arXiv subjects

Wenjun Dong

Publications and source records attributed to Wenjun Dong.

5 recordsLinked to original sources

arXiv2026

Adding Thermal Awareness to Visual Systems in Real-Time via Distilled Diffusion Models

Purely RGB-based vision models often fail to provide reliable cues in challenging scenarios such as nighttime and fog, leading to degraded performance and safety risks. Infrared imaging captures heat-emitting sources and provides critical complementary information, but existing high-fidelity fusion methods suffer from prohibitive latency, rendering them impractical for real-time edge deployment. To address this, we propose FusionProxy, a real-time image fusion module designed as a fully independent, plug-and-play component with diffusion level quality. FusionProxy exploits two complementary statistics of a teacher sample ensemble: per-pixel variance in raw image space, used to weight pixel-level supervision, and per-pixel variance inside frozen foundation backbones, used to route feature-level alignment spatially. Once trained, FusionProxy can be directly integrated into any visual perception system without joint optimization. Extensive experiments demonstrate that our method achieves superior performance on static recognition tasks and significantly enhances robustness in dynamic tasks, including closed-loop autonomous driving. Crucially, FusionProxy achieves real-time inference speeds on diverse platforms, from high-end GPUs to commodity hardware, providing a flexible and generalizable solution for all-day perception.

cs.CV↗

arXiv2026

Automated Detection and Climatological Analysis of Ripple-Scale Gravity Wave Instabilities Using a Squeeze-and-Excitation Convolutional Neural Network

All-sky OH airglow imaging provides two-dimensional observations of mesospheric gravity wave structure near ~87 km altitude. Ripple-scale instability signatures, characterized by 5-15 km horizontal wavelengths and short lifetimes, are particularly difficult to identify consistently using manual inspection. In this study, we develop a reproducible, automated detection framework based on a squeeze-and-excitation convolutional neural network (SE-CNN) trained on 41 x 41 pixel image patches, to identify ripple-scale structures in 512 x 512 pixel all-sky airglow images acquired at Yucca Ridge Field Station (40.7o N, 104.9o W). The time-differenced images are normalized using a robust median-absolute-deviation (MAD) scaling procedure to mitigate star contamination and background variability. The model is trained and validated on manually annotated ripple and non-ripple patches, then evaluated using independent test subsets. The automated detection is performed using a sliding-window approach with spatial and temporal clustering criteria for event definition. At the patch level, the classifier achieves 92\% F1-score with high precision and recall. At the event level, automated detections recover approximately 90\% of manually identified ripple events while identifying additional low-amplitude occurrences. Validated against previous manual identification study, the automated detection catalog enables objective quantification of ripple occurrence frequency, seasonal modulation, and lifetime distributions. By emphasizing methodological transparency, calibration considerations, and validation metrics, this framework establishes a scalable measurement technique for systematic detection of mesospheric instability signatures in long-term airglow image archives.

physics.ao-ph↗

arXiv2025

Imputing Missing Long-Term Spatiotemporal Multivariate Atmospheric Data with CNN-Transformer Machine Learning

Continuous physical domains are important for scientific investigations of dynamical processes in the atmosphere. However, missing data arising from operational constraints and adverse environmental conditions pose significant challenges to accurate analysis and modeling. To address this limitation, we propose a novel hybrid Convolutional Neural Network (CNN) Transformer machine learning model for multivariable atmospheric data imputation, termed CT-MVP. This framework integrates CNNs for local feature extraction with transformers for capturing long-range dependencies across time and altitude. The model is trained and evaluated on a testbed using the Specified Dynamics Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (SD-WACCM-X) dataset spanning 13 years, which provides continuous global coverage of atmospheric variables, including temperature and zonal and meridional winds. This setup ensures that the ML approach can be rigorously assessed under diverse data-gap conditions. The hybrid framework enables effective reconstruction of missing values in high-dimensional atmospheric datasets, with comparative evaluations against traditional methods and a simple transformer. The results demonstrate that CT-MVP achieves superior performance compared with traditional approaches, particularly in cases involving extended periods of missing data, and slightly outperforms a simple transformer with the same hyper-parameters.

physics.ao-ph↗

arXiv2025

A Geometry-Aware AI Emulator for the Coupled Whole Atmosphere from Earth Surface to the Ionosphere and Thermosphere

Whole-atmosphere models such as WACCM-X resolve coupling from the Earth surface to the Mesosphere-Lower-Thermosphere (MLT), and Ionosphere-Thermosphere (IT) systems with expensive computational costs. Here we introduce CAM-NET, a geometry-aware Spherical Fourier Neural Operator (SFNO) surrogate for emulating WACCM-X variability from Earth surface to IT region. CAM-NET is trained on 3-hourly WACCM-X simulations and predicts neutral winds, temperature, pressure-coordinate vertical velocity, electron density, and zonal ion drift. The framework combines a Spherical Fourier Neural Operator (SFNO) backbone with a newly developed lightweight module that extends the frozen atmospheric representation to plasma variables. For the held-out simulation, CAM-NET preserves the dominant IT morphology and remains stable during multi-day autoregressive rollouts. Spherical-harmonic diagnostics show that the model retains low-degree variability while damping high-wavenumber mesospheric structures, especially near 90 km where gravity wave breaks. CAM-NET is intended as a computationally efficient emulator of WACCM-X, rather than an operational forecasting system. These results demonstrate its potential for rapid ensemble experiments, uncertainty quantification, and sensitivity studies of large-scale coupled whole atmospheric variability.

physics.space-ph↗

arXiv2021

Human factors engineering research on single pilot operations for large commercial aircraft: Status and prospect

The civil aviation community is actively exploring and developing the solutions of single pilot operations SPO for large commercial aircraft. Human factors engineering research for SPO has been launched, and the research mainly focuses on three research solutions: flight deck airborne equipment upgrade, flight support from ground stations, and the combined SPO solution of "flight deck airborne equipment upgrade, flight support from ground stations". This paper reviews and analyzez the progress of human factors engineering research on SPO. The preliminary research outcome tends to support the combined SPO solution. However, the current human factors engineering research is not comprehensive and cannot provide a complete human factors engineering solution for SPO. For future human factors engineering research, this paper analyzes the key human factors issues on SPO and points out the gaps in the current research and the areas for future work. Finally, this paper puts forward an overall strategy and recommendations for future human factors engineering research on SPO.

cs.HC↗