公开(公告)号：US20250083311A1

公开(公告)日：2025-03-13

申请号：US18827825

申请日：2024-09-08

Applicant: SOUTHEAST UNIVERSITY

Inventor： Baoguo XU ,  Jiajin WANG ,  Xin WANG ,  Weifeng PENG ,  Aiguo SONG

IPC: B25J9/16

Abstract: Disclosed is an optimization modeling and robust control method for a soft robot based on a fusion prediction equation, including the following steps: deriving measurement coordinates based on the fusion prediction equation; designing an observation function based on the measurement coordinates; identifying a Koopman model based on the observation function; and designing a robust model predictive controller based on the Koopman model. Further disclosed are a fusion prediction equation and a derivation method thereof, which can derive correct, abundant but non-redundant measurement coordinates, overcoming the problem of single measurement coordinates in a soft robot system, thereby being conducive to simplifying a design process of the observation function and further improving the accuracy of the Koopman model for the soft robot.

公开(公告)号：US20250076986A1

公开(公告)日：2025-03-06

申请号：US18817208

申请日：2024-08-27

Applicant: SOUTHEAST UNIVERSITY

Inventor： Baoguo XU ,  Xin WANG ,  Jiajin WANG ,  Aiguo SONG

IPC: G06F3/01 ,  G06V10/80

Abstract: Disclosed are an affective haptic regulation system and method based on multimodal fusion, including a haptic optimal parameter adjustment module, a haptic generation module, a visual-auditory generation module, a multi-physiological signal acquisition module, a multi-sensory signal acquisition module, and a multimodal fusion emotion recognition module. The system can fuse multi-physiological signal features with audio and haptic modal features by acquiring a plurality of physiological signals of a user, accurately identify a current affective state of the user in real time through advanced data processing and analysis technology, seek for a haptic parameter with the help of an optimization theory, and achieve proactive regulation of affective state of the user; and the system can overcome the limitations of traditional subjective scale methods, effectively reduce the influence of unstable physiological signals on emotion recognition results, and significantly improve the accuracy of affective detection in the affective haptic regulation system.

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.