89 research outputs found
Chen Shupeng, Yang Ruwan et Lin Hui éds., Xinjingyi yu Zhonguo xibu kaifa (La nouvelle économie et le développement de l'Ouest chinois), Hong Kong, Université chinoise, 2001, 243 p.
No full textGoodman David S.G., Petit-Tung Stéphanie. Chen Shupeng, Yang Ruwan et Lin Hui éds., Xinjingyi yu Zhonguo xibu kaifa (La nouvelle économie et le développement de l'Ouest chinois), Hong Kong, Université chinoise, 2001, 243 p.. In: Perspectives chinoises, n°72, 2002. pp. 80-82
Supplemental Material - Annulus Fibrosus Repair for Lumbar Disc Herniation: A Meta-Analysis of Clinical Outcomes From Controlled Studies
No full textSupplemental Material for Annulus Fibrosus Repair for Lumbar Disc Herniation: A Meta-Analysis of Clinical Outcomes From Controlled Studies Yangbin Wang, MB, Xiaoyu He, MB, Shupeng Chen, MB, Yiyong Weng, MB, Zhihua Liu, MB, Qunlong Pan, MB, Rongmou Zhang, MM, Yizhong Li, MM, Hanshi Wang, MD, Shu Lin, and Haiming Yu, MM in Global Spine Journal.</p
Differentiable Moving Horizon Estimation for Robust Flight Control
No full textEstimating and reacting to external disturbances is of fundamental importance
for robust control of quadrotors. Existing estimators typically require
significant tuning or training with a large amount of data, including the
ground truth, to achieve satisfactory performance. This paper proposes a
data-efficient differentiable moving horizon estimation (DMHE) algorithm that
can automatically tune the MHE parameters online and also adapt to different
scenarios. We achieve this by deriving the analytical gradient of the estimated
trajectory from MHE with respect to the tuning parameters, enabling end-to-end
learning for auto-tuning. Most interestingly, we show that the gradient can be
calculated efficiently from a Kalman filter in a recursive form. Moreover, we
develop a model-based policy gradient algorithm to learn the parameters
directly from the trajectory tracking errors without the need for the ground
truth. The proposed DMHE can be further embedded as a layer with other neural
networks for joint optimization. Finally, we demonstrate the effectiveness of
the proposed method via both simulation and experiments on quadrotors, where
challenging scenarios such as sudden payload change and flying in downwash are
examined.Comment: This paper was accepted for presentation at the 60th IEEE Conference
on Decision and Control (CDC2021). The extended version here contains the
experiment results and an appendix with brief proofs for the two lemma
A GPU mapping system for real-time robot motion planning
No full text10.1109/RCAR52367.2021.95176362021 IEEE International Conference on Real-time Computing and Robotics (RCAR)762-76
Research on friction torque analysis of planetary roller screw mechanism considering load distribution
No full textRelative growth rate for trees at the growth stage is coordinated with leaf bulk modulus of elasticity and osmotic potential in a subtropical forest of China
No full textCurrent theoretical models for tree relative growth rate (RGR) have considered physiological processes based on carbon economy and nutrient-productivity relations, but have not taken water relations into account. According to the ecological energetics and Lockhart equation theories, if more energy is needed to maintain water balance in plants, less energy should be invested towards growth, resulting in a trade-off between growth and drought tolerance. Leaf pressure–volume (PV) curve parameters are the best recognised classical indicators of plant drought tolerance, however, the relationships between tree growth and leaf PV parameters have rarely been investigated in forest communities. In the study, we selected two evergreen (Lithocarpus glaber, Cyclobalanopsis oxyodon) and two deciduous species (Quercus serrata, Platycarya strobilacea) that dominate northern subtropical forests on Mt. Shennongjia, central China. For each species, we selected individuals at different developmental stages (growth vs. mature stage), and measured the relative growth rate of diameter at breast height (RGRDBH) and leaf PV curve parameters, including leaf saturated osmotic potential (Ψsat), maximum bulk elastic modulus (εmax), turgor loss osmotic potential (Ψtlp) and leaf water capacity (Cleaf). Correlation analysis and structural equation models (SEM) were applied to explore the relationships between RGRDBH and PV parameters. The results showed that: 1) at both stages, RGRDBH was positively correlated with Ψsat, but not with Ψtlp and Cleaf; 2) εmax was negatively related to RGRDBH at the growth stage; 3) the relationship between PV parameters and RGRDBH was determined mainly by inter-specific difference; 4) SEM showed that both Ψtlp and Ψsat were affected by εmax; and 5) due to the interaction between the PV parameters, Ψsat and εmax had a direct effect on RGRDBH only during the growth stage. Our results showed that the functional/structural coordination between PV parameters adjusted the RGRDBH of trees, and among different species, there exists a tradeoff between energy allocation for drought tolerance and growth. These findings can be applied to improve RGR models, and show how forests respond to warm-induced drought, which is a major threat to global forests in a future warmer climate
Neural Moving Horizon Estimation for Robust Flight Control
No full textEstimating and reacting to disturbances is crucial for robust flight control
of quadrotors. Existing estimators typically require significant tuning for a
specific flight scenario or training with extensive ground-truth disturbance
data to achieve satisfactory performance. In this paper, we propose a neural
moving horizon estimator (NeuroMHE) that can automatically tune its key
parameters modeled by a neural network and adapt to different flight scenarios.
We achieve this by deriving the analytical gradients of the MHE estimates with
respect to the MHE weighting matrices, which enables a seamless embedding of
the MHE as a learnable layer into the neural network for highly effective
learning. Interestingly, we show that the gradients can be computed efficiently
using a Kalman filter in a recursive form. Moreover, we develop a model-based
policy gradient algorithm to train NeuroMHE directly from the quadrotor
trajectory tracking error without needing the ground-truth disturbance data.
The effectiveness of NeuroMHE is verified extensively via both simulations and
physical experiments on quadrotors in various challenging flights. Notably,
NeuroMHE outperforms a state-of-the-art neural network-based estimator,
reducing force estimation errors by up to 76.7%, while using a portable neural
network that has only 7.7% of the learnable parameters of the latter. The
proposed method is general and can be applied to robust adaptive control of
other robotic systems.Comment: This paper (not the final version) has been accepted for publication
in the IEEE Transactions on Robotic
Experimental Study and Numerical Simulation on Reed Valve Flow Coefficient
No full textIn a complex simulation of a household refrigerator compressor, the flow coefficient and the corresponding effective flow area of the reed valve are key parameters which should be known previously. In general, the flow coefficient and the corresponding effective flow area can be obtained by experiment. But in fact, it is hard to measure flow coefficient of the reed valve with refrigerant flowing in a closed system. A new method is presented in this paper. A simulation model is built to get the flow coefficient of the reed valve with working medium of air. In addition, we measure the flow coefficients of air by static blow experiment and obtain flow coefficients in varying flux and valve lifts. Changing the medium to R600a, we obtained the flow coefficient of reed valve by numerical simulation. And through the compressor performance test, we validate the correction of flow coefficient indirectly. The results show that the numerical simulation results coincide well with the experiment results as the working medium was air. This model can be used to calculate flow efficient of working medium for R600a
Reprocessed MODIS Version 6.1 Leaf Area Index dataset
No full textIntroduction
The leaf area index (LAI) data sets were generated by reprocessing the MODIS version 6.1 LAI products.
The raw data used include the MODIS LAI Version 6.1 products MCD15A2H (2002.7.4-2021), MOD15A2H (2000.2.18-2002.6.26) (Myneni et al., 2021) and MODIS Land Cover Type product MCD12Q1 (2001-2021) (Friedl and Sulla-Menashe, 2022).
The algorithm is mainly based on the two-step integrated method developed by Yuan et al. (2011), and the method of background value calculation was updated.
For each year being reprocessed, the nearest 9-year data include itself are used.
We use the corresponding year's land cover data for calculating the multi-year average, local per class mean, per class mean and multi-year per class mean (ref. Fig. 4, Yuan et al., 2011).
Data description
These monthly LAI data were provided at 0.5-degree resolution covering the period 2000-2021. Data of each year is stored in one NetCDF file, namely lai_monthly_0.5_{YEAR}.nc.
For LAI data with more spatial or temporal resolutions, see Land-Atmosphere Interaction Research Group at Sun Yat-sen University (bnu.edu.cn).
Caution
The reprocessed data for downloading consists two MODIS version 6.1 products, i.e., MCD15A2H (2002.7.4-2021) and MOD15A2H (2000.2.18-2002.6.26). The prefix “MCD” stands for a combined product, whose algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA’s Terra and Aqua satellites, while “MOD” data are retrieved only from the Terra satellite. We have found that their temporal-mean values were different especially in the equatorial region, which may result in an unrealistic trend (see Lin et al., 2022 for detailed discussion). Therefore, attention should be paid when using the reprocessed products for long-term trend analysis and data starting from year 2003 (i.e., only MCD) was recommended for LAI trend study.
Data citation
Lin, W. et al., 2022. Reprocessed MODIS Version 6.1 Leaf Area Index dataset and its evaluation for land surface and climate modeling. Submitted.
Yuan, H., Dai, Y., Xiao, Z., Ji, D., Shangguan, W., 2011. Reprocessing the MODIS Leaf Area Index Products for Land Surface and Climate Modelling. Remote Sensing of Environment, 115(5), 1171-1187. doi:10.1016/j.rse.2011.01.001
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