Emc IP Holding Company LLC

Dell Products Inc.

Tongji University

Nio Nextev Limited

Telefonaktiebolaget Lm Ericsson (Publ)

Alcatel Lucent

East China University of Science and Technology

Nio Co., Ltd.

Nio (Anhui) Holding Co., Ltd.

Emc IP Holdinq Company LLC

Shanghai Sanden Behr Automotive A/c Co., Ltd.

Shanghai Cas Shenglonda Biotech (Group) Co., Ltd.

Chinese Academy of Sciences

Bin He

Zhen Jia

Danqing Sha

Anzhou Hou

Yong Wang

Jiacheng Ni

Runjie Shen

Zijia Wang

Wenlei Wu

Zhipeng Wang

Yan Chen

Si Ruo Chen

Zhenzhen Lin

Tianxiang Chen

Xuwei Tang

Ping Lu

Zhongpan Zhu

Yanmin Zhou

Pengfei Wu

Liang Shi

Mingyin Hu

Zhongqiu Lin

Kenneth Durazzo

Jinpeng Liu

Peng Li

Min Gong

Qiang Chen

Victor Fong

Zhisong Liu

Chenxi Hu

Ruixue Zhang

Yang Shen

Min Chao Zhao

Xiaowen Zhang

Bin Cheng

Lian Li

WuiChak Wong

Chendong Huang

Aron Yu

Bo Wei

ChunXi Chen

Songhai Wang

Enyuan Qian

Qing Li

Xu Chai

Hongyu Lan

Shaobiao Chai

Guoling Kong

Shuping Song

Yimin Lou

Xudong Wang

Chenxi Ding

Wenke Xu

Changhong Chen

Baorui Wu

Linjiang Li

Aijun Yu

Minxian Zhong

Xiaodong Yao

Junzhong Zou

Yonglian Zhang

Hsiaochang Chan

Qiqi Zhu

Siucheung So

Yiuwa Chung

Quan Shang

Youduan Zhang

Mengyang Liu

Yibin Ren

Bozhi Liang

Yiuwa Epithelial Cell Biology Res Center Chung

Siucheung Epithelial Cell Biol Res Center So

Hsiaochang Epithelial Cell Biol ResCenter Chan

Tunnel defect detecting method and system using unmanned aerial vehicle (UAV) are provided, and the UAV is equipped with a light-emitting diode (LED) module, a camera, a laser radar, an ultrasonic distance meter and an inertial measurement unit (IMU). The method includes: collecting images in a tunnel based on the LED module and the camera to obtain a training image set; training by using the training image set to obtain a defect detecting model, collecting real-time tunnel images, detecting suspected defects to the real-time tunnel images by the defect detecting model, obtaining pose information of the UAV based on the camera, the laser radar, the ultrasonic distance meter and the IMU to control the UAV to hover. The method can realize accurate pose estimation and defect detection in the tunnel with no GPS signals and highly symmetrical inside.

Tunnel defect detecting method and system using unmanned aerial vehicle

The embodiment of this present disclosure provides a control method of unmanned aerial vehicle (UAV) hovering in tunnel, which comprises the following steps: acquiring hovering information of hovering position of UAV; acquiring the position information of the current position of the UAV; determining flight parameters based on hovering information and position information. The flight parameters are used to control the UAV to move from the current position to the hovering position.

Method and system for hovering control of unmanned aerial vehicle in tunnel

Embodiments of the present disclosure provide an image processing method, an electronic device, and a computer program product. The method may include receiving a sub-image containing a target object and location information of the target object from a first computing node, the sub-image being intercepted from a monitoring image by the first computing node through target detection, the monitoring image being acquired by a roadside device and containing the target object, and the first computing node being located near the roadside device. The method may further include determining classification information of the target object based on the sub-image at a second computing node different from the first computing node. In addition, the method may further include generating safety hint information for the target object at the second computing node at least based on the classification information and the location information.

Image processing method, electronic device, and computer program product

Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for monitoring. The method includes receiving, at a second node, a target image from a first node, where the target image is determined as being of a first category based on performing target detection on a road monitoring image obtained by the first node, and the second node is closer to a cloud end than the first node. The method further includes determining, at the second node, a second category of the target image, where the second category is a subcategory of the first category; and in response to the second category being a preset category, sending a warning corresponding to the second category to a terminal device associated with the road monitoring image. According to the method of the embodiments of the present disclosure, the accuracy of road monitoring warnings can be improved.

Method, electronic device, and computer program product for road monitoring

The present disclosure relates to a detection method and system for an underground space by joint use of fixed sensors and unmanned aerial vehicle (UAV) movement detection. The detection system includes underground space sensor nodes and an underground space UAV. The underground space sensor nodes are configured to perform fixed monitoring based on an adaptive optimal layout strategy for an underground structural space. The underground space UAV is configured to calculate a first virtual force, and realize movement detection by means of a virtual force-guided path planning algorithm. The underground space UAV is configured to calculate the first virtual force based on the electronic telescopic anti-collision bars, a second virtual force based on a static perception probability and a third virtual force based on structural evolution knowledge, and realize a fixed node-guided UAV flight detection mode by means of the virtual force-guided path planning algorithm.

Detection method and system for underground space by joint use of fixed sensor and UAV movement detection

Embodiments of the present disclosure relate to a method, a device, and a computer program product for generating a three-dimensional (3D) object model. The method includes generating a first 3D object model based on multiple two-dimensional (2D) images of an object in different views. The method further includes acquiring metadata related to the first 3D object model by searching for information related to the object in at least one of a database and the Internet. The method further includes generating a second 3D object model by combining the first 3D object model and the metadata. The method for generating a 3D object model according to the present disclosure can automatically generate customizable and editable 3D model metadata, thereby significantly reducing labor, saving costs, improving efficiency, and improving user experience.

Method, device, and computer program product for generating 3D object model utilizing multiple metadata components of different types

Embodiments of the present disclosure relate to a method, a device, and a computer program product for communication of the Internet of Things. The method includes receiving data of a first protocol from an access point of a first network, the data of the first protocol coming from an Internet of Things device wirelessly connected to the access point; converting the data of the first protocol into data of a second protocol through a gateway, the gateway having a first protocol stack corresponding to the first protocol and a second protocol stack corresponding to the second protocol; and sending the data of the second protocol to a core network node in a second network. According to the embodiments of the present disclosure, different types of networks can be converged with low cost and high efficiency through adaptation of protocol stacks between the different types of networks.

Method, device, and computer program product for communication of internet of things

The present disclosure relates to a method, a device, and a product for searching data. The method includes: encoding a search input into a first dense vector based on a first multi-modal search model; determining, based on the first dense vector, a distilled data item corresponding to the search input from a distilled dataset corresponding to the first multi-modal search model; encoding, based on the first multi-modal search model, an original data item in an original data subset corresponding to the distilled data item in an original dataset corresponding to the distilled dataset into a second dense vector; and determining, based on the second dense vector, an original data item from the original data subset as a search result corresponding to the search input. The method for searching data according to the present disclosure can improve the efficiency and security of data storage, model reproduction, and multi-modal data management.

Method, device, and product for searching data

An apparatus comprises at least one processing device configured to establish, utilizing a communication network, a remote collaboration session between first and second computing devices associated with first and second users, and to determine data to be exchanged between the first and second computing devices as part of the remote collaboration session. The first and second computing devices each comprise at least one of an augmented reality and a virtual reality computing device. The processing device is also configured to generate different logical network slices of the communication network, each logical network slice having a set of network characteristics associated with at least one type of the data to be exchanged between the first and second computing devices as part of the remote collaboration session. The processing device is further configured to communicate the different types of data between the first and second computing devices utilizing the logical network slices.

Remote collaboration between users utilizing at least one of augmented reality and virtual reality computing devices

Embodiments disclosed herein relate to a method, a device, and a computer program product for training an image classification model. The method includes: determining a first classification result obtained by the image classification model on pixel points of a sample image in a first training iteration, wherein the first classification result indicates that each of the pixel points belongs to one of a plurality of object classes; determining a second classification result obtained by the image classification model on the pixel points of the sample image in a second training iteration; determining, based on the first classification result and the second classification result, a first set of recall rates and a second set of recall rates for the plurality of object classes; and adjusting, based on the first set of recall rates and the second set of recall rates, the image classification model to obtain a trained image classification model.

Method, device, and computer program product for training image classification model

The method includes: obtaining point cloud information collected by a depth camera, laser information collected by a lidar, and motion information of an unmanned aerial vehicle (UAV); generating a raster map based on the laser information, and obtaining pose information of the UAV based on the motion information; obtaining a map model through fusing the point cloud information, the raster map, and the pose information by a Bayesian fusion method; and correcting a latest map model by feature matching based on a previous map model.

Methods and systems for modeling poor texture tunnels based on vision-lidar coupling

Disclosed is an unmanned aerial vehicle (UAV) positioning method based on a millimeter-wave radar, including a calibration stage and a positioning stage. The calibration stage includes: acquiring ground coordinates of the unmanned aerial vehicle; and extracting feature points from radar point cloud data and get the ground coordinates of the feature points. The positioning stage includes: acquiring radar point cloud data of a current frame and pre-processing; acquiring UAV motion data and fuse the data with radar point cloud data; extracting characteristic line segment from radar point cloud data; registering the characteristic line segment of the current frame with the characteristic line segment of the previous frame, and finding matching feature points and newly added feature points; and obtaining the ground coordinates of UAV and the ground coordinates of newly added feature points based on the ground coordinates of matched feature points on the map.

Unmanned aerial vehicle positioning method based on millimeter-wave radar

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for data processing. The method for data processing includes: generating, based on a category of a target data content segment, a target tag for the target data content segment; acquiring a reference tag set, a reference tag in the reference tag set being generated based on a category of a historical data content segment; and determining redundancy of the target data content segment based on comparison between the target tag and the reference tag set. In this way, the distribution of a large amount of redundant data from an edge server to a terminal device can be avoided, thereby saving bandwidth, improving delay, and further providing a better user experience for AR/VR content experience.

Method, electronic device, and computer program product for data processing

The present disclosure relates to a method, a device, and a program product for deploying a visual resource. In one method, a resource requirement of a vision application for the visual resource in a network system is acquired. Based on the resource requirement, the visual resource which will be called by the vision application is predicted. Based on processing capabilities of various edge devices and the visual resource in the network system, an edge device located near a terminal device in the network system is identified, wherein the terminal device is configured to run the vision application. Based on a time requirement in the resource requirement, the visual resource is deployed to the edge device. Further, a corresponding device and a corresponding program product are provided.

Method, device, and computer program product for deploying visual resource

A system and a method are provided for feasibility evaluation of UAV Digital Twin based on Vicon motion capture system is disclosed, which establishes a mission feasibility evaluation model according to flight history data of a target UAV acquired by the UAV Digital Twin system. The mission feasibility evaluation model includes a UAV trajectory prediction module and a mission feasibility determination module. The UAV trajectory prediction module acquires real-time position and attitude information of the target UAV according to the Vicon motion capture system, and predicts target flight trajectory of the target UAV according to the real-time position and attitude information. The mission feasibility determination module compares the position difference between an end point of the target flight trajectory and preset designated mission point to evaluate feasibility of target mission of the target UAV.

Method for feasibility evaluation of UAV digital twin based on vicon motion capture system

Growing community of inventors

Shanghai, China

Bin He