Prediction of the stiffness of nanoclay-polypropylene composites using a monte carlo finite element analysis approach

The present work deals with the prediction of stiffness of an Indian nanoclay-reinforced polypropylene composite (that can be termed as a nanocomposite) using a Monte Carlo finite element analysis (FEA) technique. Nanocomposite samples are at first prepared in the laboratory using a torque rheometer for achieving desirable dispersion of nanoclay during master batch preparation followed up with extrusion for the fabrication of tensile test dog-bone specimens. It has been observed through SEM (scanning electron microscopy) images of the prepared nanocomposite containing a given percentage (3–9% by weight) of the considered nanoclay that nanoclay platelets tend to remain in clusters. By ascertaining the average size of these nanoclay clusters from the images mentioned, a planar finite element model is created in which nanoclay groups and polymer matrix are modeled as separate entities assuming a given homogeneous distribution of the nanoclay clusters. Using a Monte Carlo simulation procedure, the distribution of nanoclay is varied randomly in an automated manner in a commercial FEA code, and virtual tensile tests are performed for computing the linear stiffness for each case. Values of computed stiffness modulus of highest frequency for nanocomposites with different nanoclay contents correspond well with the experimentally obtained measures of stiffness establishing the effectiveness of the present approach for further applications.

How Does Effectual Logic Impact Survival of High-tech Startups in an Emerging Economy? - Evidence from 99tests.com Case Study

During the early stages of operation, high-tech startups need to overcome the liability of newness and manage high degree of uncertainty. Several high-tech startups fail due to inability to deal with skeptical customers, underdeveloped markets and limited resources in selling an offering that has no precedent. This paper leverages the principles of effectuation (a logic of entrepreneurial decision making under uncertainty) to explain the journey from creation to survival of high-tech startups in an emerging economy. Based on the 99tests.com case study, this paper suggests that early stage high-tech startups in emerging economies can increase their probability of survival by adopting the principles of effectuation.

汽车覆盖件模具激光表面强化中环带区域的测量及轨迹规划

在模具表面激光强化中,测量及轨迹规划是工艺实现的关键环节。本文针对汽车覆盖件模具表面较为常见的侧壁结构,从中抽取出更为基本的一类型面—环带,提出了一种基于特征线的环带测量及轨迹规划的方法，并且在建立环带数学模型的基础上对误差进行了分析,证明了这种方法不仅简单、高效,而且满足相应的加工工艺要求。

空间机械臂在线实时避障路径规划研究

针对目前空间机械臂避障路径规划算法计算量大难以达到在线实时规划的缺点,对空间机械臂的在线实时避障路径规划问题进行了研究和探讨.采用规则体的包络对障碍物进行建模,并借助C空间法的思想,把障碍物和机械臂映射到两个相互垂直的平面内,将机械臂工作空间的三维问题转化为二维问题,并结合二岔树逆向寻优的方法进行路径搜索,从而大大减少了计算量,达到了在线实时规划的要求.最后在空间机器人仿真系统上对其进行了仿真研究,验证了该方法的可行性.

基于特征线的棱脊重构及加工轨迹规划算法

在基于三维曲面的激光强化加工中,针对某些特殊的加工表面,采用基于特征线的测量、重构及轨迹规划方法,可在不降低精度的基础上大幅度提高效率.主要针对常见的一种曲面类型即棱脊,介绍了基于特征线的棱脊重构及加工轨迹规划方法的应用.除适用于激光强化外,该方法对于一般的刀具加工也有一定的应用价值和参考意义.

SCARA errobota: mugimendu gaitasunen eta ibilbide sorkuntzaren inplementazioa software grafikoan

[EU]Lan honen gaia SCARA errobot motaren mugimendu gaitasunen analisia egitea da, eta ibilbideen sorkuntzarako metodoekin batera software grafiko batean inplementatzea mugimenduaren simulazioa egin ahal izateko. Errobot serieen zinematikaren oinarrizko ezagutzatik hasita, mota konkretu batetara aplikatu egiten da eta honek aurkezten dituen berezitasunak garatu egiten dira, bi helburutara bideratuta: SCARA errobotaren mugimendu gaitasunak ezagutzea. Ibilbideen sorkuntzarako metodo baten inplementazioa. Hasteko, gaiaren egoera aztertu da, aplikazio nagusien eta ibilbide moten informazioa batzeko. Halaber ibilbideen sorkuntzarako metodoak arakatu dira, erabilera honetarako aproposena aurkitzeko. Jarraian, errobotaren analisia burutu da, ohizko erreminta matematikoak erabiliz, funtsezkoak diren lan eremua eta kokapen singularrak lortzeko. Ostean, software grafikoa garatu da mugimendu gaitasun hauek simulatzeko. Ohiko aplikazioetan oinarritutako ibilbideak sortzeko aukerak gehitu dira. Amaitzeko, oztopoak saihesten dituen ibilbideen sorkuntzarako metodoa inplementatu da, “pick and place” ibilbide motaren barruan.

Essays in Behavioral Decision Theory

This thesis studies decision making under uncertainty and how economic agents respond to information. The classic model of subjective expected utility and Bayesian updating is often at odds with empirical and experimental results; people exhibit systematic biases in information processing and often exhibit aversion to ambiguity. The aim of this work is to develop simple models that capture observed biases and study their economic implications.

In the first chapter I present an axiomatic model of cognitive dissonance, in which an agent's response to information explicitly depends upon past actions. I introduce novel behavioral axioms and derive a representation in which beliefs are directionally updated. The agent twists the information and overweights states in which his past actions provide a higher payoff. I then characterize two special cases of the representation. In the first case, the agent distorts the likelihood ratio of two states by a function of the utility values of the previous action in those states. In the second case, the agent's posterior beliefs are a convex combination of the Bayesian belief and the one which maximizes the conditional value of the previous action. Within the second case a unique parameter captures the agent's sensitivity to dissonance, and I characterize a way to compare sensitivity to dissonance between individuals. Lastly, I develop several simple applications and show that cognitive dissonance contributes to the equity premium and price volatility, asymmetric reaction to news, and belief polarization.

The second chapter characterizes a decision maker with sticky beliefs. That is, a decision maker who does not update enough in response to information, where enough means as a Bayesian decision maker would. This chapter provides axiomatic foundations for sticky beliefs by weakening the standard axioms of dynamic consistency and consequentialism. I derive a representation in which updated beliefs are a convex combination of the prior and the Bayesian posterior. A unique parameter captures the weight on the prior and is interpreted as the agent's measure of belief stickiness or conservatism bias. This parameter is endogenously identified from preferences and is easily elicited from experimental data.

The third chapter deals with updating in the face of ambiguity, using the framework of Gilboa and Schmeidler. There is no consensus on the correct way way to update a set of priors. Current methods either do not allow a decision maker to make an inference about her priors or require an extreme level of inference. In this chapter I propose and axiomatize a general model of updating a set of priors. A decision maker who updates her beliefs in accordance with the model can be thought of as one that chooses a threshold that is used to determine whether a prior is plausible, given some observation. She retains the plausible priors and applies Bayes' rule. This model includes generalized Bayesian updating and maximum likelihood updating as special cases.

Desarrollo de una herramienta virtual destinada al diseño de una plataforma robótica reconfigurable.

[ES]Este proyecto tiene como objeto aumentar el conocimiento concerniente a mecanismos robóticos reconfigurables, así como ponerlo en práctica. Estos mecanismos pueden lograr rápidas transiciones y son capaces de adaptarse a sí mismos a muchos entornos diferentes, conduciendo a una reducción de costes y requerimientos de espacio. Para ello, se estudia el estado del arte, de manera que se pueda reunir información sobre las principales aplicaciones y oportunidades que este campo ofrece en diferentes áreas. A continuación, se requiere llevar a cabo un análisis cinemático de un robot específico, y junto a métodos de planificación de trayectorias, su implementación en un software gráfico para simular su movimiento. La herramienta de software “Matlab” va a ser la que permitirá llevar a cabo toda la programación y representación a lo largo de todo el proyecto.

Planejamento Tributário pela Ótica do Estado: dívida ativa nos municípios fluminenses, sua relação com a Lei de Responsabilidade Fiscal e com o comportamento fiscal dos contribuintes

Ao discorrer-se sobre o tema Planejamento Tributário as considerações remetem, quase que de forma intuitiva, aos interesses unicamente das entidades contribuintes, que depositam nos seus gestores a incumbência de otimização de seus patrimônios. Entretanto, esse é um instrumental igualmente indispensável no repertório de soluções a serem adotadas pelos gestores públicos, também responsáveis em gerir da melhor forma possível o patrimônio, neste caso, da sociedade. Considerando a visão do gestor público sobre Planejamento Tributário, a qual foi chamada Ótica do Estado, este trabalho procurou trazer a dívida ativa nos municípios fluminenses para o centro da discussão, atribuindo-lhe um enfoque mais financeiro, tendo em vista que grande parte dos trabalhos que lhes são direcionados assenta-se sobre considerações jurídico-legais. Sendo assim, foram analisados dados extraídos de órgãos oficiais buscando-se verificar as relações existentes entre a cobrança de créditos inscritos em dívida ativa, os preceitos da Lei de Responsabilidade Fiscal e o comportamento dos contribuintes oriundo da postura do agente fiscalizador. Para tanto, a metodologia foi dividida em duas abordagens distintas (momentos). Para a primeira abordagem foi desenvolvida uma forma de conceituação para os níveis de recebimento de dívida ativa dos municípios, estruturada sobre a adaptação dos critérios desenvolvidos pela Associação Brasileira de Orçamento Público (ABOP); na segunda abordagem foram utilizados também de forma adaptada os parâmetros desenvolvidos pela Secretaria do Tesouro Nacional (STN) para análise das finanças municipais no Brasil. Os resultados sugerem que a maioria dos municípios fluminenses está transgredindo a LRF e que há influência no comportamento fiscal dos contribuintes.

Risk-sensitivity in sensorimotor control.

Recent advances in theoretical neuroscience suggest that motor control can be considered as a continuous decision-making process in which uncertainty plays a key role. Decision-makers can be risk-sensitive with respect to this uncertainty in that they may not only consider the average payoff of an outcome, but also consider the variability of the payoffs. Although such risk-sensitivity is a well-established phenomenon in psychology and economics, it has been much less studied in motor control. In fact, leading theories of motor control, such as optimal feedback control, assume that motor behaviors can be explained as the optimization of a given expected payoff or cost. Here we review evidence that humans exhibit risk-sensitivity in their motor behaviors, thereby demonstrating sensitivity to the variability of "motor costs." Furthermore, we discuss how risk-sensitivity can be incorporated into optimal feedback control models of motor control. We conclude that risk-sensitivity is an important concept in understanding individual motor behavior under uncertainty.

Surface excavation with model predictive control

This paper describes the application of variable-horizon model predictive control to trajectory generation in surface excavation. A nonlinear dynamic model of a surface mining machine digging in oil sand is developed as a test platform. This model is then stabilised with an inner-loop controller before being linearised to generate a prediction model. The linear model is used to design a predictive controller for trajectory generation. A variable horizon formulation is augmented with extra terms in the cost function to allow more control over digging, whilst still preserving the guarantee of finite-time completion. Simulations show the generation of realistic trajectories, motivating new applications of variable horizon MPC for autonomy that go beyond the realm of vehicle path planning. ©2010 IEEE.

Striatal activity underlies novelty-based choice in humans.

The desire to seek new and unfamiliar experiences is a fundamental behavioral tendency in humans and other species. In economic decision making, novelty seeking is often rational, insofar as uncertain options may prove valuable and advantageous in the long run. Here, we show that, even when the degree of perceptual familiarity of an option is unrelated to choice outcome, novelty nevertheless drives choice behavior. Using functional magnetic resonance imaging (fMRI), we show that this behavior is specifically associated with striatal activity, in a manner consistent with computational accounts of decision making under uncertainty. Furthermore, this activity predicts interindividual differences in susceptibility to novelty. These data indicate that the brain uses perceptual novelty to approximate choice uncertainty in decision making, which in certain contexts gives rise to a newly identified and quantifiable source of human irrationality.

Cortical substrates for exploratory decisions in humans.

Decision making in an uncertain environment poses a conflict between the opposing demands of gathering and exploiting information. In a classic illustration of this 'exploration-exploitation' dilemma, a gambler choosing between multiple slot machines balances the desire to select what seems, on the basis of accumulated experience, the richest option, against the desire to choose a less familiar option that might turn out more advantageous (and thereby provide information for improving future decisions). Far from representing idle curiosity, such exploration is often critical for organisms to discover how best to harvest resources such as food and water. In appetitive choice, substantial experimental evidence, underpinned by computational reinforcement learning (RL) theory, indicates that a dopaminergic, striatal and medial prefrontal network mediates learning to exploit. In contrast, although exploration has been well studied from both theoretical and ethological perspectives, its neural substrates are much less clear. Here we show, in a gambling task, that human subjects' choices can be characterized by a computationally well-regarded strategy for addressing the explore/exploit dilemma. Furthermore, using this characterization to classify decisions as exploratory or exploitative, we employ functional magnetic resonance imaging to show that the frontopolar cortex and intraparietal sulcus are preferentially active during exploratory decisions. In contrast, regions of striatum and ventromedial prefrontal cortex exhibit activity characteristic of an involvement in value-based exploitative decision making. The results suggest a model of action selection under uncertainty that involves switching between exploratory and exploitative behavioural modes, and provide a computationally precise characterization of the contribution of key decision-related brain systems to each of these functions.

大型公共环境人员疏散路径规划研究

大型公共环境人员疏散路径规划研究对指导安全设施建设、应急预案的制定、实施和推演都有重要意义。近年来进化计算领域兴起的分布估计算法为路径规划问题的解决提供了新的优化工具。本论文将一种典型的分布估计算法——贝叶斯优化算法，应用到大型公共环境人员疏散路径规划问题解决过程中，针对单路径人员疏散、多路径人员疏散和多目标点人员疏散三类具体的疏散路径规划问题，分别设计和实现了相应的基于贝叶斯优化的人员疏散路径规划算法，并在仿真实验中根据大型公共环境空间类型复杂和人员相对密集等特点，面向不同的规划要求和目的，求解出了满足约束条件的最优疏散路径集合。 本文提出的基于贝叶斯优化的人员疏散路径规划算法，以大型公共环境的基本信息已知为前提，基于二维环境下的拓扑空间法，按照应用系统的规划要求，通过引入疏散性能，路径安全性和易通性等参数，对疏散空间建立全局环境模型；在系统分析分布估计算法基本框架和特点的基础上，引入其中一类典型算法——贝叶斯优化算法，设计出一系列适于不同疏散路径规划问题的基于贝叶斯优化的路径规划算法。该算法用贝叶斯网络对优选路径集合建立概率模型，并由建立起的贝叶斯网络产生新的路径集合，与原有路径进行竞争，可以在找到一条最优的路径集合的同时，利用中间结果，快速找出多条较优的路径。仿真实验标明，该算法可以有效的满足大型公共环境下人员疏散的任务要求。