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Modern fatigue analysis techniques, that can provide reliable estimates of the service performance of components and structures, are finding increasing use in vehicle development programs. A major objective of such efforts is the prediction of the field performance of a fleet of vehicles as influenced by the host of design, manufacturing, and performance variables. An approach to this complex problem, based on the incorporation of probability theory in established life prediction methods, is presented. In this way, quantitative estimates of the lifetime distribution of a population are obtained based on anticipated, or specified, variations in component geometry, material processing sequences, and service loading. The application of this approach is demonstrated through a case study of an automotive transmission component.

We propose to enhance reliability based diagnosis by enhancing the fault tree model with a sensor layer for capturing evidence. We recognized the need for an automated diagnostic process that can predict and report component failure in vehicles prior to total failure of any system in the vehicle. We also want to take advantage of evidence that can be derived from sensors to reduce the amount of tests required to identify failed components.

Originally designed for measuring closed-loop contours such as those around a human thorax, the External Peripheral Instrument for Deformation Measurement (EPIDM), or chestband, was developed to improve the measurement of dummy and cadaver thoracic response during impact. In the closed-loop configuration, the chestband wraps around on itself forming a closed contour. This study investigates the use of the chestband for dynamic deformation measurements in an open-loop configuration. In the open-loop configuration, the chestband does not generally form a closed contour. This work includes enhanced procedures and algorithms for the calculation of chestband deformation contours including the determination of static and dynamic chestband contours under several boundary conditions.

The incorporation of reliability theory into a fatigue analysis algorithm is studied. This probabilistic approach gives designers the ability to quantify “real world” variations existing in the material properties, geometry, and loading of engineering components. Such information would serve to enhance the speed and accuracy of current design techniques. An automobile wheel assembly is then introduced as an example of the applications of this durability/reliability design package.

The development of the concepts, terminology and methodology of verification and validation is based on practical issues, not the philosophy of science. Different communities have tried to improve the existing terminology to one which is more comprehensible in their own field of study. All definitions follow the same concept, but they have been defined in a way to be most applicable to a specific field of study. This paper proposes the Verification, Validation, and Uncertainty Quantification (VV&UQ) framework applicable to power electronic systems. Although the steps are similar to the VV&UQ frameworks' steps from other societies, this framework is more efficient as a result of the new arrangement of the steps which makes this procedure more comprehensible. This new arrangement gives this procedure the capability of improving the model in the most efficient way.

This paper presents a novel infrastructural traffic monitoring approach that estimates traffic information by combining two sensing techniques. The traffic information can be obtained from the presented approach includes passing vehicle counts, corresponding speed estimation and vehicle classification based on size. This approach uses measurement from an array of Lidars and video frames from a camera and derives traffic information using two techniques. The first technique detects passing vehicles by using Lidars to constantly measure the distance from laser transmitter to the target road surface. When a vehicle or other objects pass by, the measurement of the distance to road surface reduces in each targeting spot, and triggers detection event. The second technique utilizes video frames from camera and performs background subtraction algorithm in each selected Region of Interest (ROI), which also triggers detection when vehicle travels through each ROI.