469 research outputs found
Learning Unmanned Aerial Vehicle Control for Autonomous Target Following
While deep reinforcement learning (RL) methods have achieved unprecedented
successes in a range of challenging problems, their applicability has been
mainly limited to simulation or game domains due to the high sample complexity
of the trial-and-error learning process. However, real-world robotic
applications often need a data-efficient learning process with safety-critical
constraints. In this paper, we consider the challenging problem of learning
unmanned aerial vehicle (UAV) control for tracking a moving target. To acquire
a strategy that combines perception and control, we represent the policy by a
convolutional neural network. We develop a hierarchical approach that combines
a model-free policy gradient method with a conventional feedback
proportional-integral-derivative (PID) controller to enable stable learning
without catastrophic failure. The neural network is trained by a combination of
supervised learning from raw images and reinforcement learning from games of
self-play. We show that the proposed approach can learn a target following
policy in a simulator efficiently and the learned behavior can be successfully
transferred to the DJI quadrotor platform for real-world UAV control
Multiply robust estimators in longitudinal studies with missing data under control-based imputation
Longitudinal studies are often subject to missing data. The ICH E9(R1)
addendum addresses the importance of defining a treatment effect estimand with
the consideration of intercurrent events. Jump-to-reference (J2R) is one
classically envisioned control-based scenario for the treatment effect
evaluation using the hypothetical strategy, where the participants in the
treatment group after intercurrent events are assumed to have the same disease
progress as those with identical covariates in the control group. We establish
new estimators to assess the average treatment effect based on a proposed
potential outcomes framework under J2R. Various identification formulas are
constructed under the assumptions addressed by J2R, motivating estimators that
rely on different parts of the observed data distribution. Moreover, we obtain
a novel estimator inspired by the efficient influence function, with multiple
robustness in the sense that it achieves -consistency if any pairs of
multiple nuisance functions are correctly specified, or if the nuisance
functions converge at a rate not slower than when using flexible
modeling approaches. The finite-sample performance of the proposed estimators
is validated in simulation studies and an antidepressant clinical trial
The Impact Mechanism of Pro-Environmental Behaviours
Based on the theory of relationship quality, the research constructed the impact mechanism model of pro-environmental behaviours by applying touristâs perceived value as the antecedent variable, while relationship quality (satisfaction and loyalty) was treated as the mediators, and place attachment as a moderator. Surveys were conducted at the Daweishan National Forest Park in Hunan Province, China, with 674 valid questionnaires collected. The empirical results provided evidence that the direct effect of perceived value of tourists on touristsâ pro-environmental behaviours is significantly positive, and the indirect effect of relationship quality (satisfaction and loyalty) between perceived value of tourists. Recommendations and strategies for further research and implementing tourist stratification management are suggested
A high-resolution marine mercury model MITgcm-ECCO2-Hg with online biogeochemistry
Mercury (Hg) is a global persistent contaminant. Modeling studies are useful means of synthesizing a current understanding of the Hg cycle. Previous studies mainly use coarse-resolution models, which makes it impossible to analyze the role of turbulence in the Hg cycle and inaccurately describes the transport of kinetic energy. Furthermore, all of them are coupled with offline biogeochemistry, and therefore they cannot respond to short-term variability in oceanic Hg concentration. In our approach, we utilize a high-resolution ocean model (MITgcm-ECCO2, referred to as âhigh-resolution-MITgcmâ) coupled with the concurrent simulation of biogeochemistry processes from the Darwin Project (referred to as âonlineâ). This integration enables us to comprehensively simulate the global biogeochemical cycle of Hg with a horizontal resolution of 1/5â. The finer portrayal of surface Hg concentrations in estuarine and coastal areas, strong western boundary flow and upwelling areas, and concentration diffusion as vortex shapes demonstrate the effects of turbulence that are neglected in previous models. Ecological events such as algal blooms can cause a sudden enhancement of phytoplankton biomass and chlorophyll concentrations, which can also result in a dramatic change in particle-bound Hg (HgaqP) sinking flux simultaneously in our simulation. In the global estuary region, including riverine Hg input in the high-resolution model allows us to reveal the outward spread of Hg in an eddy shape driven by fine-scale ocean currents. With faster current velocities and diffusion rates, our model captures the transport and mixing of Hg from river discharge in a more accurate and detailed way and improves our understanding of Hg cycle in the ocean.</p
Cattle Number Estimation on Smart Pasture Based on Multi-Scale Information Fusion
In order to solve the problem of intelligent management of cattle numbers in the pasture, a dataset of cattle density estimation was established, and a multi-scale residual cattle density estimation network was proposed to solve the problems of uneven distribution of cattle and large scale variations caused by perspective changes in the same image. Multi-scale features are extracted by multiple parallel dilated convolutions with different dilation rates. Meanwhile, aiming at the âgrid effectâ caused by the use of dilated convolution, the residual structure is combined with a small dilation rate convolution to eliminate the influence of the âgrid effectâ. Experiments were carried out on the cattle dataset and dense population dataset, respectively. The experimental results show that the proposed multi-scale residual cattle density estimation network achieves the lowest mean absolute error (MAE) and means square error (RMSE) on the cattle dataset compared with other density estimation methods. In ShanghaiTech, a dense population dataset, the density estimation results of the multi-scale residual network are also optimal or suboptimal in MAE and RMSE
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