4 resultados para OPEN-FRAME UNDERWATER VEHICLE

em Aston University Research Archive


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Knowledge maintenance is a major challenge for both knowledge management and the Semantic Web. Operating over the Semantic Web, there will be a network of collaborating agents, each with their own ontologies or knowledge bases. Change in the knowledge state of one agent may need to be propagated across a number of agents and their associated ontologies. The challenge is to decide how to propagate a change of knowledge state. The effects of a change in knowledge state cannot be known in advance, and so an agent cannot know who should be informed unless it adopts a simple ‘tell everyone – everything’ strategy. This situation is highly reminiscent of the classic Frame Problem in AI. We argue that for agent-based technologies to succeed, far greater attention must be given to creating an appropriate model for knowledge update. In a closed system, simple strategies are possible (e.g. ‘sleeping dog’ or ‘cheap test’ or even complete checking). However, in an open system where cause and effect are unpredictable, a coherent cost-benefit based model of agent interaction is essential. Otherwise, the effectiveness of every act of knowledge update/maintenance is brought into question.

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In the UK, Open Learning has been used in industrial training for at least the last decade. Trainers and Open Learning practitioners have been concerned about the quality of the products and services being delivered. The argument put forward in this thesis is that there is ambiguity amongst industrialists over the meanings of `Open Learning' and `Quality in Open Learning'. For clarity, a new definition of Open Learning is proposed which challenges the traditional learner-centred approach favoured by educationalists. It introduces the concept that there are benefits afforded to the trainer/employer/teacher as well as to the learner. This enables a focussed view of what quality in Open Learning really means. Having discussed these issues, a new quantitative method of evaluating Open Learning is proposed. This is based upon an assessment of the degree of compliance with which products meet Parts 1 & 2 of the Open Learning Code of Practice. The vehicle for these research studies has been a commercial contract commissioned by the Training Agency for the Engineering Industry Training Board (EITB) to examine the quality of Open Learning products supplied to the engineering industry. A major part of this research has been the application of the evaluation technique to a range of 67 Open Learning products (in eight subject areas). The findings were that good quality products can be found right across the price range - so can average and poor quality ones. The study also shows quite convincingly that there are good quality products to be found at less than 50. Finally the majority (24 out of 34) of the good quality products were text based.

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Recently underwater sensor networks (UWSN) attracted large research interests. Medium access control (MAC) is one of the major challenges faced by UWSN due to the large propagation delay and narrow channel bandwidth of acoustic communications used for UWSN. Widely used slotted aloha (S-Aloha) protocol suffers large performance loss in UWSNs, which can only achieve performance close to pure aloha (P-Aloha). In this paper we theoretically model the performances of S-Aloha and P-Aloha protocols and analyze the adverse impact of propagation delay. According to the observation on the performances of S-Aloha protocol we propose two enhanced S-Aloha protocols in order to minimize the adverse impact of propagation delay on S-Aloha protocol. The first enhancement is a synchronized arrival S-Aloha (SA-Aloha) protocol, in which frames are transmitted at carefully calculated time to align the frame arrival time with the start of time slots. Propagation delay is taken into consideration in the calculation of transmit time. As estimation error on propagation delay may exist and can affect network performance, an improved SA-Aloha (denoted by ISA-Aloha) is proposed, which adjusts the slot size according to the range of delay estimation errors. Simulation results show that both SA-Aloha and ISA-Aloha perform remarkably better than S-Aloha and P-Aloha for UWSN, and ISA-Aloha is more robust even when the propagation delay estimation error is large. © 2011 IEEE.

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Purpose: To investigate whether regional long-term changes in peripapillary retinal flow, measured by scanning laser Doppler flowmetry (SLDF), occur in patients with primary open angle glaucoma (POAG). Methods: 31 healthy volunteers (mean age: 65 8.3 years) and 33 POAG patients (mean age: 71.2 7.6 years) were followed up every 4 months for 16 months. Using SLDF, three images of the superior and inferior optic nerve head were obtained for each subject. A 1010-pixel frame was used to measure blood flow, volume and velocity in the four quadrants of the peripapillary retina. Central 24-2 visual field testing was carried out at each visit. Repeated measures analysis of covariance was used to assess change over time between the normal and POAG groups for the SLDF parameters. Univariate linear regression analysis for mean deviation and glaucoma change probability (GCP) analysis were used to identify visual field progression. Results: Blood volume, flow and velocity measured in the inferior nasal quadrant of the peripapillary retina decreased significantly over time for the POAG group compared to the normal group (p=0.0073, 0.0097, 0.0095 respectively). Overall, 2 glaucoma patients showed a significantly deteriorating MD slope, while 7 patients showed visual field progression with GPA. All of the patients progressing with GPA, showed change in the superior hemifield and, of those, 14% showed change in the inferior hemifield. Conclusion: Glaucoma patients showed a decrease in blood flow, volume and velocity in the inferior nasal peripapillary retina. A regional variation in microvascular retinal capillary blood flow may provide insight into the pathogenesis of glaucomatous optic neuropathy. Keywords: 331 blood supply • 554 retina • 624 visual fields