Guang Li

Dr. Guang Li

College of Control Science and Engineering, Zhejiang University, Hangzhou, China

Research Focus & Metrics

Academic Career Length

23Years (2004-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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27 March 2008 in Sensors
Verified
Journal Article
Open access
PDF file
YWYou Wang
You Wang
HXHui Xu
Hui Xu
JZJianming Zhang
Jianming Zhang
GLGuang Li
Guang Li
Demanded by modern medical diagnosis, advances in microfabrication technology have led to the development of fast, sensitive and selective electrochemical sensors for clinic analysis. This review addresses the principles behind electrochemical sensor design and fabrication, and introduces recent progress in the application of electrochemical sensors to analysis of clinical chemicals such as blood gases, electrolytes, metabolites, DNA and antibodies, including basic and applied research. Miniaturized commercial electrochemical biosensors will form the basis of inexpensive and easy to use devices for acquiring chemical information to bring sophisticated analytical capabilities to the non-specialist and general public alike in the future.

1 April 2005 in Biosensors and Bioelectronics
Verified
Journal Article
Closed access
GLGuang Li
Guang Li
JLJ.M. Liao
J.M. Liao
GHG.Q. Hu
G.Q. Hu
NMN.Z. Ma
N.Z. Ma
PWP.J. Wu
P.J. Wu

13 August 2021 in npj Flexible Electronics
Verified
Journal Article
Open access
PDF file
YWYouhua Wang
Youhua Wang
TTTianyi Tang
Tianyi Tang
YXYin Xu
Yin Xu
YBYunzhao Bai
Yunzhao Bai
LYLang Yin
Lang Yin
YHYongan Huang
Yongan Huang
The internal availability of silent speech serves as a translator for people with aphasia and keeps human–machine/human interactions working under various disturbances. This paper develops a silent speech strategy to achieve all-weather, natural interactions. The strategy requires few usage specialized skills like sign language but accurately transfers high-capacity information in complicated and changeable daily environments. In the strategy, the tattoo-like electronics imperceptibly attached on facial skin record high-quality bio-data of various silent speech, and the machine-learning algorithm deployed on the cloud recognizes accurately the silent speech and reduces the weight of the wireless acquisition module. A series of experiments show that the silent speech recognition system (SSRS) can enduringly comply with large deformation (~45%) of faces by virtue of the electricity-preferred tattoo-like electrodes and recognize up to 110 words covering daily vocabularies with a high average accuracy of 92.64% simply by use of small-sample machine learning. We successfully apply the SSRS to 1-day routine life, including daily greeting, running, dining, manipulating industrial robots in deafening noise, and expressing in darkness, which shows great promotion in real-world applications.

5 April 2006 in Chemistry – A European Journal
Verified
Journal Article
Closed access
PDF file
Xingfa (or X.) MaXingfa (or X.) Ma
Xingfa (or X.) Ma
GLGuang Li
Guang Li
MWMang Wang
Mang Wang
YCYunan Cheng
Yunan Cheng
RBRu Bai
Ru Bai
HCHongzheng Chen
Hongzheng Chen

11 July 2020 in Brain Sciences
Verified
Journal Article
Open access
PDF file
YWYou Wang
You Wang
MZMing Zhang
Ming Zhang
RWRumeng Wu
Rumeng Wu
HGHan Gao
Han Gao
MYMeng Yang
Meng Yang
GLGuang Li
Guang Li
Silent speech decoding is a novel application of the Brain–Computer Interface (BCI) based on articulatory neuromuscular activities, reducing difficulties in data acquirement and processing. In this paper, spatial features and decoders that can be used to recognize the neuromuscular signals are investigated. Surface electromyography (sEMG) data are recorded from human subjects in mimed speech situations. Specifically, we propose to utilize transfer learning and deep learning methods by transforming the sEMG data into spectrograms that contain abundant information in time and frequency domains and are regarded as channel-interactive. For transfer learning, a pre-trained model of Xception on the large image dataset is used for feature generation. Three deep learning methods, Multi-Layer Perception, Convolutional Neural Network and bidirectional Long Short-Term Memory, are then trained using the extracted features and evaluated for recognizing the articulatory muscles’ movements in our word set. The proposed decoders successfully recognized the silent speech and bidirectional Long Short-Term Memory achieved the best accuracy of 90%, outperforming the other two algorithms. Experimental results demonstrate the validity of spectrogram features and deep learning algorithms.

31 August 2022 in IEEE Robotics and Automation Letters
Verified
Journal Article
Closed access
YLYifan Liu
Yifan Liu
YWYixu Wang
Yixu Wang
XGXiaoqing Guan
Xiaoqing Guan
THTao Hu
Tao Hu
ZZZiang Zhang
Ziang Zhang
GLGuang Li
Guang Li
A spherical robot is a nonlinear, nonholonomic, and unstable system which increases the difficulty of the direction and trajectory tracking problem. In this study, we propose a new direction controller Hierarchical Terminal Sliding Mode Controller (HTSMC), an instruction planning controller called Model Prediction Control-based Planner (MPCBP), and a trajectory tracking framework named MPCBP-HTSMC-HSMC (MHH). The HTSMC is designed by integrating a fast terminal algorithm, a hierarchical method, the motion features of a spherical robot, and its dynamics. In addition, the new direction controller has an excellent control effect with a quick response speed and strong stability. MPCBP can obtain optimal commands that are then transmitted to the velocity and direction controller. Since the two torque controllers in MHH are all Lyapunov-based sliding mode controllers, the MHH framework may achieve optimal control performance while assuring stability. Finally, the two controllers eliminate the requirement for MPCBP's stability and dynamic constraints. Finally, hardware experiments demonstrate the efficacy of the HTSMC, MPCBP, and MHH.