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Chunlin Li

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7 papers

2 author rows

TAAS Journal 2026 Journal Article

Federated Meta-Learning for Autonomous System in VEC-Enabled ICVs

Chunlin Li
Kun Jiang
Sihan Zeng
Guangxuan He
Shaohua Wan

Autonomous systems in VEC-enabled ICVs face many challenges, such as self-organization, privacy breach risks, vehicle selection, and resource allocation. As a distributed training framework, Federated Meta-Learning (FML) provides a powerful tool for adaptive and efficient processing of vehicular tasks while securing vehicle data privacy in VEC-enabled ICVs. However, the high-speed mobility of vehicles leads to higher latency and communication interruptions. This article investigates the vehicle selection and resource allocation scheme, subject to the constraints on the number and the residence time of vehicles, the maximum transmission energy consumption, and the ratio of bandwidth resource allocation. It is proved to be a challenging mixed-integer nonlinear programming problem, and we formulate it as a Markov decision process (MDP). We proposed an adaptive Sum Tree-Deep Recurrent Q-network algorithm (ST-DRQN) to solve the optimal resource allocation. ST-DRQN employs an enhanced empirical selection rule and a proportional priority sampling method to address the problems of inefficient model training and slow convergence. Finally, we conducted experiments using intelligent cars equipped with Raspberry Pi to show the effectiveness of the proposed methodology. Experimental results demonstrate that ST-DRQN achieves adaptability and credibility among ICVs while reducing latency and energy costs incurred by long-term training of FML.

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YNIMG Journal 2025 Journal Article

Interictal suppression in patients with mesial temporal lobe epilepsy: A simultaneous PET/fMRI study

Jie Hu
Liwei Sun
Kun Guo
Bixiao Cui
Chenyang Yao
Jingjuan Wang
Hui Ouyang
Xu Zhang

Previous stereotactic-electroencephalography (SEEG) results have suggested that seizure-onset zones (SOZs) could be suppressed by strengthened inward connectivity from the rest of the brain during interictal periods, which might explain why people with epilepsy did not have seizures continuously. However, the limited coverage of SEEG contacts and allocation bias hindered a more comprehensive survey of interictal suppression at the whole-brain level. Previous studies also lacked a direct comparison between patients and healthy controls due to the invasive nature of SEEG. In the present study, we introduced metabolic connectivity mapping (MCM), a simultaneous FDG-PET/fMRI-based measure of effective connectivity, to evaluate the inward and outward connectivity of the SOZs in patients with mesial temporal lobe epilepsy (MTLE). Specifically, simultaneous FDG-PET/fMRI data was acquired from 23 patients with left MTLE, 24 patients with right MTLE, and 25 healthy controls. At the whole-brain level, there was significant increase of inward MCM connectivity to the SOZs, which mostly came from mesial-temporo-limbic, anterior and posterior midline regions of the default mode network (DMN) and subcortical nuclei. There was also significant decrease of outward MCM connectivity from the SOZs, which mainly projected to the regions within DMN. The increased net inward MCM to the SOZs, calculated by subtracting outward MCM from the inward MCM, was positively correlated with seizure frequency. Within DMN, MTLE patients showed decreased MCM from the SOZs to posterior cingulate cortex and right ventromedial prefrontal cortex and increased effective connectivity from posterior cingulate cortex to the SOZs. Based on the MCM patterns within DMN, we were able to classify the epileptic side of MTLE with an accuracy of 91.67 % (79.17 % for MRI-negative patients). Overall, our results provide whole-brain evidences for the interictal suppression hypothesis. We also found that the regions within DMN play a critical role in the suppression of SOZs. The pattern of such suppressive network might also serve as potential features for the localization of SOZs. Our neuroimaging results does not only provide a comprehensive understanding of interictal suppression at the whole-brain level, but also shed lights on a non-invasive and time-efficient way for SOZs localization.

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TIST Journal 2025 Journal Article

Joint Service Migration and Resource Allocation for DNN Tasks using SA‐DDQN‐DDPG in Vehicular Edge Computing

Chunlin Li
Zihao Zhang
Bingxin Wang
Mengchao Lei
Sen Liu
Aoyong Li
Shaohua Wan

With the rapid development of vehicular edge computing (VEC) and artificial intelligence (AI), the emergence of vehicle edge intelligence meets the need for real-time vehicle intelligence applications. But the execution of deep neural networks (DNNs) requires a large amount of data input, which results in a large amount of computing resources required for the execution of DNN tasks. This also brings a certain burden to the deployment of DNN tasks and the resource allocation of edge servers. In addition, due to the high mobility of vehicles in the VEC, the backhaul delay of vehicle edge intelligent task results increases, affecting the vehicle’s quality of experience (QoE). We propose a joint optimization strategy for service migration and resource allocation aimed at minimizing the average task completion delay. This strategy comprehensively considers service migration actions and edge server resource allocation, which is proved to be a mixed integer nonlinear programming (MINLP) problem, and hence we formulate it as an Markov decision process (MDP). To solve this problem, we propose a service migration algorithm based on the self-attention mechanism-based double deep Q-network and deep deterministic policy gradient algorithm (SA-DDQN-DDPG) to solve it to obtain the optimal system service migration strategy. The experimental results show that the proposed SA-DDQN-DDPG algorithm has good performance in reducing latency. The average migration latency is reduced by 40.41%, 20.7%, and 14.50% compared with always, DQN and DDQN, respectively.

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IROS Conference 2025 Conference Paper

Online Residual Model Learning for Model Predictive Control of Autonomous Surface Vehicles in Real-World Environments

Arturo Gamboa-Gonzalez
Chunlin Li
Michael Wehner
Wei Wang

Model predictive control (MPC) relies on an accurate dynamics model to achieve precise and safe robot operation. In complex and dynamic aquatic environments, developing an accurate model that captures hydrodynamic details and accounts for environmental disturbances like waves, currents, and winds is challenging for aquatic robots. In this paper, we propose an online residual model learning framework for MPC, which leverages approximate models to learn complex unmodeled dynamics and environmental disturbances in dynamic aquatic environments. We integrate offline learning from previous simulation experience with online learning from the robot’s real-time interactions with the environments. These three components—residual modeling, offline learning, and on-line learning—enable a highly sample-efficient learning process, allowing for accurate real-time inference of model dynamics in complex and dynamic conditions. We further integrate this online learning residual model into a nonlinear model predictive controller, enabling it to actively choose the optimal control actions that optimize the control performance. Extensive simulations and real-world experiments with an autonomous surface vehicle demonstrate that our residual model learning MPC significantly outperforms conventional MPCs in dynamic field environments.

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EAAI Journal 2024 Journal Article

Mutual dimensionless improved bearing fault diagnosis based on Bp-increment broad learning system in computer vision

Chunlin Li
Qintai Hu
Shuping Zhao
Jigang Wu
Jianbin Xiong

Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial for ensuring normal machinery operation. However, the nonlinear and non-stationary vibration signals generated by machinery in harsh environments pose significant challenges in distinguishing fault signals from normal ones. Although several fault diagnosis methods based on mutual dimensionless indicators (MDI) have been proposed, they often fail to achieve effective and accurate health monitoring. Hence, this paper proposes a BP-Incremental Broad Learning System (BP-INBLS) model based on the combined synergistic of two modules, to address the existing challenges. Firstly, a new mutual dimensionless indicator (VMDI) with high sensitivity and low overlap is refactored. Secondly, leveraging the advantages of incremental learning algorithms, a novel Broad Learning System (BLS) model for quickly identifying different fault types is constructed. Finally, the proposed method is validated using multiple datasets and verified through a comparative analysis with a published method based on dimensionless indicators (DI). The results demonstrate the effectiveness of the proposed method in fault diagnosis.

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JMLR Journal 2023 Journal Article

Inference for a Large Directed Acyclic Graph with Unspecified Interventions

Chunlin Li
Xiaotong Shen
Wei Pan

Statistical inference of directed relations given some unspecified interventions (i.e., the intervention targets are unknown) is challenging. In this article, we test hypothesized directed relations with unspecified interventions. First, we derive conditions to yield an identifiable model. Unlike classical inference, testing directed relations requires identifying the ancestors and relevant interventions of hypothesis-specific primary variables. To this end, we propose a peeling algorithm based on nodewise regressions to establish a topological order of primary variables. Moreover, we prove that the peeling algorithm yields a consistent estimator in low-order polynomial time. Second, we propose a likelihood ratio test integrated with a data perturbation scheme to account for the uncertainty of identifying ancestors and interventions. Also, we show that the distribution of a data perturbation test statistic converges to the target distribution. Numerical examples demonstrate the utility and effectiveness of the proposed methods, including an application to infer gene regulatory networks. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2023. ( edit, beta )

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JMLR Journal 2023 Journal Article

RankSEG: A Consistent Ranking-based Framework for Segmentation

Ben Dai
Chunlin Li

Segmentation has emerged as a fundamental field of computer vision and natural language processing, which assigns a label to every pixel/feature to extract regions of interest from an image/text. To evaluate the performance of segmentation, the Dice and IoU metrics are used to measure the degree of overlap between the ground truth and the predicted segmentation. In this paper, we establish a theoretical foundation of segmentation with respect to the Dice/IoU metrics, including the Bayes rule and Dice-/IoU-calibration, analogous to classification-calibration or Fisher consistency in classification. We prove that the existing thresholding-based framework with most operating losses are not consistent with respect to the Dice/IoU metrics, and thus may lead to a suboptimal solution. To address this pitfall, we propose a novel consistent ranking-based framework, namely RankDice/RankIoU, inspired by plug-in rules of the Bayes segmentation rule. Three numerical algorithms with GPU parallel execution are developed to implement the proposed framework in large-scale and high-dimensional segmentation. We study statistical properties of the proposed framework. We show it is Dice-/IoU-calibrated, and its excess risk bounds and the rate of convergence are also provided. The numerical effectiveness of RankDice/mRankDice is demonstrated in various simulated examples and Fine-annotated CityScapes, Pascal VOC and Kvasir-SEG datasets with state-of-the-art deep learning architectures. Python module and source code are available on Github at (https://github.com/statmlben/rankseg). [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2023. ( edit, beta )

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