Reliability estimation of regressional predictions on data streams

With today's technology it is easy to collect data continuously. Still, how to extract knowledge from potentially infinite data streams remains an open problem. Because of specific constraints, stream processing methods have to be well designed, space-efficient, computationally simple and fast. Typically, data analysis is done on a fixed history of the data stream defined by a sliding window. We usually define the quality of predictions by their average accuracy. However, when dealing with real-time data it can be also important to know the reliability of the models’ output values. In this thesis we deal with online reliability estimation of individual predictions on data streams. We consider different interval reliability estimators based on maximum likelihood, bootstrap and local neighborhood approach for working on continuous dynamic data. We implement these methods on different regression models and test them on several real and artificial regression problems with various sizes of the sliding window. Performance of the interval estimates are evaluated using the estimates of prediction interval coverage probability, the relative mean prediction interval and the combined statistic. We compare the execution times of learning algorithms with and without the reliability estimates as well as their prediction accuracy when given the same time constraint. We also analyze results visually

High performance chitosan/nanocellulose-based composite membrane for alkaline direct ethanol fuel cells

Polysaccharide anion exchange membranes (AEMs) containing chitosan (CS), cellulose nanofibrils (CNFs) and CNFs quaternized with poly(diallyldimethylammonium chloride) (CNF(P)s) were developed for use in alkaline direct ethanol fuel cells (ADEFCs). The resulting composite membranes prepared by the solvent casting process based on an experimental design were comprehensively assessed for morphology, KOH uptake, swelling ratio, EtOH permeability, mechanical properties, ionic conductivity, and cell performance. The fabricated CS-based composite membranes with CNF(P) fillers were superior to the commercial Fumatech FAA-3-50 membrane in terms of Young\u27s modulus and tensile strength (69 % and 85 % higher, respectively), ion exchange capacity (169 % higher), and ionic conductivity (228 % higher). Single fuel cell tests have shown excellent performance of the CS-based membranes with CNF and CNF(P) fillers, as they exhibited up to 86 % improvement in power density at 80 °C compared to the commercial membrane (65.1 mW/cm$^2$ vs. 35.1 mW/cm$^2$) and higher maximum power density at all test conditions