacturing method

An electronic device establishes a connection to an audio device, the electronic device displays a first image, the audio device outputs audio data synchronized with the first image, the electronic device receives a projection operation, and the electronic device sends projection information of the first image to a display device based on the projection operation. The display device displays the first image based on the projection information, and the audio device outputs the audio data synchronized with the first image.

Projection method and related apparatus

Lamp bracket

LED grow light

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

Embodiments of the present disclosure provide a reporting method and a configuration method for minimization of drive tests information, a terminal and a network device. The reporting method applied to a terminal side comprises: receiving configuration information sent by a network device for a minimization of drive tests information report; creating, according to the configuration information, a measurement log regarding minimization of drive tests information; and reporting to the network device, the measurement log regarding the minimization of drive tests information.

Reporting method and configuration method for minimization of drive tests information, terminal and network device

The present application discloses a conditional handover (CHO) processing method and apparatus, a related device, and a storage medium. The method comprises: a first network device determining that CHO configuration information is invalid and indicating to a terminal that the CHO configuration information is invalid.

Conditional handover processing method and apparatus, related device, and storage medium

The present disclosure provides a random access method, a UE and a network side device. The random access method includes: initiating random access on one or more SS blocks; and sending random access information on each SS block to the network side device.

Random access method, user equipment and network side device

The present invention relates to a method and apparatus for controlling a wind turbine. The method includes: dividing a plurality of wind turbines into at least one group based on a similarity in status information of the plurality of wind turbines; in response to having detected a fault in a first wind turbine of the plurality of wind turbines, searching a group to which the first wind turbine belongs for a second wind turbine matching status information of the first wind turbine; and controlling the first wind turbine based on parameters from the second wind turbine.

Controlling wind turbine

Embodiments of the disclosure provide a method, device and computer readable medium for authentication. According to embodiments of the present disclosure, only the first terminal device in a group of terminal devices which requests to connect to a network needs to finish the whole authentication. Upon the first terminal device successfully accesses to the network, the remaining terminal devices in the same group can ignore the authentication. In this way, time for authentication is reduced and the calculation efforts related to authentication is reduced for a large number for terminal devices.

Methods, devices and computer readable medium for authentication in communication

This disclosure provides a computer-implemented method. The method may comprise estimating a value range of a water body parameter based on measured data for a water quality indicator of a first set of time-spatial points and measured data for the water quality indicator of a second set of time-spatial points; and determining an optimal value of the water body parameter from the estimated value range by comparing the measured data for the water quality indicator of the second set and simulated data for the water quality indicator of the second set, wherein the simulated data for the water quality indicator of the second set is obtained based on a fluid dynamic model using the measured data for the water quality indicator of the first set as an input of the fluid dynamic model and using a value in the estimated value range as a parameter of the fluid dynamic model.

Water quality prediction

A handover method, information exchange method, device and computer readable storage medium are provided. In some embodiments, a first network device may acquire multi-connectivity capability information of a candidate second network device during a handover process, so that the first network device may select, based on the multi-connectivity capability information, a candidate second network device capable of providing an multi-connectivity service as a second network device.

Handover method, information exchange method, device and computer readable storage medium

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

Growing community of inventors

Beijing, China

Liang Liu