Sentence examples for characterization problems from inspiring English sources

With this new kernel, we analyzed microstructure evolution with burn-off and surface discrimination, two important carbon characterization problems.

Artificial neural networks (ANN) have recently been introduced to solve reservoir characterization problems dealing with the complex underlying relationships inherent in well log data.

The objective of this study is to provide a different formulation to design composition at certain ranges and to verify the robustness of GP for the formulation of such characterization problems.

Based on the mapping, a robust framework is developed that streamlines the formulation of likelihood functions for various soil and rock properties when estimated using different field or laboratory tests, leading to a streamlined process for applications of the proposed Bayesian framework to different site characterization problems.

First, the Article demonstrates the widespread presence of characterization problems within constitutional law, highlighting both the broad range of contexts in which these problems arise and the limited amount of attention they have thus far generated.

Characterization problems for discrete distributions using conditional distributions and their expectations have been investigated by several authors; see, e.g., Dahiya and Korwar (1977), Ruiz and Navarro (1995), and Nguyen et al. (1996).

In this article, we consider the characterization problem in design theory.

The proposed technique was finally applied to a realistic characterization problem, and its practicality has been demonstrated.

The characterization of good or sound inductions might be called the characterization problem: What distinguishes good from bad inductions?

Due to the limitation of computing power and quest for affordable solutions, the reservoir characterization problem has been assumed to be linear contrary to the reality.

However, such unifying approach would be quite unpractical because of the different nature of the group characterization problem in different dimensions (clustering for geo-temporal, classification for socio-topical) and because of the difficult interpretation of a model that blends together such diverse types of measures.