William J. Pielemeier

The use of seat effective amplitude transmissibility (SEAT) values to predict dynamic seat comfort

Abstract The Seat Effective Amplitude Transmissibility (SEAT) value is the ratio of the vibration experienced on top of the seat and the vibration that one would be exposed to when sitting directly on the vibrating floor. SEAT values have been widely used to determine the vibration isolation efficiency of a seat. In this article the subjective evaluations of six persons were compared to the SEAT values estimated from experimentally obtained transmissibility curves for 16 different automobile seats ranging from sedans to SUVs and pickups. A vertical rough road stimulus was used as input for both the subjective testing and the SEAT calculations. The SEAT values were estimated using the power spectral density of the vertical vibration input at the seat track and the measured transmissibility data to compute the response in the vertical direction at the seat top. The averaged, estimated SEAT values were compared to averaged measured values and significant correlation ( R 2 =0.94) was obtained. The subjective ratings were obtained on the Ford Vehicle Vibration Simulator using a paired comparison methodology that eliminated static comfort bias during the evaluation. The results indicated that there is good correlation ( R 2 =0.94) between the subjective ratings and the SEAT values when the subjective ratings and transmissibilities are averaged over the six subjects.

Vehicle vibration simulator

A vehicle vibration simulator to simulate occupant exposure to vibration in a motor vehicle includes a reaction mass, a seat portion, a seat actuator between the reaction mass and the seat portion for providing at least one degree of vibration freedom, a steering column portion, a steering column actuator between the reaction mass and the steering column portion for providing at least one degree of vibration freedom, a floorpan portion, a floorpan actuator between the reaction mass and the floorpan portion for providing one degree of vibration freedom, the seat actuator, steering column actuator and floorpan actuator being actuated independently and/or simultaneously to simulate occupant exposure to vehicle vibration.

A high‐resolution time–frequency representation for musical instrument signals

Analyzing musical signals to obtain the time‐varying magnitudes and frequencies of instruments’ partial frequency components is important for resynthesis, transcription, and instrument physics. Windowing techniques, including Fourier series extensions, short‐time Fourier transforms, and constant‐Q transforms, generate bias in time and frequency dictated by the uncertainty principle. This is significant to analysis requirements of such properties as attack, which involve changes over millisecond time ranges and require frequency accuracy on the order of cents. Alternatives such as the Wigner distribution avoid the uncertainty principle restriction and associated bias, but nonlinear cross products of magnitude and frequency computations are not smoothed as with windowing methods, increasing those sources of bias. All these techniques belong to Cohen’s class, a framework where this paper develops the modal distribution, exhibiting decreased total bias. Computation of the modal distribution and a constant‐Q v...

Multi-component power and frequency estimation for a discrete TFD

In a previous article of Pielemeier and Wakefield (see Proc. of IEEE Symposium on Time-Frequency and Time-Scale Analysis, Oct. 4-6, p.421-424, 1992) a discrete time-frequency distribution (TFD) in Cohens class was developed for multicomponent musical signals. This TFDs separable kernel employs low pass filtering in time to achieve limited superposition between components, and either constant-bandwidth or constant-Q smoothing in frequency. We develop instantaneous power and frequency estimators for the components analyzed by this TFD. In the literature, frequency estimators for discrete distributions often compute frequency based on discrete finite phase differences using periodic statistics. We instead start with the underlying continuous analog signal, and using linear statistics, show that estimates from the discrete distribution can be made arbitrarily accurate for the single component cast, while for the multicomponent case, the estimates are minimally biased by time smoothing. Results are demonstrated showing much less bias than common spectrogram estimators. Multicomponent examples include signals which are inharmonic and contain widely varying levels, and AM and FM signals.<<ETX>>