A model-based algorithm is proposed and tested using the simulator and operation data of a plastic pipeline prototype to locate multiple non-simultaneous leaks in the pipeline. A combination of an extended Kalman filt...
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A model-based algorithm is proposed and tested using the simulator and operation data of a plastic pipeline prototype to locate multiple non-simultaneous leaks in the pipeline. A combination of an extended Kalman filter and obtained relations from the steady state response is used in order to tackle the problem of multi-leak localization. To achieve the mentioned relations, a real pipe with two leaks is equated to a virtual pipe with a single virtual equivalent leak.
In multi-objective evolutionary algorithm (MOEA), modelling method is a crucial part. Moreover, variable linkages enable the modelling process more complex for multi-objective optimisation problems. The Karush-Kulm-Tu...
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In multi-objective evolutionary algorithm (MOEA), modelling method is a crucial part. Moreover, variable linkages enable the modelling process more complex for multi-objective optimisation problems. The Karush-Kulm-Tucker condition shows that the Pareto set of a continuous MOP with m objectives is a piecewise continuous (m-1)-dimensional manifold. How to use this regularity property to model continuous MOP with variable linkages has been the research focus. In this paper, a model-based multi-objective evolutionary algorithmbased on regression analysis (MMEA-RA) for continuous multi-objective optimisation problems with variable linkages is put forward. In the algorithm, the optimisation problem is modelled as a promising area in the decision space by a probability distribution, and the centroid of the probability distribution is (m-1)-dimensional piecewise continuous manifold. The least squares algorithm is used to build such a model. Systematic experiments have shown that, compared with two state-of-the-art algorithms, MMEA-RA performs excellent on a set of test instances with variable linkages.
The aim of this paper is to show how image points can be extracted accurately. We will restrict our search to specific points identified by corners, which are stable given a sequence. Our approach makes us of a model-...
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ISBN:
(纸本)0819416851
The aim of this paper is to show how image points can be extracted accurately. We will restrict our search to specific points identified by corners, which are stable given a sequence. Our approach makes us of a model-based corner detector. It matches a part of the image containing a corner against a predefined corner model. Once the fitting is accomplished, the position of the corner in the image can be deduced by the knowledge of the corner position in the image. The validity of our approach has been proven with 4 independent tests. It is shown that the accuracy which can be achieved is 1/10th of a pixel.
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