@phdthesis{Pham2011,
  author    = {Pham, Phu Tinh},
  title     = {Upper bound limit and shakedown analysis of elastic-plastic bounded linearly kinematic hardening structures},
  publisher = {RWTH Aachen University},
  address   = {Aachen},
  pages     = {140 S.},
  year      = {2011},
  language  = {en}
}
@article{NguyenXuanRabczukNguyenThoietal.2011,
  author    = {Nguyen-Xuan, H. and Rabczuk, T. and Nguyen-Thoi, T. and Tran, Thanh Ngoc and Nguyen-Thanh, N.},
  title     = {Computation of limit and shakedown loads using a node-based smoothed finite element method},
  series = {International Journal for Numerical Methods in Engineering},
  volume    = {90},
  journal   = {International Journal for Numerical Methods in Engineering},
  number    = {3},
  publisher = {Wiley},
  address   = {Weinheim},
  issn      = {1097-0207},
  doi       = {10.1002/nme.3317},
  pages     = {287 -- 310},
  year      = {2011},
  abstract  = {This paper presents a novel numerical procedure for computing limit and shakedown loads of structures using a node-based smoothed FEM in combination with a primal-dual algorithm. An associated primal-dual form based on the von Mises yield criterion is adopted. The primal-dual algorithm together with a Newton-like iteration are then used to solve this associated primal-dual form to determine simultaneously both approximate upper and quasi-lower bounds of the plastic collapse limit and the shakedown limit. The present formulation uses only linear approximations and its implementation into finite element programs is quite simple. Several numerical examples are given to show the reliability, accuracy, and generality of the present formulation compared with other available methods.},
  language  = {en}
}
@article{BialonskiWendlerLehnertz2011,
  author    = {Bialonski, Stephan and Wendler, Martin and Lehnertz, Klaus},
  title     = {Unraveling spurious properties of interaction networks with tailored random networks},
  series = {Plos one},
  volume    = {6},
  journal   = {Plos one},
  number    = {8},
  publisher = {Plos},
  address   = {San Francisco},
  doi       = {10.1371/journal.pone.0022826},
  pages     = {e22826},
  year      = {2011},
  abstract  = {We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erd{\"o}s-R{\´e}nyi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures - known for their complex spatial and temporal dynamics - we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis.},
  language  = {en}
}