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Xin Wu

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9 papers
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9

AAAI Conference 2026 Conference Paper

Uncovering Hidden Degeneration: A Physics-Guided Bidirectional Inference Framework for Industrial Time Series Prediction

  • Xingwang Li
  • Fei Teng
  • Xin Wu
  • Qiang Duan

Hidden degenerations in industrial time series often precede observable failures, they remain undetected by standard monitoring systems until anomalies become apparent. This gap between microscopic degradation and macroscopic observation renders conventional predictors inherently reactive, as they rely on correlations in sensor data rather than uncovering the underlying, physics‑consistent degradation states. Crucially, the microscopic mechanisms governing system evolution depend on macroscopic state variables—whose measurements are expectations over microscopic probability distributions—so purely data‑driven “top‑down” or purely physics‑guided “bottom‑up” approaches cannot forecast degeneration‑entangled industrial faults. To address these challenges, we propose a Physics-Guided Bidirectional Inference Framework that represents hidden microscopic states from macroscopic measurements. Our approach uniquely combines: (1) bottom-up physics-based simulation using Continuum Damage Mechanics to model micro-scale damage evolution under environmental stressors, and (2) top-down probabilistic inference via maximum entropy formalism to estimate latent microstate distributions from sparse sensor data. This bidirectional mechanism enables early failure prediction by bridging observable measurements with unobservable degeneration. Validation on real-world railway infrastruc datasets demonstrates significant improvements in early fault prediction compared to state-of-the-art baselines. Our method establishes a new paradigm for safety-critical industrial applications requiring reliable prediction of hidden degeneration processes.

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IROS Conference 2025 Conference Paper

Adversarial Augmentation for Task-Parameterized Underwater Skill Learning via Digital Twins *

  • Zhangpeng Tu
  • Zilin Xing
  • Xin Wu
  • Suohang Zhang
  • Canjun Yang

Learning from Demonstration (LfD) provides an efficient approach to acquiring diverse underwater skills, with task-parameterized learning enhancing the generalization of policies. However, collecting comprehensive underwater demonstrations across various conditions remains a significant challenge. In this work, we propose an adversarial trajectory augmentation method for Task Parameterized Hidden Semi-Markov Models (TP-HSMM) based on digital twins, inspired by adversarial example generation. Our method aims to improve the performance of motion policies by utilizing adversarial trajectory generation and retraining, leveraging low-cost demonstrations from digital twins. We evaluate the proposed adversarial trajectory augmentation method on two datasets. Comparative experiments demonstrate its effectiveness in reducing trajectory generation errors in new scenarios. Finally, we validate the method through an underwater humanoid plugging experiment, showing that it achieves similar performance to the baseline with fewer demonstrations.

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AAAI Conference 2025 Conference Paper

Content-free Logical Modification of Large Language Model by Disentangling and Modifying Logic Representation

  • Xin Wu
  • Yuqi Bu
  • Yifei Chen
  • Yi Cai

Despite extensive training on diverse datasets and alignment with human values, large language models (LLMs) can still generate fallacious outputs. Additionally, the validity of LLM's outputs varies significantly depending on the content. It is crucial to ensure LLMs' logical consistency across different contexts. Drawing inspiration from cognitive psychology studies, we propose a Logic Control Framework (LCF) that disentangles LLMs' hidden representations into separate content and logic spaces. Within the logic space, we use logically valid and invalid samples to construct distinct regions through contrastive learning. By moving logic representations to logically valid regions and fusing them with unchanged content representations, we significantly reduce logical fallacies in LLM outputs while maintaining content coherence. We demonstrate the effectiveness of LCF through experiments on conclusion generation and fallacy identification tasks, showing a significant improvement in logical validity and a reduction in fallacious outputs.

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IROS Conference 2024 Conference Paper

V2I-Calib: A Novel Calibration Approach for Collaborative Vehicle and Infrastructure LiDAR Systems

  • Qianxin Qu
  • Yijin Xiong
  • Guipeng Zhang
  • Xin Wu
  • Xiaohan Gao
  • Xin Gao
  • Hanyu Li
  • Shichun Guo

Cooperative LiDAR systems integrating vehicles and road infrastructure, termed V2I calibration, exhibit substantial potential, yet their deployment encounters numerous challenges. A pivotal aspect of ensuring data accuracy and consistency across such systems involves the calibration of LiDAR units across heterogeneous vehicular and infrastructural endpoints. This necessitates the development of calibration methods that are both real-time and robust, particularly those that can ensure robust performance in urban canyon scenarios without relying on initial positioning values. Accordingly, this paper introduces a novel approach to V2I calibration, leveraging spatial association information among perceived objects. Central to this method is the innovative Overall Intersection over Union (oIoU) metric, which quantifies the correlation between targets identified by vehicle and infrastructure systems, thereby facilitating the real-time monitoring of calibration results. Our approach involves identifying common targets within the perception results of vehicle and infrastructure LiDAR systems through the construction of an affinity matrix. These common targets then form the basis for the calculation and optimization of extrinsic parameters. Comparative and ablation studies conducted using the DAIR-V2X dataset substantiate the superiority of our approach. For further insights and resources, our project repository is accessible at https://github.com/MassimoQu/v2i-calib.

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AAAI Conference 2021 Short Paper

Information Block Detection in Infographic Based on Spatial Proximity and Structural Similarity (Student Abstract)

  • Jie Lin
  • Xin Wu
  • Jianwei Lu
  • Yi Cai

The infographic is a type of visualization chart used to display information. Existing infographic understanding works utilize spatial proximity to group elements into information blocks. However, these works ignore structural features such as background color and boundary, which results in poor performance towards complex infographics. We propose a Spatial and Structural Feature Extraction model to group elements based on spatial proximity and structural similarity. We introduce a new dataset for information block detection. Experiments show that our model can effectively identify the information blocks in the infographic.

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