268 research outputs found

    Human-centric intelligent systems for exploration and knowledge discovery

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    This speculative article discusses research and development relating to computational intelligence (CI) technologies comprising powerful machine-based search and exploration techniques that can generate, extract, process and present high-quality information from complex, poorly understood biotechnology domains. The integration and capture of user experiential knowledge within such CI systems in order to support and stimulate knowledge discovery and increase scientific and technological understanding is of particular interest. The manner in which appropriate user interaction can overcome problems relating to poor problem representation within systems utilising evolutionary computation (EC), machine-learning and software agent technologies is investigated. The objective is the development of user-centric intelligent systems that support an improving knowledge-base founded upon gradual problem re-definition and reformulation. Such an approach can overcome initial lack of understanding and associated uncertainty

    Improving problem definition through interactive evolutionary computation

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    Poor definition and uncertainty are primary characteristics of conceptual design processes. During the initial stages of these generally human-centric activities, little knowledge pertaining to the problem at hand may be available. The degree of problem definition will depend on information available in terms of appropriate variables, constraints, and both quantitative and qualitative objectives. Typically, the problem space develops with information gained in a dynamical process in which design optimization plays a secondary role, following the establishment of a sufficiently well-defined problem domain. This paper concentrates on background human-computer interaction relating to the machine-based generation of high-quality design information that, when presented in an appropriate manner to the designer, supports a better understanding of a problem domain. Knowledge gained from such information combined with the experiential knowledge of the designer can result in a reformulation of the problem, providing increased definition and greater confidence in the machine-based representation. Conceptual design domains related to gas turbine blade cooling systems and a preliminary air frame configuration are introduced. These are utilized to illustrate the integration of interactive evolutionary strategies that support the extraction of optimal design information, its presentation to the designer, and subsequent human-based modification of the design domain based on knowledge gained from the information received. An experimental iterative designer or evolutionary search process resulting in a better understanding of the problem and improved machine-based representation of the design domain is thus established

    PNEUMATIC HYDROPOWER SYSTEMS

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    The following thesis investigates the performance and economics of a Pneumatic Water Engine capable of extracting energy from differential heads of water in the two to three metre range. Initial concepts are discussed and a system configuration is physically modelled at a laboratory scale. Outline designs using a variety of materials are developed and these provide a basis for the estimation of a probable capital cost using standard Civil Engineering methods. The proposed system is mathematically modelled using a lumped mass approach to the complex hydrodynamics. The resultant differential equations are solved by means of a variable Runge Kutta numerical analysis. The model includes the thermodynamic aspects of the system's compressible airflow. A computer program has been developed from the mathematical model and Is utilized in a series of parametric studies. An economic assessment based upon both the average power output achieved during the parametric studies and the probable capital cost of the system is presented, together with an estimate of the cost per kilowatt-hour of the electricity produced. This assessment takes into account maintenance costs, expected value of the energy produced and the possible effects of both Water Abstraction Charges and Local Authority Rating. In addition to the parametric studies a final, more rigorous optimization of the system involving a number of the many interacting variables has been undertaken. This optimization is achieved via Cumulative Evolutionary Design techniques involving the use of Genetic Algorithms. An optimal design of the chamber shape is achieved in the same manner.Energy Technology Support Unit (ETSU), Harwel

    Phases of driven two-level systems with nonlocal dissipation

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    We study an array of two-level systems arranged on a lattice and illuminated by an external plane wave which drives a dipolar transition between the two energy levels. In this set up, the two-level systems are coupled by dipolar interactions and subject to nonlocal dissipation, so behave as an open many-body quantum system. We investigate the long-time dynamics of the system at the mean-field level, and use this to determine a phase diagram as a function of external drive and detuning. We find a multitude of phases including antiferromagnetism, spin density waves, oscillations and phase bistabilities. We investigate these phases in more detail and explain how nonlocal dissipation plays a role in the long-time dynamics. Furthermore, we discuss what features would survive in the full quantum description
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