9,622 research outputs found
Cognitive Capabilities for the CAAI in Cyber-Physical Production Systems
This paper presents the cognitive module of the cognitive architecture for
artificial intelligence (CAAI) in cyber-physical production systems (CPPS). The
goal of this architecture is to reduce the implementation effort of artificial
intelligence (AI) algorithms in CPPS. Declarative user goals and the provided
algorithm-knowledge base allow the dynamic pipeline orchestration and
configuration. A big data platform (BDP) instantiates the pipelines and
monitors the CPPS performance for further evaluation through the cognitive
module. Thus, the cognitive module is able to select feasible and robust
configurations for process pipelines in varying use cases. Furthermore, it
automatically adapts the models and algorithms based on model quality and
resource consumption. The cognitive module also instantiates additional
pipelines to test algorithms from different classes. CAAI relies on
well-defined interfaces to enable the integration of additional modules and
reduce implementation effort. Finally, an implementation based on Docker,
Kubernetes, and Kafka for the virtualization and orchestration of the
individual modules and as messaging-technology for module communication is used
to evaluate a real-world use case
An illustration of new methods in machine condition monitoring, Part I: Stochastic resonance
There have been many recent developments in the application of data-based
methods to machine condition monitoring. A powerful methodology based on machine learning
has emerged, where diagnostics are based on a two-step procedure: extraction of damage sensitive
features, followed by unsupervised learning (novelty detection) or supervised learning
(classification). The objective of the current pair of papers is simply to illustrate one state-of the-art
procedure for each step, using synthetic data representative of reality in terms of size
and complexity. The first paper in the pair will deal with feature extraction.
Although some papers have appeared in the recent past considering stochastic resonance
as a means of amplifying damage information in signals, they have largely relied on ad hoc
specifications of the resonator used. In contrast, the current paper will adopt a principled
optimisation-based approach to the resonator design. The paper will also show that a discrete
dynamical system can provide all the benefits of a continuous system, but also provide a
considerable speed-up in terms of simulation time in order to facilitate the optimisation
approach
Efficient computational strategies for doubly intractable problems with applications to Bayesian social networks
Powerful ideas recently appeared in the literature are adjusted and combined
to design improved samplers for Bayesian exponential random graph models.
Different forms of adaptive Metropolis-Hastings proposals (vertical, horizontal
and rectangular) are tested and combined with the Delayed rejection (DR)
strategy with the aim of reducing the variance of the resulting Markov chain
Monte Carlo estimators for a given computational time. In the examples treated
in this paper the best combination, namely horizontal adaptation with delayed
rejection, leads to a variance reduction that varies between 92% and 144%
relative to the adaptive direction sampling approximate exchange algorithm of
Caimo and Friel (2011). These results correspond to an increased performance
which varies from 10% to 94% if we take simulation time into account. The
highest improvements are obtained when highly correlated posterior
distributions are considered.Comment: 23 pages, 8 figures. Accepted to appear in Statistics and Computin
Unconventional MBE Strategies from Computer Simulations for Optimized Growth Conditions
We investigate the influence of step edge diffusion (SED) and desorption on
Molecular Beam Epitaxy (MBE) using kinetic Monte-Carlo simulations of the
solid-on-solid (SOS) model. Based on these investigations we propose two
strategies to optimize MBE growth. The strategies are applicable in different
growth regimes: During layer-by-layer growth one can exploit the presence of
desorption in order to achieve smooth surfaces. By additional short high flux
pulses of particles one can increase the growth rate and assist layer-by-layer
growth. If, however, mounds are formed (non-layer-by-layer growth) the SED can
be used to control size and shape of the three-dimensional structures. By
controlled reduction of the flux with time we achieve a fast coarsening
together with smooth step edges.Comment: 19 pages, 7 figures, submitted to Phys. Rev.
A memetic ant colony optimization algorithm for the dynamic travelling salesman problem
Copyright @ Springer-Verlag 2010.Ant colony optimization (ACO) has been successfully applied for combinatorial optimization problems, e.g., the travelling salesman problem (TSP), under stationary environments. In this paper, we consider the dynamic TSP (DTSP), where cities are replaced by new ones during the execution of the algorithm. Under such environments, traditional ACO algorithms face a serious challenge: once they converge, they cannot adapt efficiently to environmental changes. To improve the performance of ACO on the DTSP, we investigate a hybridized ACO with local search (LS), called Memetic ACO (M-ACO) algorithm, which is based on the population-based ACO (P-ACO) framework and an adaptive inver-over operator, to solve the DTSP. Moreover, to address premature convergence, we introduce random immigrants to the population of M-ACO when identical ants are stored. The simulation experiments on a series of dynamic environments generated from a set of benchmark TSP instances show that LS is beneficial for ACO algorithms when applied on the DTSP, since it achieves better performance than other traditional ACO and P-ACO algorithms.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and Grant EP/E060722/02
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