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Haotian Mao

Publications and source records attributed to Haotian Mao.

8 recordsLinked to original sources

VoxScene: Anchor-Conditioned Voxel Diffusion for Indoor Scene Arrangement

We present VoxScene, a novel anchor-conditioned voxel diffusion framework tailored for 3D scene synthesis. Current data-driven layout generation techniques typically rely on bounding proxies or implicit representations, which overlook volumetric structures. This geometric blindness inevitably leads to severe physical collisions and structural entanglement, particularly in densely populated environments. To overcome these limitations, we shift the paradigm to an explicit, object-centric voxel representation. Our pipeline sequentially synthesizes discrete volumetric occupancies conditioned on prior anchors and local context. By exploiting the mutually exclusive nature of discrete voxels, our approach eliminates spatial ambiguities and guarantees collision-free arrangements, even in highly complex environments. Furthermore, the synthesized high-fidelity voxel grids serve as discriminative geometric queries for downstream asset retrieval. Extensive experiments demonstrate the universality of our method, achieving state-of-the-art physical plausibility and unlocking shape diversity compared to existing layout planners.

cs.GR

LIVE-GS: LLM Powers Interactive VR Experience with Physics-Aware Gaussian Splatting

As 3D Gaussian Splatting (3DGS) emerges as a leading approach for novel view synthesis and scene reconstruction, its potential in digital asset creation has gained significant attention. An increasing number of asset libraries based on GS are being established. However, generating physics-based dynamic assets remains a time-consuming and expertise-intensive task, especially for non-experts. In this paper, we propose LIVE-GS, a highly realistic Virtual Reality (VR) system powered by Large Language Models (LLMs), which enables rapid creation of dynamic Gaussian assets and real-time VR interactions. To inform our system design, we conducted interviews to examine challenges faced by current GS-based VR systems and the specific demands of users. Based on these insights, we employed GPT-4o to analyze key physical properties of objects that significantly impact user interactions, ensuring physics-based interactions in VR align with real-world phenomena. A key innovation of LIVE-GS is its ability to predict reasonable parameters in just 10 seconds from static Gaussian assets while maintaining high-quality VR interactions. To validate our approach, we invited participants experienced in physical simulation to manually adjust physical parameters, providing a baseline for comparison in both asset quality and authoring efficiency. We also conducted a comprehensive user study to evaluate system usability and user satisfaction. Experimental results demonstrate that LIVE-GS, leveraging LLMs' scene understanding capabilities, can achieve efficient physical scene creation and natural interactions without requiring manual design or annotation.

cs.HC

Survey of Large Language Models in Extended Reality: Technical Paradigms and Application Frontiers

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation, and their integration with Extended Reality (XR) is poised to transform how users interact with immersive environments. This survey provides a comprehensive review of recent developments at the intersection of LLMs and XR, offering a structured organization of research along both technical and application dimensions. We propose a taxonomy of LLM-enhanced XR systems centered on key technical paradigms -- such as interactive agent control, XR development toolkits, and generative scene synthesis -- and discuss how these paradigms enable novel capabilities in XR. In parallel, we examine how LLM-driven techniques support practical XR applications across diverse domains, including immersive education, clinical healthcare, and industrial manufacturing. By connecting these technical paradigms with application frontiers, our survey highlights current trends, delineates design considerations, and identifies open challenges in building LLM-augmented XR systems. This work provides insights that can guide researchers and practitioners in advancing the state of the art in intelligent XR experiences.

cs.HC

Enhancing Fault Detection and Isolation in an All-Electric Auxiliary Power Unit (APU) Gas Generator by Utilizing Starter/Generator Signal

This study proposes a novel paradigm for enhancing fault detection and isolation (FDI) of gas generators in all-electric auxiliary power unit (APU) by utilizing shaft power information from the starter/generator. First, we conduct a pioneering investigation into the challenges and opportunities for FDI brought about by APU electrification. Our analysis reveals that the electrification of APU opens up new possibilities for utilizing shaft power estimates from starter/generator to improve gas generator FDI. We then provide comprehensive theoretical and analytical evidence demonstrating why, how, and to what extent, the shaft power information from the starter/generator can fundamentally enhance the estimation accuracy of system states and health parameters of the gas generator, while also identifying the key factors influencing these improvements in FDI performance. The effectiveness of the proposed paradigm and its theoretical foundations are validated through extensive Monte Carlo simulations. Furthermore, through comprehensive comparative analysis with state-of-the-art gas generator fault diagnosis methods, our experimental results not only demonstrate the superior performance of the proposed approach but also validate that the diagnostic capabilities of existing advanced FDI techniques can be substantially enhanced by incorporating shaft power information. And the observed performance improvement patterns strongly align with our theoretical analysis, verifying both the effectiveness and guiding significance of our theoretical framework. These research findings provide a unique perspective in answering three fundamental questions: why joint fault diagnosis of the starter/generator and gas generator is essential, how it can be implemented, and what factors determine its effectiveness, thereby opening up promising new avenues for FDI technologies in all-electric APU systems.

eess.SY

Joint System Modeling Approach for Fault Simulation of Start-er/Generator and Gas Generator in All-Electric APU

This paper presents a joint system modeling approach for fault simulation of all-electric auxiliary power unit (APU), integrating starter/generator turn-to-turn short circuit (TTSC) faults with gas generator gas-path faults.To address challenges in electromechanical coupling, simulation precision and computational efficiency balance, we propose a multi-rate continuous-discrete hybrid simulation architecture. This architecture treats the starter/generator as a continuous system with variable step size in Simulink, while modeling the gas generator as a discrete system with fixed step size in a dynamic-link library (DLL) environment. For the starter/generator fault modeling, a multi-loop approach is deployed to accurately simulate TTSC faults. For the gas generator, we develop an improved GasTurb-DLL modeling method (IGDM) that enhances uncertainty modeling, state-space representation, and tool chain compatibility. Finally, the proposed methodology above was implemented in a case study based on the APS5000 all-electric APU structure and parameters. Model validation was conducted by comparing simulation results--covering steady-state, transients, healthy, and fault conditions--with reference data from third-party software and literature. The close agreement confirms both the model's accuracy and the effectiveness of our modeling methodology. This work establishes a modeling foundation for investigating the opportunities and challenges in fault detection and isolation (FDI) brought by the all electrification of the APU, including joint fault estimation and diagnosis, coupled electromechanical fault characteristics.

eess.SY

Similarity for downscaled kinetic simulations of electrostatic plasmas: reconciling the large system size with small Debye length

A simple similarity has been proposed for kinetic (e.g., particle-in-cell) simulations of plasma transport that can effectively address the longstanding challenge of reconciling the tiny Debye length with the vast system size. This applies to both transport in unmagnetized plasma and parallel transport in magnetized plasmas, where the characteristics length scales are given by the Debye length, collisional mean free paths, and the system or gradient lengths. The controlled scaled variables are the configuration space, $\mathbf{x}/\mathscr{L},$ and artificial collisional rates, $\mathscr{L}μ$, which is realized through scaling the Coulomb Logarithm in the simulations, $\mathscr{L}\ln Λ.$ Whereas, the scaled time, $t/\mathscr{L}$, and electric field, $\mathscr{L}\mathbf{E}$, are automatic outcomes. The similarity properties are examined, demonstrating that the macroscopic transport physics is preserved through a similarity transformation while keeping the microscopic physics at its original scale of Debye length. To showcase the utility of this approach, two examples of 1D plasma transport problems were simulated using the VPIC code: the plasma thermal quench in tokamaks [J. Li, et al., Nuclear Fusion \textbf{63}, 066030 (2023)] and the plasma sheath in the high-recycling regime [Y. Li, et al., Physics of Plasmas \textbf{30}, 063505 (2023)].

physics.plasm-ph

Rapid assimilation of high-Z impurity ions along the magnetic field line from an ablated pellet

The assimilation of ablated high-Z impurities into the hot surrounding plasma along the magnetic field is investigated by first-principles kinetic simulations. It is found that the assimilated impurity ions, primarily driven by the ambipolar electric force, propagate steadily into the surrounding plasmas. The high-Z impurities in different charge states are mostly aligned due to the strong collisional friction among them so that the averaged impurity ions charge $\bar{Z}$ is a deciding factor. Such assimilation is led by an impurity front that is behind the cooling front due to a smaller charge-mass-ratio of the impurity ions $\bar{Z}/m_I$. With the help of a self-similar solution, the speed of the impurity front $U_s$ is shown to be primarily set by the hot surrounding plasma temperature $T_0$ with a weak dependence on the pellet plasma temperature, underscoring the collisionless nature of the impurity assimilation process. Specifically, $U_s\sim \sqrt{\bar{Z}T_0/m_I}$. The ambipolar-constrained electron conduction flux from the hot plasma is primarily responsible for the collisionless impurity assimilation process.

physics.plasm-ph

Emission of electromagnetic waves as a stopping mechanism for nonlinear collisionless ionization waves in a high-$β$ regime

A high energy density plasma embedded in a neutral gas is able to launch an outward-propagating nonlinear electrostatic ionization wave that traps energetic electrons. The trapping maintains a strong sheath electric field, enabling rapid and long-lasting wave propagation aided by field ionization. Using 1D3V kinetic simulations, we examine the propagation of the ionization wave in the presence of a transverse MG-level magnetic field with the objective to identify qualitative changes in a regime where the initial thermal pressure of the plasma exceeds the pressure of the magnetic field ($β>1$). Our key finding is that the magnetic field stops the propagation by causing the energetic electrons sustaining the wave to lose their energy by emitting an electromagnetic wave. The emission is accompanied by the magnetic field expulsion from the plasma and an increased electron loss from the trapping wave structure. The described effect provides a mechanism mitigating rapid plasma expansion for those applications that involve an embedded plasma, such as high-flux neutron production from laser-irradiated deuterium gas jets.

physics.plasm-ph