69 resultados para Graph Decomposition

em QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast


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In this study, we investigate an adaptive decomposition and ordering strategy that automatically divides examinations into difficult and easy sets for constructing an examination timetable. The examinations in the difficult set are considered to be hard to place and hence are listed before the ones in the easy set in the construction process. Moreover, the examinations within each set are ordered using different strategies based on graph colouring heuristics. Initially, the examinations are placed into the easy set. During the construction process, examinations that cannot be scheduled are identified as the ones causing infeasibility and are moved forward in the difficult set to ensure earlier assignment in subsequent attempts. On the other hand, the examinations that can be scheduled remain in the easy set.

Within the easy set, a new subset called the boundary set is introduced to accommodate shuffling strategies to change the given ordering of examinations. The proposed approach, which incorporates different ordering and shuffling strategies, is explored on the Carter benchmark problems. The empirical results show that the performance of our algorithm is broadly comparable to existing constructive approaches.

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In this work, the non-Markovian decoherence is considered in two ways. Firstly, an effective Hamiltonian approach is demonstrated to investigate the decoherence of a quantum system in a non-Markovian environment, in which complete positivity of the reduced dynamics is achieved. This method uses the notion of an effective environment, that is a subsystem of the environment that causes the decoherence. Secondly, the evolution of the system and environment is decomposed, thus partially illuminating how they would interact given that memory effects are allowed. It should be noted that beam splitters and rotators are sufficient to explain this decomposition.

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We present a novel approach to goal recognition based on a two-stage paradigm of graph construction and analysis. First, a graph structure called a Goal Graph is constructed to represent the observed actions, the state of the world, and the achieved goals as well as various connections between these nodes at consecutive time steps. Then, the Goal Graph is analysed at each time step to recognise those partially or fully achieved goals that are consistent with the actions observed so far. The Goal Graph analysis also reveals valid plans for the recognised goals or part of these goals. Our approach to goal recognition does not need a plan library. It does not suffer from the problems in the acquisition and hand-coding of large plan libraries, neither does it have the problems in searching the plan space of exponential size. We describe two algorithms for Goal Graph construction and analysis in this paradigm. These algorithms are both provably sound, polynomial-time, and polynomial-space. The number of goals recognised by our algorithms is usually very small after a sequence of observed actions has been processed. Thus the sequence of observed actions is well explained by the recognised goals with little ambiguity. We have evaluated these algorithms in the UNIX domain, in which excellent performance has been achieved in terms of accuracy, efficiency, and scalability.

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The decomposition of N2O was studied using a silica-supported Pt catalyst. The catalyst was found to exhibit short-lived activity at low temperatures to yield N-2 and O-(ads), the latter remained adsorbed on the surface and poisoned the active sites. Creation of hot-O-(ads) atoms during N2O decomposition is proposed to allow O-2 desorption at intermediate temperatures. Inclusion of H-2 as a reducing agent greatly enhanced the activity and suppressed low temperature deactivation. Simultaneous and sequential pulsing of N2O and H-2 showed that H-2 inclusion with the N2O gas stream produced the greatest activity. A mechanism involving H-(ads) addition to