8,036 research outputs found
Integrating IVHM and Asset Design
Integrated Vehicle Health Management (IVHM) describes a set of capabilities that enable effective and efficient maintenance and operation of the target vehicle. It accounts for the collection of data, conducting analysis, and supporting the decision-making process for sustainment and operation. The design of IVHM systems endeavours to account for all causes of failure in a disciplined, systems engineering, manner. With industry striving to reduce through-life cost, IVHM is a powerful tool to give forewarning of impending failure and hence control over the outcome. Benefits have been realised from this approach across a number of different sectors but, hindering our ability to realise further benefit from this maturing technology, is the fact that IVHM is still treated as added on to the design of the asset, rather than being a sub-system in its own right, fully integrated with the asset design. The elevation and integration of IVHM in this way will enable architectures to be chosen that accommodate health ready sub-systems from the supply chain and design trade-offs to be made, to name but two major benefits. Barriers to IVHM being integrated with the asset design are examined in this paper. The paper presents progress in overcoming them, and suggests potential solutions for those that remain. It addresses the IVHM system design from a systems engineering perspective and the integration with the asset design will be described within an industrial design process
RADAR: A Lightweight Tool for Requirements and Architecture Decision Analysis
Uncertainty and conflicting stakeholders' objectives make many requirements and architecture decisions particularly hard. Quantitative probabilistic models allow software architects to analyse such decisions using stochastic simulation and multi-objective optimisation, but the difficulty of elaborating the models is an obstacle to the wider adoption of such techniques. To reduce this obstacle, this paper presents a novel modelling language and analysis tool, called RADAR, intended to facilitate requirements and architecture decision analysis. The language has relations to quantitative AND/OR goal models used in requirements engineering and to feature models used in software product lines. However, it simplifies such models to a minimum set of language constructs essential for decision analysis. The paper presents RADAR's modelling language, automated support for decision analysis, and evaluates its application to four real-world examples
Integrating IVHM and asset design
Integrated Vehicle Health Management (IVHM) describes a set of capabilities that enable effective and efficient maintenance and operation of the target vehicle. It accounts for the collecting of data, conducting analysis, and supporting the decision-making process for sustainment and operation. The design of IVHM systems endeavours to account for all causes of failure in a disciplined, systems engineering, manner. With industry striving to reduce through-life cost, IVHM is a powerful tool to give forewarning of impending failure and hence control over the outcome. Benefits have been realised from this approach across a number of different sectors but, hindering our ability to realise further benefit from this maturing technology, is the fact that IVHM is still treated as added on to the design of the asset, rather than being a sub-system in its own right, fully integrated with the asset design. The elevation and integration of IVHM in this way will enable architectures to be chosen that accommodate health ready sub-systems from the supply chain and design trade-offs to be made, to name but two major benefits. Barriers to IVHM being integrated with the asset design are examined in this paper. The paper presents progress in overcoming them, and suggests potential solutions for those that remain. It addresses the IVHM system design from a systems engineering perspective and the integration with the asset design will be described within an industrial design process
Simulation modelling and visualisation: toolkits for building artificial worlds
Simulations users at all levels make heavy use of compute resources to drive computational
simulations for greatly varying applications areas of research using different simulation
paradigms. Simulations are implemented in many software forms, ranging from highly standardised
and general models that run in proprietary software packages to ad hoc hand-crafted
simulations codes for very specific applications. Visualisation of the workings or results of a
simulation is another highly valuable capability for simulation developers and practitioners.
There are many different software libraries and methods available for creating a visualisation
layer for simulations, and it is often a difficult and time-consuming process to assemble a
toolkit of these libraries and other resources that best suits a particular simulation model. We
present here a break-down of the main simulation paradigms, and discuss differing toolkits and
approaches that different researchers have taken to tackle coupled simulation and visualisation
in each paradigm
Information recovery from rank-order encoded images
The time to detection of a visual stimulus by the primate eye is recorded at
100 – 150ms. This near instantaneous recognition is in spite of the considerable
processing required by the several stages of the visual pathway to recognise and
react to a visual scene. How this is achieved is still a matter of speculation.
Rank-order codes have been proposed as a means of encoding by the primate
eye in the rapid transmission of the initial burst of information from the sensory
neurons to the brain. We study the efficiency of rank-order codes in encoding
perceptually-important information in an image. VanRullen and Thorpe built a
model of the ganglion cell layers of the retina to simulate and study the viability
of rank-order as a means of encoding by retinal neurons. We validate their model
and quantify the information retrieved from rank-order encoded images in terms
of the visually-important information recovered. Towards this goal, we apply
the ‘perceptual information preservation algorithm’, proposed by Petrovic and
Xydeas after slight modification. We observe a low information recovery due
to losses suffered during the rank-order encoding and decoding processes. We
propose to minimise these losses to recover maximum information in minimum
time from rank-order encoded images. We first maximise information recovery by
using the pseudo-inverse of the filter-bank matrix to minimise losses during rankorder
decoding. We then apply the biological principle of lateral inhibition to
minimise losses during rank-order encoding. In doing so, we propose the Filteroverlap
Correction algorithm. To test the perfomance of rank-order codes in
a biologically realistic model, we design and simulate a model of the foveal-pit
ganglion cells of the retina keeping close to biological parameters. We use this
as a rank-order encoder and analyse its performance relative to VanRullen and
Thorpe’s retinal model
Optimal Reasoning of Opposing Non-functional Requirements based on Game Theory
Goal-Oriented Requirement Engineering is a modeling technique that represents software system requirements using goals as goal models. In a competitive environment, these requirements may have opposing objectives. Therefore, there is a requirement for a goal reasoning method, which offers an alternative design option that achieves the opposing objectives of inter-dependent actors. In this paper, a multi-objective zero-sum game theory-based approach is applied for choosing an optimum strategy for dependent actors in the i* goal model. By integrating Java with IBM CPLEX optimisation tool, a simulation model based on the proposed method was developed. A successful evaluation was performed on case studies from the existing literature. Results indicate that the developed simulation model helps users to choose an optimal design option feasible in real-time competitive environments
A review of Multi-Agent Simulation Models in Agriculture
Multi-Agent Simulation (MAS) models are intended to capture emergent properties of complex systems that are not amenable to equilibrium analysis. They are beginning to see some use for analysing agricultural systems. The paper reports on work in progress to create a MAS for specific sectors in New Zealand agriculture. One part of the paper focuses on options for modelling land and other resources such as water, labour and capital in this model, as well as markets for exchanging resources and commodities. A second part considers options for modelling agent heterogeneity, especially risk preferences of farmers, and the impacts on decision-making. The final section outlines the MAS that the authors will be constructing over the next few years and the types of research questions that the model will help investigate.multi-agent simulation models, modelling, agent-based model, cellular automata, decision-making, Crop Production/Industries, Environmental Economics and Policy, Farm Management, Land Economics/Use, Livestock Production/Industries,
VARIwise: a general-purpose adaptive control simulation framework for spatially and temporally varied irrigation at sub-field scale
Irrigation control strategies may be used to improve the site-specific irrigation of cotton via lateral move and centre pivot irrigation machines. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied site-specific irrigation control strategies. VARIwise accommodates sub-field scale variations in all input parameters using a 1 m2 cell size, and permits application of differing control strategies within the field, as well as differing irrigation amounts down to this scale.
In this paper the motivation and objectives for the creation of VARIwise are discussed, the structure of the software is outlined and an example of the use and utility of VARIwise is presented. Three irrigation control strategies have been simulated in VARIwise using a cotton model with a range of input parameters including spatially variable soil properties, non-uniform irrigation application, three weather profiles and two crop varieties. The simulated yield and water use efficiency were affected by the combination of input parameters and the control strategy implemented
Addressing the sample size problem in behavioural operational research: simulating the newsvendor problem
Laboratory-based experimental studies with human participants are beneficial for testing hypotheses in behavioural operational research. However, such experiments are not without their problems. One specific problem is obtaining a sufficient sample size, not only in terms of the number of participants but also the time they are willing to devote to an experiment. In this paper, we explore how agent-based simulation (ABS) can be used to address the sample size problem and demonstrate the approach in the newsvendor setting. The decision-making strategies of a small sample of individual decision-makers are determined through laboratory experiments. The interactions of these suppliers and retailers are then simulated using an ABS to generate a large sample set of decisions. With only a small number of participants, we demonstrate that it is possible to produce similar results to previous experimental studies that involved much larger sample sizes. We conclude that ABS provides the potential to extend the scope of experimental research in behavioural operational research
- …