The contact of rough surfaces carrying pressure sensitive boundary layers

When two rough surfaces are loaded together it is well known that the area of true contact is very much smaller then the geometric area and that, consequently, local contact pressures are very much greater than the nominal value. If the asperities on each surface can be thought of as possessing smooth summits and each of the solids is elastically isotropic then the pressure distribution will consist of a series of small, but severe, Hertzian patches. However, if one of both of the surfaces in question is protected by a boundary layer then both the number and dimensions of these patches, and the form of the pressure distribution within them, will be modified. Recent experimental evidence from studies using both Atomic Force Microscopy and micro-tribometry suggests that boundary films produced by the action of commercial anti-wear additives, such as ZDTP, exhibit mechanical properties, which are affected by local values of pressure. These changes bring about further modifications to local conditions. These effects have been explored in a numerical model of rough surface contact and the implications for the mechanisms of surface distress and wear are discussed. © 2000 Elsevier Science B.V. All rights reserved.

Derivative Kernels for Noise Robust ASR

Recently there has been interest in combined gen- erative/discriminative classifiers. In these classifiers features for the discriminative models are derived from generative kernels. One advantage of using generative kernels is that systematic approaches exist how to introduce complex dependencies beyond conditional independence assumptions. Furthermore, by using generative kernels model-based compensation/adaptation tech- niques can be applied to make discriminative models robust to noise/speaker conditions. This paper extends previous work with combined generative/discriminative classifiers in several directions. First, it introduces derivative kernels based on context- dependent generative models. Second, it describes how derivative kernels can be incorporated in continuous discriminative models. Third, it addresses the issues associated with large number of classes and parameters when context-dependent models and high- dimensional features of derivative kernels are used. The approach is evaluated on two noise-corrupted tasks: small vocabulary AURORA 2 and medium-to-large vocabulary AURORA 4 task.

Derivative kernels for noise robust ASR

Recently there has been interest in combining generative and discriminative classifiers. In these classifiers features for the discriminative models are derived from the generative kernels. One advantage of using generative kernels is that systematic approaches exist to introduce complex dependencies into the feature-space. Furthermore, as the features are based on generative models standard model-based compensation and adaptation techniques can be applied to make discriminative models robust to noise and speaker conditions. This paper extends previous work in this framework in several directions. First, it introduces derivative kernels based on context-dependent generative models. Second, it describes how derivative kernels can be incorporated in structured discriminative models. Third, it addresses the issues associated with large number of classes and parameters when context-dependent models and high-dimensional feature-spaces of derivative kernels are used. The approach is evaluated on two noise-corrupted tasks: small vocabulary AURORA 2 and medium-to-large vocabulary AURORA 4 task. © 2011 IEEE.

Prediction of tread block forces for a free-rolling tyre in contact with a rough road

This paper describes a new approach to model the forces on a tread block for a free-rolling tyre in contact with a rough road. A theoretical analysis based on realistic tread mechanical properties and road roughness is presented, indicating partial contact between a tread block and a rough road. Hence an asperity-scale indentation model is developed using a semi-empirical formulation, taking into account both the rubber viscoelasticity and the tread block geometry. The model aims to capture the essential details of the contact at the simplest level, to make it suitable as part of a time-domain dynamic analysis of the coupled tyre-road system. The indentation model is found to have a good correlation with the finite element (FE) predictions and is validated against experimental results using a rolling contact rig. When coupled to a deformed tyre belt profile, the indentation model predicts normal and tangential force histories inside the tyre contact patch that show good agreement with FE predictions. © 2012 Elsevier B.V..

Scaling of transitionally-rough flow structures in DNSes of channels with riblets up to Reτ=550

The linear, drag-reducing effect of vanishingly small riblets breaks down once their size is in the transitionally-rough regime. We have previously reported that this breakdown is caused by the additional Reynolds stresses produced by the appearance of elongated spanwise rollers just above the riblet surface. These rollers are related with the Kelvin--Helmholtz instability of free shear layers, and to similar structures appearing over other rough and porous surfaces. However, because of the limited Reτ=180 in our previous DNSes, it could not be determined whether those structures scaled in inner or outer units. Furthermore, it is questionable if results in the transitionally-rough regime at Reτ=180 can be extrapolated to configurations of practical interest. At such small Reynolds numbers, roughness of transitional size can perturb a large portion of the boundary layer, which is not the case in most industrial and atmospheric applications. To clarify these issues we have conducted a set of DNSes at Reτ=550. Our results indicate that the spanwise rollers scale in wall units, and support the validity of the extrapolation to configurations of practical interest.

The Random Forest Kernel and other kernels for big data from random partitions

We present Random Partition Kernels, a new class of kernels derived by demonstrating a natural connection between random partitions of objects and kernels between those objects. We show how the construction can be used to create kernels from methods that would not normally be viewed as random partitions, such as Random Forest. To demonstrate the potential of this method, we propose two new kernels, the Random Forest Kernel and the Fast Cluster Kernel, and show that these kernels consistently outperform standard kernels on problems involving real-world datasets. Finally, we show how the form of these kernels lend themselves to a natural approximation that is appropriate for certain big data problems, allowing $O(N)$ inference in methods such as Gaussian Processes, Support Vector Machines and Kernel PCA.

Minimalistic control of biped walking in rough terrain

Toward our comprehensive understanding of legged locomotion in animals and machines, the compass gait model has been intensively studied for a systematic investigation of complex biped locomotion dynamics. While most of the previous studies focused only on the locomotion on flat surfaces, in this article, we tackle with the problem of bipedal locomotion in rough terrains by using a minimalistic control architecture for the compass gait walking model. This controller utilizes an open-loop sinusoidal oscillation of hip motor, which induces basic walking stability without sensory feedback. A set of simulation analyses show that the underlying mechanism lies in the "phase locking" mechanism that compensates phase delays between mechanical dynamics and the open-loop motor oscillation resulting in a relatively large basin of attraction in dynamic bipedal walking. By exploiting this mechanism, we also explain how the basin of attraction can be controlled by manipulating the parameters of oscillator not only on a flat terrain but also in various inclined slopes. Based on the simulation analysis, the proposed controller is implemented in a real-world robotic platform to confirm the plausibility of the approach. In addition, by using these basic principles of self-stability and gait variability, we demonstrate how the proposed controller can be extended with a simple sensory feedback such that the robot is able to control gait patterns autonomously for traversing a rough terrain. © 2010 Springer Science+Business Media, LLC.