István Vajk

Paper: Adaptive load-frequency control of the hungarian power system

The paper deals with the modelling of the power stations and the interconnected power systems for the design of the load-frequency controller of the Hungarian power system. It presents an adaptive regulator which uses the a priori known information and satisfies the multi-objective character of the control. The elaborated control strategy performs the following objectives: •-It eliminates the effect of the area load fluctuations to the tie-line power. •-It guarantees the scheduled value of the exported/imported energy. •-It reduces the commands sent to power stations. •-It satisfies the requirement with minimum cost. The paper shows the real-time experiments with the implemented adaptive regulator which is presently applied for the load-frequency control of the Hungarian power system.

Brief Identification of nonlinear errors-in-variables models

A new identification algorithm for nonlinear, but linear in parameters errors-in-variables models is presented using nonlinear, polynomial eigenvalue-eigenvector decompositions.

Identification and adaptive control of a glass furnace

The paper presents the modelling and control of a glass furnace. A portable process computer laboratory was applied. The computer was provided by a process control program and control tasks. Based on normal operating records experiments were designed and a model with three inputs and outputs of the furnace was elaborated. By means of identified models the glass level was successfully controlled by a self-tuning regulator.

A Self-tuning Extremal Controller for the Generalized Hammerstein Model

Abstract A self-tuning extremal controller is presented which provides to find the extremum and to maintain it with minimum variance using the generalized Hammerstein model as the approximate description of a nonlinear dynamic process. A simulation example is given to illustrate the work of the new regulator.

Identification methods in a unified framework

The paper derives a framework suitable to discuss the classical Koopmans-Levin (KL) and maximum likelihood (ML) algorithms to estimate parameters of errors-in-variables linear models in a unified way. Using the capability of the unified approach a new parameter estimation algorithm is presented offering flexibility to ensure acceptable variance in the estimated parameters. The developed algorithm is based on the application of Hankel matrices of variable size and can equally be considered as a generalized version of the KL method (GKL) or as a reduced version of the ML estimation. The methodology applied to derive the GKL algorithm is used to present a straightforward derivation of the subspace identification algorithm.

A novel adaptive control system for raw material blending

We discuss a new generic optimal controller structure for raw material blending in the cement industry. We focus on an important phase of the proportioning-burning-grinding operation triplet that essentially determines the cement quality; namely, the composition control of the raw mill system. We begin by describing the application of a generic optimal controller structure to this important phase, followed by a discussion of the control engineering background and the design of the realized controller. Finally, the control algorithm is discussed in a technology-independent manner.

Perturbed datasets methods for hypothesis testing and structure of corresponding confidence sets

Hypothesis testing methods that do not rely on exact distribution assumptions have been emerging lately. The method of sign-perturbed sums (SPS) is capable of characterizing confidence regions with exact confidence levels for linear regression and linear dynamical systems parameter estimation problems if the noise distribution is symmetric. This paper describes a general family of hypothesis testing methods that have an exact user chosen confidence level based on finite sample count and without relying on an assumed noise distribution. It is shown that the SPS method belongs to this family and we provide another hypothesis test for the case where the symmetry assumption is replaced with exchangeability. In the case of linear regression problems it is shown that the confidence regions are connected, bounded and possibly non-convex sets in both cases. To highlight the importance of understanding the structure of confidence regions corresponding to such hypothesis tests it is shown that confidence sets for linear dynamical systems parameter estimates generated using the SPS method can have non-connected parts, which have far reaching consequences.

Design and implementation of a video on-demand system

Recent technological advances made multimedia on-demand servers feasible. Two challenging tasks in such systems are satisfying the real-time requirement for continuous delivery of objects at specified bandwidths and efficiently servicing multiple clients simultaneously. Our project is aimed at prototype development of such a large scale server. This paper jointly addresses the issues of load balancing, responsiveness, streaming capacity and cost effectiveness of high-performance storage servers and delivery systems for data streaming applications such as video-on-demand or news-on-demand. We propose a relatively simple, flexible and robust video-server architecture.

Identification Methods in a Unified Framework

Abstract The paper derives a framework suitable to discuss the errors-in-variables (EIV) and the maximum likelihood (ML) estimation algorithms to estimate linear system parameters in a unified way. Using the capability of the unified approach a new parameter estimation algorithm is presented offering flexibility to ensure acceptable variance in the estimated parameters. The developed algorithm is based on the application of Hankel matrices of variable size and can be considered as an extended version of the EIV method.

Suffix Vector: A Space-Efficient Suffix Tree Representation

This paper introduces a new way of representing suffix trees. The basic idea behind the representation is that we are storing the nodes of the tree along with the string itself, thus edge labels can directly be read from the string. The new representation occupies less space than the best-known representation to date in case of English text and program files, though it requires slightly more space in case of DNA sequences. We also believe that our representation is clearer and thus implementing algorithms on it is easier. We also show that our representation is not only better in terms of space but it is also faster to retrieve information from the tree. We theoretically compare the running time of the matching statistics algorithm on both representations.