Sentence examples for penalization in from inspiring English sources

Penalization approaches are adopted in both scales to ensure clear topologies, i.e. SIMP (Solid Isotropic Material with Penalization) in micro-scale and PAMP (Porous Anisotropic Material with Penalization) in macro-scale.

To compare the efficacy and sensory outcome of pharmacologic and optical penalization in the treatment of moderate to mild amblyopia.

In [16], Sato proposes a different kind of penalization in order to show the existence of multi-peak nodal solutions to a Schrödinger equation with a vanishing potential.

First, to reduce spurious modes on very distorted meshes, the addition of dissipative terms as penalization in the numerical scheme is studied and compared on examples.

The PmTC with flat absorber shows the worst performance showing more than 25% penalization in terms of maximum drag and moment values in comparison with the other three collectors.

In our experiments, we show that in most tests DPM outperforms both Pregel and Fork-Join in terms of recommendation time, with a minor penalization in network usage in some scenarios.

Moreover, the regularization problem ((QP_{1}^{lambda})) always leads to a biased estimation by shrinking all the components of the vector toward zero simultaneously, and sometimes results in over-penalization in the regularization model ((QP_{1}^{lambda})) as the (ell_{1} -norm in linear compressed sensing.

However, it is common to have penalizations in the efficiency as a result of including security features.

And it is found that the existing qp-relaxation stress interpolation scheme to resolve the singularity issue in TO just shows the local optima issue in reinforcement TO design with different penalization factors in Solid Isotropic Material with Penalization (SIMP).

Inspired by the work in [1] and the alternating proximal penalization algorithm in [28], we present a two-block alternating proximal penalization algorithm for this problem in this paper.

Work is currently underway to implement the penalization approach in a wavelet basis.