592 research outputs found
Flood lamination strategy based on a three-flood-diversion-area system management
The flood lamination has for principal objective to maintain a downstream flow at a fixed lamination level. For this goal, it is necessary to proceed to the dimensioning of the river system capacity and to make sure of its management by taking into account socio-economic and environmental constraints. The use of flood diversion areas on a river has for main interest to protect inhabited downstream areas. In this paper, a flood lamination strategy aiming at deforming the wave of flood at the entrance of the zone to be protected is presented. A transportation network modeling and a flow optimization method are proposed. The flow optimization method, is based on the modeling of a Min-Cost-Max-flow problem with a linear programming formulation. The optimization algorithm used in this method is the interior-point algorithm which allows a relaxation of the solution of the problem and avoids some non feasibility cases due to the use of constraints based on real data. For a forecast horizon corresponding to the flood episode, the management method of the flood volumes is evaluated on a 2D simulator of a river equipped with a three-flood-diversion- area system. Performances show the effectiveness of the method and its ability to manage flood lamination with efficient water storage
Time and Location Aware Mobile Data Pricing
Mobile users' correlated mobility and data consumption patterns often lead to
severe cellular network congestion in peak hours and hot spots. This paper
presents an optimal design of time and location aware mobile data pricing,
which incentivizes users to smooth traffic and reduce network congestion. We
derive the optimal pricing scheme through analyzing a two-stage decision
process, where the operator determines the time and location aware prices by
minimizing his total cost in Stage I, and each mobile user schedules his mobile
traffic by maximizing his payoff (i.e., utility minus payment) in Stage II. We
formulate the two-stage decision problem as a bilevel optimization problem, and
propose a derivative-free algorithm to solve the problem for any increasing
concave user utility functions. We further develop low complexity algorithms
for the commonly used logarithmic and linear utility functions. The optimal
pricing scheme ensures a win-win situation for the operator and users.
Simulations show that the operator can reduce the cost by up to 97.52% in the
logarithmic utility case and 98.70% in the linear utility case, and users can
increase their payoff by up to 79.69% and 106.10% for the two types of
utilities, respectively, comparing with a time and location independent pricing
benchmark. Our study suggests that the operator should provide price discounts
at less crowded time slots and locations, and the discounts need to be
significant when the operator's cost of provisioning excessive traffic is high
or users' willingness to delay traffic is low.Comment: This manuscript serves as the online technical report of the article
accepted by IEEE Transactions on Mobile Computin
Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning
A central question in robotics is how to design a control system for an agile
mobile robot. This paper studies this question systematically, focusing on a
challenging setting: autonomous drone racing. We show that a neural network
controller trained with reinforcement learning (RL) outperformed optimal
control (OC) methods in this setting. We then investigated which fundamental
factors have contributed to the success of RL or have limited OC. Our study
indicates that the fundamental advantage of RL over OC is not that it optimizes
its objective better but that it optimizes a better objective. OC decomposes
the problem into planning and control with an explicit intermediate
representation, such as a trajectory, that serves as an interface. This
decomposition limits the range of behaviors that can be expressed by the
controller, leading to inferior control performance when facing unmodeled
effects. In contrast, RL can directly optimize a task-level objective and can
leverage domain randomization to cope with model uncertainty, allowing the
discovery of more robust control responses. Our findings allowed us to push an
agile drone to its maximum performance, achieving a peak acceleration greater
than 12 times the gravitational acceleration and a peak velocity of 108
kilometers per hour. Our policy achieved superhuman control within minutes of
training on a standard workstation. This work presents a milestone in agile
robotics and sheds light on the role of RL and OC in robot control
Reaching the limit in autonomous racing: Optimal control versus reinforcement learning
A central question in robotics is how to design a control system for an agile mobile robot. This paper studies this question systematically, focusing on a challenging setting: autonomous drone racing. We show that a neural network controller trained with reinforcement learning (RL) outperformed optimal control (OC) methods in this setting. We then investigated which fundamental factors have contributed to the success of RL or have limited OC. Our study indicates that the fundamental advantage of RL over OC is not that it optimizes its objective better but that it optimizes a better objective. OC decomposes the problem into planning and control with an explicit intermediate representation, such as a trajectory, that serves as an interface. This decomposition limits the range of behaviors that can be expressed by the controller, leading to inferior control performance when facing unmodeled effects. In contrast, RL can directly optimize a task-level objective and can leverage domain randomization to cope with model uncertainty, allowing the discovery of more robust control responses. Our findings allowed us to push an agile drone to its maximum performance, achieving a peak acceleration greater than 12 times the gravitational acceleration and a peak velocity of 108 kilometers per hour. Our policy achieved superhuman control within minutes of training on a standard workstation. This work presents a milestone in agile robotics and sheds light on the role of RL and OC in robot control
A flood lamination strategy based on transportation network with time delay
Over the last few years, the frequency and intensity of floods has become more marked due to the influence of climate change. The engendered problems are related to the safety of goods and persons. These considerations require predictive management that will limit water height downstream. In the literature, numerous works have described flow modeling and management. The work presented in this paper is interested in quantitative management by means of flood expansion areas placed along the river and for which we have size and location. The performance of the management system depends on the time and height of gate opening, which will influence wave mitigation. The proposed management method is based on use of a transportation network with time delay from which the volume of water to be stored is calculated
Full-Duplex MIMO Relaying Powered by Wireless Energy Transfer
We consider a full-duplex decode-and-forward system, where the wirelessly
powered relay employs the time-switching protocol to receive power from the
source and then transmit information to the destination. It is assumed that the
relay node is equipped with two sets of antennas to enable full-duplex
communications. Three different interference mitigation schemes are studied,
namely, 1) optimal 2) zero-forcing and 3) maximum ratio combining/maximum ratio
transmission. We develop new outage probability expressions to investigate
delay-constrained transmission throughput of these schemes. Our analysis show
interesting performance comparisons of the considered precoding schemes for
different system and link parameters.Comment: Accepted for IEEE International Workshop on Signal Processing
Advances in Wireless Communications (SPAWC 2015), Invited pape
Dead Time Compensation for High-Flux Ranging
Dead time effects have been considered a major limitation for fast data
acquisition in various time-correlated single photon counting applications,
since a commonly adopted approach for dead time mitigation is to operate in the
low-flux regime where dead time effects can be ignored. Through the application
of lidar ranging, this work explores the empirical distribution of detection
times in the presence of dead time and demonstrates that an accurate
statistical model can result in reduced ranging error with shorter data
acquisition time when operating in the high-flux regime. Specifically, we show
that the empirical distribution of detection times converges to the stationary
distribution of a Markov chain. Depth estimation can then be performed by
passing the empirical distribution through a filter matched to the stationary
distribution. Moreover, based on the Markov chain model, we formulate the
recovery of arrival distribution from detection distribution as a nonlinear
inverse problem and solve it via provably convergent mathematical optimization.
By comparing per-detection Fisher information for depth estimation from high-
and low-flux detection time distributions, we provide an analytical basis for
possible improvement of ranging performance resulting from the presence of dead
time. Finally, we demonstrate the effectiveness of our formulation and
algorithm via simulations of lidar ranging.Comment: Revision with added estimation results, references, and figures, and
modified appendice
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