67 research outputs found
Criterion and parameter analysis in aircraft shimmy study
Aircraft shimmy, a dynamic instability phenomenon of the landing gear has been a problem for over half a century. It is important to predict and control nose landing gear shimmy effectively in aircraft design phase. Simulation with typical cases is a better way compared to the tests on real aircraft to investigate early stage design and give modification suggestions at a reasonable cost .In this paper, the simulation for a certain type of aircraft is presented based on actual data. In the rigid-flexible coupling model of aircraft, non-linear factors are considered, such as airframe flexibility, steering clearance and tire parameters. The model is checked with test results of static and modal experiments and proved with sufficient accuracy. Figures of stable region are presented, formed by taxing speed and critical anti-shimmy damping coefficient. Accordingly, details of shimmy criterion are discussed and effects of factors mentioned above are studied. The result shows that self-alignment torque coefficient, relaxation length of tire, and steering clearance of nose landing gear affect critical damping coefficient significantly
UniWorld: Autonomous Driving Pre-training via World Models
In this paper, we draw inspiration from Alberto Elfes' pioneering work in
1989, where he introduced the concept of the occupancy grid as World Models for
robots. We imbue the robot with a spatial-temporal world model, termed
UniWorld, to perceive its surroundings and predict the future behavior of other
participants. UniWorld involves initially predicting 4D geometric occupancy as
the World Models for foundational stage and subsequently fine-tuning on
downstream tasks. UniWorld can estimate missing information concerning the
world state and predict plausible future states of the world. Besides,
UniWorld's pre-training process is label-free, enabling the utilization of
massive amounts of image-LiDAR pairs to build a Foundational Model.The proposed
unified pre-training framework demonstrates promising results in key tasks such
as motion prediction, multi-camera 3D object detection, and surrounding
semantic scene completion. When compared to monocular pre-training methods on
the nuScenes dataset, UniWorld shows a significant improvement of about 1.5% in
IoU for motion prediction, 2.0% in mAP and 2.0% in NDS for multi-camera 3D
object detection, as well as a 3% increase in mIoU for surrounding semantic
scene completion. By adopting our unified pre-training method, a 25% reduction
in 3D training annotation costs can be achieved, offering significant practical
value for the implementation of real-world autonomous driving. Codes are
publicly available at https://github.com/chaytonmin/UniWorld.Comment: 8 pages, 5 figures. arXiv admin note: substantial text overlap with
arXiv:2305.1882
Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders
Current perception models in autonomous driving heavily rely on large-scale
labelled 3D data, which is both costly and time-consuming to annotate. This
work proposes a solution to reduce the dependence on labelled 3D training data
by leveraging pre-training on large-scale unlabeled outdoor LiDAR point clouds
using masked autoencoders (MAE). While existing masked point autoencoding
methods mainly focus on small-scale indoor point clouds or pillar-based
large-scale outdoor LiDAR data, our approach introduces a new self-supervised
masked occupancy pre-training method called Occupancy-MAE, specifically
designed for voxel-based large-scale outdoor LiDAR point clouds. Occupancy-MAE
takes advantage of the gradually sparse voxel occupancy structure of outdoor
LiDAR point clouds and incorporates a range-aware random masking strategy and a
pretext task of occupancy prediction. By randomly masking voxels based on their
distance to the LiDAR and predicting the masked occupancy structure of the
entire 3D surrounding scene, Occupancy-MAE encourages the extraction of
high-level semantic information to reconstruct the masked voxel using only a
small number of visible voxels. Extensive experiments demonstrate the
effectiveness of Occupancy-MAE across several downstream tasks. For 3D object
detection, Occupancy-MAE reduces the labelled data required for car detection
on the KITTI dataset by half and improves small object detection by
approximately 2% in AP on the Waymo dataset. For 3D semantic segmentation,
Occupancy-MAE outperforms training from scratch by around 2% in mIoU. For
multi-object tracking, Occupancy-MAE enhances training from scratch by
approximately 1% in terms of AMOTA and AMOTP. Codes are publicly available at
https://github.com/chaytonmin/Occupancy-MAE.Comment: Accepted by TI
Occ-BEV: Multi-Camera Unified Pre-training via 3D Scene Reconstruction
Multi-camera 3D perception has emerged as a prominent research field in
autonomous driving, offering a viable and cost-effective alternative to
LiDAR-based solutions. However, existing multi-camera algorithms primarily rely
on monocular image pre-training, which overlooks the spatial and temporal
correlations among different camera views. To address this limitation, we
propose a novel multi-camera unified pre-training framework called Occ-BEV,
which involves initially reconstructing the 3D scene as the foundational stage
and subsequently fine-tuning the model on downstream tasks. Specifically, a 3D
decoder is designed for leveraging Bird's Eye View (BEV) features from
multi-view images to predict the 3D geometry occupancy to enable the model to
capture a more comprehensive understanding of the 3D environment. One
significant advantage of Occ-BEV is that it can utilize a vast amount of
unlabeled image-LiDAR pairs for pre-training. The proposed multi-camera unified
pre-training framework demonstrates promising results in key tasks such as
multi-camera 3D object detection and semantic scene completion. When compared
to monocular pre-training methods on the nuScenes dataset, Occ-BEV demonstrates
a significant improvement of 2.0% in mAP and 2.0% in NDS for 3D object
detection, as well as a 0.8% increase in mIOU for semantic scene completion.
codes are publicly available at https://github.com/chaytonmin/Occ-BEV.Comment: 8 pages, 5 figure
An autonomous ultra-wide band-based attitude and position determination technique for indoor mobile laser scanning
Mobile laser scanning (MLS) has been widely used in three-dimensional (3D) city modelling data collection, such as Google cars for Google Map/Earth. Building Information Modelling (BIM) has recently emerged and become prominent. 3D models of buildings are essential for BIM. Static laser scanning is usually used to generate 3D models for BIM, but this method is inefficient if a building is very large, or it has many turns and narrow corridors. This paper proposes using MLS for BIM 3D data collection. The positions and attitudes of the mobile laser scanner are important for the correct georeferencing of the 3D models. This paper proposes using three high-precision ultra-wide band (UWB) tags to determine the positions and attitudes of the mobile laser scanner. The accuracy of UWB-based MLS 3D models is assessed by comparing the coordinates of target points, as measured by static laser scanning and a total station survey
Contrastive Label Disambiguation for Self-Supervised Terrain Traversability Learning in Off-Road Environments
Discriminating the traversability of terrains is a crucial task for
autonomous driving in off-road environments. However, it is challenging due to
the diverse, ambiguous, and platform-specific nature of off-road
traversability. In this paper, we propose a novel self-supervised terrain
traversability learning framework, utilizing a contrastive label disambiguation
mechanism. Firstly, weakly labeled training samples with pseudo labels are
automatically generated by projecting actual driving experiences onto the
terrain models constructed in real time. Subsequently, a prototype-based
contrastive representation learning method is designed to learn distinguishable
embeddings, facilitating the self-supervised updating of those pseudo labels.
As the iterative interaction between representation learning and pseudo label
updating, the ambiguities in those pseudo labels are gradually eliminated,
enabling the learning of platform-specific and task-specific traversability
without any human-provided annotations. Experimental results on the RELLIS-3D
dataset and our Gobi Desert driving dataset demonstrate the effectiveness of
the proposed method.Comment: 9 pages, 11 figure
Integrated prediction of green bond return under the dual risks of climate change and energy crisis
Prediction of bond return is a classic problem in financial area, providing an important basis for portfolio construction and risk management. The sustainable investment attribute of green bonds has been favored by investors, so that green bonds have become an important component for major asset allocation. However, due to the specific investment focus of green bonds, investors’ return expectations are influenced not only by traditional corporate bond factors, but also by related factors such as climate change and energy transition. Against the backdrop of increasingly severe climate risks and the global energy crisis, this paper analyses the volatility characteristics of China’s green bonds at multiple time scales, and introduces exogenous variables such as returns of the alternative financial assets, climate risks and returns of energy markets for prediction. Based on the LSTM model, the volatility of green bond yield at different time scales is separately predicted using optimal exogenous variable before integration. It is found that the new integrated prediction model can significantly improve the forecasting performance compared to traditional single LSTM models and simple decomposition-integrated models. Further, both climate risks and energy markets variables have a significant improvement effect on predicting green bond in low-frequency item, while energy markets variables also have a better predictive effect on trend items. Building on the use of only LSTM model, it could be further enhanced by integrating more algorithms to select the best single model for each component, further improve the prediction accuracy and provide a more effective quantitative tool for investment decision-making and risk management in related fields
Thermodynamically favorable reactions shape the archaeal community affecting bacterial community assembly in oil reservoirs
Microbial community assembly mechanisms are pivotal for understanding the ecological functions of microorganisms in biogeochemical cycling in Earth’s ecosystems, yet rarely investigated in the context of deep terrestrial ecology. Here, the microbial communities in the production waters collected from water injection wells and oil production wells across eight oil reservoirs throughout northern China were determined and analyzed by proportional distribution analysis and null model analysis. A ‘core’ microbiota consisting of three bacterial genera, including Arcobacter, Pseudomonas and Acinetobacter, and eight archaeal genera, including Archaeoglobus, Methanobacterium, Methanothermobacter, unclassified Methanobacteriaceae, Methanomethylovorans, Methanoculleus, Methanosaeta and Methanolinea, was found to be present in all production water samples. Canonical correlation analysis reflected that the core archaea were significantly influenced by temperature and reservoir depth, while the core bacteria were affected by the combined impact of the core archaea and environmental factors. Thermodynamic calculations indicate that bioenergetic constraints are the driving force that governs the enrichment of two core archaeal guilds, aceticlastic methanogens versus hydrogenotrophic methanogens, in low- and high-temperature oil reservoirs, respectively. Collectively, our study indicates that microbial community structures in wells of oil reservoirs are structured by the thermodynamic window of opportunity, through which the core archaeal communities are accommodated directly followed by the deterministic recruiting of core bacterial genera, and then the stochastic selection of some other microbial members from local environments. Our study enhances the understanding of the microbial assembly mechanism in deep terrestrial habitats. Meanwhile, our findings will support the development of functional microbiota used for bioremediation and bioaugmentation in microbial enhanced oil recovery
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