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Xi'an, China

Xi'an Shiyou University is a university in Xi'an, China. Wikipedia.

She Y.,Xian Shiyou University
International Journal of Approximate Reasoning | Year: 2014

This paper is mainly devoted to establishing a kind of graded reasoning method in the context of rough logic. To this end, a weak form of deduction theorem in rough logic is firstly obtained, then, based upon the weak deduction theorem and the notion of rough truth degree, a new kind of graded reasoning method in rough logic is presented. Moreover, to embody the idea of rough approximations, the notions of graded rough upper consequence and graded rough lower consequence are also proposed, which can be treated as the logical counterpart of rough upper and lower approximation, respectively. Compared with the existing graded reasoning method, the proposed method in the present paper does not employ the notion of rough similarity degree, and hence their fundamental starting points are different, however, they are also closely related, accordingly, a comparative study is performed between these two different graded reasoning methods. Lastly, based on the proposed graded reasoning method, the notions of rough (upper, lower) consistency degree are also proposed and their properties are investigated in detail. © 2013 Elsevier Inc. All rights reserved. Source

Yao Y.,University of Regina | She Y.,Xian Shiyou University
Information Sciences | Year: 2016

There exist several approaches to rough set approximations in a multigranulation space, namely, a family of equivalence relations. In this paper, we propose a unified framework to classify and compare existing studies. An underlying principle is to explain rough sets in a multigranulation space through rough sets derived by using individual equivalence relations. Two basic models are suggested. One model is based on a combination of a family of equivalence relations into an equivalence relation and the construction of approximations with respect to the combined relation. By combining equivalence relations through set intersection and union, respectively, we construct two sub-models. The other model is based on the construction of a family of approximations from a set of equivalence relations and a combination of the family of approximations. By using set intersection and union to combine a family of approximations, respectively, we again build two sub-models. As a result, we have a total of four models. We examine these models and give conditions under which some of them become the same. © 2015 Elsevier Inc. Source

Two quantitative structure property relationship (QSPR) models for predicting soot-water partition coefficients (Ksc) of 25 persistent organic pollutants (POPs) were developed. One model was established with linear artificial neural network (L-ANN), the other model was developed by using back propagation artificial neural network (BP-ANN). Leave one out cross validation was adopted to assess the predictive ability of the developed models. For the L-ANN model, the square of correlation coefficient (R2) between the predicted and experimental logKSC is 0.8358 and the RMS%RE is 6.32 for all the compounds. For the BP-ANN model, R2 is 0.9628 and the RMS%RE is 4.12 for all the compounds. The result of leave one out cross validation demonstrates that both L-ANN and BP-ANN are practicable for developing the QSPR model for KSC of the investigated POPs. However, the model established with BP-ANN is better than the model established with L-ANN in prediction accuracy. It is shown that BP-ANN is a promising method for developing QSPR models for KSC of POPs. © 2010 Elsevier Ltd. Source

Cui L.,Xian Shiyou University | Cui L.,TU Darmstadt | Wang P.,TU Darmstadt
International Journal of Fatigue | Year: 2014

The flexibility of steam turbine components is currently a key issue in terms of the fluctuations in the power supply due to regenerative energy. Conventional steam power plants must run at varying utilization levels. Life estimation methods according to standards, e.g. ASME Code N47 and TR, assess the influences of creep and fatigue separately under the assumption of isothermal conditions at the maximum operating temperature. The influence of thermomechanical fatigue (TMF) loading still requires a significant number of experimental studies. Further, the interaction of creep and fatigue is not adequately taken into account. Thus, new lifetime estimation methods are required for the monitoring, re-engineering and new design of power plant components. In this paper, both a phenomenological and a constitutive crack initiation lifetime estimation model for steam turbine components are introduced. The effectiveness of each method is shown by recalculation of uniaxial as well as multiaxial service-type creep-fatigue experiments on high-chromium 10%Cr stainless rotor steel. Finally, the two models are compared with respect to different aspects, such as the type and number of necessary experiments to determine model parameters, the prerequisite for the application and the limitations of each model. © 2013 Elsevier Ltd. All rights reserved. Source

Jun T.,Xian Shiyou University
ICCET 2010 - 2010 International Conference on Computer Engineering and Technology, Proceedings | Year: 2010

Image segmentation is a classic subject in the field of image processing and also is a hotspot and focus of image processing techniques. With the improvement of computer processing capabilities and the increased application of color image, the color image segmentation are more and more concerned by the researchers. Color image segmentation methods can be seen as an extension of the gray image segmentation method in the color images, but many of the original gray image segmentation methods can not be directly applied to color images. This requires to improve the method of original gray image segmentation method according to the color image which have the feature of rich information or research a new image segmentation method it specially used in color image segmentation. This article proposes a color image segmentation method of automatic seed region growing on basis of the region with the combination of the watershed algorithm with seed region growing algorithm which based on the traditional seed region growing algorithm. © 2010 IEEE. Source

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