We argue that any attempt to classify dynamical properties from nonlinear finite time-series data requires a mechanistic model fitting the data better than piecewise linear models according to standard model selection criteria. Such a procedure seems necessary but still not sufficient.

Determining parameters which describe the performance of a solid oxide fuel cell requires the solution of an inverse problem. Two formulations have been presented in the literature; a convolutional approach or a direct quadrature approach. A complete study and analysis of the direct quadrature method, which leads to two systems for the unknown signal given measured complex data, known as the di...

We consider the problem of locating a user’s position from a set of noisy pseudoranges to a group of satellites. We consider both the nonlinear least squares formulation of the problem, which is nonconvex and nonsmooth, and the nonlinear squared least squares variant, in which the objective function is smooth, but still nonconvex. We show that the squared least squares problem can be reformulat...

In this paper, we develop methods for outlier removal and noise reduction based on weighted local linear smoothing for a set of noisy points sampled from a nonlinear manifold. The methods can be used by manifold learning methods such as Isomap, LLE and LTSA as a preprocessing procedure so as to obtain a more accurate reconstruction of the underlying nonlinear manifolds. Weighted principal compo...

Long term prediction such as multi-step time series prediction is a challenging prognostics problem. This paper proposes an improved AR time series model called ND-AR model (Nonlinear Degradation AutoRegression) for Remaining Useful Life (RUL) estimation of lithium-ion batteries. The nonlinear degradation feature of the lithiumion battery capacity degradation is analyzed and then the non-linear...

The regression model for a two-segments titration curve with a break-point at the end-point is analyzed. Both linear and nonlinear shapes of the titration curve segments are treated. An effective and simple method discriminates which of two segments is linear or curved. The point and interval estimates of the end-point are calculated by the nonlinear least squares of curve fitting technique. Th...

Linear approximations of nonlinear systems can be obtained by fitting a linear model to data from a nonlinear system, for example, using the prediction-error method. In many situations, the type of linear model and the model orders are selected after estimating several models and evaluating them using various validation techniques. Two commonly used validation methods for linear models are spec...

This paper addresses the design of a model-based 3D object pose estimation algorithm, which is one of the major techniques to develop a robust robotic vision system using a monocular camera. The proposed system first extracts line features of a captured image by using edge detection and Hough transform techniques. Given a CAD model of the object-of-interest, the 6-DOF pose of the object can the...

– Prediction of financial time series using artificial neural networks has been the subject of many publications, even if the predictability of financial series remains a subject of scientific debate in the financial literature. Facing this difficulty, analysts often consider a large number of exogenous indicators, which makes the fitting of neural networks extremely difficult. In this paper, w...

GRAPPA linearly combines the undersampled k-space signals to estimate the missing k-space signals where the coefficients are obtained by fitting to some auto-calibration signals (ACS) sampled with Nyquist rate based on the shift-invariant property. At high acceleration factors, GRAPPA reconstruction can suffer from a high level of noise even with a large number of auto-calibration signals. In t...