System Identification of a Cantilever Beam with Interferometer Measurement Using Adaptive Filters

Laser interferometry, commonly used in high-precision motion control systems, is rarely adopted in experimental vibration analysis because its installation and mounting is invasive to dynamical systems. However, metrology systems that already utilize laser interferometry, such as profilometry in semiconductor manufacturing, may benefit from interferometer feedback for signal processing. This study investigates the use of laser interferometry for system identification through a piezoelectrically actuated cantilevered beam. The model of the beam including piezo actuators and optical measurement components are established through the Euler-Bernoulli beam theory. From the method of separation of variables, the continuous system is transformed into a discrete system represented in a state-space form. By performing the Laplace transformation of the state-space form, we obtain the analytical transfer function of interferometer displacement versus actuator input, which is then validated numerically and experimentally. Adaptive filters based on FIR and IIR are designed to identify the transfer function. Because of the slow convergence of such filters, a recursive LMS algorithm is designed to accelerate computation. It is experimentally demonstrated that the precision measurement of interferometer can lead to highly accurate results of system identification

Measures and Models for Estimating and Predicting Cognitive Fatigue

We analyzed EEG and ERPs in a fatiguing mental task and created statistical models for single subjects. Seventeen subjects (4 F, 18-38 y) viewed 4-digit problems (e.g., 3+5-2+7=15) on a computer, solved the problems, and pressed keys to respond (intertrial interval = 1 s). Subjects performed until either they felt exhausted or three hours had elapsed. Re- and post-task measures of mood (Activation Deactivation Adjective Checklist, Visual Analogue Mood Scale) confirmed that fatigue increased and energy decreased over time. We tested response times (RT); amplitudes of ERP components N1, P2, P300, readiness potentials; and amplitudes of frontal theta and parietal alpha rhythms for change as a function of time. For subjects who completed 3 h (n=9) we analyzed 12 15-min blocks. For subjects who completed at least 1.5 h (n=17), we analyzed the first-, middle-, and last 100 error-free trials. Mean RT rose from 6.7 s to 8.5 s over time. We found no changes in the amplitudes of ERP components. In both analyses, amplitudes of frontal theta and parietal alpha rose by 30% or more over time. We used 30-channel EEG frequency spectra to model the effects of time in single subjects using a kernel partial least squares classifier. We classified 3.5s EEG segments as being from the first 100 or the last 100 trials, using random sub-samples of each class. Test set accuracies ranged from 63.9% to 99.6% correct. Only 2 of 17 subjects had mean accuracies lower than 80%. The results suggest that EEG accurately classifies periods of cognitive fatigue in 90% of subjects

Indigo backstaining during cellulase washing

We have attempted to understand the mechanisms of indigo backstaining during enzyme stone washing by separately analyzing dye staining levels on different cotton surfaces during the process. The high ability of the cellulase enzyme protein to bind to cotton cellulose is the major cause of backstaining. Further studies of commercial cellulases show that the pH of maximum cellulase activity may not be the same pH of maximum of cellulase binding capacity to the substrate, and also that indigo dyes have completely different affinities for cellulase proteins from different fungal origins