Search

Channel network identification is an important practice in not only hydrologic analysis but also hydraulic computation. In this paper, a new algorithm, watershed merging, is proposed to automatically identify and extract channel networks. In the water‐merging algorithm, based on the fact that the sink cell of a dendritic watershed is either a depression cell or a flat cell, a macroscale approach is proposed to treat the depression and flat areas (DAFA) and determine the flow direction within those areas, where the conventional D8 slope calculation fails. The separated neighboring watersheds are merged together using information of neighboring watersheds instead of the D8 cells. This progressive merging process starts from small neighboring watershed to larger ones. The example and applications demonstrated that the proposed watershed‐merging algorithm is effective in resolving the DAFA problems and identifying channel networks. Key Points: A simple and effective new algorithm, watershed merging, is proposed to identify and extract channel networks from a digital elevation modelThe watershed‐merging algorithm is conceptually clear and can effectively resolve channel networks in the DAFA of a digital elevation modelThe watershed‐merging algorithm preserves the original DEM, and no local elevation adjustments or corrections are needed [ABSTRACT FROM AUTHOR]

The pattern recognition of a partial discharge (PD) is critical to evaluate the insulation condition of electric equipment of high voltage. However, much attention had been paid to recognise PD types which are known, but it is ignored that the types which did not appear previously. To solve the above problems, a method to recognise unknown PD types based on improved support vector data description (SVDD) algorithm is introduced in this study. Tri‐training algorithm and double thresholds set based on Otsu algorithm are used to improve the traditional SVDD classifiers. PD samples collected from different artificial defects models are finally classified by the improved fuzzy c‐means clustering algorithm. Experiments compared the improved SVDD with existing one‐class classification methods such as SVDD, one‐class support vector machine and probability density function estimation. The results show that the proposed method has much higher recognition accuracy. It is verified that the improved SVDD is an efficient method which can be applied to the recognition of unknown PD types. [ABSTRACT FROM AUTHOR]

The pattern recognition of partial discharge (PD) is critical to evaluate the insulation condition and locate the defect of a power transformer. The existing pattern recognition methods fail to make use of the inter‐relations of the extracted features of PD signals. In fact, the inter‐relations can show distinct dissimilarities between different classes of the signals. To overcome the defect of existing pattern recognition methods, the variable predictive model‐based class discrimination (VPMCD), a new pattern recognition method, is introduced for the pattern recognition of PD in this study. However, the original VPMCD lacks general expression ability of the inter‐relations of extracted features and inherits the shortcomings of least squares (LS) regression. To overcome the above defects, an improved VPMCD based on kernel partial LS (KPLS) regression, which is called as KPLS‐VPMCD, is proposed in this study. Experiments and analyses are implemented using both UCI datasets and the extracted features of PD signals. The experiments show that the performance of the proposed KPLS‐VPMCD is better than those of the existing methods such as VPMCD, back propagation neural networks, and support vector machines. The conclusion is that KPLS‐VPMCD is an efficient supervised learning algorithm with consistency and good performance for PD pattern recognition. [ABSTRACT FROM AUTHOR]

Both the feature extraction method and pattern recognition method are of great importance to assess the health condition of a power transformer. Since the partial discharge (PD) signals of the transformer are non‐stationary and non‐linear, and the existing pattern recognition methods fail to capitalise on the inter‐relations between the extracted features of the signals, a novel pattern recognition method, namely variable predictive model based class discrimination (VPMCD) is introduced for the PD pattern recognition in this research. However, the parameters of VPMCD are estimated by using least squares (LS) regression which is sensitive to multiple correlations between independent variables. Fortunately, partial LS (PLS) regression is usable even if features are highly correlated or the number of trained samples is very small. It is novelly adopted to overcome the defects of LS regression. Therefore, an automatic PD source classifier based on PLS and VPMCD, i.e. PLS‐VPMCD, is put forward in this study. PD signal features from either pulse shape characterisations or phase‐resolved PD are extracted. Then, the features are used as the input vectors of PLS‐VPMCD classifier. PD signals sampled from four artificial defect models are adopted for the algorithms testing. Compared with the original VPMCD and back propagation recognition methods, PLS‐VPMCD has much higher recognition accuracy. [ABSTRACT FROM AUTHOR]

This paper presents an application of the advancing reduction technique for 2D hybrid mesh generation (triangles + quadrilaterals). Based on an initial rectangle mesh (RM) covering the whole domain, the advancing reduction technique coarsens the base RM in a marching way from the boundary to the interior of the domain so that different zones of sub-RMs with different edge lengths are recognized. These sub-RMs are connected to each other with the so-called transition layers which consist of the transition triangles and quadrilaterals. As demonstrated by examples, the proposed method is simple, efficient, and easy to implement. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY This paper presents a two-dimensional finite element model for simulating dynamic propagation of weakly dispersive waves. Shallow water equations including extra non-hydrostatic pressure terms and a depth-integrated vertical momentum equation are solved with linear distributions assumed in the vertical direction for the non-hydrostatic pressure and the vertical velocity. The model is developed based on the platform of a finite element model, CCHE2D. A physically bounded upwind scheme for the advection term discretization is developed, and the quasi second-order differential operators of this scheme result in no oscillation and little numerical diffusion. The depth-integrated non-hydrostatic wave model is solved semi-implicitly: the provisional flow velocity is first implicitly solved using the shallow water equations; the non-hydrostatic pressure, which is implicitly obtained by ensuring a divergence-free velocity field, is used to correct the provisional velocity, and finally the depth-integrated continuity equation is explicitly solved to satisfy global mass conservation. The developed wave model is verified by an analytical solution and validated by laboratory experiments, and the computed results show that the wave model can properly handle linear and nonlinear dispersive waves, wave shoaling, diffraction, refraction and focusing. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]