52 resultados para Programmable logic controller
Resumo:
Objectives The administration of unfractionated heparin (UFH) prior to carotid clamping during carotid endarterectomy (CEA) transiently increases the platelet aggregation response to arachidonic acid (AA) despite the use of aspirin. We hypothesized that this phenomenon might be reduced by using low molecular weight heparin (LMWH) resulting in fewer emboli in the early post-operative period. Methods 183 aspirinated patients undergoing CEA were randomised to 5000 IU UFH (n = 91) or 2500 IU LMWH (dalteparin, n = 92) prior to carotid clamping. End-points were: transcranial Doppler (TCD) measurement of embolisation, effect on bleeding and platelet aggregation to AA and adenosine 5′-diphosphate (ADP). Results Patients randomised to UFH had twice the odds of experiencing a higher number of emboli in the first 3 h after CEA, than those randomised to LMWH (p = 0.04). This was not associated with increased bleeding (mean time from flow restoration to operation end: 23 min (UFH) vs. 24 min (LMWH), p = 0.18). Platelet aggregation to AA increased significantly following heparinisation, but was unaffected by heparin type (p = 0.90). The platelets of patients randomised to LMWH exhibited significantly lower aggregation to ADP compared to UFH (p < 0.0001). Conclusions Intravenous LMWH is associated with a significant reduction in post-operative embolisation without increased bleeding. The higher rate of embolisation seen with UFH may be mediated by increased platelet aggregation to ADP, rather than to AA.
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This paper describes the development and validation of a novel web-based interface for the gathering of feedback from building occupants about their environmental discomfort including signs of Sick Building Syndrome (SBS). The gathering of such feedback may enable better targeting of environmental discomfort down to the individual as well as the early detection and subsequently resolution by building services of more complex issues such as SBS. The occupant's discomfort is interpreted and converted to air-conditioning system set points using Fuzzy Logic. Experimental results from a multi-zone air-conditioning test rig have been included in this paper.
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This paper develops cycle-level FPGA circuits of an organization for a fast path-based neural branch predictor Our results suggest that practical sizes of prediction tables are limited to around 32 KB to 64 KB in current FPGA technology due mainly to FPGA area of logic resources to maintain the tables. However the predictor scales well in terms of prediction speed. Table sizes alone should not be used as the only metric for hardware budget when comparing neural-based predictor to predictors of totally different organizations. This paper also gives early evidence to shift the attention on to the recovery from mis-prediction latency rather than on prediction latency as the most critical factor impacting accuracy of predictions for this class of branch predictors.
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This paper offers general guidelines for the development of effective visual languages. That is, languages for constructing diagrams that can be easily and readily interpreted and manipulated by the human reader. We use these guidelines first to examine classical AND/OR trees as a representation of logical proofs, and second to design and evaluate a visual language for representing proofs in LofA: a Logic of Dependability Arguments, for which we provide a brief motivation and overview.
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This paper presents a clocking pipeline technique referred to as a single-pulse pipeline (PP-Pipeline) and applies it to the problem of mapping pipelined circuits to a Field Programmable Gate Array (FPGA). A PP-pipeline replicates the operation of asynchronous micropipelined control mechanisms using synchronous-orientated logic resources commonly found in FPGA devices. Consequently, circuits with an asynchronous-like pipeline operation can be efficiently synthesized using a synchronous design methodology. The technique can be extended to include data-completion circuitry to take advantage of variable data-completion processing time in synchronous pipelined designs. It is also shown that the PP-pipeline reduces the clock tree power consumption of pipelined circuits. These potential applications are demonstrated by post-synthesis simulation of FPGA circuits. (C) 2004 Elsevier B.V. All rights reserved.
Resumo:
In this study a minimum variance neuro self-tuning proportional-integral-derivative (PID) controller is designed for complex multiple input-multiple output (MIMO) dynamic systems. An approximation model is constructed, which consists of two functional blocks. The first block uses a linear submodel to approximate dominant system dynamics around a selected number of operating points. The second block is used as an error agent, implemented by a neural network, to accommodate the inaccuracy possibly introduced by the linear submodel approximation, various complexities/uncertainties, and complicated coupling effects frequently exhibited in non-linear MIMO dynamic systems. With the proposed model structure, controller design of an MIMO plant with n inputs and n outputs could be, for example, decomposed into n independent single input-single output (SISO) subsystem designs. The effectiveness of the controller design procedure is initially verified through simulations of industrial examples.
Resumo:
This study explores the implications of an organization moving toward service-dominant logic (S-D logic) on the sales function. Driven by its customers’ needs, a service orientation by its nature requires personal interaction and sales personnel are in an ideal position to develop offerings with the customer. However, the development of S-D logic may require sales staff to develop additional skills. Employing a single case study, the study identified that sales personnel are quick to appreciate the advantages of S-D logic for customer satisfaction and six specific skills were highlighted and explored. Further, three propositions were identified: in an organization adopting S-D logic, the sales process needs to elicit needs at both embedded-value and value-in-use levels. In addition, the sales process needs to coproduce not just goods and service attributes but also attributes of the customer’s usage processes. Further, the sales process needs to coproduce not just goods and service attributes but also attributes of the customer’s usage processes.
Resumo:
A neural network enhanced self-tuning controller is presented, which combines the attributes of neural network mapping with a generalised minimum variance self-tuning control (STC) strategy. In this way the controller can deal with nonlinear plants, which exhibit features such as uncertainties, nonminimum phase behaviour, coupling effects and may have unmodelled dynamics, and whose nonlinearities are assumed to be globally bounded. The unknown nonlinear plants to be controlled are approximated by an equivalent model composed of a simple linear submodel plus a nonlinear submodel. A generalised recursive least squares algorithm is used to identify the linear submodel and a layered neural network is used to detect the unknown nonlinear submodel in which the weights are updated based on the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model therefore the nonlinear submodel is naturally accommodated within the control law. Two simulation studies are provided to demonstrate the effectiveness of the control algorithm.