82,322 research outputs found
A multi-objective framework for the optimisation of life-cycle costs of wind turbines
Peer reviewedPublisher PD
A probabilistic model for information and sensor validation
This paper develops a new theory and model for information and sensor validation. The model represents relationships between variables using Bayesian networks and utilizes probabilistic propagation to estimate the expected values of variables. If the estimated value of a variable differs from the actual value, an apparent fault is detected. The fault is only apparent since it may be that the estimated value is itself based on faulty data. The theory extends our understanding of when it is possible to isolate real faults from potential faults and supports the development of an algorithm that is capable of isolating real faults without deferring the problem to the use of expert provided domain-specific rules. To enable practical adoption for real-time processes, an any time version of the algorithm is developed, that, unlike most other algorithms, is capable of returning improving assessments of the validity of the sensors as it accumulates more evidence with time. The developed model is tested by applying it to the validation of temperature sensors during the start-up phase of a gas turbine when conditions are not stable; a problem that is known to be challenging. The paper concludes with a discussion of the practical applicability and scalability of the model
Scalability of Genetic Programming and Probabilistic Incremental Program Evolution
This paper discusses scalability of standard genetic programming (GP) and the
probabilistic incremental program evolution (PIPE). To investigate the need for
both effective mixing and linkage learning, two test problems are considered:
ORDER problem, which is rather easy for any recombination-based GP, and TRAP or
the deceptive trap problem, which requires the algorithm to learn interactions
among subsets of terminals. The scalability results show that both GP and PIPE
scale up polynomially with problem size on the simple ORDER problem, but they
both scale up exponentially on the deceptive problem. This indicates that while
standard recombination is sufficient when no interactions need to be
considered, for some problems linkage learning is necessary. These results are
in agreement with the lessons learned in the domain of binary-string genetic
algorithms (GAs). Furthermore, the paper investigates the effects of
introducing utnnecessary and irrelevant primitives on the performance of GP and
PIPE.Comment: Submitted to GECCO-200
- …