1 code implementation • 6 Mar 2024 • Jiajia Li, Dong Chen, Xunyuan Yin, Zhaojian Li
In this study, we assess the effectiveness of a semi-supervised learning framework for multi-class weed detection, employing two well-known object detection frameworks, namely FCOS and Faster-RCNN.
no code implementations • 21 Jan 2024 • Xinda Qi, Dong Chen, Zhaojian Li, Xiaobo Tan
In this paper, we propose a novel technique, Back-stepping Experience Replay (BER), that is compatible with arbitrary off-policy reinforcement learning (RL) algorithms.
no code implementations • 13 Nov 2023 • Zhaojian Li, Bin Zhao, Yuan Yuan
To this end, a metric to measure the spatial perception of audio is proposed for the first time.
1 code implementation • 16 Oct 2023 • Jiajia Li, Raju Thada Magar, Dong Chen, Feng Lin, Dechun Wang, Xiang Yin, Weichao Zhuang, Zhaojian Li
Soybeans are a critical source of food, protein and oil, and thus have received extensive research aimed at enhancing their yield, refining cultivation practices, and advancing soybean breeding techniques.
no code implementations • 13 Aug 2023 • Xubo Gu, Hanyu Bai, Xiaofan Cui, Juner Zhu, Weichao Zhuang, Zhaojian Li, Xiaosong Hu, Ziyou Song
Due to the increasing volume of Electric Vehicles in automotive markets and the limited lifetime of onboard lithium-ion batteries (LIBs), the large-scale retirement of LIBs is imminent.
1 code implementation • 13 Aug 2023 • Jiajia Li, Mingle Xu, Lirong Xiang, Dong Chen, Weichao Zhuang, Xunyuan Yin, Zhaojian Li
These models are trained on a large amount of data from multiple domains and modalities.
1 code implementation • 4 Aug 2023 • Dong Chen, Kaixiang Zhang, Yongqiang Wang, Xunyuan Yin, Zhaojian Li, Dimitar Filev
Connected and autonomous vehicles (CAVs) promise next-gen transportation systems with enhanced safety, energy efficiency, and sustainability.
no code implementations • 29 Jun 2023 • Amin Vahidi-Moghaddam, Kaian Chen, Kaixiang Zhang, Zhaojian Li, Yan Wang, Kai Wu
Despite great successes, model predictive control (MPC) relies on an accurate dynamical model and requires high onboard computational power, impeding its wider adoption in engineering systems, especially for nonlinear real-time systems with limited computation power.
no code implementations • 16 Jun 2023 • Haoxuan Dong, Weichao Zhuang, Guoyuan Wu, Zhaojian Li, Guodong Yin, Ziyou Song
To potentially mitigate the negative effect of preceding vehicles on eco-driving control at the signalized intersection, this paper proposes an overtakingenabled eco-approach control (OEAC) strategy.
no code implementations • 7 Jun 2023 • Amin Vahidi-Moghaddam, Kaixiang Zhang, Zhaojian Li, Xunyuan Yin, Ziyou Song, Yan Wang
In this work, an extended NE (ENE) framework is developed to systematically adapt the nominal control to both state and preview perturbations.
no code implementations • 2 Jun 2023 • Dongjun Li, Kaixiang Zhang, Haoxuan Dong, Qun Wang, Zhaojian Li, Ziyou Song
In this paper, we employ a data-enabled predictive control (DeePC) scheme to address the eco-driving of mixed traffic flows with diverse behaviors of human drivers.
1 code implementation • 24 May 2023 • Jiajia Li, Dong Chen, Xinda Qi, Zhaojian Li, Yanbo Huang, Daniel Morris, Xiaobo Tan
In addition, a systematic review of various agricultural applications exploiting these label-efficient algorithms, such as precision agriculture, plant phenotyping, and postharvest quality assessment, is presented.
no code implementations • 8 Mar 2023 • Pengyu Chu, Zhaojian Li, Kaixiang Zhang, Dong Chen, Kyle Lammers, Renfu Lu
One key technology to fully enable efficient automated harvesting is accurate and robust apple detection, which is challenging due to complex orchard environments that involve varying lighting conditions and foliage/branch occlusions.
no code implementations • 7 Nov 2022 • Kaixiang Zhang, Yang Zheng, Chao Shang, Zhaojian Li
In this letter, we propose a simple yet effective singular value decomposition (SVD) based strategy to reduce the optimization problem dimension in data-enabled predictive control (DeePC).
1 code implementation • 18 Oct 2022 • Dong Chen, Xinda Qi, Yu Zheng, Yuzhen Lu, Zhaojian Li
In this paper, we present the first work of applying diffusion probabilistic models (also known as diffusion models) to generate high-quality synthetic weed images based on transfer learning.
no code implementations • 26 Sep 2022 • Kaixiang Zhang, Kaian Chen, Xinfan Lin, Yusheng Zheng, Xunyun Yin, Xiaosong Hu, Ziyou Song, Zhaojian Li
Fast charging of lithium-ion batteries has gained extensive research interests, but most of existing methods are either based on simple rule-based charging profiles or require explicit battery models that are non-trivial to identify accurately.
no code implementations • 22 May 2022 • Kaixiang Zhang, Kaian Chen, Zhaojian Li, Jun Chen, Yang Zheng
Data-driven predictive control of connected and automated vehicles (CAVs) has received increasing attention as it can achieve safe and optimal control without relying on explicit dynamical models.
no code implementations • 16 May 2022 • Wubing B. Qin, Zhaojian Li
This paper investigates the lateral control problem in vehicular path-following when the feedback sensor(s) are mounted at an arbitrary location in the longitudinal symmetric axis.
no code implementations • 11 Nov 2021 • Wei Zhou, Dong Chen, Jun Yan, Zhaojian Li, Huilin Yin, Wanchen Ge
In this paper, we formulate the lane-changing decision making of multiple AVs in a mixed-traffic highway environment as a multi-agent reinforcement learning (MARL) problem, where each AV makes lane-changing decisions based on the motions of both neighboring AVs and HDVs.
no code implementations • 13 Oct 2021 • Mohammad R. Hajidavalloo, Zhaojian Li, Xin Xia, Ali Louati, Minghui Zheng, Weichao Zhuang
Promising results on extensive simulations and hardware-in-the-loop experiments show that the proposed collaborative estimation can significantly enhance estimation and iteratively improve the performance from vehicle to vehicle, despite vehicle heterogeneity, model uncertainty, and measurement noises.
no code implementations • 20 Jun 2021 • Nan Li, Kaixiang Zhang, Zhaojian Li, Vaibhav Srivastava, Xiang Yin
In this paper, we propose a novel cloud-assisted model predictive control (MPC) framework in which we systematically fuse a cloud MPC that uses a high-fidelity nonlinear model but is subject to communication delays with a local MPC that exploits simplified dynamics (due to limited computation) but has timely feedback.
no code implementations • 19 Jun 2021 • Mohammad R. Hajidavalloo, Joel Cosner, Zhaojian Li, Wei-Che Tai, Ziyou Song
In this paper, we propose a new EHSA design -- inerter pendulum vibration absorber (IPVA) -- that integrates an electromagnetic rotary EHSA with a nonlinear pendulum vibration absorber.
3 code implementations • 12 May 2021 • Dong Chen, Mohammad Hajidavalloo, Zhaojian Li, Kaian Chen, Yongqiang Wang, Longsheng Jiang, Yue Wang
On-ramp merging is a challenging task for autonomous vehicles (AVs), especially in mixed traffic where AVs coexist with human-driven vehicles (HDVs).
no code implementations • 19 Oct 2020 • Pengyu Chu, Zhaojian Li, Kyle Lammers, Renfu Lu, Xiaoming Liu
Robotic apple harvesting has received much research attention in the past few years due to growing shortage and rising cost in labor.
no code implementations • 11 Jul 2020 • Zhaonan Qu, Kaixiang Lin, Zhaojian Li, Jiayu Zhou, Zhengyuan Zhou
For strongly convex and convex problems, we also characterize the corresponding convergence rates for the Nesterov accelerated FedAvg algorithm, which are the first linear speedup guarantees for momentum variants of FedAvg in convex settings.
no code implementations • 10 Oct 2019 • Dong Chen, Longsheng Jiang, Yue Wang, Zhaojian Li
The predicted decisions are incorporated in the safety constraints for reinforcement learning in training and in implementation.
1 code implementation • 11 Mar 2019 • Tianshu Chu, Jie Wang, Lara Codecà, Zhaojian Li
Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal control (ATSC) in complex urban traffic networks, and deep neural networks further enhance its learning power.