Results 221 to 230 of about 993,116 (266)

A fatigue model with local sensitivity analysis

Fatigue & Fracture of Engineering Materials & Structures, 2007
ABSTRACTThe goal of this paper is two fold. First, it introduces a general parametric lifetime model for high‐cycle fatigue regime derived from physical, statistical, engineering and dimensional analysis considerations. The proposed model has two threshold parameters and three Weibull distribution parameters.
E. CASTILLO, A. FERNÁNDEZ‐CANTELI, A. S. HADI, M. LÓPEZ‐AENLLE   +3 more
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Hoffman's Error Bound, Local Controllability, and Sensitivity Analysis

SIAM Journal on Control and Optimization, 2000
Summary: Our aim is to present sufficient conditions ensuring Hoffman's error bound for lower semicontinuous nonconvex inequality systems and to analyze its impact on the local controllability, implicit function theorem for (non-Lipschitz) multivalued mappings, generalized equations (variational inequalities), and sensitivity analysis and on other ...
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On the Local Sensitivity Analysis of the Inoperability Input‐Output Model

Risk Analysis, 2011
Natural and man‐made disasters are currently a source of major concern for contemporary societies. In order to understand their economic impacts, the inoperability input‐output model has recently gained recognition among scholars. In a recent paper, Percoco (2006) has proposed an extension of the model to map the technologically most important sectors ...
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Stable locality sensitive discriminant analysis for image recognition

Neural Networks, 2014
Locality Sensitive Discriminant Analysis (LSDA) is one of the prevalent discriminant approaches based on manifold learning for dimensionality reduction. However, LSDA ignores the intra-class variation that characterizes the diversity of data, resulting in unstableness of the intra-class geometrical structure representation and not good enough ...
Quanxue Gao, Jingjing Liu, Kai Cui, Hailin Zhang, Xiaogang Wang   +4 more
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Local Sensitivity Analysis with Constraints

2017
This chapter, which is our last on deterministic methods, addresses the removal of a typical assumption in sensitivity analysis.
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A Novel Local Sensitive Frontier Analysis for Feature Extraction

2009
In this paper, an efficient feature extraction method, named local sensitive frontier analysis (LSFA), is proposed. LSFA tries to find instances near the crossing of the multi-manifold, which are sensitive to classification, to form the frontier automatically.
Chao Wang 0071, De-Shuang Huang, Bo Li 0002   +2 more
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Large Scale Sentiment Analysis with Locality Sensitive BitHash

2015
As social media data rapidly grows, sentiment analysis plays an increasingly more important role in classifying users’ opinions, attitudes and feelings expressed in text. However, most studies have been focused on the effectiveness of sentiment analysis, while ignoring the storage efficiency when processing large-scale high-dimensional text data.
Wenhao Zhang 0003, Jianqiu Ji, Jun Zhu 0001, Hua Xu 0003, Bo Zhang 0010   +4 more
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Two-dimensional locality sensitive discriminant analysis

2008 International Conference on Wavelet Analysis and Pattern Recognition, 2008
Recently, locality sensitive discriminant analysis (LSDA) was proposed for dimensionality reduction. As far as matrix data, such as images, they are often vectorized for LSDA algorithm to find the intrinsic manifold structure. Such a matrix-to-vector transform may cause the loss of some structural information residing in original 2D images.
null Yantao Wei, null Hong Li, null Tian Xia   +2 more
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Sensitivity analysis with respect to a local perturbation of the material property

Asymptotic Analysis, 2006
In the present work, the notion of topological sensitivity is extended to the case of a local perturbation of the properties of the material constitutive of the domain. As a model example, we consider the problem −div(α ε A∇u ε )+β ε
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Sensitivity analysis of parallel applications to local and non-local interference

2007
The environment in which a parallel application is executed has high impact on the performance of the application due to interference caused by various factors in the execution environment. A detailed understanding of the sensitivity of the application to the parameters describing the execution environment can be of great help in (a) predicting a ...
Vaddadi P. Chandu, Karandeep Singh
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