The Music Box : Caprice

https://digitalcommons.library.umaine.edu/mmb-ps/3140/thumbnail.jp

Customer loads of two-wheeled vehicles

Customer usage profiles are the most unknown influences in vehicle design targets and they play an important role in durability analysis. This publication presents a customer load acquisition system for two-wheeled vehicles that utilises the vehicle's onboard signals. A road slope estimator was developed to reveal the unknown slope resistance force with the help of a linear Kalman filter. Furthermore, an automated mass estimator was developed to consider the correct vehicle loading. The mass estimation is performed by an extended Kalman filter. Finally, a model-based wheel force calculation was derived, which is based on the superposition of forces calculated from measured onboard signals. The calculated wheel forces were validated by measurements with wheel–load transducers through the comparison of rainflow matrices. The calculated wheel forces correspond with the measured wheel forces in terms of both quality and quantity. The proposed methods can be used to gather field data for improved vehicle design loads

Road classification for two-wheeled vehicles

This publication presents a three-part road classification system that utilises the vehicle's onboard signals of two-wheeled vehicles. First, a curve estimator was developed to identify and classify road curves. In addition, the curve estimator continuously classifies the road curviness. Second, the road slope was evaluated to determine the hilliness of a given road. Third, a modular road profile estimator has been developed to classify the road profile according to ISO 8608, which utilises the vehicle's transfer functions. The road profile estimator continuously classifies the driven road. The proposed methods for the classification of curviness, hilliness, and road roughness have been validated with measurements. The road classification system enables the collection of vehicle-independent field data of two-wheeled vehicles. The road properties are part of the customer usage profiles which are essential to define vehicle design targets

DECONVOLUTION-BASED PHYSIOLOGICAL SIGNAL SIMPLFICATION FOR PERIODICAL PARAMETER ESTIMATION

The estimation of physiological parameters from raw sensor signals is absolutely crucial in modern clinical applications. A wide variety of these parameters incorporate a periodic nature, such as the heart rate or the respiration rate. This property can be exploited for their estimation. Particularly challenging is the processing of novel, unobtrusive measurement techniques, which are characterized by complex, time-varying waveforms. Simple peak detection algorithms are often not suited for these applications. One way to tackle these challenges is a preprocessing step for the simplification of the physiological signals. A novel deconvolution based approach for this preprocessing is introduced and evaluated in this paper. Two deconvolution methods are regarded, the “Minimum Entropy Deconvolution” (MED) and the “Maximum Correlated Kurtosis Deconvolution” (MCKD). Important parameters are outlined and examined. Finally, the methods are validated using artificial as well as real clinical signals to demonstrate their potential