Position control of linear ultrasonic motor

Master'sMASTER OF ENGINEERIN

Investigating the build-up of precedence effect using reflection masking

The auditory processing level involved in the build‐up of precedence [Freyman et al., J. Acoust. Soc. Am. 90, 874–884 (1991)] has been investigated here by employing reflection masked threshold (RMT) techniques. Given that RMT techniques are generally assumed to address lower levels of the auditory signal processing, such an approach represents a bottom‐up approach to the buildup of precedence. Three conditioner configurations measuring a possible buildup of reflection suppression were compared to the baseline RMT for four reflection delays ranging from 2.5–15 ms. No buildup of reflection suppression was observed for any of the conditioner configurations. Buildup of template (decrease in RMT for two of the conditioners), on the other hand, was found to be delay dependent. For five of six listeners, with reflection delay=2.5 and 15 ms, RMT decreased relative to the baseline. For 5‐ and 10‐ms delay, no change in threshold was observed. It is concluded that the low‐level auditory processing involved in RMT is not sufficient to realize a buildup of reflection suppression. This confirms suggestions that higher level processing is involved in PE buildup. The observed enhancement of reflection detection (RMT) may contribute to active suppression at higher processing levels

Aeronautical enginnering: A cumulative index to a continuing bibliography (supplement 312)

This is a cumulative index to the abstracts contained in NASA SP-7037 (301) through NASA SP-7073 (311) of Aeronautical Engineering: A Continuing Bibliography. NASA SP-7037 and its supplements have been compiled by the Center for AeroSpace Information of the National Aeronautics and Space Administration (NASA). This cumulative index includes subject, personal author, corporate source, foreign technology, contract number, report number, and accession number indexes

Pattern Classification by an Incremental Learning Fuzzy Neural Network

To detect and identify defects in machine condition health monitoring, classical neural classifiers, such as Multilayer Perceptron (MLP) neural networks, are proposed to supervise the monitored system. A drawback of classical neural classifiers, off-line and iterative learning algorithms, is a long training time. In addition, they are often stuck at local minima, unable to achieve the optimum solution. Furthennore, in an operating mode, it is possible that new faults are developing while a monitored system is running. These new classes of defects need to be instantly detected and distinguished from those that have been trained to the classifier. Those classical neural classifiers need to be retrained by both old and new patterns in order to learn new patterns without forgetting the learned patterns. Conventional classifiers cannot detect and learn the new fault types on-line real-time. Using incremental learning algorithms in the monitoring system it is possible to detect those new defects of machine conditions with the system operating while maintaining oLd knowledge. Inspired by the promising properties of an incremental learning algorithm named Fuzzy ARTMAP Neural Network, a new algorithm suitable for pattern classification based on fuzzy neural networks called an Incremental Learning Fuzzy Neuron Network (ILFN) is developed. The ILFN uses Gaussian neurons to represent the distributions of the input space, while the fuzzy ARTMAP neural network uses hyperboxes. The ILFN employs a hybrid supervised and unsupervised learning scheme to generate its prototypes. The network is a self-organized classifier with the capability of adaptive learning of new information without forgetting old knowledge. The classifier can detect new classes of patterns and update its parameters while in an operating mode. Moreover, it is an on-line (real-time) and fast learning algorithm without knowing a priori information. In addition, it has the capability to make soft (fuzzy) and hard (crisp) decisions, and.it is able to classify both linear separable and nonlinear separable problems. To prove the concept, simulations have been performed with the vibration data known as the Westland Data Set. This data set was obtained from the Internet at http://wisdom.ar1.psu.edulWestland/ collected from U.S. Navy CH-46E helicopters maintained by Applied Research Laboratory (ARL) at Penn State University. Using a simple Fast Fourier Transform (FFT) technique for the feature extraction part, the network, capable of one-pass, on-line, and incremental learning performed quite well. Training by various torque levels, the network achieved 100% correct prediction for the same torque level of testing data. Furthermore, the classification performance of the network has been tested using other benchmark data, such as the Fisher's Iris data, the two-spiral problem, and a vowel data set. Comparison studies among other well-known classifiers were preformed. The ILFN was found competitive with or even superior to many classifiers

Applications of EMG in Clinical and Sports Medicine

This second of two volumes on EMG (Electromyography) covers a wide range of clinical applications, as a complement to the methods discussed in volume 1. Topics range from gait and vibration analysis, through posture and falls prevention, to biofeedback in the treatment of neurologic swallowing impairment. The volume includes sections on back care, sports and performance medicine, gynecology/urology and orofacial function. Authors describe the procedures for their experimental studies with detailed and clear illustrations and references to the literature. The limitations of SEMG measures and methods for careful analysis are discussed. This broad compilation of articles discussing the use of EMG in both clinical and research applications demonstrates the utility of the method as a tool in a wide variety of disciplines and clinical fields

Technology 2001: The Second National Technology Transfer Conference and Exposition, volume 2

Proceedings of the workshop are presented. The mission of the conference was to transfer advanced technologies developed by the Federal government, its contractors, and other high-tech organizations to U.S. industries for their use in developing new or improved products and processes. Volume two presents papers on the following topics: materials science, robotics, test and measurement, advanced manufacturing, artificial intelligence, biotechnology, electronics, and software engineering