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Brake systems are strongly related with safety of vehicles. Therefore a reliable design of the brake system is critical as vehicles operate in a wide range of environmental conditions, fulfilling different security requirements. Particularly, countries with mountainous geography expose vehicles to aggressive variations in altitude and road grade. These variations affect the performance of the brake system. In order to study how these changes affect the brake system, two approaches were considered. The first approach was centered on the development of an analytical model for the longitudinal dynamics of the vehicle during braking maneuvers. This model was developed at system-level, considering the whole vehicle. This allowed the understanding of the relation between the braking force and the altitude and road grade, for different fixed deceleration requirement scenarios. The second approach was focused on the characterization of the vacuum servo operation.

Neural networks for whole ion mobility spectra from a standardized data base of 1295 spectra for 195 chemicals at various concentrations showed 92% successful classifications by functional group was throughout a range of concentrations. Application of neural networks in a two tier design where chemicals were first identified by class and as individual substances eliminated all but one false positive out of 161 test spectra. These findings establish that ion mobility spectra, even with low resolution instrumentation, contain sufficient detail to permit development of automated identification systems. Under certain conditions of temperature and moisture in the IMS drift tube, the identification of “blind unknowns” was better than 90%. This suggests that the volatile organic analyzer can be extended to completely unknown chemicals during air quality monitoring.