Temperature dependences of oscillation parameters in ferroelectric liquid crystals

Oscillation processes have been revealed in the course of reversible polarization study in ferroelectric liquid crystals. The oscillation frequency of polarization vector has been found to be from 1 to 30 kHz. The oscillation parameters were studied as functions of temperature. Temperature dependences of the oscillation amplitude and damping decrement have been measured.

A procedure for automatically estimating model parameters in optical motion capture

Model-based optical motion capture systems require knowledge of the position of the markers relative to the underlying skeleton, the lengths of the skeleton's limbs, and which limb each marker is attached to. These model parameters are typically assumed and entered into the system manually, although techniques exist for calculating some of them, such as the position of the markers relative to the skeleton's joints. We present a fully automatic procedure for determining these model parameters. It tracks the 2D positions of the markers on the cameras' image planes and determines which markers lie on each limb before calculating the position of the underlying skeleton. The only assumption is that the skeleton consists of rigid limbs connected with ball joints. The proposed system is demonstrated on a number of real data examples and is shown to calculate good estimates of the model parameters in each. © 2004 Elsevier B.V. All rights reserved.

Experimental investigation into parameters governing corner interactions for transonic shock wave/ boundary layer interactions

Experiments were conducted investigating the interaction between a normal shock wave and a corner boundary layer in a constant area rectangular duct. Active corner suction and passive blowing were applied to manipulate the natural corner flows developing in the working section of the Cambridge University supersonic wind tunnel. In addition robust vane micro-vortex generators were applied to the corners of the working section. Experiments were conducted at Mach numbers of M∞=1.4 and 1.5. Flow visualisation was carried out through schlieren and surface oil flow, while static pressures were recorded via floor tappings. The results indicate that an interplay occurs between the corner flow and the centre line flow. It is believed that corner flow separation acts to induce a shock bifurcation, which in turn leads to a smearing of the adverse pressure gradient elsewhere. In addition the blockage effect from the corners was seen to result in a reacceleration of the subsonic post-shock flow. As a result manipulation of the corner regions allows a separated or attached centre line flow to be observed at the same Mach number. Copyright © 2010 by Babinsky, Burton, Bruce.

Natural actor and belief critic: Reinforcement algorithm for learning parameters of dialogue systems modelled as POMDPs

This article presents a novel algorithm for learning parameters in statistical dialogue systems which are modeled as Partially Observable Markov Decision Processes (POMDPs). The three main components of a POMDP dialogue manager are a dialogue model representing dialogue state information; a policy that selects the system's responses based on the inferred state; and a reward function that specifies the desired behavior of the system. Ideally both the model parameters and the policy would be designed to maximize the cumulative reward. However, while there are many techniques available for learning the optimal policy, no good ways of learning the optimal model parameters that scale to real-world dialogue systems have been found yet. The presented algorithm, called the Natural Actor and Belief Critic (NABC), is a policy gradient method that offers a solution to this problem. Based on observed rewards, the algorithm estimates the natural gradient of the expected cumulative reward. The resulting gradient is then used to adapt both the prior distribution of the dialogue model parameters and the policy parameters. In addition, the article presents a variant of the NABC algorithm, called the Natural Belief Critic (NBC), which assumes that the policy is fixed and only the model parameters need to be estimated. The algorithms are evaluated on a spoken dialogue system in the tourist information domain. The experiments show that model parameters estimated to maximize the expected cumulative reward result in significantly improved performance compared to the baseline hand-crafted model parameters. The algorithms are also compared to optimization techniques using plain gradients and state-of-the-art random search algorithms. In all cases, the algorithms based on the natural gradient work significantly better. © 2011 ACM.

Single trial learning of external dynamics: What can the brain teach us about learning mechanisms?

Humans have exceptional abilities to learn new skills, manipulate tools and objects, and interact with our environment. In order to be successful at these tasks, our brain has developed learning mechanisms to deal with and compensate for the constantly changing dynamics of the world. If this mechanism or mechanisms can be understood from a computational point of view, then they can also be used to drive the adaptability and learning of robots. In this paper, we will present a new technique for examining changes in the feedforward motor command due to adaptation. This technique can then be utilized for examining motor adaptation in humans and determining a computational algorithm which explains motor learning. © 2007.

Learning the dynamics of the external world: Brain inspired learning for robotic applications

Humans have exceptional abilities to learn new skills, manipulate tools and objects, and interact with our environment. In order to be successful at these tasks, our brain has become exceptionally well adapted to learning to deal not only with the complex dynamics of our own limbs but also with novel dynamics in the external world. While learning of these dynamics includes learning the complex time-varying forces at the end of limbs through the updating of internal models, it must also include learning the appropriate mechanical impedance in order to stabilize both the limb and any objects contacted in the environment. This article reviews the field of human learning by examining recent experimental evidence about adaptation to novel unstable dynamics and explores how this knowledge about the brain and neuro-muscular system can expand the learning capabilities of robotics and prosthetics. © 2006.

Why do firms acquire external technologies? Understanding the motivations for technology acquisitions

Why do firms acquire external technologies? Previous research indicates that there are a wide variety of motivations. These include the need to acquire valuable knowledge-based resources, to improve strategic flexibility, to experiment), to overcome organisational inertia, to mitigate risk and uncertainty, to reduce costs and development time in new product development, and the perception that the firm has the absorptive capacity to integrate acquisitions. In this paper we provide an in-depth literature review of the motivations for the acquisition of external technologies by firms. We find that these motivations can be broadly classed into four categories: (1) the development of technological capabilities, (2) the development of strategic options, (3) efficiency improvements, and (4) responses to the competitive environment. In light of this categorisation, we comment on how these different motivations connect to the wider issues of technology acquisition. © 2010 IEEE.

Understanding university academics' internal and external knowledge interactions in different disciplines: evidence from university in South Korea

Most of the literature on the role of universities in innovation assumes that academics¡¯ knowledge interacts only with industry and knowledge transfer occurs only or mainly in the technological and scientific fields. We question these assumptions, suggesting academics¡¯ internal and external knowledge interact across disciplines. Using national survey data, this paper tries to show the heterogeneity of university teachers¡¯ knowledge interactions across wider disciplines. Also, this paper explores the patterns of university academics¡¯ internal knowledge interactions with other academics within academia and the university academics¡¯ external knowledge interactions with industry, such as small and medium enterprises (SMEs) and major Korean firms, Chaebols. We found that there are heterogeneities of academics¡¯ knowledge interactions across the disciplines.

Implications of local lifecycle analyses and low carbon fuel standard design on gasohol transportation fuels

State and regional policies, such as low carbon fuel standards (LCFSs), increasingly mandate that transportation fuels be examined according to their greenhouse gas (GHG) emissions. We investigate whether such policies benefit from determining fuel carbon intensities (FCIs) locally to account for variations in fuel production and to stimulate improvements in FCI. In this study, we examine the FCI of transportation fuels on a lifecycle basis within a specific state, Minnesota, and compare the results to FCIs using national averages. Using data compiled from 18 refineries over an 11-year period, we find that ethanol production is highly variable, resulting in a 42% difference between carbon intensities. Historical data suggests that lower FCIs are possible through incremental improvements in refining efficiency and the use of biomass for processing heat. Stochastic modeling of the corn ethanol FCI shows that gains in certainty due to knowledge of specific refinery inputs are overwhelmed by uncertainty in parameters external to the refiner, including impacts of fertilization and land use change. The LCA results are incorporated into multiple policy scenarios to demonstrate the effect of policy configurations on the use of alternative fuels. These results provide a contrast between volumetric mandates and LCFSs. © 2011 Elsevier Ltd.