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Yunfeng Yang

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EAAI Journal 2025 Journal Article

Django-based framework database for leakage detection using machine learning for water distribution networks

  • Yiwei Xie
  • Mengze Gao
  • Fan Luo
  • Ao Zhou
  • Yunfeng Yang
  • Jian Hu
  • Wei Jiang
  • Yuanyao Ye

Leakage in water supply pipe networks is a critical issue, with traditional detection methods being inefficient and error-prone. Acoustic-based leak detection often lacks standardized databases, limiting its effectiveness. This study proposes an integrated system using MySQL, Python, and Django for managing and analyzing acoustic leakage data. The system incorporates Variable Modal Decomposition (VMD), Wavelet Threshold Noise Reduction, Feature Extraction, and Support Vector Machine (SVM) for accurate leak detection. Experimentation on 413 labeled acoustic samples achieved classification accuracies of 96. 1% (training set) and 97. 4% (test set). This approach enhances detection precision and offers a scalable solution for real-time monitoring, with significant practical implications for improving water distribution system management and decision-making.

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IROS Conference 2025 Conference Paper

Point Cloud-Based Control Barrier Functions for Model Predictive Control in Safety-Critical Navigation of Autonomous Mobile Robots

  • Faduo Liang
  • Yunfeng Yang
  • Shi-Lu Dai

In this work, we propose a novel motion planning algorithm to facilitate safety-critical navigation for autonomous mobile robots. The proposed algorithm integrates a real-time dynamic obstacle tracking and mapping system that categorizes point clouds into dynamic and static components. For dynamic point clouds, the Kalman filter is employed to estimate and predict their motion states. Based on these predictions, we extrapolate the future states of dynamic point clouds, which are subsequently merged with static point clouds to construct the forward-time-domain (FTD) map. By combining control barrier functions (CBFs) with nonlinear model predictive control, the proposed algorithm enables the robot to effectively avoid both static and dynamic obstacles. The CBF constraints are formulated based on risk points identified through collision detection between the predicted future states and the FTD map. Experimental results from both simulated and real-world scenarios demonstrate the efficacy of the proposed algorithm in complex environments. In simulation experiments, the proposed algorithm is compared with two baseline approaches, showing superior performance in terms of safety and robustness in obstacle avoidance. The source code is released for the reference of the robotics community.

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v2026.09.13