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Chen Lu

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10 papers

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EAAI Journal 2026 Journal Article

An adaptive maintenance decision methodology integrating multiagent-based modelling and simulation with a multistage evolutionary game model

Xu An
Dong Zhou
Huixing Meng
Peikai Qu
Ziyue Guo
Chen Lu

Dynamic interactions among multiple agents in maintenance scenarios are common. Flexible maintenance strategies can enhance system reliability, reduce downtime, and minimize operational costs to achieve system health management in long-term operations. Therefore, investigating the complexity and interdependence of these agents in performing maintenance operations from the perspective of system maintenance decision-making is beneficial. In this paper, we propose the integration of multiagent-based modelling and simulation (MABMS) with a multistage evolutionary game (EG) model for the development of adaptive maintenance strategies. In the proposed method, MABMS is applied to describe the interactions among various agents. Stakeholders related to agents in MABMS are regarded as players in a game. Game theory can thus be adopted to model the strategic decision-making of stakeholders at different maintenance stages. We subsequently established a multistage EG to study the strategies of both competition and cooperation among agent-related stakeholders. Stakeholders at different stages optimize their strategies on the basis of feedback from agents and the results of relevant maintenance stages. Finally, by improving decision-making across different maintenance stages, a dynamic maintenance strategy is established to enhance system reliability and reduce downtime. The obtained results indicate that the proposed approach yields improvements in maintenance efficiency and decision-making adaptability.

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EAAI Journal 2026 Journal Article

Imbalanced fault diagnosis of electromechanical systems under unseen operating conditions: a heterogeneous domain generalization framework combining digital twin knowledge and data

Xuanyuan Su
Kaixin Jin
Yongzhe Ma
Chen Lu
Laifa Tao

Imbalanced data and diverse operating conditions (OCs) are two common issues in fault diagnosis, which are generally addressed by data-driven artificial intelligence (AI) variants focusing on data generation and transfer learning. However, applying these approaches to complex electromechanical systems (EMS) remains challenging, as extended faults and diverse OCs create harsh data situations, such as scarcity of fault data and unseen OCs, thus limiting the efficacy of purely data-driven paradigms. This paper proposes a data and knowledge-combined intelligent fault diagnosis framework. Firstly, a collaborative hierarchical modeling mechanism is proposed to construct a full-system digital twin (DT) for EMS, which generates two modalities of information: DT fault data and DT knowledge, enriching both the scale and type of the available dataset. Furthermore, a heterogeneous domain generalization network (HDGN) is proposed to achieve generalized fault diagnosis from both data and knowledge perspectives. By embedding prior DT knowledge, domain-invariance is stably retained from the data. Driven by the triplet specific similarity loss, domain-specific discriminative representations are adaptively learned by multi-channels from the knowledge-embedded data. The resulting HDGN progressively improves model generalization to unseen OCs with well-balanced stability and adaptiveness. The experimental results demonstrate the proposed method's effectiveness and superiority, providing a reference for AI applications in industrial scenarios with imbalanced data and unseen OCs.

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AIIM Journal 2025 Journal Article

Disentangled global and local features of multi-source data variational autoencoder: An interpretable model for diagnosing IgAN via multi-source Raman spectral fusion techniques

Wei Shuai
Xuecong Tian
Enguang Zuo
Xueqin Zhang
Chen Lu
Jin Gu
Chen Chen
Xiaoyi Lv

A single Raman spectrum reflects limited molecular information. Effective fusion of the Raman spectra of serum and urine source domains helps to obtain richer feature information. However, most of the current studies on immunoglobulin A nephropathy (IgAN) based on Raman spectroscopy are based on small sample data and low signal-to-noise ratio. If a multi-source data fusion strategy is directly adopted, it may even reduce the accuracy of disease diagnosis. To this end, this paper proposes a data enhancement and spectral optimization method based on variational autoencoders to obtain reconstructed Raman spectra with doubled sample size and improved signal-to-noise ratio. In the diagnosis of IgAN in multi-source domain Raman spectra, this paper builds a global and local feature decoupled variational autoencoder (DMSGL-VAE) model based on multi-source data. First, the statistical features after spectral segmentation are extracted, and the latent variables obtained by the variational encoder are decoupled through the decoupling module. The global representation and local representation obtained represent the global shared information and local unique information of the serum and urine source domains, respectively. Then, the cross-source reconstruction loss and decoupling loss are used to constrain the decoupling, and the effectiveness of the decoupling is proved quantitatively and qualitatively. Finally, the features of different source domains were integrated to diagnose IgAN, and the results were analyzed for important features using the SHapley Additive exPlanations algorithm. The experimental results showed that the AUC value of the DMSGL-VAE model for diagnosing IgAN on the test set was as high as 0. 9958. The SHAP algorithm was used to further prove that proteins, hydroxybutyrate, and guanine are likely to be common biological fingerprint substances for the diagnosis of IgAN by serum and urine Raman spectroscopy. In summary, the DMSGL-VAE model designed based on Raman spectroscopy in this paper can achieve rapid, non-invasive, and accurate screening of IgAN in terms of classification performance. And interpretable analysis may help doctors further understand IgAN and make more efficient diagnostic measures in the future.

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NeurIPS Conference 2025 Conference Paper

Sequential Multi-Agent Dynamic Algorithm Configuration

Chen Lu
Ke Xue
Lei Yuan
Yao Wang
Yaoyuan Wang
Sheng Fu
Chao Qian

The performance of an algorithm often critically depends on its hyperparameter configuration. Dynamic algorithm configuration (DAC) is a recent trend in automated machine learning, which can dynamically adjust the algorithm’s configuration during the execution process and relieve users from tedious trial-and-error tuning tasks. Recently, multi-agent reinforcement learning (MARL) approaches have improved the configuration of multiple heterogeneous hyperparameters, making various parameter configurations for complex algorithms possible. However, many complex algorithms have inherent inter-dependencies among multiple parameters (e. g. , determining the operator type first and then the operator's parameter), which are, however, not considered in previous approaches, thus leading to sub-optimal results. In this paper, we propose the sequential multi-agent DAC (Seq-MADAC) framework to address this issue by considering the inherent inter-dependencies of multiple parameters. Specifically, we propose a sequential advantage decomposition network, which can leverage action-order information through sequential advantage decomposition. Experiments from synthetic functions to the configuration of multi-objective optimization algorithms demonstrate Seq-MADAC's superior performance over state-of-the-art MARL methods and show strong generalization across problem classes. Seq-MADAC establishes a new paradigm for the widespread dependency-aware automated algorithm configuration. Our code is available at https: //github. com/lamda-bbo/seq-madac.

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EAAI Journal 2024 Journal Article

A rail defect detection framework under class-imbalanced conditions based on improved you only look once network

Yu Ding
Qin Zhao
Tianhao Li
Chen Lu
Laifa Tao
Jian Ma

In real rail operations, defects that can lead to serious accidents occur at very low frequencies, resulting in sample scarcity and class imbalances in rail defect datasets. Under imbalanced conditions, rail defect detection models tend to be biased toward majority classes and ignore minority classes, which further leads to inaccurate defect detection results. Therefore, a two-stage rail defect detection framework based on a latent diffusion model (LDM) and an improved You Only Look Once (YOLO) network operating under imbalanced conditions is proposed. This framework aims to enhance the detection performance achieved on imbalanced defect datasets through data augmentation and model improvements. First, the LDM is used to generate many defects with extremely small sample sizes and provide high-quality generated samples to expand the original imbalanced dataset. Furthermore, a coordinate attention module and a feature fusion module are integrated into the original YOLO version 8 (YOLOv8) model to improve its detection capabilities on imbalanced datasets. The coordinate attention mechanism enhances its focus on the positional information of various defects, whereas the feature fusion module enhances its ability to fuse the multiscale features of different defects. The results of the case study demonstrate that sample generation and filtration can provide high-quality samples for dataset augmentation purposes, alleviating the impact of minority defects on the overall detection accuracy. The results of the comparison and ablation experiments show that the improved YOLOv8 model has better detection performance than that of the comparison methods due to its introduction of coordinate attention and feature fusion modules.

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EAAI Journal 2023 Journal Article

Autonomous dispatch trajectory planning on flight deck: A search-resampling-optimization framework

Xinwei Wang
Bai Li
Xichao Su
Haijun Peng
Lei Wang
Chen Lu
Chao Wang

There is a growing expectation to realize the autonomous dispatch on flight deck, where dispatch trajectory planning is seen as the key technique. Optimal-control based method has shown great advantages in high degree of constraint satisfaction over its counterparts in the last decade. However, it suffers from low computational efficiency even numerical divergence under scenarios with complicated obstacles. To deal with such an issue, a search-resampling-optimization (SRO) framework is proposed in this paper. A hybrid A* algorithm is employed to generate a coarse path according to the boundary conditions in the search stage. Then a resampling process is implemented to pave a series of safe dispatch corridors (SDCs) along the coarse path. Finally, by replacing the common one-to-one collision-avoidance with the constructed within-SDC constraints, an optimal control problem whose scale is totally independent of the number of obstacles can be formulated. The resampled result is further fed into the optimization stage to facilitate the numerical solution. Dispatch trajectory planning for taxiing aircraft and tractor can be treated uniformly under this framework. And numerical simulations demonstrate that the SRO framework is efficient and robust even with narrow accessible tunnels. The SRO is inherently flexible and can be easily extended to the trajectory planning problem in other fields. A video of the main idea and numerical simulations in this paper is available at www. bilibili. com/video/BV1tP4y1d7xy/.

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EAAI Journal 2023 Journal Article

Conditional probability based multi-objective cooperative task assignment for heterogeneous UAVs

Xiaohua Gao
Lei Wang
Xinyong Yu
Xichao Su
Yu Ding
Chen Lu
Haijun Peng
Xinwei Wang

In actual air combat, there is an inevitable risk that an unmanned aerial vehicle (UAV) will be destroyed. However, this risk is rarely considered in the mission planning phase. In this paper, we focus on cooperative mission assignment for heterogeneous UAVs. We develop a multi-objective optimization model to find a balance between mission gains and UAV losses. The objective function is expressed using conditional probability theory by introducing the probabilities of mission success and UAV loss. Munitions loading capacity, time constraints, and priority constraints are modeled as constraints. To solve this combinatorial problem, an improved multi-objective genetic algorithm, which incorporates a natural chromosome encoding format and specially designed genetic operators, is developed. An efficient unlocking method is constructed to address the unavoidable dead-lock phenomenon meanwhile maintaining the population randomness. Numerical simulations for different problem sizes and ammunition stocks are performed, and the proposed algorithm is compared with the Multi-objective Particle Swarm Optimization and the Multi-objective Grey Wolf Optimization, respectively, using different unlocking approaches. The simulation and comparison results demonstrate the practical value and effectiveness of the developed model and the proposed algorithm.

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JMLR Journal 2023 Journal Article

Contextual Stochastic Block Model: Sharp Thresholds and Contiguity

Chen Lu
Subhabrata Sen

We study community detection in the “contextual stochastic block model" (Yan and Sarkar (2020), Deshpande et al. (2018)). Deshpande et al. (2018) studied this problem in the setting of sparse graphs with high-dimensional node-covariates. Using the non-rigorous “cavity method" from statistical physics (Mezard and Montanari (2009)), they calculated the sharp limit for community detection in this setting, and verified that the limit matches the information theoretic threshold when the average degree of the observed graph is large. They conjectured that the limit should hold as soon as the average degree exceeds one. We establish this conjecture, and characterize the sharp threshold for detection and weak recovery. [abs] [ pdf ][ bib ] &copy JMLR 2023. ( edit, beta )

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NeurIPS Conference 2020 Conference Paper

Exponential ergodicity of mirror-Langevin diffusions

Sinho Chewi
Thibaut Le Gouic
Chen Lu
Tyler Maunu
Philippe Rigollet
Austin Stromme

Motivated by the problem of sampling from ill-conditioned log-concave distributions, we give a clean non-asymptotic convergence analysis of mirror-Langevin diffusions as introduced in Zhang et al. (2020). As a special case of this framework, we propose a class of diffusions called Newton-Langevin diffusions and prove that they converge to stationarity exponentially fast with a rate which not only is dimension-free, but also has no dependence on the target distribution. We give an application of this result to the problem of sampling from the uniform distribution on a convex body using a strategy inspired by interior-point methods. Our general approach follows the recent trend of linking sampling and optimization and highlights the role of the chi-squared divergence. In particular, it yields new results on the convergence of the vanilla Langevin diffusion in Wasserstein distance.

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NeurIPS Conference 2020 Conference Paper

SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence

Sinho Chewi
Thibaut Le Gouic
Chen Lu
Tyler Maunu
Philippe Rigollet

Stein Variational Gradient Descent (SVGD), a popular sampling algorithm, is often described as the kernelized gradient flow for the Kullback-Leibler divergence in the geometry of optimal transport. We introduce a new perspective on SVGD that instead views SVGD as the kernelized gradient flow of the chi-squared divergence. Motivated by this perspective, we provide a convergence analysis of the chi-squared gradient flow. We also show that our new perspective provides better guidelines for choosing effective kernels for SVGD.

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