1,696 research outputs found
Data-Driven Dynamic Robust Resource Allocation: Application to Efficient Transportation
The transformation to smarter cities brings an array of emerging urbanization challenges. With the development of technologies such as sensor networks, storage devices, and cloud computing, we are able to collect, store, and analyze a large amount of data in real time. Modern cities have brought to life unprecedented opportunities and challenges for allocating limited resources in a data-driven way. Intelligent transportation system is one emerging research area, in which sensing data provides us opportunities for understanding spatial-temporal patterns of demand human and mobility. However, greedy or matching algorithms that only deal with known requests are far from efficient in the long run without considering demand information predicted based on data.
In this dissertation, we develop a data-driven robust resource allocation framework to consider spatial-temporally correlated demand and demand uncertainties, motivated by the problem of efficient dispatching of taxi or autonomous vehicles. We first present a receding horizon control (RHC) framework to dispatch taxis towards predicted demand; this framework incorporates both information from historical record data and real-time GPS location and occupancy status data. It also allows us to allocate resource from a globally optimal perspective in a longer time period, besides the local level greedy or matching algorithm for assigning a passenger pick-up location of each vacant vehicle. The objectives include reducing both current and anticipated future total idle driving distance and matching spatial-temporal ratio between demand and supply for service quality. We then present a robust optimization method to consider spatial-temporally correlated demand model uncertainties that can be expressed in closed convex sets. Uncertainty sets of demand vectors are constructed from data based on theories in hypothesis testing, and the sets provide a desired probabilistic guarantee level for the performance of dispatch solutions. To minimize the average resource allocation cost under demand uncertainties, we develop a general data-driven dynamic distributionally robust resource allocation model. An efficient algorithm for building demand uncertainty sets that compatible with various demand prediction methods is developed. We prove equivalent computationally tractable forms of the robust and distributionally robust resource allocation problems using strong duality. The resource allocation problem aims to balance the demand-supply ratio at different nodes of the network with minimum balancing and re-balancing cost, with decision variables on the denominator that has not been covered by previous work.
Trace-driven analysis with real taxi operational record data of San Francisco shows that the RHC framework reduces the average total idle distance of taxis by 52%, and evaluations with over 100GB of New York City taxi trip data show that robust and distributionally robust dispatch methods reduce the average total idle distance by 10% more compared with non-robust solutions. Besides increasing the service efficiency by reducing total idle driving distance, the resource allocation methods in this dissertation also reduce the demand-supply ratio mismatch error across the city
Behavior Planning For Connected Autonomous Vehicles Using Feedback Deep Reinforcement Learning
With the development of communication technologies, connected autonomous
vehicles (CAVs) can share information with each other. We propose a novel
behavior planning method for CAVs to decide actions such as whether to change
lane or keep lane based on the observation and shared information from
neighbors, and to make sure that there exist corresponding control maneuvers
such as acceleration and steering angle to guarantee the safety of each
individual autonomous vehicle. We formulate this problem as a hybrid partially
observable Markov decision process (HPOMDP) to consider objectives such as
improving traffic flow efficiency and driving comfort and safety requirements.
The discrete state transition is determined by the proposed feedback deep
Q-learning algorithm using the feedback action from an underlying controller
based on control barrier functions. The feedback deep Q-learning algorithm we
design aims to solve the critical challenge of reinforcement learning (RL) in a
physical system: guaranteeing the safety of the system while the RL is
exploring the action space to increase the reward. We prove that our method
renders a forward invariant safe set for the continuous state physical dynamic
model of the system while the RL agent is learning. In experiments, our
behavior planning method can increase traffic flow and driving comfort compared
with the intelligent driving model (IDM). We also validate that our method
maintains safety during the learning process.Comment: conferenc
An adaptive RKHS regularization for Fredholm integral equations
Regularization is a long-standing challenge for ill-posed linear inverse
problems, and a prototype is the Fredholm integral equation of the first kind.
We introduce a practical RKHS regularization algorithm adaptive to the discrete
noisy measurement data and the underlying linear operator. This RKHS arises
naturally in a variational approach, and its closure is the function space in
which we can identify the true solution. We prove that the RKHS-regularized
estimator has a mean-square error converging linearly as the noise scale
decreases, with a multiplicative factor smaller than the commonly-used
-regularized estimator. Furthermore, numerical results demonstrate that
the RKHS-regularizer significantly outperforms -regularizer when either
the noise level decays or when the observation mesh refines.Comment: 18 page
PS-TRUST: Provably Secure Solution for Truthful Double Spectrum Auctions
Truthful spectrum auctions have been extensively studied in recent years.
Truthfulness makes bidders bid their true valuations, simplifying greatly the
analysis of auctions. However, revealing one's true valuation causes severe
privacy disclosure to the auctioneer and other bidders. To make things worse,
previous work on secure spectrum auctions does not provide adequate security.
In this paper, based on TRUST, we propose PS-TRUST, a provably secure solution
for truthful double spectrum auctions. Besides maintaining the properties of
truthfulness and special spectrum reuse of TRUST, PS-TRUST achieves provable
security against semi-honest adversaries in the sense of cryptography.
Specifically, PS-TRUST reveals nothing about the bids to anyone in the auction,
except the auction result. To the best of our knowledge, PS-TRUST is the first
provably secure solution for spectrum auctions. Furthermore, experimental
results show that the computation and communication overhead of PS-TRUST is
modest, and its practical applications are feasible.Comment: 9 pages, 4 figures, submitted to Infocom 201
Deconfinement Phase Transition Heating and Thermal Evolution of Neutron Stars
The deconfinement phase transition will lead to the release of latent heat
during spins down of neutron stars if the transition is the first-order one.We
have investigated the thermal evolution of neutron stars undergoing such
deconfinement phase transition. The results show that neutron stars may be
heated to higher temperature.This feature could be particularly interesting for
high temperature of low-magnetic field millisecond pulsar at late stage.Comment: 4 pages, to be published by American Institute of Physics, ed. D.Lai,
X.D.Li and Y.F.Yuan, as the Proceedings of the conference Astrophysics of
Compact Object
Spatial-Temporal-Aware Safe Multi-Agent Reinforcement Learning of Connected Autonomous Vehicles in Challenging Scenarios
Communication technologies enable coordination among connected and autonomous
vehicles (CAVs). However, it remains unclear how to utilize shared information
to improve the safety and efficiency of the CAV system. In this work, we
propose a framework of constrained multi-agent reinforcement learning (MARL)
with a parallel safety shield for CAVs in challenging driving scenarios. The
coordination mechanisms of the proposed MARL include information sharing and
cooperative policy learning, with Graph Convolutional Network (GCN)-Transformer
as a spatial-temporal encoder that enhances the agent's environment awareness.
The safety shield module with Control Barrier Functions (CBF)-based safety
checking protects the agents from taking unsafe actions. We design a
constrained multi-agent advantage actor-critic (CMAA2C) algorithm to train safe
and cooperative policies for CAVs. With the experiment deployed in the CARLA
simulator, we verify the effectiveness of the safety checking, spatial-temporal
encoder, and coordination mechanisms designed in our method by comparative
experiments in several challenging scenarios with the defined hazard vehicles
(HAZV). Results show that our proposed methodology significantly increases
system safety and efficiency in challenging scenarios.Comment: This paper has been accepted by the 2023 IEEE International
Conference on Robotics and Automation (ICRA 2023). 6 pages, 5 figure
Learning Robotic Ultrasound Scanning Skills via Human Demonstrations and Guided Explorations
Medical ultrasound has become a routine examination approach nowadays and is
widely adopted for different medical applications, so it is desired to have a
robotic ultrasound system to perform the ultrasound scanning autonomously.
However, the ultrasound scanning skill is considerably complex, which highly
depends on the experience of the ultrasound physician. In this paper, we
propose a learning-based approach to learn the robotic ultrasound scanning
skills from human demonstrations. First, the robotic ultrasound scanning skill
is encapsulated into a high-dimensional multi-modal model, which takes the
ultrasound images, the pose/position of the probe and the contact force into
account. Second, we leverage the power of imitation learning to train the
multi-modal model with the training data collected from the demonstrations of
experienced ultrasound physicians. Finally, a post-optimization procedure with
guided explorations is proposed to further improve the performance of the
learned model. Robotic experiments are conducted to validate the advantages of
our proposed framework and the learned models
Networked Realization of Discrete-Time Controllers
We study the problem of mapping discrete-time linear controllers into potentially higher order linear controllers with predefined structural constraints. Our work has been motivated by the Wireless Control Network (WCN) architecture, where the network itself behaves as a distributed, structured dynamical compensator. We make connections to model reduction theory to derive a method for the controller embedding based on minimization of the H∞-norm of the error system. This allows us to frame the problem as synthesis of optimal structured linear controllers, which enables the utilization of design-time iterative procedures for systems’ approximation. Finally, we illustrate the use of the mapping procedure by embedding PID controllers into the WCN substrate, and show how to reduce the computation overhead of the approximation procedure
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