Z. W. Li

Hefei University of Technology

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Zhaoneng Jiang

Hefei University of Technology

M.L.S. Teo

National University of Singapore

Zhenxing Wang

Huazhong University of Science and Technology

Zhengyu Wang

Hefei University of Technology

Xuchang Liu

Marine Design & Research Institute of China

Ling Bing Kong

Civil Aviation Flight University of China

Y. B. Gan

University of Jinan

Guochen Lin

Chinese Academy of Sciences

Le Ma

Institute of Earth Environment

Daoming Wang

Hefei University of Technology

Cooperative Institutions

Hefei University of Technology

Chinese Academy of Sciences

National University of Singapore

Southeast University

Huazhong University of Science and Technology

Harbin Engineering University

Southeast University

State Key Laboratory of Millimeter Waves

Nanjing University of Science and Technology

Hwa Chong Institution

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Development of Magneto-dielectric Materials based on Lithium Ferrite Ceramics for Miniaturization of Antennas

M.L.S. Teo Ling Bing Kong Z. W. Li Guochen Lin Y. B. Gan

Magneto

10.1142/9789812709547_0022

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Transformer-based segmented learning for kinematics modelling of a cable-driven parallel continuum robot

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science (2024)

Xuchang Liu Zhengyu Wang Z. W. Li Le Ma Daoming Wang

Continuum robots with structural compliance exhibit significant operational potential in unstructured environments. However, theoretical kinematics modelling for continuum robots is complex due to their uncertain nonlinear characteristics. Although learning-based algorithms can effectively address the inherent challenges, unfortunately they frequently fail to capture internal information in robotic systems involving multiple spatial mappings, which reduces the accuracy of data-driven approaches in such applications. In this regard, we propose a segmented learning approach applied to kinematic modelling for a cable-driven parallel continuum robot (PCR), using Transformer networks to segmentally learn the mapping from task space to configuration space, and from configuration space to actuation space. Additionally, in order to evaluate the performance of the proposed approach, we employ multiple sets of neural-network models and various segmented learning configurations in the experiments. The experimental results across different trajectories show that, the Transformer network model under the segmented learning approach achieves superior trajectory tracking accuracy, compared to state-of-the-art modelling approaches.

10.1177/09544062241306683

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A Dual-Polarization Broadband Coupling Feed Dipole Antenna Based on Artificial Magnetic Conductor

The Applied Computational Electromagnetics Society Journal (ACES) (2024)

Zhaoneng Jiang Z. W. Li Zhenxing Wang

A dual-polarized broadband coupled feed dipole antenna loaded with artificial magnetic conductor (AMC) structure is proposed. The proposed AMC structure with 4×4 elements shown in this paper consists of four perfect electrical conductor (PEC) rectangular strips and a ring to reduce the profile height of the dual-polarized dipole antenna. In addition, the dual-polarization antenna adopts coupling feed microstrip to excite two pairs of bow-tie patch. The overall size of the coupling feed dipole antenna is 1.1λ0×1.1λ0×0.09λ0 at 5.5 GHz. Measured results shows that the proposed dual-polarization antenna loaded AMC exhibit a 25.2% common working bandwidth (4.75-6.12 GHz). The isolation is less than -14 dB, and the peak gain is 10.6 dBi. This dipole antenna with AMC structure has the virtues of low profile, wide band, and good radiation performance and it has the potential to be used in C-band communication.

Dual-polarization interferometry

10.13052/2024.aces.j.390103

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