Attila Magyar

This paper introduces a modeling and a control approach for Lotka-Volterra systems that are interconnected through population size-dependent migration flows. First, a control-oriented model is proposed for networks of Lotka-Volterra systems. Based on this model, a decentralized control method is introduced which assures that the states of each Lotka-Volterra system in the network can be driven into a prescribed setpoint regardless of migration. The results have been generalized to quasi-polynomial systems, and networks of Lotka-Volterra systems having interconnections with distributed delay. Simulation experiments are also presented in the paper to show the implementability of the theoretical results.

The imbalance between supply and demand is a critical factor in the operation of the power system, as it leads to a change in the system frequency. Therefore, it is essential to be able to predict its value from historical, measured and forecast data. Based on the assumption that system imbalance is correlated with measured values of system variables as well as predictions of exogenous variables, this work proposes a multi-step version of the autoregressive distributed lag model for the short-term forecast of system imbalance. The proposed forecasting model has been compared with a long short-term memory network-based procedure as well as with an extratree regression model using real data. The results show that the proposed multi-step autoregressive forecasting model outperforms the others in all three evaluation metrics. Since, in many cases, it is sufficient to specify the sign of the imbalance, this paper introduces the concept of sign accuracy as a function of the predicted imbalance and evaluates it for the investigated solutions.

Fast charging is an attractive way of charging batteries; however, it may result in an undesired degradation of battery performance and lifetime because of the increase in battery temperature during fast charge. In this paper we propose a simple optimized fuzzy controller that is responsible for the regulation of the charging current of a battery charging system. The basis of the method is a simple dynamic equivalent circuit type model of the Li-ion battery that takes into account the temperature dependency of the model parameters, too. Since there is a tradeoff between the charging speed determined by the value of the charging current and the increase in temperature of the battery, the proposed fuzzy controller is applied for controlling the charging current as a function of the temperature. The controller is optimized using a genetic algorithm to ensure a jointly minimal charging time and battery temperature increase during the charging. The control method is adaptive in the sense that we use parameter estimation of an underlying dynamic battery model to adapt to the actual status of the battery after each charging. The performance and properties of the proposed optimized charging control system are evaluated using a simulation case study. The evaluation was performed in terms of the charge profiles, using the fitness values of the individuals, and in terms of the charge performance on the actual battery. The proposed method has been evaluated compared to the conventional contant current-constant voltage methods. We have found that the proposed GA-fuzzy controller gives a slightly better performance in charging time while significantly decreasing the temperature increase.

Balancing in a distributed generation network is an increasingly difficult task because of the increasing number of residential prosumers on the power network. This paper proposes a framework for the estimation, as well as the prediction of the power flexibility of residential prosumers. In order to quantify the residential buildings’ demand flexibility, a thermoelectric simulation model of a typical residential house was developed based on first engineering principles. Based on the calculated flexibility values, a simple prediction method was used to give a short-term forecast of the prosumer flexibility. The results were validated by simulation experiments incorporating real data for four different scenarios.

In this paper a new, easy-to-use algorithm is presented to support telerehabilitation of people with movement disabilities. This algorithm can adapt to the needs of the patients as it combines the cognitive property of intelligent decision-making systems with the human-computer interaction-based movement therapy. This algorithm is built upon the earlier work of the authors and it uses six various mean techniques to classify gesture descriptors. The preliminary results and a plan of evaluating the algorithm are presented in this paper.

In this paper, a solution is presented to support both existing and future movement rehabilitation applications. The presented method combines the advantages of human computer interaction based movement therapy with the cognitive property of intelligent decision making systems. With this solution, therapy could be fully adapted to the needs of the patients and conditions while maintaining a sense of success in them, thereby motivating them. In our modern digital age, the development of HCI interfaces walk together with the growing of user needs for them. The available technologies have limitations, which can reduce the effectiveness of modern input devices, such as the Kinect sensor or any other similar sensors. In this article, multiple newly developed and modified methods are introduced with the aim to overcome these limitations. This methods can fully adapt the movement pattern recognition to the users’ skills. The main are to apply this method, is movement rehabilitation, where the supervisor, therapist can personalize the rehabilitation exercises due to the Distance Vector Based Gesture Recognition (DVGR), Reference Distance Based Synchronous/Asynchronous Movement Recognition (RDSMR/RDAMR) and the Real-Time Adaptive Movement Pattern Classification (RAMPC) methods. Keywords: cognitive infocommunication, rehabilitation exercises, motivation, Kinect sensor, adaptive interface controller, real-time gesture recognition and classification

Parameter estimation of electrical vehicle batteries in the presence of temperature effect is addressed in this work. A simple parametric temperature dependent battery model is used for this purpose where the temperature dependence is described by static relationships. A two-step method is used that includes a parameter estimation step of the key parameters at different temperatures followed by a static optimization step that determines the temperature coefficients of the corresponding parameters. It was found that the temperature dependent parameter characteristics can be reliably estimated from charging profiles only. The proposed method can be used as a computationally effective way of determining the key battery parameters at a given temperature from their actual estimated values and from their previously determined static temperature dependence. The proposed parameter estimation method was verified by simulation experiments on a more complex battery model that also describes the detailed dynamic thermal behavior of the battery.

This paper is devoted to investigate electrochemical storage systems for example car batteries. The aim of the authors is to obtain temperature and time dependent parameters by measurement. Based on the collected voltage, current and temperature date authors build a generally useful battery model and evaluate lifetime and state parameters of different systems. This method is suitable to investigate every two pole storage system for example batteries, capacitors etc. Examinations were performed in a climate chamber when the environmental temperature and the temperature change in time are controlled by a computer.