This paper introduces the uses of Robust Dynamic State Estimation (RDSE) with Phasor Measurement Unit (PMU), The M-Estimators Quadratic Linear (QL) and Square Root (SR) Estimators have been used. For the solution of the M-estimators problem, Iteratively Re-weighted Least Squares Estimation (IRLS) method is applied. In this work, we used the Decoupled Current Measurement (DCM) method to include ...

In this paper, we present a novel way to design biorthogonal and paraunitary linear phase(LPPUFB) lter banks. The square error of the perfect reconstruction condition is expressed in quadratic form of lter coe cients and the cost function is minimized by solving linear equation iteratively without nonlinear optimization. With some modi cations, the method can be extended to the design of paraun...

This paper addresses the problem of classifying minerals common in siliciclastic and carbonate rocks. Twelve chemical elements are mapped from thin sections by energy dispersive spectroscopy in a scanning electron microscope (SEM). Extensions to traditional multivariate statistical methods are applied to perform the classification. First, training and validation sets are grown from one or a few...

In this work, a Genetic Algorithm boosted Least Square Support Vector Machine model by a set of linear equations instead of a quadratic program, which is improved version of Support Vector Machine model, was used for estimation of 98 pure compounds second virial coefficient. Compounds were classified to the different groups. Finest parameters were obtained by Genetic Algorithm method ...

In this paper we characterise involutions that become metabolic over a quadratic field extension attained by adjoining a square root.

In this paper, we consider the problem of minimizing a continuously differentiable function on Stiefel manifold. To solve problem, develop geodesic-free proximal point algorithm equipped with Euclidean distance that does not require use Riemannian metric. The proposed method can be regarded as an iterative fixed-point repeatedly applies operator to initial point. addition, establish global conv...

When solving a quadratic program (QP), one can improve the numerical stability of any QP solver by performing proximal-point outer iterations, resulting in sequence better conditioned QPs. In this letter we present method which, for given multi-parametric (mpQP) and polyhedral set parameters, determines which sequences QPs will have to be solved when using iterations. By knowing sequence, bound...

In this paper, we propose a stochastic proximal gradient method to train ternary weight neural networks (TNN). The proposed method features weight ternarization via an exact formula of proximal operator. Our experiments show that our trained TNN are able to preserve the state-of-the-art performance on MNIST and CIFAR-10 benchmark datesets.

In this paper we consider the existence of the maximal and mean square stabilizing solutions for a set of generalized coupled algebraic Riccati equations (GCARE for short) associated to the infinite-horizon stochastic quadratic optimal control problem of discrete-time Markov jump with multiplicative noise linear systems. The weighting matrices of the state and control for the quadratic part are...

A general method to combine several estimators of the same quantity is investigated. In the spirit of model and forecast averaging, the final estimator is computed as a weighted average of the initial ones, where the weights are constrained to sum to one. In this framework, the optimal weights, minimizing the quadratic loss, are entirely determined by the mean square error matrix of the vector ...