4,099 research outputs found
IMPLEMENTATION AND UNIFORM MANAGEMENT OF MODELLING ENTITIES IN A MASSIVELY FEATURE-OBJECT ORIENTED ADVANCED CAD ENVIRONMENT
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
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
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
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
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
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Considerations in designing a cybernetic simple 'learning' model; and an overview of the problem of modelling learning
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Learning is viewed as a central feature of living systems and must be manifested in any artifact that claims to exhibit general intelligence. The central aims of the thesis are twofold: (1) - To review and critically assess the empirical and theoretical aspects of learning as have been addressed in a multitude of disciplines, with the aim of extracting fundamental features and elements. (2) - To develop a more systematic approach to the cybernetic modelling of learning than has been achieved hitherto. In pursuit of aim (1) above the following discussions are included: Historical and Philosophical backgrounds; Natural learning, both physiological and psychological aspects; Hierarchies of learning identified in the evolutionary, functional and developmental senses; An extensive section on the general problem of modelling of learning and the formal tools, is included as a link between aims (1) and (2). Following this a systematic and historically oriented study of cybernetic and other related approaches to the problem of modelling of learning is presented. This then leads to the development of a state-of-the-art general purpose experimental cybernetic learning model. The programming and use of this model is also fully described, including an elaborate scheme for the manifestation of simple learning
AI-based design methodologies for hot form quench (HFQĀ®)
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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