Jun Wang

Dr. Jun Wang


Research Focus & Metrics

Academic Career Length

9Years (2018-Present)

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

Representative Publications

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17 February 2024 in Drones
Verified
Journal Article
Open access
PDF file
SJShipeng Jiao
Shipeng Jiao
JWJun Wang
Jun Wang
YHYuchen Hua
Yuchen Hua
YZYe Zhuang
Ye Zhuang
XYXuetian Yu
Xuetian Yu
In the face of external disturbances affecting the trajectory tracking of quadrotors, a control scheme targeted at accurate position and attitude trajectory tracking was designed. Initially, a quadrotor dynamic model, essential for control design, was derived. Adaptive integral backstepping control (AIBS) was then employed within the position loop, enabling the upper boundaries of disturbances to be estimated through adaptive estimation. Subsequently, a new adaptive backstepping fast nonsingular integral terminal sliding mode control (ABFNITSM) was proposed to enable adherence to the desired Euler angles. Rapid convergence and accurate tracking were facilitated by the incorporation of the nonsingular terminal sliding mode and an integral component. The dead zone technique was deployed to curtail estimation errors, while a saturation function was used to eradicate the phenomenon of chattering. Finally, to validate the proposed control scheme, simulation experiments were conducted in the Simulink environment, and the results were contrasted with those obtained from traditional integral terminal sliding mode control (ITSM) and integral backstepping control (IBS), providing evidence of the effectiveness of the proposed method.

5 February 2019 in IEEE Access
Verified
Journal Article
Open access
JWJun Wang
Jun Wang
JPJianmin Pang
Jianmin Pang
XLXiaonan Liu
Xiaonan Liu
FYFeng Yue
Feng Yue
JTJie Tan
Jie Tan
LFLiguo Fu
Liguo Fu

8 January 2024 in Preprints.org
Verified
Preprint
Open access
PDF file
SJShipeng Jiao
Shipeng Jiao
JWJun Wang
Jun Wang
YHYuchen Hua
Yuchen Hua
YZYe Zhuang
Ye Zhuang
XYXuetian Yu
Xuetian Yu
To address the trajectory tracking issue for quadrotors working under the influence of external disturbances, a control scheme is designed for position and attitude trajectory tracking. Firstly, the quadrotor dynamic model is derived for control design. Secondly, adaptive integral backstepping control (AIBS) is employed in the position loop in which adaptive estimation is used to estimate the upper bounds of disturbances. Subsequently, a new adaptive backstepping fast nonsingular integral terminal sliding mode control (ABFNITSM) is proposed to follow desired Euler angles. The intro-duction of fast nonsingular terminal sliding mode and integral element is to realize fast convergence and accurate tracking. Dead zone technique is applied to minimize estimation errors. Besides, a saturation function is utilized to eliminate chattering phenomenon. Finally, simulation experiments are conducted under the Simulink environment. The proposed control is verified by comparing it with traditional integral terminal sliding mode control (ITSM) and integral backstepping control (IBS).

1 January 2021 in Wireless Communications and Mobile Computing
Verified
Journal Article
Open access
PDF file
MLMingliang Li
Mingliang Li
JPJianmin Pang
Jianmin Pang
FYFeng Yue
Feng Yue
FLFudong Liu
Fudong Liu
JWJun Wang
Jun Wang
JTJie Tan
Jie Tan
Dynamic binary translation (DBT) is gaining importance in mobile computing. Mobile Edge Computing (MEC) augments mobile devices with powerful servers, whereas edge servers and smartphones are usually based on heterogeneous architecture. To leverage high‐performance resources on servers, code offloading is an ideal approach that relies on DBT. In addition, mobile devices equipped with multicore processors and GPU are becoming ubiquitous. Migrating x86_64 application binaries to mobile devices by using DBT can also make a contribution to providing various mobile applications, e.g., multimedia applications. However, the translation efficiency and overall performance of DBT for application migration are not satisfactory, because of runtime overhead and low quality of the translated code. Meanwhile, traditional DBT systems do not fully exploit the computational resources provided by multicore processors, especially when translating sequential guest applications. In this work, we focus on leveraging ubiquitous multicore processors to improve DBT performance by parallelizing sequential applications during translation. For that, we propose LLPEMU, a DBT framework that combines binary translation with polyhedral optimization. We investigate the obstacles of adapting existing polyhedral optimization in compilers to DBT and present a feasible method to overcome these issues. In addition, LLPEMU adopts static‐dynamic combination to ensure that sequential binaries are parallelized while incurring low runtime overhead. Our evaluation results show that LLPEMU outperforms QEMU significantly on the PolyBench benchmark.

12 June 2018
Verified
Book Chapter
Closed access
PDF file
JWJun Wang
Jun Wang
JPJianmin Pang
Jianmin Pang
LFLiguo Fu
Liguo Fu
ZSZheng Shan
Zheng Shan
FYFeng Yue
Feng Yue
JZJiahao Zhang
Jiahao Zhang

1 January 2026 in IEEE Transactions on Geoscience and Remote Sensing
Verified
Journal Article
Closed access
YHYiguo He
Yiguo He
JZJunjie Zhu
Junjie Zhu
JWJun Wang
Jun Wang
YLYiying Li
Yiying Li
XZXiaoyu Zhang
Xiaoyu Zhang
KYKe Yang
Ke Yang