Predrag Pucar

Estimation of residence time in continuous flow systems with dynamics

A method for estimation of residence time in continuous flow systems with varying dynamics is presented. By resampling, i.e., choosing time instants different from the given sampling instants, and interpolation between measured data points, we obtain a continuous flow system with constant residence time expressed in the new resampled time vector. We assume the flow patterns in the systems are invariant. The new data set is then used for identification of parameters in a chosen model structure. From the identified model, the residence time is readily calculated and a procedure for that is briefly described. The presented method is readily extended to enable use in recursive identification. In that case, however, as an improvement of tracking ability of an ordinary recursive routine.

Parametrization and Conditioning of Hinging Hyperplane Models

Recently a new model class has emerged in the field of non-linear black-box modeling; the hinging hyperplane models. The hinging hyperplane model is closely related to the well known neural net mod ...

Three techniques for state order estimation of hidden Markov models

Three examples of techniques that can be used for state order estimation of hidden Markov models are given. The methods are also exemplified using real laser range data, and the computational burden of the three methods is discussed. Two techniques, maximum description length and maximum a posteriori estimate, are shown to be very similar under certain circumstances. The third technique, predictive least squares, is novel in this context.

Identification Aspects of Inter-Sample Input Behavior

Abstract In this contribution aspects of inter-sample input signal behavior are examined. The starting point is that parametric identification always is performed on basis of discrete-time data. This is valid for identification of discrete-time models as well as continuous-time models. The usual assumptions on the input signal are; i) it is band-limited, ii) it is piecewise constant or iii) it is piecewise linear. One point made in this paper is that if a discrete-time model is used, the best possible (in the model structure) adjustment to data is made. This is independent of the assumption on the input signal. However, a transformation of the obtained discrete model to a continuous one is not possible without additional assumptions on the input signal. The other point made is that the frequency functions of the discrete models very well coincides with the frequency functions of the discretized continuous time models and the continuous time transfer function fitted in the frequency domain.

Segmentation of Laser Range Images with Respect to Range and Variance

Segmentation is a first step towards successful tracking and object recognition in 2-D pictures. Mostly the pictures are segmented with respect to quantities as range, intensity, etc. Here a method is presented for segmentation of 2-D laser range pictures with respect to both range and variance simultaneously. This is very useful since man-made objects differ from the background in the terrain by their smoothness. The approach is based on modeling horizontal scans of the terrain as piecewise constant functions. Since the environment has a complicated and irregular structure we use multiple models for modeling different segments in the laser range image. The switching between different models, i.e., ranges belonging to different segments in a horizontal scan, are modeled by a hidden Markov model. The method is of relatively low computational complexity and the maximal complexity can be controlled by the user. Real data is used for illustration of the method.