Yan He

Prof. Dr. Yan He


Research Focus & Metrics

Academic Career Length

22Years (2005-Present)

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

Representative Publications

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1 May 2020 in Chemical Engineering Journal
Verified
Journal Article
Closed access
JGJiang-Shan Gao
Jiang-Shan Gao
ZLZhiming Liu
Zhiming Liu
YLYan Lin
Yan Lin
YTYuanzheng Tang
Yuanzheng Tang
TLTongtong Lian
Tongtong Lian
Yan HeYan He
Yan He

17 October 2018 in Journal of Materials Chemistry A
Verified
Journal Article
Closed access
PDF file
Yan HeYan He
Yan He
FWFei Wang
Fei Wang
Graphene-carried –SO 3 − and nano-Ag achieved completion of CO 2 hydrate formation finished within 200–300 min with gas consumption reaching 7.6 mmol gas per mL water.

14 February 2024 in Advanced Functional Materials
Verified
Journal Article
Closed access
PWPeng Wang
Peng Wang
KWKe Wang
Ke Wang
YLYunjie Liu
Yunjie Liu
HLHuifang Li
Huifang Li
YGYun Guo
Yun Guo
SGShaojun Guo
Shaojun Guo

9 March 2022 in Journal of Materials Chemistry A
Verified
Journal Article
Closed access
PDF file
XWXiaojun Wang
Xiaojun Wang
QWQing Wei
Qing Wei
HLHaichang Li
Haichang Li
JSJingrui Sun
Jingrui Sun
Huifang LiHuifang Li
Huifang Li
Zhiming LiuZhiming Liu
Zhiming Liu
Iron-chalcogenide-based materials are promising electrode candidates in electrochemical energy storage devices because of their high specific capacities, abundant resources, low cost and environmental friendliness.

7 July 2022 in ACS Applied Nano Materials
Verified
Journal Article
Closed access
DZDa Zhang
Da Zhang
RDRuixin Ding
Ruixin Ding
YTYuanzheng Tang
Yuanzheng Tang
LMLianxiang Ma
Lianxiang Ma
Yan HeYan He
Yan He

17 March 2022 in Frontiers in Neurorobotics
Verified
Journal Article
Open access
SLSha Luo
Sha Luo
DCDianming Chu
Dianming Chu
Qingdang LiQingdang Li
Qingdang Li
Yan HeYan He
Yan He
A multi-objective full-parameter optimization particle swarm optimization (MOFOPSO) algorithm is proposed in this paper to overcome the drawbacks of poor accuracy, low efficiency, and instability of the existing algorithms in the inverse kinematics(IK) solution of the manipulator. In designing the multi-objective function, the proposed algorithm considers the factors such as position, posture, and joint. To improve PSO, the proposed algorithm comprehensively analyzes all factors affecting the global and local searching abilities. Based on this, the initial population is designed following the localized uniform distribution method. Meanwhile, the inertia weight, asynchronous learning factor, and time factor are respectively designed by introducing the iteration factor. Finally, this paper tests the performance of MOFOPSO with three typical functions to obtain a better inverse kinematics solution of the 6-DOF manipulator. Also, six other algorithms are taken for performance comparison. The experimental results indicate that the proposed method not only ensures the stability of the manipulator but also achieves high accuracy and efficiency in solving the inverse kinematics of the 6-DOF manipulator.