30 resultados para feature based modelling

em Aston University Research Archive


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Random distributed feedback (DFB) fiber lasers have attracted a great attention since first demonstration [1]. Despite big advance in practical laser systems, random DFB fiber laser spectral properties are far away to be understood or even numerically modelled. Up to date, only generation power could be calculated and optimized numerically [1,2] or analytically [3] within the power balance model. However, spectral and statistical properties of random DFB fiber laser can not be found in this way. Here we present first numerical modelling of the random DFB fiber laser, including its spectral and statistical properties, using NLSE-based model. © 2013 IEEE.

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Homogenous secondary pyrolysis is category of reactions following the primary pyrolysis and presumed important for fast pyrolysis. For the comprehensive chemistry and fluid dynamics, a probability density functional (PDF) approach is used; with a kinetic scheme comprising 134 species and 4169 reactions being implemented. With aid of acceleration techniques, most importantly Dimension Reduction, Chemistry Agglomeration and In-situ Tabulation (ISAT), a solution within reasonable time was obtained. More work is required; however, a solution for levoglucosan (C6H10O5) being fed through the inlet with fluidizing gas at 500 °C, has been obtained. 88.6% of the levoglucosan remained non-decomposed, and 19 different decomposition product species were found above 0.01% by weight. A homogenous secondary pyrolysis scheme proposed can thus be implemented in a CFD environment and acceleration techniques can speed-up the calculation for application in engineering settings.

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Aircraft manufacturing industries are looking for solutions in order to increase their productivity. One of the solutions is to apply the metrology systems during the production and assembly processes. Metrology Process Model (MPM) (Maropoulos et al, 2007) has been introduced which emphasises metrology applications with assembly planning, manufacturing processes and product designing. Measurability analysis is part of the MPM and the aim of this analysis is to check the feasibility for measuring the designed large scale components. Measurability Analysis has been integrated in order to provide an efficient matching system. Metrology database is structured by developing the Metrology Classification Model. Furthermore, the feature-based selection model is also explained. By combining two classification models, a novel approach and selection processes for integrated measurability analysis system (MAS) are introduced and such integrated MAS could provide much more meaningful matching results for the operators. © Springer-Verlag Berlin Heidelberg 2010.

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Softeam has over 20 years of experience providing UML-based modelling solutions, such as its Modelio modelling tool, and its Constellation enterprise model management and collaboration environment. Due to the increasing number and size of the models used by Softeam’s clients, Softeam joined the MONDO FP7 EU research project, which worked on solutions for these scalability challenges and produced the Hawk model indexer among other results. This paper presents the technical details and several case studies on the integration of Hawk into Softeam’s toolset. The first case study measured the performance of Hawk’s Modelio support using varying amounts of memory for the Neo4j backend. In another case study, Hawk was integrated into Constellation to provide scalable global querying of model repositories. Finally, the combination of Hawk and the Epsilon Generation Language was compared against Modelio for document generation: for the largest model, Hawk was two orders of magnitude faster.

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Manufacturing planning and control systems are fundamental to the successful operations of a manufacturing organisation. 10 order to improve their business performance, significant investment is made by companies into planning and control systems; however, not all companies realise the benefits sought Many companies continue to suffer from high levels of inventory, shortages, obsolete parts, poor resource utilisation and poor delivery performance. This thesis argues that the fit between the planning and control system and the manufacturing organisation is a crucial element of success. The design of appropriate control systems is, therefore, important. The different approaches to the design of manufacturing planning and control systems are investigated. It is concluded that there is no provision within these design methodologies to properly assess the impact of a proposed design on the manufacturing facility. Consequently, an understanding of how a new (or modified) planning and control system will perform in the context of the complete manufacturing system is unlikely to be gained until after the system has been implemented and is running. There are many modelling techniques available, however discrete-event simulation is unique in its ability to model the complex dynamics inherent in manufacturing systems, of which the planning and control system is an integral component. The existing application of simulation to manufacturing control system issues is limited: although operational issues are addressed, application to the more fundamental design of control systems is rarely, if at all, considered. The lack of a suitable simulation-based modelling tool does not help matters. The requirements of a simulation tool capable of modelling a host of different planning and control systems is presented. It is argued that only through the application of object-oriented principles can these extensive requirements be achieved. This thesis reports on the development of an extensible class library called WBS/Control, which is based on object-oriented principles and discrete-event simulation. The functionality, both current and future, offered by WBS/Control means that different planning and control systems can be modelled: not only the more standard implementations but also hybrid systems and new designs. The flexibility implicit in the development of WBS/Control supports its application to design and operational issues. WBS/Control wholly integrates with an existing manufacturing simulator to provide a more complete modelling environment.

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Discrete event simulation is a popular aid for manufacturing system design; however in application this technique can sometimes be unnecessarily complex. This paper is concerned with applying an alternative technique to manufacturing system design which may well provide an efficient form of rough-cut analysis. This technique is System Dynamics, and the work described in this paper has set about incorporating the principles of this technique into a computer based modelling tool that is tailored to manufacturing system design. This paper is structured to first explore the principles of System Dynamics and how they differ from Discrete Event Simulation. The opportunity for System Dynamics is then explored, and this leads to defining the capabilities that a suitable tool would need. This specification is then transformed into a computer modelling tool, which is then assessed by applying this tool to model an engine production facility. Read More: http://www.worldscientific.com/doi/abs/10.1142/S0219686703000228

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Web-based distributed modelling architectures are gaining increasing recognition as potentially useful tools to build holistic environmental models, combining individual components in complex workflows. However, existing web-based modelling frameworks currently offer no support for managing uncertainty. On the other hand, the rich array of modelling frameworks and simulation tools which support uncertainty propagation in complex and chained models typically lack the benefits of web based solutions such as ready publication, discoverability and easy access. In this article we describe the developments within the UncertWeb project which are designed to provide uncertainty support in the context of the proposed ‘Model Web’. We give an overview of uncertainty in modelling, review uncertainty management in existing modelling frameworks and consider the semantic and interoperability issues raised by integrated modelling. We describe the scope and architecture required to support uncertainty management as developed in UncertWeb. This includes tools which support elicitation, aggregation/disaggregation, visualisation and uncertainty/sensitivity analysis. We conclude by highlighting areas that require further research and development in UncertWeb, such as model calibration and inference within complex environmental models.

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As more of the economy moves from traditional manufacturing to the service sector, the nature of work is becoming less tangible and thus, the representation of human behaviour in models is becoming more important. Representing human behaviour and decision making in models is challenging, both in terms of capturing the essence of the processes, and also the way that those behaviours and decisions are or can be represented in the models themselves. In order to advance understanding in this area, a useful first step is to evaluate and start to classify the various types of behaviour and decision making that are required to be modelled. This talk will attempt to set out and provide an initial classification of the different types of behaviour and decision making that a modeller might want to represent in a model. Then, it will be useful to start to assess the main methods of simulation in terms of their capability in representing these various aspects. The three main simulation methods, System Dynamics, Agent Based Modelling and Discrete Event Simulation all achieve this to varying degrees. There is some evidence that all three methods can, within limits, represent the key aspects of the system being modelled. The three simulation approaches are then assessed for their suitability in modelling these various aspects. Illustration of behavioural modelling will be provided from cases in supply chain management, evacuation modelling and rail disruption.

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In the "Thatcher illusion" a face, in which the eyes and mouth are inverted relative to the rest of the face, looks grotesque when shown upright but not when inverted. In four experiments we investigated the contribution of local and global processing to this illusion in normal observers. We examined inversion effects (i.e., better performance for upright than for inverted faces) in a task requiring discrimination of whether faces were or were not "thatcherized". Observers made same/different judgements to isolated face parts (Experiments 1-2) and to whole faces (Experiments 3-4). Face pairs had the same or different identity, allowing for different processing strategies using feature-based or configural information, respectively. In Experiment 1, feature-based matching of same-person face parts yielded only a small inversion effect for normal face parts. However, when feature-based matching was prevented by using the face parts of different people on all trials (Experiment 2) an inversion effect occurred for normal but not for thatcherized parts. In Experiments 3 and 4, inversion effects occurred with normal but not with thatcherized whole faces, on both same- and different-person matching tasks. This suggests that a common configural strategy was used with whole (normal) faces. Face context facilitated attention to misoriented parts in same-person but not in different-person matching. The results indicate that (1) face inversion disrupts local configural processing, but not the processing of image features, and (2) thatcherization disrupts local configural processing in upright faces.

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Physically based distributed models of catchment hydrology are likely to be made available as engineering tools in the near future. Although these models are based on theoretically acceptable equations of continuity, there are still limitations in the present modelling strategy. Of interest to this thesis are the current modelling assumptions made concerning the effects of soil spatial variability, including formations producing distinct zones of preferential flow. The thesis contains a review of current physically based modelling strategies and a field based assessment of soil spatial variability. In order to investigate the effects of soil nonuniformity a fully three dimensional model of variability saturated flow in porous media is developed. The model is based on a Galerkin finite element approximation to Richards equation. Accessibility to a vector processor permits numerical solutions on grids containing several thousand node points. The model is applied to a single hillslope segment under various degrees of soil spatial variability. Such variability is introduced by generating random fields of saturated hydraulic conductivity using the turning bands method. Similar experiments are performed under conditions of preferred soil moisture movement. The results show that the influence of soil variability on subsurface flow may be less significant than suggested in the literature, due to the integrating effects of three dimensional flow. Under conditions of widespread infiltration excess runoff, the results indicate a greater significance of soil nonuniformity. The recognition of zones of preferential flow is also shown to be an important factor in accurate rainfall-runoff modelling. Using the results of various fields of soil variability, experiments are carried out to assess the validity of the commonly used concept of `effective parameters'. The results of these experiments suggest that such a concept may be valid in modelling subsurface flow. However, the effective parameter is observed to be event dependent when the dominating mechanism is infiltration excess runoff.

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Multi-agent systems are complex systems comprised of multiple intelligent agents that act either independently or in cooperation with one another. Agent-based modelling is a method for studying complex systems like economies, societies, ecologies etc. Due to their complexity, very often mathematical analysis is limited in its ability to analyse such systems. In this case, agent-based modelling offers a practical, constructive method of analysis. The objective of this book is to shed light on some emergent properties of multi-agent systems. The authors focus their investigation on the effect of knowledge exchange on the convergence of complex, multi-agent systems.

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Simulation is an effective method for improving supply chain performance. However, there is limited advice available to assist practitioners in selecting the most appropriate method for a given problem. Much of the advice that does exist relies on custom and practice rather than a rigorous conceptual or empirical analysis. An analysis of the different modelling techniques applied in the supply chain domain was conducted, and the three main approaches to simulation used were identified; these are System Dynamics (SD), Discrete Event Simulation (DES) and Agent Based Modelling (ABM). This research has examined these approaches in two stages. Firstly, a first principles analysis was carried out in order to challenge the received wisdom about their strengths and weaknesses and a series of propositions were developed from this initial analysis. The second stage was to use the case study approach to test these propositions and to provide further empirical evidence to support their comparison. The contributions of this research are both in terms of knowledge and practice. In terms of knowledge, this research is the first holistic cross paradigm comparison of the three main approaches in the supply chain domain. Case studies have involved building ‘back to back’ models of the same supply chain problem using SD and a discrete approach (either DES or ABM). This has led to contributions concerning the limitations of applying SD to operational problem types. SD has also been found to have risks when applied to strategic and policy problems. Discrete methods have been found to have potential for exploring strategic problem types. It has been found that discrete simulation methods can model material and information feedback successfully. Further insights have been gained into the relationship between modelling purpose and modelling approach. In terms of practice, the findings have been summarised in the form of a framework linking modelling purpose, problem characteristics and simulation approach.

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The supply chain can be a source of competitive advantage for the firm. Simulation is an effective tool for investigating supply chain problems. The three main simulation approaches in the supply chain context are System Dynamics (SD), Discrete Event Simulation (DES) and Agent Based Modelling (ABM). A sample from the literature suggests that whilst SD and ABM have been used to address strategic and planning problems, DES has mainly been used on planning and operational problems., A review of received wisdom suggests that historically, driven by custom and practice, certain simulation techniques have been focused on certain problem types. A theoretical review of the techniques, however, suggests that the scope of their application should be much wider and that supply chain practitioners could benefit from applying them in this broader way.

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Returnable transport equipment (RTE) such as pallets form an integral part of the supply chain and poor management leads to costly losses. Companies often address this matter by outsourcing the management of RTE to logistics service providers (LSPs). LSPs are faced with the task to provide logistical expertise to reduce RTE related waste, whilst differentiating their own services to remain competitive. In the current challenging economic climate, the role of the LSP to deliver innovative ways to achieve competitive advantage has never been so important. It is reported that radio frequency identification (RFID) application to RTE enables LSPs such as DHL to gain competitive advantage and offer clients improvements such as loss reduction, process efficiency improvement and effective security. However, the increased visibility and functionality of RFID enabled RTE requires further investigation in regards to decision‐making. The distributed nature of the RTE network favours a decentralised decision‐making format. Agents are an effective way to represent objects from the bottom‐up, capturing the behaviour and enabling localised decision‐making. Therefore, an agent based system is proposed to represent the RTE network and utilise the visibility and data gathered from RFID tags. Two types of agents are developed in order to represent the trucks and RTE, which have bespoke rules and algorithms in order to facilitate negotiations. The aim is to create schedules, which integrate RTE pick‐ups as the trucks go back to the depot. The findings assert that: - agent based modelling provides an autonomous tool, which is effective in modelling RFID enabled RTE in a decentralised utilising the real‐time data facility. ‐ the RFID enabled RTE model developed enables autonomous agent interaction, which leads to a feasible schedule integrating both forward and reverse flows for each RTE batch. ‐ the RTE agent scheduling algorithm developed promotes the utilisation of RTE by including an automatic return flow for each batch of RTE, whilst considering the fleet costs andutilisation rates. ‐ the research conducted contributes an agent based platform, which LSPs can use in order to assess the most appropriate strategies to implement for RTE network improvement for each of their clients.

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In recent years, the boundaries between e-commerce and social networking have become increasingly blurred. Many e-commerce websites support the mechanism of social login where users can sign on the websites using their social network identities such as their Facebook or Twitter accounts. Users can also post their newly purchased products on microblogs with links to the e-commerce product web pages. In this paper, we propose a novel solution for cross-site cold-start product recommendation, which aims to recommend products from e-commerce websites to users at social networking sites in 'cold-start' situations, a problem which has rarely been explored before. A major challenge is how to leverage knowledge extracted from social networking sites for cross-site cold-start product recommendation. We propose to use the linked users across social networking sites and e-commerce websites (users who have social networking accounts and have made purchases on e-commerce websites) as a bridge to map users' social networking features to another feature representation for product recommendation. In specific, we propose learning both users' and products' feature representations (called user embeddings and product embeddings, respectively) from data collected from e-commerce websites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users' social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation. Experimental results on a large dataset constructed from the largest Chinese microblogging service Sina Weibo and the largest Chinese B2C e-commerce website JingDong have shown the effectiveness of our proposed framework.