Learning Multirobot Hose Transportation and Deployment by Distributed Round-Robin Q-Learning

Multi-Agent Reinforcement Learning (MARL) algorithms face two main difficulties: the curse of dimensionality, and environment non-stationarity due to the independent learning processes carried out by the agents concurrently. In this paper we formalize and prove the convergence of a Distributed Round Robin Q-learning (D-RR-QL) algorithm for cooperative systems. The computational complexity of this algorithm increases linearly with the number of agents. Moreover, it eliminates environment non sta tionarity by carrying a round-robin scheduling of the action selection and execution. That this learning scheme allows the implementation of Modular State-Action Vetoes (MSAV) in cooperative multi-agent systems, which speeds up learning convergence in over-constrained systems by vetoing state-action pairs which lead to undesired termination states (UTS) in the relevant state-action subspace. Each agent's local state-action value function learning is an independent process, including the MSAV policies. Coordination of locally optimal policies to obtain the global optimal joint policy is achieved by a greedy selection procedure using message passing. We show that D-RR-QL improves over state-of-the-art approaches, such as Distributed Q-Learning, Team Q-Learning and Coordinated Reinforcement Learning in a paradigmatic Linked Multi-Component Robotic System (L-MCRS) control problem: the hose transportation task. L-MCRS are over-constrained systems with many UTS induced by the interaction of the passive linking element and the active mobile robots.

Mapeamento de difusão no reconhecimento e reconstrução de sinais

Em muitas representações de objetos ou sistemas físicos se faz necessário a utilização de técnicas de redução de dimensionalidade que possibilitam a análise dos dados em baixas dimensões, capturando os parâmetros essenciais associados ao problema. No contexto de aprendizagem de máquina esta redução se destina primordialmente à clusterização, reconhecimento e reconstrução de sinais. Esta tese faz uma análise meticulosa destes tópicos e suas conexões que se encontram em verdadeira ebulição na literatura, sendo o mapeamento de difusão o foco principal deste trabalho. Tal método é construído a partir de um grafo onde os vértices são os sinais (dados do problema) e o peso das arestas é estabelecido a partir do núcleo gaussiano da equação do calor. Além disso, um processo de Markov é estabelecido o que permite a visualização do problema em diferentes escalas conforme variação de um determinado parâmetro t: Um outro parâmetro de escala, Є, para o núcleo gaussiano é avaliado com cuidado relacionando-o com a dinâmica de Markov de forma a poder aprender a variedade que eventualmente seja o suporte do dados. Nesta tese é proposto o reconhecimento de imagens digitais envolvendo transformações de rotação e variação de iluminação. Também o problema da reconstrução de sinais é atacado com a proposta de pré-imagem utilizando-se da otimização de uma função custo com um parâmetro regularizador, γ, que leva em conta também o conjunto de dados iniciais.

Is it possible to measure social entrepreneurship in firms?

[EN] This study defines and proposes a measurement scale for social entrepreneurship (SE) in its broadest sense. The broad definition of SE covers for-profit firms that use social aims as a core component of their strategy. By pursuing social aims, these firms can boost the value of their products or services for consumers or exploit new business areas. Under this broad definition of SE, profit-seeking and the pursuit of social aims converge, thereby revealing a form of SE that has received little attention in either theoretical or empirical research. To fill this research gap, the present study proposes a measurement scale to measure broad SE in firms. The process used to build the scale draws upon research by Churchill (1979) and DeVellis (1991) and combines the Delphi technique, a pre-test questionnaire and structural equation modelling. The main aim of this research is to develop a scale capable of measuring broad SE in firms. The theoretical basis for the scale is supported by an empirical study in the hotel sector. The scale provides a valid, reliable instrument for measuring broad SE in firms. The scale meets all sociometric properties required of measurement scales in the social sciences, namely dimensionality, reliability and validity.

Uso da calibração multivariada para a predição de propriedades físico-químicas de misturas de óleo de soja e biodiesel

O objetivo deste trabalho foi estabelecer um modelo empregando-se ferramentas de regressão multivariada para a previsão do teor em ésteres metílicos e, simultaneamente, de propriedades físico-químicas de misturas de óleo de soja e biodiesel de soja. O modelo foi proposto a partir da correlação das propriedades de interesse com os espectros de reflectância total atenuada no infravermelho médio das misturas. Para a determinação dos teores de ésteres metílicos foi utilizada a cromatografia líquida de alta eficiência (HPLC), podendo esta ser uma técnica alternativa aos método de referência que utilizam a cromatografia em fase gasosa (EN 14103 e EN 14105). As propriedades físico-químicas selecionadas foram índice de refração, massa específica e viscosidade. Para o estudo, foram preparadas 11 misturas com diferentes proporções de biodiesel de soja e de óleo de soja (0-100 % em massa de biodiesel de soja), em quintuplicata, totalizando 55 amostras. A região do infravermelho estudada foi a faixa de 3801 a 650 cm-1. Os espectros foram submetidos aos pré-tratamentos de correção de sinal multiplicativo (MSC) e, em seguida, à centralização na média (MC). As propriedades de interesse foram submetidas ao autoescalamento. Em seguida foi aplicada análise de componentes principais (PCA) com a finalidade de reduzir a dimensionalidade dos dados e detectar a presença de valores anômalos. Quando estes foram detectados, a amostra era descartada. Os dados originais foram submetidos ao algoritmo de Kennard-Stone dividindo-os em um conjunto de calibração, para a construção do modelo, e um conjunto de validação, para verificar a sua confiabilidade. Os resultados mostraram que o modelo proposto por PLS2 (Mínimos Quadrados Parciais) foi capaz de se ajustar bem os dados de índice de refração e de massa específica, podendo ser observado um comportamento aleatório dos erros, indicando a presença de homocedasticidade nos valores residuais, em outras palavras, o modelo construído apresentou uma capacidade de previsão para as propriedades de massa específica e índice de refração com 95% de confiança. A exatidão do modelo foi também avaliada através da estimativa dos parâmetros de regressão que são a inclinação e o intercepto pela Região Conjunta da Elipse de Confiança (EJCR). Os resultados confirmaram que o modelo MIR-PLS desenvolvido foi capaz de prever, simultaneamente, as propriedades índice de refração e massa específica. Para os teores de éteres metílicos determinados por HPLC, foi também desenvolvido um modelo MIR-PLS para correlacionar estes valores com os espectros de MIR, porém a qualidade do ajuste não foi tão boa. Apesar disso, foi possível mostrar que os dados podem ser modelados e correlacionados com os espectros de infravermelho utilizando calibração multivariada

Átomos hidrogenoides em espaços com dimensionalidade D6=3: os casos não relativístico e relativístico

A possibilidade da existência de átomos de hidrogênio estáveis em dimensões superiores a três é abordada. O problema da dimensionalidade é visto como um problema de Física, no qual relacionam-se algumas leis físicas com a dimensão espacial. A base da análise deste trabalho faz uso das equações de Schrödinger (não relativística) e de Dirac (relativística). Nos dois casos, utiliza-se a generalização tanto do setor cinemático bem como o setor de interação coulombiana para variar o parâmetro topológico dimensão. Para o caso não relativístico, os auto-valores de energia e as auto-funções são obtidas através do método numérico de Numerov. Embora existam soluções em espaços com dimensões superiores, os resultados obtidos no presente trabalho indicam que a natureza deve, de alguma maneira, se manifestar em um espaço tridimensional.

First-principles prediction of doped graphane as a high-temperature electron-phonon superconductor.

We predict by first-principles calculations that p-doped graphane is an electron-phonon superconductor with a critical temperature above the boiling point of liquid nitrogen. The unique strength of the chemical bonds between carbon atoms and the large density of electronic states at the Fermi energy arising from the reduced dimensionality give rise to a giant Kohn anomaly in the optical phonon dispersions and push the superconducting critical temperature above 90 K. As evidence of graphane was recently reported, and doping of related materials such as graphene, diamond, and carbon nanostructures is well established, superconducting graphane may be feasible.

The modified Kanerva model for automatic speech recognition

A parallel processing network derived from Kanerva's associative memory theory Kanerva 1984 is shown to be able to train rapidly on connected speech data and recognize further speech data with a label error rate of 0·68%. This modified Kanerva model can be trained substantially faster than other networks with comparable pattern discrimination properties. Kanerva presented his theory of a self-propagating search in 1984, and showed theoretically that large-scale versions of his model would have powerful pattern matching properties. This paper describes how the design for the modified Kanerva model is derived from Kanerva's original theory. Several designs are tested to discover which form may be implemented fastest while still maintaining versatile recognition performance. A method is developed to deal with the time varying nature of the speech signal by recognizing static patterns together with a fixed quantity of contextual information. In order to recognize speech features in different contexts it is necessary for a network to be able to model disjoint pattern classes. This type of modelling cannot be performed by a single layer of links. Network research was once held back by the inability of single-layer networks to solve this sort of problem, and the lack of a training algorithm for multi-layer networks. Rumelhart, Hinton & Williams 1985 provided one solution by demonstrating the "back propagation" training algorithm for multi-layer networks. A second alternative is used in the modified Kanerva model. A non-linear fixed transformation maps the pattern space into a space of higher dimensionality in which the speech features are linearly separable. A single-layer network may then be used to perform the recognition. The advantage of this solution over the other using multi-layer networks lies in the greater power and speed of the single-layer network training algorithm. © 1989.

Multi-dimensional conditional moment closure modelling applied to a heavy-duty common-rail diesel engine

A multi-dimensional combustion code implementing the Conditional Moment Closure turbulent combustion model interfaced with a well-established RANS two- phase flow field solver has been employed to study a broad range of operating conditions for a heavy duty direct-injection common-rail Diesel engine. These conditions include different loads (25%, 50%, 75% and full load) and engine speeds (1250 and 1830 RPM) and, with respect to the fuel path, different injection timings and rail pressures. A total of nine cases have been simulated. Excellent agreement with experimental data has been found for the pressure traces and the heat release rates, without adjusting any model constants. The chemical mechanism used contains a detailed NOx sub-mechanism. The predicted emissions agree reasonably well with the experimental data considering the range of operating points and given no adjustments of any rate constants have been employed. In an effort to identify CPU cost reduction potential, various dimensionality reduction strategies have been assessed. Furthermore, the sensitivity of the predictions with respect to resolution in particular relating to the CMC grid has been investigated. Overall, the results suggest that the presented modelling strategy has considerable predictive capability concerning Diesel engine combustion without requiring model constant calibration based on experimental data. This is true particularly for the heat release rates predictions and, to a lesser extent, for NOx emissions where further progress is still necessary. © 2009 SAE International.

VGs for a normal SBLI with a downstream diffuser

New types of vortex generators for boundary layer control were investigated experimentally in a flow field which contains a Mach 1.4 normal Shockwave followed by a subsonic diffuser. A parametric study of device height and distance upstream of the normal shock was undertaken with two novel devices: ramped-vanes and split-ramps. Flowfield diagnostics included high-speed Schlieren, oil flow visualization, and pitot-static pressure measurements. A number of flowfield parameters including flow separation, pressure recovery, centerline incompressible boundary layer shape factor, and shock stability were analyzed and compared to the baseline. All configurations tested yielded an elimination of centerline flow separation with the presence of the vortex generators. However, the devices also tended to increase the three-dimensionality of the flow with increased side-wall interaction. When located 25δo upstream of the normal shock, the largest ramped-vane device (whose height was about 0.75 the incoming uncontrolled boundary layer thickness, δo) yielded the smallest centerline incompressible shape factor and the least streamwise oscillations of the normal shock. However, additional studies are needed to better understand the corner interaction effects, which are substantial. © 2010 by the American Institute of Aeronautics and Astronautics, Inc.

Three-dimensional effects on sliding and waving wings

Like large insects, micro air vehicles operate at low Reynolds numbers O(1; 000 - 10; 000) in a regime characterized by separated flow and strong vortices. The leading-edge vortex has been identified as a significant source of high lift on insect wings, but the conditions required for the formation of a stably attached leading-edge vortex are not yet known. The waving wing is designed to model the translational phase of an insect wing stroke by preserving the unsteady starting and stopping motion as well as three-dimensionality in both wing geometry (via a finite-span wing) and kinematics (via wing rotation). The current study examines the effect of the spanwise velocity gradient on the development of the leading-edge vortex along the wing as well as the effects of increasing threedimensionalityby decreasing wing aspect ratio from four to two. Dye flow visualization and particle image velocimetry reveal that the leading-edge vortices that form on a sliding or waving wing have a very high aspect ratio. The structure of the flow is largely two-dimensional on both sliding and waving wings and there is minimal interaction between the leading-edge vortices and the tip vortex. Significant spanwise flow was observed on the waving wing but not on the sliding wing. Despite the increased three-dimensionality on the aspect ratio 2 waving wing, there is no evidence of an attached leading-edge vortex and the structure of the flow is very similar to that on the higher-aspect-ratio wing and sliding wing. © Copyright 2010.

Microvortex generators applied to flowfield containing a normal shock wave and diffuser

The flow through a terminating shock wave and the subsequent subsonic diffuser typically found in supersonic inlets has been simulated using a small-scale wind tunnel. Experiments have been conducted at an inflow Mach number of 1.4 using a dual-channel working section to produce a steady near-normal shock wave. The setup was designed so that the location of the shock wave could be varied relative to the diffuser. As the near-normal shock wave was moved downstream and into the diffuser, an increasingly distorted, three-dimensional, and separated flow was observed. Compared with the interaction of a normal shock wave in a constant area duct, the addition of the diffuser resulted in more prominent corner interactions. Microvortex generators were added to determine their potential for removing flow separation. Although these devices were found to reduce the extent of separation, they significantly increased three-dimensionality and even led to a large degree of flow asymmetry in some configurations. Copyright © 2011 by Neil Titchener and Holger Babinsky.

Model-based approaches to handling additive noise in reverberant environments

Model-based approaches to handle additive and convolutional noise have been extensively investigated and used. However, the application of these schemes to handling reverberant noise has received less attention. This paper examines the extension of two standard additive/convolutional noise approaches to handling reverberant noise. The first is an extension of vector Taylor series (VTS) compensation, reverberant VTS, where a mismatch function including reverberant noise is used. The second scheme modifies constrained MLLR to allow a wide-span of frames to be taken into account and projected into the required dimensionality. To allow additive noise to be handled, both these schemes are combined with standard VTS. The approaches are evaluated and compared on two tasks, MC-WSJ-AV, and a reverberant simulated version of AURORA-4. © 2011 IEEE.

Optimal design of Functionally Graded Materials using a procedural model and Particle Swarm Optimization

A new method for the optimal design of Functionally Graded Materials (FGM) is proposed in this paper. Instead of using the widely used explicit functional models, a feature tree based procedural model is proposed to represent generic material heterogeneities. A procedural model of this sort allows more than one explicit function to be incorporated to describe versatile material gradations and the material composition at a given location is no longer computed by simple evaluation of an analytic function, but obtained by execution of customizable procedures. This enables generic and diverse types of material variations to be represented, and most importantly, by a reasonably small number of design variables. The descriptive flexibility in the material heterogeneity formulation as well as the low dimensionality of the design vectors help facilitate the optimal design of functionally graded materials. Using the nature-inspired Particle Swarm Optimization (PSO) method, functionally graded materials with generic distributions can be efficiently optimized. We demonstrate, for the first time, that a PSO based optimizer outperforms classical mathematical programming based methods, such as active set and trust region algorithms, in the optimal design of functionally graded materials. The underlying reason for this performance boost is also elucidated with the help of benchmarked examples. © 2011 Elsevier Ltd. All rights reserved.

A Study of Fan-Distortion Interaction Within the NASA Rotor 67 Transonic Stage

The performance of a transonic fan operating within nonuniform inlet flow remains a key concern for the design and operability of a turbofan engine. This paper applies computational methods to improve the understanding of the interaction between a transonic fan and an inlet total pressure distortion. The test case studied is the NASA rotor 67 stage operating with a total pressure distortion covering a 120-deg sector of the inlet flow field. Full-annulus, unsteady, three-dimensional CFD has been used to simulate the test rig installation and the full fan assembly operating with inlet distortion. Novel post-processing methods have been applied to extract the fan performance and features of the interaction between the fan and the nonuniform inflow. The results of the unsteady computations agree well with the measurement data. The local operating condition of the fan at different positions around the annulus has been tracked and analyzed, and this is shown to be highly dependent on the swirl and mass flow redistribution that the rotor induces ahead of it due to the incoming distortion. The upstream flow effects lead to a variation in work input that determines the distortion pattern seen downstream of the fan stage. In addition, the unsteady computations also reveal more complex flow features downstream of the fan stage, which arise due to the three dimensionality of the flow and unsteadiness. © 2012 American Society of Mechanical Engineers.

Multiwall nanotubes, multilayers, and hybrid nanostructures: new frontiers for technology and Raman spectroscopy.

Technological progress is determined, to a great extent, by developments in material science. Breakthroughs can happen when a new type of material or new combinations of known materials with different dimensionality and functionality are created. Multilayered structures, being planar or concentric, are now emerging as major players at the forefront of research. Raman spectroscopy is a well-established characterization technique for carbon nanomaterials and is being developed for layered materials. In this issue of ACS Nano, Hirschmann et al. investigate triple-wall carbon nanotubes via resonant Raman spectroscopy, showing how a wealth of information can be derived about these complex structures. The next challenge is to tackle hybrid heterostructures, consisting of different planar or concentric materials, arranged "on demand" to achieve targeted properties.