Dynamic response of human and primate head and neck to +Gy impact acceleration. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Dynamic response of finite length MAGLEV vehicles subjected to crosswind gusts. Final report.

Transportation Department, Office of Systems Engineering, Washington, D.C.

NONSAP : a structural analysis program for static and dynamic response of nonlinear systems /

Photocopy of original: Berkeley : Structural Engineering Laboratory, University of California, 1974.

A method for predicting the aerodynamic loads and dynamic response of the rotor blades of a tandem-rotor helicopter.

"USAAVLABS technical report 67-38."

Dynamic response of materials to intense impulsive loading.

Includes bibliographical references.

Dynamic, convex, and robust optimization with Bayesian learning for response-guided dosing

Thesis (Ph.D.)--University of Washington, 2016-08

Extension of dynamic model of impulse behavior of concentrated grounds at high currents

A dynamic model which describes the impulse behavior of concentrated grounds at high currents is described in this paper. This model is an extension of previous models in that it can successfully account for the surge behavior of concentrated grounds over a much wider range of current densities. It is able to describe the well known effect of ionization of soil as well as the observed effect of discrete breakdowns and filamentary arc paths at much higher currents. Results of verification against experimental results are also presented.

Dynamic response: Real-time adaptation for music emotion

Music plays an enormous role in today's computer games; it serves to elicit emotion, generate interest and convey important information. Traditional gaming music is fixed at the event level, where tracks loop until a state change is triggered. This behaviour however does not reflect musically the in-game state between these events. We propose a dynamic music environment, where music tracks adjust in real-time to the emotion of the in-game state. We are looking to improve the affective response to symbolic music through the modification of structural and performative characteristics through the application of rule-based techniques. In this paper we undertake a multidiscipline approach, and present a series of primary music-emotion structural rules for implementation. The validity of these rules was tested in small study involving eleven participants, each listening to six permutations from two musical works. Preliminary results indicate that the environment was generally successful in influencing the emotion of the musical works for three of the intended four directions (happier, sadder & content/dreamier). Our secondary aim of establishing that the use of music-emotion rules, sourced predominantly from Western classical music, could be applied with comparable results to modern computer gaming music was also largely successfully.

Dynamic response of spring mounted flexible platforms carring rotating machinery

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

Self-Learning Fuzzy Spiking Neural Network as a Nonlinear Pulse-Position Threshold Detection Dynamic System Based on Second-Order Critically Damped Response Units

Architecture and learning algorithm of self-learning spiking neural network in fuzzy clustering task are outlined. Fuzzy receptive neurons for pulse-position transformation of input data are considered. It is proposed to treat a spiking neural network in terms of classical automatic control theory apparatus based on the Laplace transform. It is shown that synapse functioning can be easily modeled by a second order damped response unit. Spiking neuron soma is presented as a threshold detection unit. Thus, the proposed fuzzy spiking neural network is an analog-digital nonlinear pulse-position dynamic system. It is demonstrated how fuzzy probabilistic and possibilistic clustering approaches can be implemented on the base of the presented spiking neural network.