Összefoglaló. A tanulmányban a hallgatói vélemények kiértékelésének egy lehetséges módját mutatjuk be. A hallgatói vélemények tipikusan szubjektívek, ezért nehéz mérőszámokat rendelni hozzájuk. Így a hallgatói véleményeztetést páros összehasonlításokkal tettük meg, és az így kialakult adatok alapján Thurstone-módszerrel sorba rendeztük az egyes tantárgyakat és oktatókat, számszerűsítettük a köztük kialakuló különbségeket. Munkánkban a Pannon Egyetem Mérnöki Karán BSc képzésen oktatott alapvető fontosságú tárgyak kiértékelését végeztük el néhány szempont alapján, de a módszer alkalmazható más tárgyakra, illetve szempontokra is. Summary. In this paper, a method for evaluation of students’ opinions is presented. These opinions are subjective therefore it is difficult to characterize them by numbers/marks. We asked the opinions by comparing the objects in pairs and the data are evaluated by the Thurstone method. Applying this method, we could rank the objects (subjects or teachers) and we could also determine numerical values for presenting the differences between them. During this research, we have investigated the basic courses of the BSc studies of the Faculty of Engineering of the University of Pannonia by some view of points, but the method is suitable for evaluating other subjects and other questions as well.

This study introduces particle filtering (PF) for the tracking and fault diagnostics of complex process systems. In process systems, model equations are often nonlinear and environmental noise is non-Gaussian. We propose a method for state estimation and fault detection in a wastewater treatment system. The contributions of the paper are the following: (1) A method is suggested for sensor placement based on the state estimation performance; (2) based on the sensitivity analysis of the particle filter parameters, a tuning method is proposed; (3) a case study is presented to compare the performances of the classical PF and intelligent particle filtering (IPF) algorithms; (4) for fault diagnostics purposes, bias and impact sensor faults were examined; moreover, the efficiency of fault detection was evaluated. The results verify that particle filtering is applicable and highly efficient for tracking and fault diagnostics tasks in process systems.