Dynamic graphs are defined in a linear space as a one-parameter group of transformations of the graph space into itself.
This flight-path angle profile is designed with required altitude, flight-path angle and range-to-go, and then optimized as a one-parameter search problem to meet the velocity constraint.
We then continue this solution as a one-parameter family of orbit segments subject to the boundary conditions textstylebegin{cases} u(0) in L_{2}^{a}, u(1) in widehat{varSigma }_{1}.
The IWWA framework can be applied as a one-parameter approach (IWWA1) in which the MRP for the wind and wave conditions is assumed to be the same or a two-parameter approach (IWWA2) in which the MRPs associated with wind and wave conditions are related to a joint probability density function characterizing the wind and wave conditions at the site.
In this way, we can represent a suitable submanifold of (S^{s}_{varepsilon }) as a one-parameter family of orbit segments that enter along (W^{s}(S^{s}_{varepsilon })), and then stay extremely close to (S^{s}_{varepsilon }) before exiting along (W^{u}(S^{s}_{varepsilon })).
We then continue the resulting extended orbit segment as a one-parameter family of orbit segments subject to the boundary conditions textstylebegin{cases} u(0) in varSigma ^{s}, u(1) in L^{s}, end{cases} (23) where (varSigma ^{s}) is a codimension-one submanifold far away from S and transverse to the stable manifold of the trajectory (c^{s}_{varepsilon }).
The Rasch model [ 14], sometimes referred to as a one-parameter logistic model under IRT, provides a mathematical framework against which test developers can compare their data.
The standard CRM uses a simple parametric model, such as a one-parameter power model or two-parameter logistic regression mode, to characterize the relationship between dose level and the probability of experiencing a dose limiting toxicity (DLT).
An image is represented as a one-parameter family of smoothed images, the scale-space representation, parameterized by the size of the Gaussian smoothing kernel (Lindeberg, 1994): Given a subtomogram, f, a Gaussian smoothing can be expressed as the convolution between f and : In the smoothed subtomogram, all features with a size smaller than σ are filtered out, whereas others are preserved.
