Tongji University

General Electric Company

Inventec Corp.

Inventec (pudong) Technology Corporation

Shanghai Micro Electronics Equipment (group) Co., Ltd.

Jee Automation Equipment (shanghai) Co., Ltd.

Other

China Petroleum & Chemical Corporation

Chia Tai Tianqing Pharmaceutical Group Co., Ltd.

Shanghai Research Institute of Petrochemical Technology, Sinopec

Stubhub, Inc.

Sq Technology(shanghai) Corporation

Shanghai Institute of Biological Products Co., Ltd.

Robert Boschgmbh

Shanghai Micro Electronics Equipment Co., Ltd.

Lin Zhang

Hong Chen

Qiang Meng

Rongjie Yu

Hanghang Liu

Jinlong Hong

Bin Li

James Jun Xu

Rolando Luis Martinez

Lei Liu

Jian Xu

Xuexin Ding

Venkata Subramanya Sarma Devarakonda

Wei Pan

Ting Wang

Jun Chen

Jing Li

Chao Yan

Jun Jiang

Chang Liu

Hui Calvin Yang

Ashley Gibson

Fei Liu

Xin Tian

Zaiku Xie

Werner Schiemann

Joachim Joos

Weiji Liu

Xiaolei Liu

Yingshun Jiang

Fangkang Shen

Yanqi Guo

Haobo Sun

Chunli Shen

Zhigang Zhang

Cass Patrick Costello

Hai Xia

Liwei Wu

Xiaowen Zhang

Zhijie Zhu

Jiahu Wu

Lin Guo

Xin Zhao

Dong Dong Yao

Qiu-Yue Duan

Yangdong Wang

Shenglin Chen

Xin-Ying Xie

Su Liu

Zhihua Li

Haibo Zhou

Aoxiang Li

Minsheng Jiao

Yuing Guo

Aidong Qu

Jingye Zhu

Jianhua Qiu

Xiaofei Song

Hongyuan Liang

Fanhong Xu

Chengyi Zhang

Jiaxing Lu

Luyi Jiang

Chunlai Zhao

Lina Wu

Yuesheng Xie

Xuefei Fan

Jin Lu

Jinyuan Shi

Wangren Xu

Zifang Zhu

Yuxuan Wang

Xinxuan Kong

Yanjun Huang

Haoqi Hu

Subing Duan

Bo Deng

Shanrui Zhang

Binxin Zhou

Wenqian Jiao

Junjie Su

Rolando Martinez

A boundary-scan standard pin test system is disclosed. In the system, to-be-tested slots are electrically connected to form different to-be-tested series chains, or riser cards which are inserted into the to-be-tested slots are electrically connected to each other through first boundary-scan input interfaces and first boundary-scan output interfaces thereof or through second boundary-scan input interfaces and second boundary-scan output interfaces thereof, to form different riser-card series chains; a TAP controller provides a boundary-scan format test signal to a to-be-tested series chain or a riser-card series chain to make a test card perform a boundary-scan standard pin test, so as to achieve the technical effect of efficiency of providing the boundary-scan standard pin test for the to-be-tested slots on different to-be-tested boards by a testing machine.

JTAG standard pin test system

The present application belongs to the field of medicine, and in particular relates to a crystal of a diarylthiohydantoin compound, a preparation method therefor, and use thereof in the preparation of medicaments for treating related diseases which are androgen-mediated.

Crystal of diarylthiohydantoin compound

A catalyst contains a metal oxide, and a molecular sieve, in a crystal form, having a topological pore structure. The metal oxide is centrally distributed on the surface of the molecular sieve. Grains of the molecular sieve are exposed to at least three families of crystal planes. The family of crystal plane with the largest pore size in topology is occupied by the metal oxide by no more than 30%, preferably no more than 20%, or no more than 10%.

Catalyst including molecular sieve having topological pore structure, preparation method therefor and use thereof

The present invention provides a real-time path planning method taking into account dynamic properties of vehicles; the method includes a calculation without connecting to internet of a reachable set and an online path planning; in the calculation without connecting to internet of the reachable set, all vehicle safety states are traversed by means of the vehicle model and the wheel model, thereby predicting the position set capable of being reached by the vehicle at a next moment; in the online path planning, by means of calculating the position set capable of being reached by the vehicle at the next moment with connecting to internet, non-linear dynamic constraints are provided for the exploration of the artificial potential field method, achieving the purpose of real-time planning while taking into account dynamic properties of the vehicles.

Real-time path planning method for intelligent driving taking into account dynamic properties of high-precision vehicles

The present disclosure relates to a real-time control method for an additional yaw moment of a distributed drive electric vehicle, including the following steps: acquiring and inputting a real-time motion state of the distributed drive electric vehicle into a vehicle dynamics model, using a yaw rate and a sideslip angle of the distributed drive electric vehicle as tracking targets to suppress actuation energy, and performing optimization calculation on an additional yaw moment to acquire an amount of the additional yaw moment distributed for each tire; and in the optimization calculation process, a linear expression of the sideslip angle with respect to the additional yaw moment and a linear expression of the yaw rate with respect to the additional yaw moment are constructed, so as to perform search calculation on the additional yaw moment.

Real-time control method for additional yaw moment of distributed drive electric vehicle

The present disclosure relates to a multi-constraint optimal distribution method for a torque vectoring of a distributed drive electric vehicle, which includes: obtaining a vehicle real-time motion state, taking a pre-constructed tire rotation dynamics model as a control object of an optimal distribution problem, establishing an objective function for tracking a desired additional yaw moment, tracking a desired tire slip ratio, and suppressing a motor output energy consumption to a minimum value, establishing a corresponding system constraint, solving the optimal distribution problem, and obtaining an optimal distribution solution for a torque vectoring of each tire. Compared with the prior art, the present disclosure can effectively track a desired control target, can also effectively constrain an important state and a control input of the vehicle, and can avoid wrong solutions or no solution caused by an optimization problem.

Multi-constraint optimal distribution method for torque vectoring of distributed drive electric vehicle

A coordinated control method for electric vehicles having independent four-wheel driving and steering, comprising the steps of: calculating to obtain a desired value of yaw velocity according to the steering angle and the current vehicle driving speed, and limiting the desired value of yaw velocity according to the current road adhesion condition; constructing an optimization problem according to the current vehicle motion state and the desired value of yaw velocity, and solving the optimization problem to obtain a desired active rear wheel steering angle control variable and a desired additional yaw moment control variable; calculating to obtain an additional torque of each wheel according to a desired additional yaw moment control variable, obtaining a desired active rear wheel steering angle, and sending the additional torque of each wheel and the desired active rear wheel steering angle to an executor of the vehicle for performing a coordinated control.

Coordinated control method for electric vehicles having independent four-wheel driving and steering

Disclosed is a decision-making and planning integrated method for a nonconservative intelligent vehicle in a complex heterogeneous environment, including the following steps: offline establishing and training a social interaction knowledge learning model; obtaining state data of the traffic participants and state data of an intelligent vehicle online in real time, and splicing the state data to obtain an environmental state; using the environmental state as an input to the trained social interaction knowledge learning model to obtain predicted trajectories of all traffic participants including the nonconservative intelligent vehicle; updating the environmental state based on the predicted trajectories; and inputting the updated environmental state to the social interaction knowledge learning model to complete trajectory decision-making and planning for the nonconservative intelligent vehicle by iteration, where a planned trajectory of the nonconservative intelligent vehicle is a splicing result of a first point of a predicted trajectory obtained by each iteration.

Decision-making and planning integrated method for nonconservative intelligent vehicle

Disclosed is a method for predicting a trajectory of a traffic participant in a complex heterogeneous environment, including the following steps: obtaining traffic participant information in a complex heterogeneous environment; arranging and numbering traffic participant classes based on the class information, to obtain serial numbers of the traffic participant classes; establishing a position graph, a velocity graph, an acceleration graph, and a class graph, into each of which expert experience is introduced; and capturing topological structure relationships and time dependence relationships to obtain a position hidden state, a velocity hidden state, an acceleration hidden state, and a class hidden state; classifying the position hidden state, the velocity hidden state, the acceleration hidden state, and the class hidden state to obtain a hidden state set of traffic participants; and decoding hidden states of the traffic participants separately using a corresponding decoder to obtain future trajectory predictions of the traffic participants.

Method for predicting trajectory of traffic participant in complex heterogeneous environment

A vehicle state estimation method based on adaptive total variation denoising (TVD) filtering includes the following steps: step 1: collection and preprocessing of an original signal of a vehicle; step 2: noise level evaluation, step 3: Teager-Kaiser energy evaluation; step 4: optimization problem construction; and step 5: application of a filtered signal in the step 4 in the estimation of a vehicle state. The vehicle state estimation method is mainly based on the global noise level characteristic and the local intensity change characteristic of the vehicle system state data, and adaptive filtering of parameters is achieved by means of a TVD filtering method. The signal is denoised to the maximum extent, peak information of the signal is retained while the data smoothness is maintained, and then the signal is used for vehicle state estimation, working condition identification and the like.

Vehicle state estimation method based on adaptive total variation denoising filtering

Disclosed is a steering wheel torque feedback optimization control method for a differential braking system, including: estimating a tire lateral force of a vehicle; predicting a state of the vehicle at a next moment under a non-differential torque condition; predicting a state of the vehicle at the next moment under a differential torque condition with an additional yaw moment; calculating tire lateral forces at the next moment under the differential torque condition and the non-differential torque condition separately based on the predicted states of the vehicle at the next moment under the differential torque condition and the non-differential torque condition, and obtaining a predicted one-step change value of a front tire lateral force by performing subtraction; performing integral calculation based on time, and obtaining a continuous change amount of the tire lateral force; and correcting a desired moment of a steering power motor.

Steering wheel torque feedback optimization control method for differential braking system

Disclosed is an intelligent driving test method with corner cases dynamically completed based on human feedback, including the following steps: obtaining an initial state of a real environment; building an original scene driver based on reinforcement learning, and correcting behavior selection to obtain an exploratory behavior; testing in a testing environment, performing expert evaluation, and building a dynamic corner case completion library based on the human feedback; building an imitation learning driver based on the human feedback, updating a policy based on test data in the dynamic corner case completion library, training the imitation learning driver, and outputting a corner case reproduction behavior; obtaining the initial state of the real environment and an initial environmental state of the dynamic corner case completion library, and selecting a scene driver; and outputting a corresponding behavior, and testing in the testing environment based on the corresponding behavior to obtain a test result.

Intelligent driving test method with corner cases dynamically completed based on human feedback

The present disclosure relates to a self-adaptive guided advanced driver assistance system (ADAS) considering a driving skill difference between drivers, including a driving skill classification module, configured to calculate a vehicle stability margin based on a vehicle state, and obtain a corresponding driving skill classification result with the vehicle stability margin and a driver state as inputs of a driving skill classification model; a skill learning range classification module, configured to obtain the vehicle stability margin and a distance between a vehicle and a lane line boundary, and use a skill learning range classification model to obtain a skill learning range classification result; and a self-adaptive guided driving right allocation module, configured to realize driving right allocation control based on the driving skill classification result and the skill learning range classification result, and generate an assisted steering torque acting on a vehicle steering system.

Self-adaptive guided advanced driver assistance system considering driving skill difference between drivers

Provided is an anti-HER3 humanized monoclonal antibody. The antibody binds HER3 antigen, has high affinity and biological activity and low immunogenicity, and is stable in structure. The antibody can be used for preparing drugs for preventing or treating HER3-related diseases.

Anti-HER3 humanized monoclonal antibody

Disclosed is a high voltage signal and low voltage signal sampling and transmission system based on a high voltage MCU, including a sampling unit, a high voltage processing unit, a communication unit and a low voltage processing unit. The sampling unit includes a bus voltage sampling module, a phase voltage detection module and an IGBT temperature detection module. The high voltage processing unit adopts the high voltage MCU, and the high voltage MCU is configured to perform state monitoring and analog-to-digital conversion on the three-way analog detection signals, and output a digital signal to the communication unit; the communication unit adopts an isolated communication unit to transmit the three-way digital signal converted by the high voltage MCU to the low voltage processing unit; the low voltage processing unit adopts a low voltage MCU to realize sampling and communication of high and low voltage sampling signals.

High voltage signal and low voltage signal sampling and transmission system based on high voltage MCU

Growing community of inventors

Shanghai, China

Lin Zhang