Other

University of Electronic Science and Technology of China

Chengdu University of Technology

Xihua University

Science & Technology Department of Sichuan Province

Liang Chen

Jie Tang

Hong Wen

Yi Zhang

Huanhuan Song

Xiao-Yu Li

Xiao Yang

Peng Hu

Zhiliang Zhao

Xiaoyue Lai

Aike Kan

Rui Luo

Chunpeng He

Kaiyu Qin

Yebin Wang

Jason Xu

Wei Yang

Xingyang Tan

Xinchen Xu

Rui Du

An airframe structure and an unmanned aerial vehicle thereof is disclosed, which belongs to the technical field of the unmanned aerial vehicle. The airframe structure includes: a main airframe; at least two main wings, which are symmetrically provided on the two sides of the main airframe, and which are provided with a propeller and a motor to provide power to the propeller; an upper vertical tail wing, which is provided at the upper end of the main airframe; and a lower vertical tail wing, which is provided at the lower end of the main airframe. The characteristics of a fixed-wing unmanned aerial vehicle and a multi-rotor-wing unmanned aerial vehicle are considered.

Airframe structure and unmanned aerial vehicle thereof with upper and lower vertical tail wings

The invention relates to the field of tourism risk prediction, and particularly to a high land tourism safety risk warning method based on reinforcement learning, comprising: step S1, storing a tourism data set in a historical time period of a tourist site in a database, and based on the database, initializing a tourism risk warning indication function Q and a risk target function T; step S2, based on a tourism risk assessment factor sequence and combined with the selected tourism data set in the historical time period, training the tourism risk warning indication function Q and the risk target function T; and step 3, combined with an input time parameter and the trained tourism risk warning indication function Q, obtaining a warning action under the time parameter, and obtaining a risk assessment rank of the tourist site.

High land tourism safety risk warning method based on reinforcement learning

A method for physical layer secure transmission against an arbitrary number of eavesdropping antennas includes: S: communication between legitimate transmitter Alice and legitimate receiver Bob is confirmed; S: Alice randomly generates a key bit bwith Mbits, maps the key bit binto a key symbol K, and performs an XOR on the key bit band to-be-transmitted confidential information b to obtain an encrypted bits b; S: Bob transmits a pilot sequence to Alice, and Alice calculates a candidate precoding space W and transmits modulated symbol streams s=(s, . . , s) by using precoding W(e); S: Bob measures received signal strength of each antenna, estimates the corresponding antenna vector e, inversely maps the vector e to obtain key symbols and key bits, and demodulates the received symbol streams in sequence at each activated antenna to obtain demodulated ciphertext bits; S: Bob performs an XOR on observed key bits and the demodulated ciphertext bits to obtain the confidential information.

Physical layer secure communication against an eavesdropper with arbitrary number of eavesdropping antennas

3D printer

Methods and apparatus are provided for communicating a message to multiple radio groups (). An exemplary method () involves configuring, by an initiating radio device (), a header portion of a message () for a multi-group communication session including the plurality of radio groups () and transmitting the message (). Each respective radio device () of each respective radio group () is configured to provide output () corresponding to a content portion of the message in response to identifying its own radio group () in the header portion of the message ().

Multi-group communications and related devices

Growing community of inventors

Chengdu, China

Liang Chen