156,872 research outputs found
New developments around the μCRL tool set1 1http://www.cwi.nl/~mcrl
AbstractSome recent developments in the μCRL tool set are presented. New analysis techniques are a symbolic model checker, and a visualizer for huge state spaces. Also various transformations are presented. At symbolic level, theorem proving, data flow analysis, and confluence checking are used to obtain considerable state space reductions. At the concrete level, distributed implementations of state space generation and minimization are recent. We mention the successful application of the tools to the verification of large data-intensive distributed systems
Distributed Branching Bisimulation Minimization by Inductive Signatures
We present a new distributed algorithm for state space minimization modulo
branching bisimulation. Like its predecessor it uses signatures for refinement,
but the refinement process and the signatures have been optimized to exploit
the fact that the input graph contains no tau-loops.
The optimization in the refinement process is meant to reduce both the number
of iterations needed and the memory requirements. In the former case we cannot
prove that there is an improvement, but our experiments show that in many cases
the number of iterations is smaller. In the latter case, we can prove that the
worst case memory use of the new algorithm is linear in the size of the state
space, whereas the old algorithm has a quadratic upper bound.
The paper includes a proof of correctness of the new algorithm and the
results of a number of experiments that compare the performance of the old and
the new algorithms
Cooperation and Storage Tradeoffs in Power-Grids with Renewable Energy Resources
One of the most important challenges in smart grid systems is the integration
of renewable energy resources into its design. In this work, two different
techniques to mitigate the time varying and intermittent nature of renewable
energy generation are considered. The first one is the use of storage, which
smooths out the fluctuations in the renewable energy generation across time.
The second technique is the concept of distributed generation combined with
cooperation by exchanging energy among the distributed sources. This technique
averages out the variation in energy production across space. This paper
analyzes the trade-off between these two techniques. The problem is formulated
as a stochastic optimization problem with the objective of minimizing the time
average cost of energy exchange within the grid. First, an analytical model of
the optimal cost is provided by investigating the steady state of the system
for some specific scenarios. Then, an algorithm to solve the cost minimization
problem using the technique of Lyapunov optimization is developed and results
for the performance of the algorithm are provided. These results show that in
the presence of limited storage devices, the grid can benefit greatly from
cooperation, whereas in the presence of large storage capacity, cooperation
does not yield much benefit. Further, it is observed that most of the gains
from cooperation can be obtained by exchanging energy only among a few energy
harvesting sources
Application of A Distributed Nucleus Approximation In Grid Based Minimization of the Kohn-Sham Energy Functional
In the distributed nucleus approximation we represent the singular nucleus as
smeared over a smallportion of a Cartesian grid. Delocalizing the nucleus
allows us to solve the Poisson equation for theoverall electrostatic potential
using a linear scaling multigrid algorithm.This work is done in the context of
minimizing the Kohn-Sham energy functionaldirectly in real space with a
multiscale approach. The efficacy of the approximation is illustrated
bylocating the ground state density of simple one electron atoms and
moleculesand more complicated multiorbital systems.Comment: Submitted to JCP (July 1, 1995 Issue), latex, 27pages, 2figure
Modelling the Interconnected Synchronous Generators and its State Estimations
© 2018 IEEE. In contrast to the traditional centralized power system state estimation approaches, this paper investigates the optimal filtering problem for distributed dynamic systems. Particularly, the interconnected synchronous generators are modeled as a state-space linear equation where sensors are deployed to obtain measurements. As the synchronous generator states are unknown, the estimation is required to know the operating conditions of large-scale power networks. Availability of the system states gives the designer an accurate picture of power networks to avoid blackouts. Basically, the proposed algorithm is based on the minimization of the mean squared estimation error, and the optimal gain is determined by exchanging information with their neighboring estimators. Afterward, the convergence of the developed algorithm is proved so that it can be applied to real-time applications in modern smart grids. Simulation results demonstrate the efficacy of the developed algorithm
Improving Connectionist Energy Minimization
Symmetric networks designed for energy minimization such as Boltzman machines
and Hopfield nets are frequently investigated for use in optimization,
constraint satisfaction and approximation of NP-hard problems. Nevertheless,
finding a global solution (i.e., a global minimum for the energy function) is
not guaranteed and even a local solution may take an exponential number of
steps. We propose an improvement to the standard local activation function used
for such networks. The improved algorithm guarantees that a global minimum is
found in linear time for tree-like subnetworks. The algorithm, called activate,
is uniform and does not assume that the network is tree-like. It can identify
tree-like subnetworks even in cyclic topologies (arbitrary networks) and avoid
local minima along these trees. For acyclic networks, the algorithm is
guaranteed to converge to a global minimum from any initial state of the system
(self-stabilization) and remains correct under various types of schedulers. On
the negative side, we show that in the presence of cycles, no uniform algorithm
exists that guarantees optimality even under a sequential asynchronous
scheduler. An asynchronous scheduler can activate only one unit at a time while
a synchronous scheduler can activate any number of units in a single time step.
In addition, no uniform algorithm exists to optimize even acyclic networks when
the scheduler is synchronous. Finally, we show how the algorithm can be
improved using the cycle-cutset scheme. The general algorithm, called
activate-with-cutset, improves over activate and has some performance
guarantees that are related to the size of the network's cycle-cutset.Comment: See http://www.jair.org/ for any accompanying file
Optimizing Age of Information in Wireless Networks with Perfect Channel State Information
Age of information (AoI), defined as the time elapsed since the last received
update was generated, is a newly proposed metric to measure the timeliness of
information updates in a network. We consider AoI minimization problem for a
network with general interference constraints, and time varying channels. We
propose two policies, namely, virtual-queue based policy and age-based policy
when the channel state is available to the network scheduler at each time step.
We prove that the virtual-queue based policy is nearly optimal, up to a
constant additive factor, and the age-based policy is at-most factor 4 away
from optimality. Comparing with our previous work, which derived age optimal
policies when channel state information is not available to the scheduler, we
demonstrate a 4 fold improvement in age due to the availability of channel
state information
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