The research results are related to problems of control of biological processes in a wastewater treatment plant. The aim was to show whether dynamic control of wastewater treatment processes using on-line nitrogen sensors and more advanced control algorithms can contribute to the more efficient plant operation. In the paper it is shown that use of new sensors it contributes to better effluent quality and significantly reduces the operating costs, while the use of more advanced model-based control is reasonable in the cases of highly loaded influent and strict effluent requirements.
COBISS.SI-ID: 20679463
The topic addressed in this research work was the problem of the detailed quality end-test of vacuum cleaner motors. The paper focuses on the sound analysis module and provides two main contributions. First, an analysis of sound sources is performed and a set of appropriate features is suggested. Second, efficient signal processing algorithms are developed in order to detect and localise bearing faults, defects in fan impeller, improper brush–commutator contacts and rubbing of rotating surfaces. A thorough laboratory study shows that the designed diagnostic modules provide accurate diagnosis.
COBISS.SI-ID: 18802471
This achievement addresses an innovative self-tuning nonlinear controller, appropriate for the implementation on a programmable logic controller. The process under control is represented by a set of low-order local linear models whose parameters are identified using an online learning procedure. The controller monitors and evaluates the control performance of the closed-loop system. It was implemented on a programmable logic controller and tested on a field test application for control of pressure on a hydraulic valve.
COBISS.SI-ID: 19833639
The identification of Hammerstein model is very demanding and most often requires frequency rich excitation signals that could be rarely used in practice. However, if a special form of the excitation signal is used, the nonlinear static function could be represented by a piecewise-linear function. In this way, the obtained model is linear in the parameters, and could be used for the approximation of various discontinuous nonlinear functions. The identification method adapted to this model is derived from recursive least squares method.
COBISS.SI-ID: 18745127
This work verifies the hypothesis that, associated with the state of anaesthesia, characteristic changes exist in both cardio-respiratory and cerebral oscillator parameters and couplings, perhaps varying with the depth of anaesthesia. We applied non-linear dynamics and information theory to seek evidence of causal relationships between the cardiac, respiratory and slow delta-oscillations. The results of this research are important as they show that such an approach can be used to identify different stages of anaesthesia, which is still not solved in practice.
COBISS.SI-ID: 22373081