122 research outputs found
Optimal Online Transmission Policy for Energy-Constrained Wireless-Powered Communication Networks
This work considers the design of online transmission policy in a
wireless-powered communication system with a given energy budget. The system
design objective is to maximize the long-term throughput of the system
exploiting the energy storage capability at the wireless-powered node. We
formulate the design problem as a constrained Markov decision process (CMDP)
problem and obtain the optimal policy of transmit power and time allocation in
each fading block via the Lagrangian approach. To investigate the system
performance in different scenarios, numerical simulations are conducted with
various system parameters. Our simulation results show that the optimal policy
significantly outperforms a myopic policy which only maximizes the throughput
in the current fading block. Moreover, the optimal allocation of transmit power
and time is shown to be insensitive to the change of modulation and coding
schemes, which facilitates its practical implementation.Comment: 7 pages, accepted by ICC 2019. An extended version of this paper is
accepted by IEEE TW
Robust neurooptimal control for a robot via adaptive dynamic programming
We aim at the optimization of the tracking control of a robot to improve the robustness, under the effect of unknown nonlinear perturbations. First, an auxiliary system is introduced, and optimal control of the auxiliary system can be seen as an approximate optimal control of the robot. Then, neural networks (NNs) are employed to approximate the solution of the Hamilton-Jacobi-Isaacs equation under the frame of adaptive dynamic programming. Next, based on the standard gradient attenuation algorithm and adaptive critic design, NNs are trained depending on the designed updating law with relaxing the requirement of initial stabilizing control. In light of the Lyapunov stability theory, all the error signals can be proved to be uniformly ultimately bounded. A series of simulation studies are carried out to show the effectiveness of the proposed control
DGMem: Learning Visual Navigation Policy without Any Labels by Dynamic Graph Memory
In recent years, learning-based approaches have demonstrated significant
promise in addressing intricate navigation tasks. Traditional methods for
training deep neural network navigation policies rely on meticulously designed
reward functions or extensive teleoperation datasets as navigation
demonstrations. However, the former is often confined to simulated
environments, and the latter demands substantial human labor, making it a
time-consuming process. Our vision is for robots to autonomously learn
navigation skills and adapt their behaviors to environmental changes without
any human intervention. In this work, we discuss the self-supervised navigation
problem and present Dynamic Graph Memory (DGMem), which facilitates training
only with on-board observations. With the help of DGMem, agents can actively
explore their surroundings, autonomously acquiring a comprehensive navigation
policy in a data-efficient manner without external feedback. Our method is
evaluated in photorealistic 3D indoor scenes, and empirical studies demonstrate
the effectiveness of DGMem.Comment: 8 pages, 6 figure
Finite-time synchronization for a class of dynamical complex networks with nonidentical nodes and uncertain disturbance
Sparse Pedestrian Character Learning for Trajectory Prediction
Pedestrian trajectory prediction in a first-person view has recently
attracted much attention due to its importance in autonomous driving. Recent
work utilizes pedestrian character information, \textit{i.e.}, action and
appearance, to improve the learned trajectory embedding and achieves
state-of-the-art performance. However, it neglects the invalid and negative
pedestrian character information, which is harmful to trajectory representation
and thus leads to performance degradation. To address this issue, we present a
two-stream sparse-character-based network~(TSNet) for pedestrian trajectory
prediction. Specifically, TSNet learns the negative-removed characters in the
sparse character representation stream to improve the trajectory embedding
obtained in the trajectory representation stream. Moreover, to model the
negative-removed characters, we propose a novel sparse character graph,
including the sparse category and sparse temporal character graphs, to learn
the different effects of various characters in category and temporal
dimensions, respectively. Extensive experiments on two first-person view
datasets, PIE and JAAD, show that our method outperforms existing
state-of-the-art methods. In addition, ablation studies demonstrate different
effects of various characters and prove that TSNet outperforms approaches
without eliminating negative characters
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