Facilitation of learning induced by both random and gradual visuomotor task variation.

Motor task variation has been shown to be a key ingredient in skill transfer, retention, and structural learning. However, many studies only compare training of randomly varying tasks to either blocked or null training, and it is not clear how experiencing different nonrandom temporal orderings of tasks might affect the learning process. Here we study learning in human subjects who experience the same set of visuomotor rotations, evenly spaced between -60° and +60°, either in a random order or in an order in which the rotation angle changed gradually. We compared subsequent learning of three test blocks of +30°→-30°→+30° rotations. The groups that underwent either random or gradual training showed significant (P < 0.01) facilitation of learning in the test blocks compared with a control group who had not experienced any visuomotor rotations before. We also found that movement initiation times in the random group during the test blocks were significantly (P < 0.05) lower than for the gradual or the control group. When we fit a state-space model with fast and slow learning processes to our data, we found that the differences in performance in the test block were consistent with the gradual or random task variation changing the learning and retention rates of only the fast learning process. Such adaptation of learning rates may be a key feature of ongoing meta-learning processes. Our results therefore suggest that both gradual and random task variation can induce meta-learning and that random learning has an advantage in terms of shorter initiation times, suggesting less reliance on cognitive processes.

Morphological decomposition in Arabic ASR systems

In recent years, the use of morphological decomposition strategies for Arabic Automatic Speech Recognition (ASR) has become increasingly popular. Systems trained on morphologically decomposed data are often used in combination with standard word-based approaches, and they have been found to yield consistent performance improvements. The present article contributes to this ongoing research endeavour by exploring the use of the 'Morphological Analysis and Disambiguation for Arabic' (MADA) tools for this purpose. System integration issues concerning language modelling and dictionary construction, as well as the estimation of pronunciation probabilities, are discussed. In particular, a novel solution for morpheme-to-word conversion is presented which makes use of an N-gram Statistical Machine Translation (SMT) approach. System performance is investigated within a multi-pass adaptation/combination framework. All the systems described in this paper are evaluated on an Arabic large vocabulary speech recognition task which includes both Broadcast News and Broadcast Conversation test data. It is shown that the use of MADA-based systems, in combination with word-based systems, can reduce the Word Error Rates by up to 8.1 relative. © 2012 Elsevier Ltd. All rights reserved.

THE SENSITIVITY OF 2D COMPRESSOR INCIDENCE RANGE TO IN-SERVICE GEOMETRIC VARIATION

The loss mechanisms which control 2D incidence range are discussed with an emphasis on determining which real in-service geometric variations will have the largest impact. For the majority of engine compressor blades (Minlet>0.55) both the negative and positive incidence limits are controlled by supersonic patches. It is shown that these patches are highly sensitive to the geometric variations close to, and around the leading edge. The variations used in this study were measured from newly manufactured as well as ex-service blades. Over most the high pressure compressor considered, it was shown that manufacture variations dominated. The first part of the paper shows that, despite large geometric variations (~10% of leading edge thickness), the incidence range responded in a linear way. The result of this is that the geometric variations have little effect on the mean incidence range of a row of blades. In the second part of the paper a region of the design space is identified where non-linear behavior can result in a 10% reduction in positive incidence range. The mechanism for this is reported and design guidelines for its avoidance offered. In the final part of the paper, the linear behavior at negative incidence and the transonic nature of the flow is exploited to design a robust asymmetric leading edge with a 5% increase in incidence range.

Environment induced variation in the photoconductivity of graphene observed by terahertz spectroscopy

Chemical vapour deposition (CVD) grown graphene sheets were investigated using optical-pump terahertz-probe spectroscopy, revealing a dramatic variation in the photoinduced terahertz conductivity of graphene in different atmospheres. © 2012 IEEE.

On the variation of the 2DEG charge density with the density of the surface donor traps in AiGaN/GaN transistors

Gallium nitride (GaN) has a bright future in high voltage device owing to its remarkable physical properties and the possibility of growing heterostructures on silicon substrates. GaN High Electron Mobility Transistors (HEMTs) are expected to make a strong impact in off line applications and LED drives. However, unlike in silicon-based power devices, the on-state resistance of HEMT devices is hugely influenced by donor and acceptor traps at interfaces and in the bulk. This study focuses on the influence of donor traps located at the top interface between the semiconductor layer and the silicon nitride on the 2DEG density. It is shown through TCAD simulations and analytical study that the 2DEG charge density has an 'S' shape variation with two distinctive 'flat' regions, wherein it is not affected by the donor concentration, and one linear region. wherein the channel density increases proportionally with the donor concentration. We also show that the upper threshold value of the donor concentration within this 'S' shape increases significantly with the AIGaN thickness and the Al mole fraction and is highly affected by the presence of a thin GaN cap layer. © 2013 IEEE.

Experimental study of the effects of temperature variation on droplet size and wetness fraction in a low-pressure model steam turbine

The three-stage low-pressure model steam turbine at the Institute of Thermal Turbomachinery and Machinery Laboratory (ITSM) was used to study the impact of three different steam inlet temperatures on the homogeneous condensation process and the resulting wetness topology. The droplet spectrum as well as the particle number concentration were measured in front of the last stage using an optical-pneumatic probe. At design load, condensation starts inside the stator of the second stage. A change in the steam inlet temperature is able to shift the location of condensation onset within the blade row up- or downstream and even into adjoining blade passages, which leads to significantly different local droplet sizes and wetness fractions due to different local expansion rates. The measured results are compared to steady three-dimensional computational fluid dynamics calculations. The predicted nucleation zones could be largely confirmed by the measurements. Although the trend of measured and calculated droplet size across the span is satisfactory, there are considerable differences between the measured and computed droplet spectrum and wetness fractions. © IMechE 2013 Reprints and permissions: sagepub.co.uk/ journalsPermissions.nav.

Morphological computation of multi-gaited robot locomotion based on free vibration.

In recent years, there has been increasing interest in the study of gait patterns in both animals and robots, because it allows us to systematically investigate the underlying mechanisms of energetics, dexterity, and autonomy of adaptive systems. In particular, for morphological computation research, the control of dynamic legged robots and their gait transitions provides additional insights into the guiding principles from a synthetic viewpoint for the emergence of sensible self-organizing behaviors in more-degrees-of-freedom systems. This article presents a novel approach to the study of gait patterns, which makes use of the intrinsic mechanical dynamics of robotic systems. Each of the robots consists of a U-shaped elastic beam and exploits free vibration to generate different locomotion patterns. We developed a simplified physics model of these robots, and through experiments in simulation and real-world robotic platforms, we show three distinctive mechanisms for generating different gait patterns in these robots.

Morphological computation for adaptive behavior and cognition

Traditionally, in robotics, artificial intelligence and neuroscience, there has been a focus on the study of the control or the neural system itself. Recently there has been an increasing interest in the notion of embodiment not only in robotics and artificial intelligence, but also in the neurosciences, psychology and philosophy. In this paper, we introduce the notion of morphological computation, and demonstrate how it can be exploited on the one hand for designing intelligent, adaptive robotic systems, and on the other hand for understanding natural systems. While embodiment has often been used in its trivial meaning, i.e. "intelligence requires a body", the concept has deeper and more important implications, concerned with the relation between physical and information (neural, control) processes. Morphological computation is about connecting body, brain and environment. A number of case studies are presented to illustrate the concept. We conclude with some speculations about potential lessons for neuroscience and robotics. © 2006 Elsevier B.V. All rights reserved.