Energy absorption behaviours of CSM-based GFRC plates with hemispherical features

In the present study, the mechanical behaviour of CSM (chopped strand mat)-based GFRC (glass fibre-reinforced composite) plates with single and multiple hemispheres under compressive loads has been investigated both experimentally and numerically. The basic stress-strain behaviours arc identified with quasi-static tests on two-ply coupon laminates and short cylinders, and these are followed up with compressive tests in a UTM (universal testing machine) on single- and multiple-hemisphere plates. The ability of an explicit LS-DYNA solver in predicting the complex material behaviour of composite hemispheres, including failure, is demonstrated. The relevance and scalability of the present class of structural components as `force-multipliers' and `energy-multipliers' have been justified by virtue of findings that as the number of hemispheres in a panel increased from one to four, peak load and average absorbed energy rose by factors of approximately four and six, respectively. The performance of a composite hemisphere has been compared to similar-sized steel and aluminium hemispheres, and the former is found to be of distinctly higher specific energy than the steel specimen. A simulation-based study has also been carried out on a composite 2 x 2-hemisphere panel under impact loads and its behaviour approaching that of an ideal energy absorber has been predicted. In summary, the present investigation has established the efficacy of composite plates with hemispherical force multipliers as potential energy-absorbing countermeasures and the suitability of CAE (computer-aided engineering) for their design.

Measurement of effective permeability of reinforcement mats using sensitivity analysis

A methodology using sensitivity analysis is proposed to measure the effective permeability which includes the interaction of the resin and the reinforcement. Initially, mold-filling experiments were performed at isothermal conditions on the test specimen and the positions of the flow front were tracked with time using a flow visualization method. Following this, mold-filling experiments were simulated using a commercial software to obtain the positions of the flow front with time at the process conditions used for experiments. Several iterations were performed using different trial values of the permeability until the experimentally tracked and simulated positions of the flow front with time were matched. Finally, the value of the permeability thus obtained was validated by comparing the positions obtained by performing the experiments at different process conditions with the positions obtained by simulating the experiments. In this study, woven roving and chopped strand mats of E-class glass fiber and unsaturated polyester resin were used for the experiments. From the results, it was found that the measured permeabilities were consistent with varying process conditions. POLYM. COMPOS., 2012. (c) 2012 Society of Plastics Engineers

A summer monsoon pump to keep the Bay of Bengal salty

The Bay of Bengal receives a large influx of freshwater from precipitation and river discharge. Outflow of excess freshwater and inflow of saltier water is required to prevent the bay from freshening. Relatively fresh water flows out of the bay along its boundaries and inflow of saltier water occurs via the Summer Monsoon Current (SMC), which flows eastward from the Arabian Sea into the bay. This saltier water, however, slides under the lighter surface water of the bay. Maintaining the salt balance of the bay therefore demands upward mixing of this saltier, subsurface water. Here, we show that an efficient mechanism for this mixing is provided by upward pumping of saltier water in several bursts during the summer monsoon along the meandering path of the SMC. Advection by currents can then take this saltier water into the rest of the basin, allowing the bay to stay salty despite a large net freshwater input.

Phytoplankton size structure in the southern Bay of Bengal modified by the Summer Monsoon Current and associated eddies: Implications on the vertical biogenic flux

The present study combines field and satellite observations to investigate how hydrographical transformations influence phytoplankton size structure in the southern Bay of Bengal during the peak Southwest Monsoon/Summer Monsoon (July-August). The intrusion of the Summer Monsoon Current (SMC) into the Bay of Bengal and associated changes in sea surface chemistry, traceable eastward up to 90 degrees E along 8 degrees N, seems to influence biology of the region significantly. Both in situ and satellite (MODIS) data revealed low surface chlorophyll except in the area influenced by the SMC During the study period, two well-developed cydonic eddies (north) and an anti-cyclonic eddy (south), closely linked to the main eastward flow of the SMC, were sampled. Considering the capping effect of the low-saline surface water that is characteristic of the Bay of Bengal, the impact of the cyclonic eddy, estimated in terms of enhanced nutrients and chlorophyll, was mostly restricted to the subsurface waters (below 20 m depth). Conversely, the anti-cyclonic eddy aided by the SMC was characterized by considerably higher nutrient concentration and chlorophyll in the upper water column (upper 60 m), which was contrary to the general characteristic of such eddies. Albeit smaller phytoplankton predominated the southern Bay of Bengal (60-95% of the total chlorophyll), the contribution of large phytoplankton was double in the regions influenced by the SMC and associated eddies. Multivariate analysis revealed the extent to which SMC-associated eddies spatially influence phytoplankton community structure. The study presents the first direct quantification of the size structure of phytoplankton from the southern Bay of Bengal and demonstrates that the SMC-associated hydrographical ramifications significantly increase the phytoplankton biomass contributed by larger phytoplankton and thereby influence the vertical opal and organic carbon flux in the region. (C) 2014 Elsevier B.V. All rights reserved.

Evaluación comparativa de sistemas pastoriles naturales con y sin árboles, en condiciones del trópico húmedo, Nueva Guinea (RAAS)

Con el Objeto de caracterizar los sistemas pastoriles del municipio de Nueva Guinea, se realizó el presente trabajo. El cual se llevó a cabo en el Municipio de Nueva Guinea. El propósito general del estudio fue determinar los efectos (positivos y negativos), en las diferentes variables de los principales componentes (Pasto, Animal, Suelo y Árbol) en dos sistemas de explotación ganadero 1) con y 2) sin árboles, en sistemas extensivos, evaluados comparativamente, y así determinar el impacto que causa la ganadería y el mal uso de la tierra en el Municipio. Se tomaron 4 fincas, dos con árboles consideradas como sistemas silvopastoriles (SSP) y dos sin árboles consideradas como sistemas monocultivo (SMc). El estudio se ejecutó mediante una metodología en la que no se intervino sobre la actividad rutinaria de los productores, para la realización de la toma de datos, realizándose durante el proceso productivo y con la presencia del ganado en cada una de las fincas evaluadas, lo que facilitó la aceptación de cada uno de los productores al proporcionar la información requerida. En el componente pasto se determinó la producción de biomasa fresca y seca, referida como la disponibilidad, además se determinó la altura, cobertura, regeneración, compatibilidad, composición botánica de la pastura, en ambos sistemas pastoriles. En el componente animal se cuantifico la producción de leche y carne (peso vivo del animal). En el componente suelo sus características física y química, así como los factores que inciden sobre su degradación, y en el componente arbóreo las características diamétricas, altura, área basal y volumen de las especies existentes más. La producción de biomasa fresca y seca fue mejor en los sistemas de monocultivo que en los silvopastoriles, pero esta diferencia se revierte cuando se analiza esta variable en conjunto con las otras variables, sobre todo cobertura, donde al analizar los SSP tienen un 87% de producción en un 53% del área que presentaron los SMc. Por lo que los sistemas sin árboles son los que mejor comportamiento presentaron en el presente estudio. Si se analizan de manera conjunta los sistemas con árboles resultan ser más productivos. El componente pasto fue afectado por una serie de factores, tales como manejo, número de animales presente en el pastoreo, presencia de árboles y estado de la finca o la finalidad que realmente posea dicho sistema. La composición botánica de la pastura estaba conformada principalmente por pasto Retana, el cual por mal manejo, permitió la presencia de otras especies vegetales no forrajeras, las que en determinado momento sobrepasaban el 60% de cobertura. Las condiciones climáticas influyeron sobre el rendimiento de la pastura en la producción animal se determinó que estaba influenciada por el grupo racial, no determinándose la influencia de los sistemas en estudio en la productividad de los mismos. En las fincas donde había árboles (SSP), estos abarcaban áreas de hasta 70%, pero no tenían utilidad en la producción animal. Comúnmente los árboles estaban dispersos en los potreros, con áreas de copas que limitan el desarrollo de las pasturas (pasto ratana). Existía un manejo inadecuado de los suelos con pasto con y sin árboles, donde las altas precipitaciones, el sobre pastoreo y la tala intensiva de los bosques han dejado a los suelos expuestos a la erosión hídrica, induciendo que gran cantidad de rocas de origen básico afloraran a la superficie. Los sistemas silvopastoriles son una alternativa para las necesidades climáticas del hato ganadero en época de sequía

Filtering via approximate Bayesian computation

Approximate Bayesian computation (ABC) has become a popular technique to facilitate Bayesian inference from complex models. In this article we present an ABC approximation designed to perform biased filtering for a Hidden Markov Model when the likelihood function is intractable. We use a sequential Monte Carlo (SMC) algorithm to both fit and sample from our ABC approximation of the target probability density. This approach is shown to, empirically, be more accurate w.r.t.~the original filter than competing methods. The theoretical bias of our method is investigated; it is shown that the bias goes to zero at the expense of increased computational effort. Our approach is illustrated on a constrained sequential lasso for portfolio allocation to 15 constituents of the FTSE 100 share index.

Forward Smoothing Using Sequential Monte Carlo

Sequential Monte Carlo (SMC) methods are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models. We propose a new SMC algorithm to compute the expectation of additive functionals recursively. Essentially, it is an on-line or "forward only" implementation of a forward filtering backward smoothing SMC algorithm proposed by Doucet, Godsill and Andrieu (2000). Compared to the standard \emph{path space} SMC estimator whose asymptotic variance increases quadratically with time even under favorable mixing assumptions, the non asymptotic variance of the proposed SMC estimator only increases linearly with time. We show how this allows us to perform recursive parameter estimation using an SMC implementation of an on-line version of the Expectation-Maximization algorithm which does not suffer from the particle path degeneracy problem.

Sequential decision making in general state space models

Many problems in control and signal processing can be formulated as sequential decision problems for general state space models. However, except for some simple models one cannot obtain analytical solutions and has to resort to approximation. In this thesis, we have investigated problems where Sequential Monte Carlo (SMC) methods can be combined with a gradient based search to provide solutions to online optimisation problems. We summarise the main contributions of the thesis as follows. Chapter 4 focuses on solving the sensor scheduling problem when cast as a controlled Hidden Markov Model. We consider the case in which the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. In sensor scheduling, our aim is to minimise the variance of the estimation error of the hidden state with respect to the action sequence. We present a novel SMC method that uses a stochastic gradient algorithm to find optimal actions. This is in contrast to existing works in the literature that only solve approximations to the original problem. In Chapter 5 we presented how an SMC can be used to solve a risk sensitive control problem. We adopt the use of the Feynman-Kac representation of a controlled Markov chain flow and exploit the properties of the logarithmic Lyapunov exponent, which lead to a policy gradient solution for the parameterised problem. The resulting SMC algorithm follows a similar structure with the Recursive Maximum Likelihood(RML) algorithm for online parameter estimation. In Chapters 6, 7 and 8, dynamic Graphical models were combined with with state space models for the purpose of online decentralised inference. We have concentrated more on the distributed parameter estimation problem using two Maximum Likelihood techniques, namely Recursive Maximum Likelihood (RML) and Expectation Maximization (EM). The resulting algorithms can be interpreted as an extension of the Belief Propagation (BP) algorithm to compute likelihood gradients. In order to design an SMC algorithm, in Chapter 8 uses a nonparametric approximations for Belief Propagation. The algorithms were successfully applied to solve the sensor localisation problem for sensor networks of small and medium size.

An overview of sequential Monte Carlo methods for parameter estimation in general state-space models

Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, are numerical techniques based on Importance Sampling for solving the optimal state estimation problem. The task of calibrating the state-space model is an important problem frequently faced by practitioners and the observed data may be used to estimate the parameters of the model. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed for this task accompanied with a discussion of their advantages and limitations.

Sequential Monte Carlo computation of the score and observed information matrix in state-space models with application to parameter estimation

Sequential Monte Carlo (SMC) methods are popular computational tools for Bayesian inference in non-linear non-Gaussian state-space models. For this class of models, we propose SMC algorithms to compute the score vector and observed information matrix recursively in time. We propose two different SMC implementations, one with computational complexity $\mathcal{O}(N)$ and the other with complexity $\mathcal{O}(N^{2})$ where $N$ is the number of importance sampling draws. Although cheaper, the performance of the $\mathcal{O}(N)$ method degrades quickly in time as it inherently relies on the SMC approximation of a sequence of probability distributions whose dimension is increasing linearly with time. In particular, even under strong \textit{mixing} assumptions, the variance of the estimates computed with the $\mathcal{O}(N)$ method increases at least quadratically in time. The $\mathcal{O}(N^{2})$ is a non-standard SMC implementation that does not suffer from this rapid degrade. We then show how both methods can be used to perform batch and recursive parameter estimation.

Convergence of the auxiliary particle implementation of the PHD filter

Optimal Bayesian multi-target filtering is in general computationally impractical owing to the high dimensionality of the multi-target state. The Probability Hypothesis Density (PHD) filter propagates the first moment of the multi-target posterior distribution. While this reduces the dimensionality of the problem, the PHD filter still involves intractable integrals in many cases of interest. Several authors have proposed Sequential Monte Carlo (SMC) implementations of the PHD filter. However, these implementations are the equivalent of the Bootstrap Particle Filter, and the latter is well known to be inefficient. Drawing on ideas from the Auxiliary Particle Filter (APF), a SMC implementation of the PHD filter which employs auxiliary variables to enhance its efficiency was proposed by Whiteley et. al. Numerical examples were presented for two scenarios, including a challenging nonlinear observation model, to support the claim. This paper studies the theoretical properties of this auxiliary particle implementation. $\mathbb{L}_p$ error bounds are established from which almost sure convergence follows.

Auxiliary particle implementation of the probability hypothesis density filter

Optimal Bayesian multi-target filtering is, in general, computationally impractical owing to the high dimensionality of the multi-target state. The Probability Hypothesis Density (PHD) filter propagates the first moment of the multi-target posterior distribution. While this reduces the dimensionality of the problem, the PHD filter still involves intractable integrals in many cases of interest. Several authors have proposed Sequential Monte Carlo (SMC) implementations of the PHD filter. However, these implementations are the equivalent of the Bootstrap Particle Filter, and the latter is well known to be inefficient. Drawing on ideas from the Auxiliary Particle Filter (APF), we present a SMC implementation of the PHD filter which employs auxiliary variables to enhance its efficiency. Numerical examples are presented for two scenarios, including a challenging nonlinear observation model.

TrustedPals plataformarako middlewarearen inplementazioa eta simulazioa

Sistema banatuak zenbait konputagailu edo gailu autonomoaz osaturiko sareak dira, non algoritmo banatuen bidez partaide guztien lana koordinatzen da entitate bakarra izatearen irudia emanez. Eredu honi esker sistemaren sendotasuna handitzen da, posible baita sistemak aurrera jarraitzea zenbait partaidek huts egin arren. Sistema banatuak diseinatzeak badu zenbait zailtasun, prozesu guztien arteko koordinazioa lortu behar baita. Erronka nagusietako bat adostasuna edo consensus lortzea da; hau da, prozesu guztiak ados jartzea zerbait erabaki behar dutenean. Ingurune desberdinetan planteatu badaiteke ere, lan honetan Byzantine ingurunean egingo da. Ingurune honetan partaideen hutsegiteak ausaz gerta daitezke eta edozein momentutan. Horrez gain, hutsegite horiek edozein motakoak izan daitezke, hala nola, prozesu bat bertan behera geratzea edota prozesu baten eskaera okerra edo lekuz kanpokoa egitea. Aurkeztutako consensus arazoa garrantzi handikoa da sistema banatuen arloan, honen bitartez beste hainbat helburu lortu baitaitezke. Horien artean Secure Multy-party Computation (SMC) dugu, non sare banatu bateko partaide guztiek adostasuna lotu behar dute partaide bakoitzaren informazioa gainontzekoei ezkutatuz. Horren adibide bezala “aberatsaren arazoa” azaldu ohi da, non partaide guztiek aurkitu behar dute zein den beraien artean aberatsena, partaide bakoitzak gainontzekoen “aberastasuna” ezagutu ahal izan gabe. SMC erabili daiteke soluzioa emateko planteamendu bera jarraitzen duten aplikazio erreal askori, hala nola, enkante pribatuak edo bozketak. SMC inplementatu ahal izateko TrustedPals izeneko plataforma dugu, non diseinu modularra jarraituz smartcard bat eta algoritmo banatuak konbinatzen dira lehenengo consensus eta ondoren SMC lortzeko. Karrera amaierako proiektu honen helburua TrustedPals proposamenaren alde praktikoa jorratzea izango da. Horretarako proposamenaren algoritmo banatuak inplementatu eta simulatuko dira zenbait probetako kasuetan. Simulazioak bideratzeko gertaera diskretuko NS-3 simulagailuan erabiliko da. Simulazio eszenario desberdinak inplementatuko dira eta ondoren emaitzak aztertuko dira.