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North China Electric Power University

Academic institutionasia · cn
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Research library99linked papers
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Selected work

Representative Papers

Large Language Models to Enhance Multi-task Drone Operations in Simulated Environments

Jan 13, 2026

This work proposes a natural language–based control framework to lower the barrier to multi-task drone operation. By fine-tuning the CodeT5 model on (natural language instruction, executable code) pairs generated by ChatGPT, the system automatically translates user commands into executable scripts that drive drones in a high-fidelity AirSim/Unreal Engine simulation environment. This study presents the first integration of large language models with a high-fidelity drone simulation platform, enabling complex task execution through intuitive linguistic input. Experimental results demonstrate that the approach achieves strong instruction comprehension and reliable task execution in simulation, significantly enhancing human–drone interaction efficiency and laying a foundation for real-world natural language control of autonomous aerial systems.

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Federated Learning at the Forefront of Fairness: A Multifaceted Perspective

Aug 01, 2024International Joint Conference on Artificial Intelligence

This work addresses the fairness challenges in federated learning arising from client heterogeneity, which often leads to uneven model performance across participants. To tackle this issue, the paper proposes a systematic taxonomy framework that unifies performance-oriented and capability-oriented fairness strategies, clarifying the technical pathways of existing approaches. Through a comprehensive literature review and taxonomic analysis, the authors construct a structured evaluation metric system for fairness, identifying key challenges and outlining promising directions for future research. This study provides a coherent theoretical foundation, a unified classification perspective, and a forward-looking roadmap to advance fairness-aware federated learning.

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Recent publications

Latest Papers

CausalSplat: Towards Comprehensive Hierarchical Reasoning in 3D Gaussian Splatting

Aug 11, 2026

Existing 3D Gaussian splatting methods struggle to model implicit intentions, complex spatial constraints, and commonsense reasoning, limiting their applicability in embodied interaction scenarios. This work proposes CausalSplat, a novel framework that introduces, for the first time, a comprehensive reasoning task within 3D Gaussian segmentation. By integrating vision-language models with 3D scene graphs, CausalSplat decouples explicit structural perception from implicit logical reasoning to achieve hierarchical holistic understanding. We establish Causal-LERF and Causal-ScanNet, new benchmark datasets encompassing commonsense, spatial, functional, and counterfactual reasoning, on which our method achieves state-of-the-art performance. Furthermore, CausalSplat demonstrates strong generalization capabilities on standard referring and open-vocabulary 3D segmentation tasks.

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Two-Step MV-DeepONet: Probabilistic Operator Learning for Uncertainty Propagation Driven by Random Input Fields

Aug 09, 2026

This work addresses forward uncertainty propagation in complex physical systems driven by random field inputs by proposing a two-stage mean-variance DeepONet framework that explicitly models the off-diagonal covariance structure among output field variables. By decoupling the learning of output basis functions from the mapping of inputs to expansion coefficients, the method shifts probabilistic modeling from a high-dimensional output space to a low-dimensional orthogonal coefficient subspace, thereby preserving spatial dependencies without explicitly parameterizing the full covariance matrix. Through basis orthonormalization, subspace rotation, and low-rank covariance compression, the model efficiently predicts conditional off-diagonal covariances in a single forward pass. Demonstrated on multiple PDEs and hypersonic aerothermal problems, the approach significantly outperforms Prob-DeepONet in generalization, accurately recovers spatial correlations, and generates structured uncertainty bands.

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