23,622 research outputs found
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles
Vehicle to Vehicle (V2V) communication has a great potential to improve
reaction accuracy of different driver assistance systems in critical driving
situations. Cooperative Adaptive Cruise Control (CACC), which is an automated
application, provides drivers with extra benefits such as traffic throughput
maximization and collision avoidance. CACC systems must be designed in a way
that are sufficiently robust against all special maneuvers such as cutting-into
the CACC platoons by interfering vehicles or hard braking by leading cars. To
address this problem, a Neural- Network (NN)-based cut-in detection and
trajectory prediction scheme is proposed in the first part of this paper. Next,
a probabilistic framework is developed in which the cut-in probability is
calculated based on the output of the mentioned cut-in prediction block.
Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed
which incorporates this cut-in probability to enhance its reaction against the
detected dangerous cut-in maneuver. The overall system is implemented and its
performance is evaluated using realistic driving scenarios from Safety Pilot
Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I
Impact of time-variant turbulence behavior on prediction for adaptive optics systems
For high contrast imaging systems, the time delay is one of the major
limiting factors for the performance of the extreme adaptive optics (AO)
sub-system and, in turn, the final contrast. The time delay is due to the
finite time needed to measure the incoming disturbance and then apply the
correction. By predicting the behavior of the atmospheric disturbance over the
time delay we can in principle achieve a better AO performance. Atmospheric
turbulence parameters which determine the wavefront phase fluctuations have
time-varying behavior. We present a stochastic model for wind speed and model
time-variant atmospheric turbulence effects using varying wind speed. We test a
low-order, data-driven predictor, the linear minimum mean square error
predictor, for a near-infrared AO system under varying conditions. Our results
show varying wind can have a significant impact on the performance of wavefront
prediction, preventing it from reaching optimal performance. The impact depends
on the strength of the wind fluctuations with the greatest loss in expected
performance being for high wind speeds.Comment: 10 pages, 8 figures; Accepted to JOSA A March 201
Adaptive traffic signal control using approximate dynamic programming
This paper presents a study on an adaptive traffic signal controller for real-time operation. The controller aims for three operational objectives: dynamic allocation of green time, automatic adjustment to control parameters, and fast revision of signal plans. The control algorithm is built on approximate dynamic programming (ADP). This approach substantially reduces computational burden by using an approximation to the value function of the dynamic programming and reinforcement learning to update the approximation. We investigate temporal-difference learning and perturbation learning as specific learning techniques for the ADP approach. We find in computer simulation that the ADP controllers achieve substantial reduction in vehicle delays in comparison with optimised fixed-time plans. Our results show that substantial benefits can be gained by increasing the frequency at which the signal plans are revised, which can be achieved conveniently using the ADP approach
- …