Helsinki University of Technology

About: Helsinki University of Technology is a based out in . It is known for research contribution in the topics: Artificial neural network & Finite element method. The organization has 8962 authors who have published 20136 publications receiving 723787 citations. The organization is also known as: TKK & Teknillinen korkeakoulu.

Decision Support by Interval SMART/SWING—Incorporating Imprecision in the SMART and SWING Methods

Abstract: Interval judgments are a way of handling preferential and informational imprecision in multicriteria decision analysis (MCDA). In this article, we study the use of intervals in the simple multiattribute rating technique (SMART) and SWING weighting methods. We generalize the methods by allowing the reference attribute to be any attribute, not just the most or the least important one, and by allowing the decision maker to reply with intervals to the weight ratio questions to account for his/her judgmental imprecision. We also study the practical and procedural implications of using imprecision intervals in these methods. These include, for example, how to select the reference attribute to identify as many dominated alternatives as possible. Based on the results of a simulation study, we suggest guidelines for how to carry out the weighting process in practice. Computer support can be used to make the process visual and interactive. We describe the WINPRE software for interval SMART/SWING, preference assessment by imprecise ratio statements (PAIRS), and preference programming. The use of interval SMART/SWING is illustrated by a job selection example.

Enzymatic hydrolysis of native cellulose nanofibrils and other cellulose model films: effect of surface structure.

Abstract: Model films of native cellulose nanofibrils, which contain both crystalline cellulose I and amorphous domains, were used to investigate the dynamics and activities of cellulase enzymes. The enzyme binding and degradation of nanofibril films were compared with those for other films of cellulose, namely, Langmuir−Schaefer and spin-coated regenerated cellulose, as well as cellulose nanocrystal cast films. Quartz crystal microbalance with dissipation (QCM-D) was used to monitor the changes in frequency and energy dissipation during incubation at varying enzyme concentrations and experimental temperatures. Structural and morphological changes of the cellulose films upon incubation with enzymes were evaluated by using atomic force microscopy. The QCM-D results revealed that the rate of enzymatic degradation of the nanofibril films was much faster compared to the other types of cellulosic films. Higher enzyme loads did not dramatically increase the already fast degradation rate. Real-time measurements of the cou...

Linear classification of low-resolution EEG patterns produced by imagined hand movements

Abstract: Electroencephalograph (EEG)-based brain-computer interfaces (BCI's) require on-line detection of mental states from spontaneous EEG signals, In this framework, surface Laplacian (SL) transformation of EEG signals has proved to improve the recognition scores of imagined motor activity. The results the authors obtained in the first year of an European project named adaptive brain interfaces (ABI) suggest that: (1) the detection of mental imagined activity can be obtained by using the signal space projection (SSP) method as a classifier and (2) a particular type of electrodes can be used in such a BCI device, reconciling the benefits of SL waveforms and the need for the use of few electrodes. Recognition of mental activity was attempted on both raw and SL-transformed EEG data from five healthy people performing two mental tasks, namely imagined right and left hand movements.