Author name cluster

Lihong Xu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

EAAI Journal 2025 Journal Article

Evolutionary dynamic multiobjective optimization using a Bayesian vector autoregression prediction model

Kai Gao
Wenxiang Jiang
Lihong Xu

Dynamic multiobjective optimization problems (DMOPs), inherently involving the simultaneous optimization of conflicting objectives under time-varying environments, exhibit ubiquitous presence in real-world applications spanning diverse engineering disciplines. A prevalent limitation in existing dynamic multiobjective optimization evolutionary algorithms (DMOEAs) lies in inadequate utilization of historical information and neglect of interdependencies among decision variables, which frequently induces suboptimal initial population predictions deviating from true Pareto optimal sets. To mitigate these limitations, we propose MOEA/D-BVAR, a novel DMOEA framework incorporating a Bayesian vector autoregressive (BVAR) model that conceptualizes solution dynamics through holistic vector forecasting rather than isolated variable-specific prediction. The algorithm initially clusters variables via mutual information correlation analysis, subsequently constructing BVAR models for each cluster to project their evolutionary trajectories. Independently varying variables are rapidly predicted through differential forecasting models. A multivariate interaction optimization mechanism enhances search efficiency. Comprehensive empirical evaluations on 14 benchmark suites compare MOEA/D-BVAR against six state-of-the-art DMOEAs developed over the past five years. Statistical analysis of experimental outcomes demonstrates the proposed algorithm’s superior competitiveness in handling complex DMOPs.

Details DOI

EAAI Journal 2024 Journal Article

HFM: A hybrid fusion method for underwater image enhancement

Shunmin An
Lihong Xu
Zhichao Deng
Huapeng Zhang

Different underwater images captured by different devices may exhibit varying degrees of nonlinear rendering, leading to white balance distortion. Meanwhile, wavelength attenuation and light scattering associated with distance can cause color shifts, reduced visibility, and decreased contrast in underwater images. This will result in difficult access to underwater information, which in turn will affect the application of advanced vision. Considering these degradation issues, we propose a hybrid fusion method for underwater image enhancement, called HFM. In terms of technical contributions, we introduce a color and white balance correction module that addresses color and white balance distortion in underwater images using the gray world principle and a nonlinear color mapping function. We design a visibility recovery module based on type-II fuzzy sets and a contrast enhancement module using curve transformation. Besides, inspired by image fusion methods, we propose an underwater image perception fusion module that focuses on two different tasks simultaneously, fusing underwater images of visibility and contrast. Therefore, the proposed method can effectively solve the problems of white balance distortion, color shift, low visibility and low contrast in underwater images, and achieves optimal results in application tests of geometric rotation estimation, feature point matching and edge detection. Through comparative experiments analyzed on four real scene datasets, the proposed method achieves superior results compared to 14 state-of-the-art underwater image enhancement methods. The code is publicly available at: https: //github. com/An-Shunmin/HFM.

Details DOI

IROS Conference 2010 Conference Paper

Adaptive fuzzy control for trajectory tracking of Mobile Robot

Yuming Liang
Lihong Xu
Ruihua Wei
Haigen Hu

Trajectory tracking of the mobile robot is one research hot for the robot. For the control system of the two-wheeled differential drive mobile robot being in nonhonolomic system and the complex relations among the control parameters, it is difficult to solve the problem based on traditional mathematics model. A new control scheme combined with the fuzzy PD (Proportional and Differential) control and the separate integral control is proposed in this paper. The control scheme can not only make full use of the advantage of the fuzzy control, but also have the good steady state tracking ability of the integral control. However, this control scheme introduces so many parameters which are difficult to optimize. In order to realize the online adaptive learning of the control parameters, the modified VFSA (Very Fast Simulated Annealing) is used. The simulation results show that the method is feasible, and can quickly approach the conference trajectory in a short time, and the trajectory tracking error is very small.

Details

ICRA Conference 2009 Conference Paper

A framework for modeling steady turning of robotic fish

Qingsong Hu
Dawn R. Hedgepeth
Lihong Xu
Xiaobo Tan 0001

In this paper we present a novel framework for computing the steady turning motion of a robotic fish undergoing periodic body and/or tail deformation. Taking the turning radius and the angular velocity as unknowns, we obtain the absolute motion trajectories of points on the ldquospinal columnrdquo of robotic fish by superimposing relative body/tail motions on the rigid body circular motion. The hydrodynamic reactive force and the resulting moment are then computed from the motion trajectories, using Lighthill's large-amplitude elongated-body theory, in terms of the two turning parameters. By integrating the dynamics of rigid body motion and averaging out oscillations, implicit equations involving the turning parameters can be established and solved. We also discuss the plan of applying the proposed framework to the modeling of steady turning maneuvers of biomimetic robotic propelled by an ionic polymer-metal composite (IPMC) caudal fin.

Details