Search Results for author: Jianping He

Found 17 papers, 1 papers with code

Inverse Reinforcement Learning with Unknown Reward Model based on Structural Risk Minimization

no code implementations27 Dec 2023 Chendi Qu, Jianping He, Xiaoming Duan, Jiming Chen

A simplistic model is less likely to contain the real reward function, while a model with high complexity leads to substantial computation cost and risks overfitting.

Model Selection

Observation-based Optimal Control Law Learning with LQR Reconstruction

no code implementations27 Dec 2023 Chendi Qu, Jianping He, Xiaoming Duan

Designing controllers to generate various trajectories has been studied for years, while recently, recovering an optimal controller from trajectories receives increasing attention.

AE-GPT: Using Large Language Models to Extract Adverse Events from Surveillance Reports-A Use Case with Influenza Vaccine Adverse Events

no code implementations28 Sep 2023 Yiming Li, Jianfu Li, Jianping He, Cui Tao

Though Vaccines are instrumental in global health, mitigating infectious diseases and pandemic outbreaks, they can occasionally lead to adverse events (AEs).

Unidentifiability of System Dynamics: Conditions and Controller Design

no code implementations28 Aug 2023 Xiangyu Mao, Jianping He

In this paper, we investigate the problem of dynamic unidentifiability and design the controller to make the system dynamics unidentifiable.

Preserving Topology of Network Systems: Metric, Analysis, and Optimal Design

no code implementations31 Jul 2023 Yushan Li, Zitong Wang, Jianping He, Cailian Chen, Xinping Guan

More importantly, we amend the noise design by introducing one-lag time dependence, achieving the zero state deviation and the non-zero topology inference error in the asymptotic sense simultaneously.

Revisiting Estimation Bias in Policy Gradients for Deep Reinforcement Learning

no code implementations20 Jan 2023 Haoxuan Pan, Deheng Ye, Xiaoming Duan, Qiang Fu, Wei Yang, Jianping He, Mingfei Sun

We show that, despite such state distribution shift, the policy gradient estimation bias can be reduced in the following three ways: 1) a small learning rate; 2) an adaptive-learning-rate-based optimizer; and 3) KL regularization.

Continuous Control reinforcement-learning +1

Toward Global Sensing Quality Maximization: A Configuration Optimization Scheme for Camera Networks

1 code implementation28 Nov 2022 Xuechao Zhang, Xuda Ding, Yi Ren, Yu Zheng, Chongrong Fang, Jianping He

Then, we form a single quantity that measures the sensing quality of the targets by the camera network.

Multi-period Optimal Control for Mobile Agents Considering State Unpredictability

no code implementations19 Jun 2022 Chendi Qu, Jianping He, Jialun Li

This paper aims at the trade-off between the control performance and state unpredictability of mobile agents in long time horizon.

Stochastic Optimization

SVR-based Observer Design for Unknown Linear Systems: Complexity and Performance

no code implementations14 May 2022 Xuda Ding, Han Wang, Jianping He, Cailian Chen, Xinping Guan

The variances of the estimation error and the fluctuations in performance are smaller with a properly-designed parameter $\gamma$ compared with the OLS methods.

I Can Read Your Mind: Control Mechanism Secrecy of Networked Dynamical Systems under Inference Attacks

no code implementations7 May 2022 Jianping He, Yushan Li, Lin Cai, Xinping Guan

Considering the latest inference attacks that enable stealthy and precise attacks into NDSs with observation-based learning, this article focuses on a new security aspect, i. e., how to protect control mechanism secrets from inference attacks, including state information, interaction structure and control laws.

Inference Attack

Local Topology Inference of Mobile Robotic Networks under Formation Control

no code implementations30 Apr 2022 Yushan Li, Jianping He, Lin Cai, Xinping Guan

We focus on the local topology inference problem of MRNs under formation control, where an inference robot with limited observation range can manoeuvre among the formation robots.

Safety-Aware Optimal Control for Motion Planning with Low Computing Complexity

no code implementations28 Apr 2022 Xuda Ding, Han Wang, Jianping He, Cailian Chen, Kostas Margellos, Antonis Papachristodoulou

Simulations demonstrates that BRSCA has a higher probability of finding feasible solutions, reduces the computation time by about 17. 4% and the energy cost by about four times compared to other methods in the literature.

Motion Planning

Resilient Average Consensus: A Detection and Compensation Approach

no code implementations22 Feb 2022 Wenzhe Zheng, Zhiyu He, Jianping He, Chengcheng Zhao, Chongrong Fang

We characterize the adverse impact of misbehaving nodes in a distributed manner via two-hop communication information and develop a deterministic detection-compensation-based consensus (D-DCC) algorithm with a decaying fault-tolerant error bound.

System Identification with Variance Minimization via Input Design

no code implementations2 Feb 2022 Xiangyu Mao, Jianping He, Chengcheng Zhao

Next, an input design method is proposed to deal with the uncertainty and obtain stable identification results by minimizing the variance.

Sneak Attack against Mobile Robotic Networks under Formation Control

no code implementations4 Jun 2021 Yushan Li, Jianping He, Xuda Ding, Lin Cai, Xinping Guan

The security of mobile robotic networks (MRNs) has been an active research topic in recent years.

Topology Inference for Network Systems: Causality Perspective and Non-asymptotic Performance

no code implementations2 Jun 2021 Yushan Li, Jianping He, Cailian Chen, Xinping Guan

Along with this line, we analyze the non-asymptotic inference performance of the proposed method by taking the OLS estimator as a reference, covering both asymptotically and marginally stable systems.

Efficient Metropolitan Traffic Prediction Based on Graph Recurrent Neural Network

no code implementations2 Nov 2018 Xiaoyu Wang, Cailian Chen, Yang Min, Jianping He, Bo Yang, Yang Zhang

Traffic prediction is a fundamental and vital task in Intelligence Transportation System (ITS), but it is very challenging to get high accuracy while containing low computational complexity due to the spatiotemporal characteristics of traffic flow, especially under the metropolitan circumstances.

Traffic Prediction

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