50 resultados para 7140-216


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Abrasion by hard particles is responsible for wear in many practical situations, but can also be used constructively in grinding and polishing processes. A brief overview of abrasion is presented, followed by an historical survey of polishing and a discussion of laboratory abrasion test methods.

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A new idea of power device, which contains highly nitrogen-doped CVD diamond and Schottky contact, is proposed to actualise a power device with diamond. Two-dimensional simulation is conducted using ISE TCAD device simulator. While comparably high current is obtained in a transient simulation as expected, this current does not contribute to the drain-source current because of the symmetry of the device. Using an asymmetric structure or bias conditions, the device has high potential as an electric device for extremely high power, high frequency and high temperature. © 2003 Elsevier Science B.V. All rights reserved.

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This paper presents direct growth of horizontally aligned carbon nanotubes (CNTs) between two predefined various inter-spacing up to tens of microns of electrodes (pads) and its use as CNT field-effect transistors (CNT-FETs). The catalytic metals were prepared, consisting of iron (Fe), aluminum (Al) and platinum (Pt) triple layers, on the thermal silicon oxide substrate (Pt/Al/Fe/SiO2). Scanning electron microscopy measurements of CNT-FETs from the as-grown samples showed that over 80% of the nanotubes are grown across the catalytic electrodes. Moreover, the number of CNTs across the catalytic electrodes is roughly controllable by adjusting the growth condition. The Al, as the upper layer on Fe electrode, not only plays a role as a barrier to prevent vertical growth but also serves as a porous medium that helps in forming smaller nano-sized Fe particles which would be necessary for lateral growth of CNTs. Back-gate field effect transistors were demonstrated with the laterally aligned CNTs. The on/off ratios in all the measured devices are lower than 100 due to the drain leakage current. ©2010 IEEE.

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This review will focus on four areas of motor control which have recently been enriched both by neural network and control system models: motor planning, motor prediction, state estimation and motor learning. We will review the computational foundations of each of these concepts and present specific models which have been tested by psychophysical experiments. We will cover the topics of optimal control for motor planning, forward models for motor prediction, observer models of state estimation arid modular decomposition in motor learning. The aim of this review is to demonstrate how computational approaches, as well as proposing specific models, provide a theoretical framework to formalize the issues in motor control.