Lorant Kovacs - Academia.edu (original) (raw)

Papers by Lorant Kovacs

arXiv (Cornell University), Jul 29, 2016

The paper is concerned with developing novel multiuser detection algorithms. The main stress is o... more The paper is concerned with developing novel multiuser detection algorithms. The main stress is on blind decorrelation of weakly stationary processes which can successfully combat multiuser interference (MUI) and intersymbol interference (ISI). The detector architecture includes an adaptive channel identifier (capable of blind decorrelation), followed by a stochastic Hopfield net which performs the task of multiuser detection. The key contribution to mobile communication lies in two factors: (i) blind decorrelation which can identify the channel even in the case of fading; (ii) the use of the stochastic Hopfield net which can guarantee good performance due to its ability to reach the global minimum of the associated quadratic form. The proposed detector structure is proven to be superior to the RAKE or Viterbi receivers. The results are demonstrated by simulations as well.

Nowadays air traffic is one of the most annoying environmental noise sources. A lot of people liv... more Nowadays air traffic is one of the most annoying environmental noise sources. A lot of people living near airports are disturbed by aircraft noise. The prediction of the effect on disturbance of new air traffic related facilities or procedures are very complex and uncertain. On the other hand, listening tests and/or field studies related to aircraft noise are expensive and time consuming as well, especially in the case of large number of subjects. This paper aims to introduce a novel prediction tool, which can estimate the disturbance of residents living near to airports caused by air traffic. The prediction tool to be presented consists of a multi-level pre-processor and a Radial Basis Function Neural Network approximator. The pre-processor itself is based on statistical pre-filtering and hierarchical clustering techniques. The new tool has been developed based on the data of a recent field study around a large European airport. Its performance has been evaluated by cross validatio...

IEEE VTS 53rd Vehicular Technology Conference, Spring 2001. Proceedings (Cat. No.01CH37202), 2001

In this paper some blind adaptive methods are introduced for multiuser detection (MUD). The detec... more In this paper some blind adaptive methods are introduced for multiuser detection (MUD). The detector architecture contains a channel identifier followed by a stochastic Hopfield (1985) net. Blind channel identification is proposed to be carried out by either the Kohonen (see Self-Organizing Maps, Springer, 2000) algorithm or by a novel adaptive decorrelation technique. Based on the estimated channel parameters the

International Symposium on VIPromCom Video/Image Processing and Multimedia Communications

EURASIP Journal on Advances in Signal Processing, Jul 26, 2010

2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)

Power consuming users and buildings with different power consumption patterns may be treated with... more Power consuming users and buildings with different power consumption patterns may be treated with different conditions and can be taken into consideration with different parameters during capacity planning and distribution. Thus the automated, unsupervised categorization of power consumers is a very important task of smart power transmission systems. Knowing the behavioral categories of power consumers better models can be created which can be used for better behavior forecast which is an important task for load balancing. One of the existing best solutions for consumer classification is the consumption forecast based scheme which applies nonlinear forecast techniques to determine the class assignment for new consumers. In this paper, we present new results on the classification of consumers using recurrent neural networks in the forecast based classification framework. The results are compared with existing classification methods using real, measured power consumption data. We demonstrate that consumer classification performed by recurrent neural networks can outperform existing methods as in several cases the correct class assignment rate is near to 100%.

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2017

Classification of different power consumers is a very important task in smart power transmission ... more Classification of different power consumers is a very important task in smart power transmission grids as the different type of consumers may be treated with different conditions. Furthermore, the power suppliers can use the category information of consumers to forecast better their behavior which is a relevant task for load balancing. In this paper, we present performance results on the classification of consumers using deep learning based classification scheme in smart grid systems. The results are compared with existing classification methods using real, measured power consumption data. We demonstrate that consumer classification performed by neural networks can outperform existing, traditional tools as in several cases the correct class assignment rate is greater than 0.97.

2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2016

The increasing presence of renewable energy sources and the novel consumption types will obviousl... more The increasing presence of renewable energy sources and the novel consumption types will obviously cause the increase of the fluctuation of electrical power in households. In order to better manage the electrical power consumption and production the integration of information and communication technologies and power grid is necessary, which is obviously a recent research topic. The availability of large amount of measurement data provided by household's smart meter(s) offers new possibilities in analyzing the internal structure of power consumption data series. One of them is discovering typical power consumption patterns with their duration-distributions. Our recent achievements in this direction are presented in the paper, namely a novel on-line, Jensen-Shannon divergence-based adaptive and automatic segmentation algorithm, the segment descriptors and the results of clustering using Kohonen's self-organizing map.

2018 19th International Carpathian Control Conference (ICCC), 2018

The model reference adaptive control (MRAC) system built with radial basis function (RBF) neural ... more The model reference adaptive control (MRAC) system built with radial basis function (RBF) neural network proved to be useful in controlling nonlinear plants. In this paper a novel method is proposed for designing RBF-MRAC neurocontrollers. The resulted RBF network is optimal in minimal norm-product sense, and the method can be used in case of plants' known nonlinear function. The robustness of the resulted controller was investigated with simulations concerning both parameter tolerances of the nonlinearity and abrupt disturbances. In case of unknown nonlinearity a heuristic method is given, which also resulted in a robust solution. The simulation results of the neurocontroller are also presented in the paper in case of several nonlinearities.

A kozelmultban kifejlesztesre kerult egy a Smart Gridbe illeszkedő fogyasztasengedelyezesi eljara... more A kozelmultban kifejlesztesre kerult egy a Smart Gridbe illeszkedő fogyasztasengedelyezesi eljaras, amely eljaras a fogyasztoi oldal befolyasolasanak problemajat (Demand Side Management) az egyes fogyasztok statisztikai leiroi alapjan oldja meg. A fogyasztoi oldal befolyasolasaval a villamosenergia kereslet a megtermelt mennyiseghez igazithato, lehetőve teve a megujulo energiatermelők nagyobb mertekű kihasznalasat. Az uj eljaras erzekeny a statisztikai leirok pontossagara, amely leirok nemstacionariusak, sőt tovabbi parameterektől is fuggenek, peldaul jelenlet, hőmerseklet, stb. Cikkunkben megvizsgaljuk, hogy radialis bazisfuggvenyes neuralis halozattal milyen hatekonysaggal becsulhetők meg idővarians statisztikai leirok. A modszer hatekonysagat nyilvanosan elerhető villamos fogyasztasi adatbazis felhasznalasaval ertekeltuk numerikusan. Estimation Of Statistical Parameters Of Electric Appliances Based On Time Of Day Information Recently the Consumption Admission Control algorithm ha...

In this paper we investigate the applicability of the Chernoff inequality in finding an upper bou... more In this paper we investigate the applicability of the Chernoff inequality in finding an upper bound on the probability of the lower tail of the aggregate load. The importance of Demand Side Management (DSM) programs in power networks has increased recently, especially because of the new challenges like intensive use of renewable energy sources (wind, photovoltaic) and the expected high penetration of Electric Vehicles (EV). We show that Chernoff bound has the potential to be incorporated in DSM algorithms to integrate energy storage (e.g. batteries) elements into the power grid and facilitate load shifting.

This paper proposes a simple fuzzy control design for a hybrid electric vehicle with a series con... more This paper proposes a simple fuzzy control design for a hybrid electric vehicle with a series connected powertrain system. In course of the research a complex system model was used which consists of three main components, i.e. the driver modeling subsystem, the control subsystem, and the subsystem modeling the hybrid vehicle. The primary objective was to develop a controller that ensures a low level dissipation in case of a predefined driving cycle by controlling the electric motor, the internal combustion engine, and the generator. In order to minimize fuel consumption and to take into consideration some other requirements a complex cost function was defined as objective function for the tuning process. A hill climbing type optimization approach was used for the tuning of the system. Keywords: fuzzy control, hybrid vehicle

Kybernetika, 2018

Institute of Mathematics of the Czech Academy of Sciences provides access to digitized documents ... more Institute of Mathematics of the Czech Academy of Sciences provides access to digitized documents strictly for personal use. Each copy of any part of this document must contain these Terms of use.

Annals of Operations Research, 2019

We propose a randomized gradient method for handling a convex function whose gradient computation... more We propose a randomized gradient method for handling a convex function whose gradient computation is demanding. The method bears a resemblance to the stochastic approximation family. But in contrast to stochastic approximation, the present method builds a model problem. The approach is adapted to probability maximization and probabilistic constrained problems. We discuss simulation procedures for gradient estimation.

Tehnicki vjesnik - Technical Gazette, 2017

New generation electricity network called Smart Grid is a recently conceived vision for a cleaner... more New generation electricity network called Smart Grid is a recently conceived vision for a cleaner, more efficient and cheaper electricity system. One of the major challenges of electricity network is that generation and consumption should be balanced at every moment. This paper introduces a new concept for controlling the demand side by the means of automatically enabling/disabling electric appliances to make sure that the demand is in match with the available supplies, based on the statistical characterization of the need. In our new approach instead of using hard limits we estimate the tail probability of the demand distribution and control system by using the principles and the results of statistical resource management.

Tehnicki vjesnik - Technical Gazette, 2016

Original scientific paper In the present paper the constant parameters of the (car following) Int... more Original scientific paper In the present paper the constant parameters of the (car following) Intelligent Driver Model are calibrated so as to obtain correct flow capacities of a signalized intersection. The calibration is based on measured time-headway parameters of the consecutive vehicles. The measurement data were taken from the work published by Dey et al. The parameters were calibrated partly by numeric calculations and partly by computer simulation, which was developed by the authors of this paper. The simulated environment was a single lane road terminated by a signalized intersection with a queue of stopped cars. As the result of the calibration, the model produced the measured saturated time-headway constant in the stationary flow phase and gave a good approximation of the consecutive time-headways in the initial transient phase. It was found that the calibrated constants of the Intelligent Driver Model considerably differ from the values proposed for modelling traffic on motorways: in a signalized intersection situation higher maximal acceleration and lower safe time gap parameters should be applied. More precisely, the differences are about 30 % in each case.

2016 International Conference on Smart Systems and Technologies (SST), 2016

In this paper we prove the convexity of the Chernoff bound in the context of estimating the overc... more In this paper we prove the convexity of the Chernoff bound in the context of estimating the overconsumption in a bottom-up consumption modelling framework in smart grid. We also introduce three application areas for the Chernoff bound in the context of power systems, more specifically the reliability assessment, consumption admission control and capacity sizing. The proof of convexity shows the method to be a computationally feasible tool for these three application areas.