4,099 research outputs found

    IMPLEMENTATION AND UNIFORM MANAGEMENT OF MODELLING ENTITIES IN A MASSIVELY FEATURE-OBJECT ORIENTED ADVANCED CAD ENVIRONMENT

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    Today we are spectators of the transition process in computer aided design from traditional geometry based on design systems to advanced computer-based engineering systems. The key is the feature technology that allows both integrating and managing modelling entities in a coherent way. Feature technology is developing rapidly. New research topics and contexts are emerging from time to time. This paper introduces concept, design and technological feature-objects to support operational, structural and morphological modelling of mechanical products. First, the feature-centred approaches to conceptual design are summarized and evaluated. Then an implementation of concept feature-objects and the methodology for using them is presented. The strength of concept feature-objects is in their morphology inclusive nature. They appear as parametrized three-dimensional skeletons providing geometrical representations for the modelled engineering conceptions. A concept feature-object models the physical ports, contact surfaces related to ports, bones between ports, DOF of ports, relevant physical parameters, scientific and empirical descriptions of intentional transformations and environmental effects. Concept feature-objects are related to design feature-objects that, in turn, are constructed of a relevant set of technological feature-entities. Concept feature-objects refer to the configurable and parametrized design feature-objects through an indexing mechanism. The conceptions have been tested during the programming and further development of the authors' PRODES system

    Recurrent neural networks and adaptive motor control

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    This thesis is concerned with the use of neural networks for motor control tasks. The main goal of the thesis is to investigate ways in which the biological notions of motor programs and Central Pattern Generators (CPGs) may be implemented in a neural network framework. Biological CPGs can be seen as components within a larger control scheme, which is basically modular in design. In this thesis, these ideas are investigated through the use of modular recurrent networks, which are used in a variety of control tasks. The first experimental chapter deals with learning in recurrent networks, and it is shown that CPGs may be easily implemented using the machinery of backpropagation. The use of these CPGs can aid the learning of pattern generation tasks; they can also mean that the other components in the system can be reduced in complexity, say, to a purely feedforward network. It is also shown that incremental learning, or 'shaping' is an effective method for building CPGs. Genetic algorithms are also used to build CPGs; although computational effort prevents this from being a practical method, it does show that GAs are capable of optimising systems that operate in the context of a larger scheme. One interesting result from the GA is that optimal CPGs tend to have unstable dynamics, which may have implications for building modular neural controllers. The next chapter applies these ideas to some simple control tasks involving a highly redundant simulated robot arm. It was shown that it is relatively straightforward to build CPGs that represent elements of pattern generation, constraint satisfaction. and local feedback. This is indirect control, in which errors are backpropagated through a plant model, as well as the ePG itself, to give errors for the controller. Finally, the third experimental chapter takes an alternative approach, and uses direct control methods, such as reinforcement learning. In reinforcement learning, controller outputs have unmodelled effects; this allows us to build complex control systems, where outputs modulate the couplings between sets of dynamic systems. This was shown for a simple case, involving a system of coupled oscillators. A second set of experiments investigates the use of simplified models of behaviour; this is a reduced form of supervised learning, and the use of such models in control is discussed

    Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches

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    In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area

    Defining Interaction within Immersive Virtual Environments

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    PhDThis thesis is concerned with the design of Virtual Environments (YEs) - in particular with the tools and techniques used to describe interesting and useful environments. This concern is not only with respect to the appearance of objects in the VE but also with their behaviours and their reactions to actions of the participants. The main research hypothesis is that there are several advantages to constructing these interactions and behaviours whilst remaining immersed within the VE which they describe. These advantages include the fact that editing is done interactively with immediate effect and without having to resort to the usual edit-compile-test cycle. This means that the participant doesn't have to leave the VE and lose their sense of presence within it, and editing tasks can take advantage of the enhanced spatial cognition and naturalistic interaction metaphors a VE provides. To this end a data flow dialogue architecture with an immersive virtual environment presentation system was designed and built. The data flow consists of streams of data that originate at sensors that register the body state of the participant, flowing through filters that modify the streams and affect the yE. The requirements for such a system and the filters it should contain are derived from two pieces of work on interaction metaphors, one based on a desktop system using a novel input device and the second a navigation technique for an immersive system. The analysis of these metaphors highlighted particular tasks that such a virtual environment dialogue architecture (VEDA) system might be used to solve, and illustrate the scope of interactions that should be accommodated. Initial evaluation of the VEDA system is provided by moderately sized demonstration environments and tools constructed by the author. Further evaluation is provided by an in-depth study where three novice VE designers were invited to construct VEs with the VEDA system. This highlighted the flexibility that the VEDA approach provides and the utility of the immersive presentation over traditional techniques in that it allows the participant to use more natural and expressive techniques in the construction process. In other words the evaluation shows how the immersive facilities of VEs can be exploited in the process of constructing further VEs

    A Predictive Fuzzy-Neural Autopilot for the Guidance of Small Motorised Marine Craft

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    This thesis investigates the design and evaluation of a control system, that is able to adapt quickly to changes in environment and steering characteristics. This type of controller is particularly suited for applications with wide-ranging working conditions such as those experienced by small motorised craft. A small motorised craft is assumed to be highly agile and prone to disturbances, being thrown off-course very easily when travelling at high speed 'but rather heavy and sluggish at low speeds. Unlike large vessels, the steering characteristics of the craft will change tremendously with a change in forward speed. Any new design of autopilot needs to be to compensate for these changes in dynamic characteristics to maintain near optimal levels of performance. This study identities the problems that need to be overcome and the variables involved. A self-organising fuzzy logic controller is developed and tested in simulation. This type of controller learns on-line but has certain performance limitations. The major original contribution of this research investigation is the development of an improved self-adaptive and predictive control concept, the Predictive Self-organising Fuzzy Logic Controller (PSoFLC). The novel feature of the control algorithm is that is uses a neural network as a predictive simulator of the boat's future response and this network is then incorporated into the control loop to improve the course changing, as well as course keeping capabilities of the autopilot investigated. The autopilot is tested in simulation to validate the working principle of the concept and to demonstrate the self-tuning of the control parameters. Further work is required to establish the suitability of the proposed novel concept to other control

    AI-based design methodologies for hot form quench (HFQĀ®)

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    This thesis aims to develop advanced design methodologies that fully exploit the capabilities of the Hot Form Quench (HFQĀ®) stamping process in stamping complex geometric features in high-strength aluminium alloy structural components. While previous research has focused on material models for FE simulations, these simulations are not suitable for early-phase design due to their high computational cost and expertise requirements. This project has two main objectives: first, to develop design guidelines for the early-stage design phase; and second, to create a machine learning-based platform that can optimise 3D geometries under hot stamping constraints, for both early and late-stage design. With these methodologies, the aim is to facilitate the incorporation of HFQ capabilities into component geometry design, enabling the full realisation of its benefits. To achieve the objectives of this project, two main efforts were undertaken. Firstly, the analysis of aluminium alloys for stamping deep corners was simplified by identifying the effects of corner geometry and material characteristics on post-form thinning distribution. New equation sets were proposed to model trends and design maps were created to guide component design at early stages. Secondly, a platform was developed to optimise 3D geometries for stamping, using deep learning technologies to incorporate manufacturing capabilities. This platform combined two neural networks: a geometry generator based on Signed Distance Functions (SDFs), and an image-based manufacturability surrogate model. The platform used gradient-based techniques to update the inputs to the geometry generator based on the surrogate model's manufacturability information. The effectiveness of the platform was demonstrated on two geometry classes, Corners and Bulkheads, with five case studies conducted to optimise under post-stamped thinning constraints. Results showed that the platform allowed for free morphing of complex geometries, leading to significant improvements in component quality. The research outcomes represent a significant contribution to the field of technologically advanced manufacturing methods and offer promising avenues for future research. The developed methodologies provide practical solutions for designers to identify optimal component geometries, ensuring manufacturing feasibility and reducing design development time and costs. The potential applications of these methodologies extend to real-world industrial settings and can significantly contribute to the continued advancement of the manufacturing sector.Open Acces
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