Abstract In recent years, the evolution of computer-aided design technology has established a new method to improve digital model creations. The is called Parametric Modeling. this paper, 3D metallic models are printed using novel Modeling approach. goal approach use parametric features simulate and print objects Laser Wire Additive Manufacturing (LWAM) process. proposed includes pattern creati...

Parametric and feature-based design is a modern approach for product modeling to save time and money during the product development and manufacturing process, especially in the preliminary stages of design. Parametric modeling can be used to improve customer satisfaction by facilitating rapid product customization to achieve specific customer requirements. In this paper, we discuss a parametric...

A set of probabilistic predictions is well calibrated if the events that are predicted to occur with probability p do in fact occur about p fraction of the time. Well calibrated predictions are particularly important when machine learning models are used in decision analysis. This paper presents two new non-parametric methods for calibrating outputs of binary classification models: a method bas...

Lorenz curves and second-order dominance criteria are known to be sensitive to data contamination in the right tail of the distribution. We propose two ways of dealing with the problem: (1) Estimate Lorenz curves using parametric models for income distributions, and (2) Combine empirical estimation with a parametric (robust) estimation of the upper tail of the distribution using the Pareto mode...

Many reduced dimension STAP algorithms have been developed for airborne radar applications which rely on a stationary Doppler component of the interference in order to maintain acceptableperformance. Two cases where this assumption is violated are when the gmund clutter contains intrinsic clutter motion (ICM) and when hot clutter is present. In addition to the non-stationa y Doppler component, ...

Deep dictionary learning seeks multiple dictionaries at different image scales to capture complementary coherent characteristics. We propose a method for learning a hierarchy of synthesis dictionaries with an image classification goal. The dictionaries and classification parameters are trained by a classification objective, and the sparse features are extracted by reducing a reconstruction loss...

In this paper, we use the parametric form of fuzzy numbers, and aniterative approach for obtaining approximate solution for a classof fuzzy nonlinear Fredholm integral equations of the second kindis proposed. This paper presents a method based on Newton-Cotesmethods with positive coefficient. Then we obtain approximatesolution of the fuzzy nonlinear integral equations by an iterativeapproach.