Chinese Academy of Sciences

Beijing Jingdong Century Trading Co., Ltd.

Beijing Institute of Technology

Beijing Wodong Tianjun Information Technology Co., Ltd.

Capital Medical University

Zhijing Nanotech Co., Ltd

Jingjing Wang

Yongyou Yang

Qingyun Di

Fei Li

Ran Zhang

Haizheng Zhong

Qijun Xie

Jiangxu Wu

Jin Guo

Xiuhua Guo

Ni Gao

Yanxia Luo

Miaofeng Hu

Shiwei Ma

Mingjun Nie

The present disclosure provides an information acquiring method and apparatus, and an attention degree detecting system. The information acquiring method includes: acquiring a video frame captured by a video capturing equipment detecting whether a pedestrian exists in the video frame by using a first deep-learning model; processing the video frame by using a second deep-learning model to generate a pedestrian bounding box of the pedestrian under a condition that the pedestrian exists in the video frame, and assigning a corresponding pedestrian identifier to the pedestrian bounding box; identifying an image in the pedestrian bounding box by using a third deep-learning model to identify feature information of the pedestrian; and writing a pedestrian historical data into a database, wherein the pedestrian historical data comprises a timestamp and an identifier of the video frame, coordinate information of the pedestrian bounding box, the pedestrian identifier and the feature information of the pedestrian.

Method and device for acquiring information, and system for detecting attention degree

The present application discloses a composite light-emitting material, a production method thereof, and use thereof, wherein the composite light-emitting material has a perovskite nanomaterial and a matrix; the perovskite nanomaterial comprises γ-CsPbIand an addition element M; and the addition element M is selected from at least one of Li, Na, K, and Rb.

Composite light-emitting material, production method thereof, and use thereof

Disclosed are a signal transmission method, device and equipment based on drilling equipment, and a medium, the signal transmission is applied to a signal transmission system of the drilling equipment, the signal transmission system includes a master node and a plurality of slave nodes, and the master node communicates with the slave nodes through a 1-wire bus. The signal transmission method includes: sending, by the master node, an instruction signal coupled by a TTL signal and a data request signal to a specified slave node, wherein the data request signal is a critical identifiable signal with the same period as the TTL signal so as to ensure that the system can identify the logic level of the instruction signal and occupy the 1-wire bus in real time; and receiving, by the specified slave node, the instruction signal, decoupling the instruction signal to obtain the data request signal, obtaining a reply signal according to the data request signal, coupling the reply signal with the TTL signal to obtain a feedback signal, and sending the feedback signal to the master node.

Signal transmission method, device and equipment based on drilling equipment, and medium

Disclosed is a data transmission method. The method includes: sending, instruction information of data transmission to a slave node in a preset first cycle; judging, by the slave node, whether data should be sent according to the instruction information of data transmission; sending the data to the a master node if the slave node judges that the data needs to be sent; and sending, by other slave nodes, the data sequentially to the master node according to a preset slave node priority, an electric potential condition and a data state of the other slave nodes in a preset second cycle. In a preset first cycle, data is actively requested from a slave node, and in a preset second cycle, other slave nodes can actively send the data to a master node according to a preset slave node priority, an electric potential condition and a data state of the other slave nodes.

Data transmission method and equipment based on single line

Disclosed is a method for establishing a prediction model based on a multidimensional texture of brain nuclear magnetic resonance images. Images are segmented using a region growing method, a contourlet transform method is used to extract an edge texture feature parameter of ROIs, a multidimensional database is established, and a prediction model is established using various data mining methods, comprising a Gaussian process, a support vector machine, a random forest, a Lasso regression and a semi-supervised support vector machine. The ROIs comprise a hippocampus region and an entorhinal cortex region.

Method for establishing prediction model based on multidimensional texture of brain nuclear magnetic resonance images

Growing community of inventors

Beijing, China

Jingjing Wang