公开(公告)号：US20220354411A1

公开(公告)日：2022-11-10

申请号：US17634418

申请日：2020-11-30

Applicant: SOUTHEAST UNIVERSITY

Inventor： Baoguo XU ,  Leying DENG ,  Yifei WANG ,  Xin WANG ,  Aiguo SONG

IPC: A61B5/372 ,  A61B5/00

Abstract: Disclosed is a natural movement electroencephalogram (EEG) recognition method based on source localization and a brain network, which includes the following steps: (1) performing multi-channel EEG measurement for natural movements; (2) preprocessing acquired EEG signals, and extracting the movement-related cortical potential (MRCP), and θ, α, β, and γ rhythms; (3) determining a lead field matrix of the signals, calculating initial solutions of sources by means of L1 regularization constraint, and then performing iteration by means of successive over-relaxation to obtain a source localization result; (4) by using the sources as nodes, calculating PLV between each pair of sources at each time point by means of short-time sliding window, and establishing brain networks; and (5) calculating a network adjacency matrix at each time point and five brain network indicators, introducing these features into a classifier for training and testing, and conducting a statistical test for the brain network indicators. The present disclosure makes improvements to the conventional source localization method by using the T-wMNE algorithm in combination with successive over-relaxation, and establishes brain networks by using the sources as nodes, thus improving the EEG decoding accuracy for natural movements and revealing the neural mechanism of the human body.

公开(公告)号：US20240359320A1

公开(公告)日：2024-10-31

申请号：US18246860

申请日：2022-08-12

Applicant: Southeast University

Inventor： Baoguo XU ,  Yifei WANG ,  Xin WANG ,  Jiajing WANG ,  Aiguo SONG

IPC: B25J9/16

CPC classification number: B25J9/163

Abstract: Disclosed in the present disclosure is a method for identifying skills of a human-machine cooperation robot based on a generative adversarial imitation learning, which includes: firstly, defining classifications of human-machine cooperation skills that needed to be conducted; conducing demonstrations on different classifications of the skills by human experts, and collecting image information and data in the demonstrations to make calibrations; identifying the image information by means of image processing, extracting effective feature vectors capable of clearly distinguishing the different classifications of the skills and taking the effective feature vectors as demonstration teaching data; training a plurality of discriminators respectively by utilizing the acquired demonstration teaching data through a method of the generative adversarial imitation learning; extracting user's data after the training and putting the data into different discriminators, and taking a discriminator corresponding to a maximum value eventually output as an output result of identifying the skills. The present disclosure innovatively combines a computer image recognition with the famous generative adversarial imitation learning in a imitation learning, which has short training time and high learning efficiencies.

公开(公告)号：US20230081697A1

公开(公告)日：2023-03-16

申请号：US17992132

申请日：2022-11-22

Applicant: SOUTHEAST UNIVERSITY

Inventor： Wei GE ,  Chongyang LI ,  Leidong ZHENG ,  Yifei WANG

IPC: G06F21/60

Abstract: The present disclosure discloses an information security application-oriented reconfigurable system chip compiler and an automatic compilation method. The method includes the following steps: firstly, inputting a source program of a cryptographic algorithm; then, executing a software compilation function syntax check of the source program, and when the check result is passed, performing compilation mapping using a compiler; next, executing the cryptographic algorithm by simulation running using a simulator, and generating a configuration code by a simulator array; and finally, guiding a hardware behavior operation using a binary configuration code file generated by the simulator. The reconfigurable system chip compiler includes a source program input module, a software compilation function verification module, a compilation mapping module, a simulation execution module, a configuration code generation module, and a hardware debugging module.