Such a model is also useful for studying communication protocols, scheduling algorithms, and congestion control schemes for ATM networks. In this paper, a model-based leak detection methodology for fluid pumping systems is developed. The novelty of this approach lies in modelling the leak position as a point between a real and an imaginary valve. The equations that describe the resulting dynamic system are then put into an input-output form suitable for least squares estimation. In this way, classic parameter-estimation based detection methods are applied to moving windows of system data.
Computer simulation illustrates the feasibility of the method.
This paper presents a back-propagation neural network method to estimate traffic flow volumes on signalized intersections. Based on volume estimates, an optimum cycle is then determined. We take five-minute intervals as term basis for volume estimation. Actual traffic flow data for the four main streets of the intersection have been collected for four weeks on a five-minute basis. Two weeks of the data were used for training the network.