Chinese Academy of Sciences

Empire Technology Development LLC

Google Inc.

Beijing Ten Dimensions Technology Co., Ltd.

Zhen Shen

Feiyue Wang

Gang Xiong

Xue Ping Liu

Xuefeng Song

Qi Cheng Li

Chao Guo

Zuo Yan

Xiuqin Shang

Jun Yan

Xiao Wang

Yuqing Li

Yi Xie

Li Wan

Jiantong Li

Hang Gao

Jiawei Liao

Mengzhen Kang

Meihua Zhao

Can Luo

Xisong Dong

Zhengpeng Wu

Fei-Yue Wang

An online monitoring device of 3D printing equipment includes a signal collection module, a signal processing module, a feature extraction module, a monitoring module and a knowledge base module. A vibration signal of a preset component of the 3D printing equipment is collected by a vibration sensor. The collected vibration signal of each preset component is converted from an analog signal to a digital signal and the spectrum characteristics are extracted. Based on the spectrum characteristics of each preset component, the operation state type of the preset component is obtained by a comparative analysis model. The knowledge base module is configured to store newly added samples and initial samples of the 3D printing equipment. The initial samples include spectrum characteristic information and corresponding fault category of known faults, and the newly added samples include spectrum characteristic information and corresponding fault category of new faults.

Online monitoring device and system for a 3D printing device

A neural network-based error compensation method for 3D printing includes: compensating an input model by a deformation network/inverse deformation network constructed and trained according to a 3D printing deformation function/inverse deformation function, and performing the 3D printing based on the compensated model. Training samples of the deformation network/inverse deformation network include to-be-printed model samples and printed model samples. The deformation network constructed according to the 3D printing deformation function is marked as a first network. During training of the first network, the to-be-printed model samples are used as real input models, and the printed model samples are used as real output models. The inverse deformation network constructed according to the 3D printing inverse deformation function is marked as a second network. During training of the second network, the printed model samples are used as real input models, and the to-be-printed model samples are used as real output models.

Neural network-based error compensation method, system and device for 3D printing

The present invention relates to a technical field of 3D printing, and more particularly to a 3D printer spray nozzle structure and a method thereof for controlling speed and precision. According to the present invention, a feeding pipeline is embedded in an external shell, the feeding pipeline and an extruder are coaxially connected; the extruder is driven by a driving device, so as to rotate relative to the feeding pipeline. A rotation angle of the extruder relative to the feeding pipeline is controlled by rotation of a motor, for controlling a filament area actually sprayed by the extrude, in such a manner that printing speed and precision is controlled for suiting different requirements of different printing area. The present invention controls the printing speed and precision, for improving overall printing speed with precision requirements satisfied, and is applicable to 3D printer spray nozzle structure and controlling.

3D printer spray nozzle structure and method thereof for controlling speed and precision

A pruning robot system, which comprises: a signal tag device () for detecting and storing information of trees and crops and positioning information, and assisting positioning; a robot () comprising a central processing device () for storing and analyzing data information of each part of the robot () and issuing action instructions to each part of the robot (), and a positioning and navigating device () for positioning and navigating the robot (), and for planning a path and providing obstacle-avoiding navigation for the robot () according to an electronic map; a cloud platform terminal (), which is in connection and communication with the central processing device () of the robot () and is used for storing data of trees and crops as well as detection data of the robot (), and for planning a path for the robot () through computing and experimenting according to the information data; a map building device () for building a three-dimensional electronic map corresponding to the plantation through field-detection by the robot (). The pruning robot system realizes positioning in the plantation, robot () path planning, pruning information collection and automatic pruning.

Pruning robot system

In one example, a system may include a display including a display screen; a display controller configured to display visual information on the display screen of the display screen, control at least portions of one or more pixels corresponding to the display screen to render the display screen transparent; and a depth camera, positioned behind the display screen, that is configured to obtain depth image information of one or more objects in front of the display screen when at least portions of the one or more pixels are rendered transparent.

Detection of user gestures

A method includes receiving landing page information from a content provider, the landing page information being representative of one or more graphical aspects of a landing page associated with the content provider, generating a landing page image representative of the landing page based at least in part on the landing page information, the landing page image being different from the landing page, specifying an association between the landing page image and a content item associated with the content provider, generating a control for presentation along with the content item that, when activated, causes the display of the landing page image providing, in association with a resource, the content item in combination with the control.

Providing content

A 3D printer spray nozzle includes a feeding pipeline, an extruder located under the feeding pipeline, an external housing and a driving device; wherein the feeding pipeline is embedded in the external housing, the extruder is coaxially fixed under the feeding pipeline, a center of gravity of a cross section area of an inner channel of the feeding pipeline and that of the extruder are located on a same axis which is perpendicular to the cross section area of the inner channel of the feeding pipeline and that of the extruder, the feeding pipeline is driven by the driving device to rotate around the axis relative to the extruder, thereby aiming at different rotation angles, widths of extruding forming areas of the extruder at a same direction are different so as to adjust a cross section area of a sprayed filament.

3D printer spray nozzle capable of adjusting cross section areas of extruded materials and method for controlling printing speed and precision of the 3D printer spray nozzle

Technologies are generally described for systems and methods effective to efficiently schedule a workload in a cloud computing system. In one example, calendar data is collected from respective sets of devices associated with respective sets of subscribers and a workload to be performed at a specific time or range of time is predicted based in part on an analysis of calendar data. Moreover, timing data associated with a set of predicted requests is determined based on the analysis and at least a portion of cloud computing resources that service the set of predicted requests are dynamically scheduled based on timing data.

Workload prediction for network-based computing

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Beijing, China

Zhen Shen