843 resultados para hybrid intelligent systems


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Score following has been an important area of research in AI and music since the mid 80's. Various systems were developed, but they were predominantly for providing automated accompaniment to live concert performances, dealing mostly with issues relating to pitch detection and identification of embellished melodies. They have a big potential in the area of education where student performers benefit in practice situations. Current accompaniment systems are not designed to deal with errors that may occur during practising. In this paper we present a system developed to provide accompaniment for students practising at home. First a survey of score following will be given. Then the capabilities of the system will be explained, and the results from the first experiments of the monophonic score following system will be presented.

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In this paper, we present a random iterative graph based hyper-heuristic to produce a collection of heuristic sequences to construct solutions of different quality. These heuristic sequences can be seen as dynamic hybridisations of different graph colouring heuristics that construct solutions step by step. Based on these sequences, we statistically analyse the way in which graph colouring heuristics are automatically hybridised. This, to our knowledge, represents a new direction in hyper-heuristic research. It is observed that spending the search effort on hybridising Largest Weighted Degree with Saturation Degree at the early stage of solution construction tends to generate high quality solutions. Based on these observations, an iterative hybrid approach is developed to adaptively hybridise these two graph colouring heuristics at different stages of solution construction. The overall aim here is to automate the heuristic design process, which draws upon an emerging research theme on developing computer methods to design and adapt heuristics automatically. Experimental results on benchmark exam timetabling and graph colouring problems demonstrate the effectiveness and generality of this adaptive hybrid approach compared with previous methods on automatically generating and adapting heuristics. Indeed, we also show that the approach is competitive with the state of the art human produced methods.

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The solubility and uniform distribution of lanthanide complexes in sol-get glasses can be improved by covalently linking the complexes to the sol-gel matrix. In this study, several lanthanide beta-diketonate complexes (Ln = Nd, Sm, Eu, Tb, Er, Yb) were immobilized on a 1,10-phenanthroline functionalized sol-gel glass. For the europium(Ill) complex, a sol-gel material of diethoxydimethylsilane (DEDMS) with polymer-like properties was derived. For the other lanthanide complexes, the sol-gel glass was prepared by using a matrix of tetramethoxysilane (TMOS) and DEDMS. Both systems were prepared under neutral reaction conditions. High-resolution emission and excitation spectra were recorded. The luminescence lifetimes were measured. (c) 2004 Elsevier B.V. All rights reserved.

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This paper describes the development of a novel metaheuristic that combines an electromagnetic-like mechanism (EM) and the great deluge algorithm (GD) for the University course timetabling problem. This well-known timetabling problem assigns lectures to specific numbers of timeslots and rooms maximizing the overall quality of the timetable while taking various constraints into account. EM is a population-based stochastic global optimization algorithm that is based on the theory of physics, simulating attraction and repulsion of sample points in moving toward optimality. GD is a local search procedure that allows worse solutions to be accepted based on some given upper boundary or ‘level’. In this paper, the dynamic force calculated from the attraction-repulsion mechanism is used as a decreasing rate to update the ‘level’ within the search process. The proposed method has been applied to a range of benchmark university course timetabling test problems from the literature. Moreover, the viability of the method has been tested by comparing its results with other reported results from the literature, demonstrating that the method is able to produce improved solutions to those currently published. We believe this is due to the combination of both approaches and the ability of the resultant algorithm to converge all solutions at every search process.

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Dwindling fossil fuel resources and pressures to reduce greenhouse gas (GHG) emissions will result in a more diverse range of generation portfolios for future electricity systems. Irrespective of the portfolio mix the overarching requirement for all electricity suppliers and system operators is that supply instantaneously meets demand and that robust operating standards are maintained to ensure a consistent supply of high quality electricity to end-users. Therefore all electricity market participants will ultimately need to use a variety of tools to balance the power system. Thus the role of demand side management (DSM) with energy storage will be paramount to integrate future diverse generation portfolios. Electric water heating (EWH) has been studied previously, particularly at the domestic level to provide load control, peak shave and to benefit end-users financially with lower bills, particularly in vertically integrated monopolies. In this paper, a continuous Direct Load Control (DLC) EWH algorithm is applied in a liberalized market environment using actual historical electricity system and market data to examine the potential energy savings, cost reductions and electricity system operational improvements.

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Dwindling fossil fuel resources and pressures to reduce greenhouse gas emissions will result in a more diverse range of generation portfolios for future electricity systems. Irrespective of the portfolio mix the overarching requirement for all electricity suppliers and system operators is to instantaneously meet demand, to operate to standards and reduce greenhouse gas emissions. Therefore all electricity market participants will ultimately need to use a variety of tools to balance the power system. Thus the role of demand side management with energy storage will be paramount to integrate future diverse generation portfolios. Electric water heating has been studied previously, particularly at the domestic level to provide load control, peak shave and to bene?t end-users ?nancially with lower bills, particularly in vertically integrated monopolies. In this paper a number of continuous direct load control demand response based electric water heating algorithms are modelled to test the effectiveness of wholesale electricity market signals to study the system bene?ts. The results are compared and contrasted to determine which control algorithm showed the best potential for energy savings, system marginal price savings and wind integration.

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The paper introduces a new modeling approach that represents the waiting times in an accident and emergency (A&E) department in a UK based national health service (NHS) hospital. The technique uses Bayesian networks to capture the heterogeneity of arriving patients by representing how patient covariates interact to influence their waiting times in the department. Such waiting times have been reviewed by the NHS as a means of investigating the efficiency of A&E departments (emergency rooms) and how they operate. As a result activity targets are now established based on the patient total waiting times with much emphasis on trolley waits.