13,087 research outputs found
Engineering failure analysis and design optimisation with HiP-HOPS
The scale and complexity of computer-based safety critical systems, like those used in the transport and manufacturing industries, pose significant challenges for failure analysis. Over the last decade, research has focused on automating this task. In one approach, predictive models of system failure are constructed from the topology of the system and local component failure models using a process of composition. An alternative approach employs model-checking of state automata to study the effects of failure and verify system safety properties. In this paper, we discuss these two approaches to failure analysis. We then focus on Hierarchically Performed Hazard Origin & Propagation Studies (HiP-HOPS) - one of the more advanced compositional approaches - and discuss its capabilities for automatic synthesis of fault trees, combinatorial Failure Modes and Effects Analyses, and reliability versus cost optimisation of systems via application of automatic model transformations. We summarise these contributions and demonstrate the application of HiP-HOPS on a simplified fuel oil system for a ship engine. In light of this example, we discuss strengths and limitations of the method in relation to other state-of-the-art techniques. In particular, because HiP-HOPS is deductive in nature, relating system failures back to their causes, it is less prone to combinatorial explosion and can more readily be iterated. For this reason, it enables exhaustive assessment of combinations of failures and design optimisation using computationally expensive meta-heuristics. (C) 2010 Elsevier Ltd. All rights reserved
Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks
Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models
Constraint-Based Heuristic On-line Test Generation from Non-deterministic I/O EFSMs
We are investigating on-line model-based test generation from
non-deterministic output-observable Input/Output Extended Finite State Machine
(I/O EFSM) models of Systems Under Test (SUTs). We propose a novel
constraint-based heuristic approach (Heuristic Reactive Planning Tester (xRPT))
for on-line conformance testing non-deterministic SUTs. An indicative feature
of xRPT is the capability of making reasonable decisions for achieving the test
goals in the on-line testing process by using the results of off-line bounded
static reachability analysis based on the SUT model and test goal
specification. We present xRPT in detail and make performance comparison with
other existing search strategies and approaches on examples with varying
complexity.Comment: In Proceedings MBT 2012, arXiv:1202.582
Finding the Core-Genes of Chloroplasts
Due to the recent evolution of sequencing techniques, the number of available
genomes is rising steadily, leading to the possibility to make large scale
genomic comparison between sets of close species. An interesting question to
answer is: what is the common functionality genes of a collection of species,
or conversely, to determine what is specific to a given species when compared
to other ones belonging in the same genus, family, etc. Investigating such
problem means to find both core and pan genomes of a collection of species,
\textit{i.e.}, genes in common to all the species vs. the set of all genes in
all species under consideration. However, obtaining trustworthy core and pan
genomes is not an easy task, leading to a large amount of computation, and
requiring a rigorous methodology. Surprisingly, as far as we know, this
methodology in finding core and pan genomes has not really been deeply
investigated. This research work tries to fill this gap by focusing only on
chloroplastic genomes, whose reasonable sizes allow a deep study. To achieve
this goal, a collection of 99 chloroplasts are considered in this article. Two
methodologies have been investigated, respectively based on sequence
similarities and genes names taken from annotation tools. The obtained results
will finally be evaluated in terms of biological relevance
A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends
Computer vision (CV) is a big and important field
in artificial intelligence covering a wide range of applications.
Image analysis is a major task in CV aiming to extract, analyse
and understand the visual content of images. However, imagerelated
tasks are very challenging due to many factors, e.g., high
variations across images, high dimensionality, domain expertise
requirement, and image distortions. Evolutionary computation
(EC) approaches have been widely used for image analysis with
significant achievement. However, there is no comprehensive
survey of existing EC approaches to image analysis. To fill
this gap, this paper provides a comprehensive survey covering
all essential EC approaches to important image analysis tasks
including edge detection, image segmentation, image feature
analysis, image classification, object detection, and others. This
survey aims to provide a better understanding of evolutionary
computer vision (ECV) by discussing the contributions of different
approaches and exploring how and why EC is used for
CV and image analysis. The applications, challenges, issues, and
trends associated to this research field are also discussed and
summarised to provide further guidelines and opportunities for
future research
Genetic Programming for Object Detection : a Two-Phase Approach with an Improved Fitness Function
This paper describes two innovations that improve the efficiency and effectiveness of a genetic programming approach to object detection problems. The approach uses genetic programming to construct object detection programs that are applied, in a moving window fashion, to the large images to locate the objects of interest. The first innovation is to break the GP search into two phases with the first phase applied to a selected subset of the training data, and a simplified fitness function. The second phase is initialised with the programs from the first phase, and uses the full set of training data with a complete fitness function to construct the final detection programs. The second innovation is to add a program size component to the fitness function. This approach is examined and compared with a neural network approach on three object detection problems of increasing difficulty. The results suggest that the innovations increase both the effectiveness and the efficiency of the genetic programming search, and also that the genetic programming approach outperforms a neural network approach for the most difficult data set in terms of the object detection accuracy
Comparison of Single- and Multi- Objective Optimization Quality for Evolutionary Equation Discovery
Evolutionary differential equation discovery proved to be a tool to obtain
equations with less a priori assumptions than conventional approaches, such as
sparse symbolic regression over the complete possible terms library. The
equation discovery field contains two independent directions. The first one is
purely mathematical and concerns differentiation, the object of optimization
and its relation to the functional spaces and others. The second one is
dedicated purely to the optimizational problem statement. Both topics are worth
investigating to improve the algorithm's ability to handle experimental data a
more artificial intelligence way, without significant pre-processing and a
priori knowledge of their nature. In the paper, we consider the prevalence of
either single-objective optimization, which considers only the discrepancy
between selected terms in the equation, or multi-objective optimization, which
additionally takes into account the complexity of the obtained equation. The
proposed comparison approach is shown on classical model examples -- Burgers
equation, wave equation, and Korteweg - de Vries equation
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