Modeling of the Division Point of Different Propagation Mechanisms in the Near-Region Within Arched Tunnels

An accurate characterization of the near-region propagation of radio waves inside tunnels is of practical importance for the design and planning of advanced communication systems. However, there has been no consensus yet on the propagation mechanism in this region. Some authors claim that the propagation mechanism follows the free space model, others intend to interpret it by the multi-mode waveguide model. This paper clarifies the situation in the near-region of arched tunnels by analytical modeling of the division point between the two propagation mechanisms. The procedure is based on the combination of the propagation theory and the three-dimensional solid geometry. Three groups of measurements are employed to verify the model in different tunnels at different frequencies. Furthermore, simplified models for the division point in five specific application situations are derived to facilitate the use of the model. The results in this paper could help to deepen the insight into the propagation mechanism within tunnel environments.

Propagation Mechanism Analysis Before the Break Point Inside Tunnels

There is no unanimous consensus yet on the propagation mechanism before the break point inside tunnels. Some deem that the propagation mechanism follows the free space model, others argue that it should be described by the multimode waveguide model. Firstly, this paper analyzes the propagation loss in two mechanisms. Then, by conjunctively using the propagation theory and the three-dimensional solid geometry, a generic analytical model for the boundary between the free space mechanism and the multi-mode waveguide mechanism inside tunnels has been presented. Three measurement campaigns validate the model in different tunnels at different frequencies. Furthermore, the condition of the validity of the free space model used in tunnel environment has been discussed in some specific situations. Finally, through mathematical derivation, the seemingly conflicting viewpoints on the free space mechanism and the multi-mode waveguide mechanism have been unified in some specific situations by the presented generic model. The results in this paper can be helpful to gain deeper insight and better understanding of the propagation mechanism inside tunnels

PWM Control of a Buck Converter with an Amorphous Core Coil

Pulse-width modulation is widely used to control electronic converters. One of the most topologies used for high DC voltage/low DC voltage conversion is the Buck converter. It is obtained as a second order system with a LC filter between the switching subsystem and the load. The use of a coil with an amorphous magnetic material core instead of air core lets design converters with smaller size. If high switching frequencies are used for obtaining high quality voltage output, the value of the auto inductance L is reduced throughout the time. Then, robust controllers are needed if the accuracy of the converter response must not be affected by auto inductance and load variations. This paper presents a robust controller for a Buck converter based on a state space feedback control system combined with an additional virtual space variable which minimizes the effects of the inductance and load variations when a not-toohigh switching frequency is applied. The system exhibits a null steady-state average error response for the entire range of parameter variations. Simulation results are presented.

An approach to operational modal analysis using the expectation maximization algorithm

This paper presents the Expectation Maximization algorithm (EM) applied to operational modal analysis of structures. The EM algorithm is a general-purpose method for maximum likelihood estimation (MLE) that in this work is used to estimate state space models. As it is well known, the MLE enjoys some optimal properties from a statistical point of view, which make it very attractive in practice. However, the EM algorithm has two main drawbacks: its slow convergence and the dependence of the solution on the initial values used. This paper proposes two different strategies to choose initial values for the EM algorithm when used for operational modal analysis: to begin with the parameters estimated by Stochastic Subspace Identification method (SSI) and to start using random points. The effectiveness of the proposed identification method has been evaluated through numerical simulation and measured vibration data in the context of a benchmark problem. Modal parameters (natural frequencies, damping ratios and mode shapes) of the benchmark structure have been estimated using SSI and the EM algorithm. On the whole, the results show that the application of the EM algorithm starting from the solution given by SSI is very useful to identify the vibration modes of a structure, discarding the spurious modes that appear in high order models and discovering other hidden modes. Similar results are obtained using random starting values, although this strategy allows us to analyze the solution of several starting points what overcome the dependence on the initial values used.

Characterising emergent semantics in Twitter lists

Twitter lists organise Twitter users into multiple, often overlapping, sets. We believe that these lists capture some form of emergent semantics, which may be useful to characterise. In this paper we describe an approach for such characterisation, which consists of deriving semantic relations between lists and users by analyzing the cooccurrence of keywords in list names. We use the vector space model and Latent Dirichlet Allocation to obtain similar keywords according to co-occurrence patterns. These results are then compared to similarity measures relying on WordNet and to existing Linked Data sets. Results show that co-occurrence of keywords based on members of the lists produce more synonyms and more correlated results to that of WordNet similarity measures.

Operational modal analisys using joint statistical analysis of multiple records

In Operational Modal Analysis (OMA) of a structure, the data acquisition process may be repeated many times. In these cases, the analyst has several similar records for the modal analysis of the structure that have been obtained at di�erent time instants (multiple records). The solution obtained varies from one record to another, sometimes considerably. The differences are due to several reasons: statistical errors of estimation, changes in the external forces (unmeasured forces) that modify the output spectra, appearance of spurious modes, etc. Combining the results of the di�erent individual analysis is not straightforward. To solve the problem, we propose to make the joint estimation of the parameters using all the records. This can be done in a very simple way using state space models and computing the estimates by maximum-likelihood. The method provides a single result for the modal parameters that combines optimally all the records.

A unified framework for linear function approximation of value functions in stochastic control

This paper contributes with a unified formulation that merges previ- ous analysis on the prediction of the performance ( value function ) of certain sequence of actions ( policy ) when an agent operates a Markov decision process with large state-space. When the states are represented by features and the value function is linearly approxi- mated, our analysis reveals a new relationship between two common cost functions used to obtain the optimal approximation. In addition, this analysis allows us to propose an efficient adaptive algorithm that provides an unbiased linear estimate. The performance of the pro- posed algorithm is illustrated by simulation, showing competitive results when compared with the state-of-the-art solutions.

Non uniform embedding based on relevance analysis with reduced computational complexity: application to the detection of pathologies from biosignal recordings

Nonlinear analysis tools for studying and characterizing the dynamics of physiological signals have gained popularity, mainly because tracking sudden alterations of the inherent complexity of biological processes might be an indicator of altered physiological states. Typically, in order to perform an analysis with such tools, the physiological variables that describe the biological process under study are used to reconstruct the underlying dynamics of the biological processes. For that goal, a procedure called time-delay or uniform embedding is usually employed. Nonetheless, there is evidence of its inability for dealing with non-stationary signals, as those recorded from many physiological processes. To handle with such a drawback, this paper evaluates the utility of non-conventional time series reconstruction procedures based on non uniform embedding, applying them to automatic pattern recognition tasks. The paper compares a state of the art non uniform approach with a novel scheme which fuses embedding and feature selection at once, searching for better reconstructions of the dynamics of the system. Moreover, results are also compared with two classic uniform embedding techniques. Thus, the goal is comparing uniform and non uniform reconstruction techniques, including the one proposed in this work, for pattern recognition in biomedical signal processing tasks. Once the state space is reconstructed, the scheme followed characterizes with three classic nonlinear dynamic features (Largest Lyapunov Exponent, Correlation Dimension and Recurrence Period Density Entropy), while classification is carried out by means of a simple k-nn classifier. In order to test its generalization capabilities, the approach was tested with three different physiological databases (Speech Pathologies, Epilepsy and Heart Murmurs). In terms of the accuracy obtained to automatically detect the presence of pathologies, and for the three types of biosignals analyzed, the non uniform techniques used in this work lightly outperformed the results obtained using the uniform methods, suggesting their usefulness to characterize non-stationary biomedical signals in pattern recognition applications. On the other hand, in view of the results obtained and its low computational load, the proposed technique suggests its applicability for the applications under study.

Aplicación de la metodología GloBeM al servicio de cloud Hydra

El presente Proyecto de Fin de Máster consiste en crear una herramienta software capaz de monitorizar y gestionar la actividad de Hydra, una herramienta de gestión de entornos distribuidos, para que su estrategia de balanceo de carga se adecúe al modelo creado por GloBeM, una metodología de análisis de entornos distribuidos. GloBeM, que es una metodología externa, puede analizar y crear un modelo de máquina de estados finitos a partir de un sistema distribuido concreto. Hydra, una herramienta también externa, es un sistema de gestión de entornos cloud recientemente desarrollado y de código abierto, con un sistema de balanceo de carga efectivo pero algo limitado. El software construido recoge el modelo creado por GloBeM y lo analiza. A partir de ahí, monitoriza en tiempo real y a una frecuencia determinada la actividad de Hydra y el sistema cloud que ésta gestiona, y reconfigura sus parámetros para que su desempeño se ciña a lo estipulado por el modelo de GloBeM, extendiendo así el sistema de balanceo de carga original de Hydra.---ABSTRACT---This Master's Thesis Project involves creating a software able to monitor and manage the activity of Hydra, a tool for managing distributed environments, in order to adjust its load balancing strategy to the model created by GloBeM, an analysis methodology for distributed environments. GloBeM, which is an external methodology, can analyse and create a finite-state machine model from a particular cloud system. Hydra, also an external tool, is an open source management system for cloud environments recently developed, with a relatively limited system of load balancing. The created software gets the model created by GloBeM as an input and analyses it. From there, it monitors in real time and at a certain frequency Hydra’s activity and the cloud system that it manages, and reconfigures its parameters to adjust its performance to the stipulations by the GloBeM’s model, extending Hydra's original load balancing system.

Monte Carlo limit cycle characterization

The fixed point implementation of IIR digital filters usually leads to the appearance of zero-input limit cycles, which degrade the performance of the system. In this paper, we develop an efficient Monte Carlo algorithm to detect and characterize limit cycles in fixed-point IIR digital filters. The proposed approach considers filters formulated in the state space and is valid for any fixed point representation and quantization function. Numerical simulations on several high-order filters, where an exhaustive search is unfeasible, show the effectiveness of the proposed approach.

Comment: Is memristor a dynamic element?

The authors present a charge/flux formulation of the equations of memristive circuits, which seemingly show that the memristor should not be considered as a dynamic circuit element. Here, is shown that this approach implicitly reduces the dynamic analysis to a certain subset of the state space in such a way that the dynamic contribution of memristors is hidden. This reduction might entail a substantial loss of information, regarding e.g. the local stability properties of the circuit. Two examples illustrate this. It is concluded that the memristor, even with its unconventional features, must be considered as a dynamic element.

Asymptotic optimality of RESTART estimators in highly dependable systems

Abstract We consider a wide class of models that includes the highly reliable Markovian systems (HRMS) often used to represent the evolution of multi-component systems in reliability settings. Repair times and component lifetimes are random variables that follow a general distribution, and the repair service adopts a priority repair rule based on system failure risk. Since crude simulation has proved to be inefficient for highly-dependable systems, the RESTART method is used for the estimation of steady-state unavailability and other reliability measures. In this method, a number of simulation retrials are performed when the process enters regions of the state space where the chance of occurrence of a rare event (e.g., a system failure) is higher. The main difficulty involved in applying this method is finding a suitable function, called the importance function, to define the regions. In this paper we introduce an importance function which, for unbalanced systems, represents a great improvement over the importance function used in previous papers. We also demonstrate the asymptotic optimality of RESTART estimators in these models. Several examples are presented to show the effectiveness of the new approach, and probabilities up to the order of 10-42 are accurately estimated with little computational effort.

How the brain keeps the eyes still

The brain can hold the eyes still because it stores a memory of eye position. The brain’s memory of horizontal eye position appears to be represented by persistent neural activity in a network known as the neural integrator, which is localized in the brainstem and cerebellum. Existing experimental data are reinterpreted as evidence for an “attractor hypothesis” that the persistent patterns of activity observed in this network form an attractive line of fixed points in its state space. Line attractor dynamics can be produced in linear or nonlinear neural networks by learning mechanisms that precisely tune positive feedback.

Microcanonical finite-size scaling in second-order phase transitions with diverging specific heat

A microcanonical finite-size ansatz in terms of quantities measurable in a finite lattice allows extending phenomenological renormalization the so-called quotients method to the microcanonical ensemble. The ansatz is tested numerically in two models where the canonical specific heat diverges at criticality, thus implying Fisher renormalization of the critical exponents: the three-dimensional ferromagnetic Ising model and the two-dimensional four-state Potts model (where large logarithmic corrections are known to occur in the canonical ensemble). A recently proposed microcanonical cluster method allows simulating systems as large as L = 1024 Potts or L= 128 (Ising). The quotients method provides accurate determinations of the anomalous dimension, η, and of the (Fisher-renormalized) thermal ν exponent. While in the Ising model the numerical agreement with our theoretical expectations is very good, in the Potts case, we need to carefully incorporate logarithmic corrections to the microcanonical ansatz in order to rationalize our data.

Real time optimization in chemical process: evaluation of strategies, improvements and industrial application.

The increasing economic competition drives the industry to implement tools that improve their processes efficiencies. The process automation is one of these tools, and the Real Time Optimization (RTO) is an automation methodology that considers economic aspects to update the process control in accordance with market prices and disturbances. Basically, RTO uses a steady-state phenomenological model to predict the process behavior, and then, optimizes an economic objective function subject to this model. Although largely implemented in industry, there is not a general agreement about the benefits of implementing RTO due to some limitations discussed in the present work: structural plant/model mismatch, identifiability issues and low frequency of set points update. Some alternative RTO approaches have been proposed in literature to handle the problem of structural plant/model mismatch. However, there is not a sensible comparison evaluating the scope and limitations of these RTO approaches under different aspects. For this reason, the classical two-step method is compared to more recently derivative-based methods (Modifier Adaptation, Integrated System Optimization and Parameter estimation, and Sufficient Conditions of Feasibility and Optimality) using a Monte Carlo methodology. The results of this comparison show that the classical RTO method is consistent, providing a model flexible enough to represent the process topology, a parameter estimation method appropriate to handle measurement noise characteristics and a method to improve the sample information quality. At each iteration, the RTO methodology updates some key parameter of the model, where it is possible to observe identifiability issues caused by lack of measurements and measurement noise, resulting in bad prediction ability. Therefore, four different parameter estimation approaches (Rotational Discrimination, Automatic Selection and Parameter estimation, Reparametrization via Differential Geometry and classical nonlinear Least Square) are evaluated with respect to their prediction accuracy, robustness and speed. The results show that the Rotational Discrimination method is the most suitable to be implemented in a RTO framework, since it requires less a priori information, it is simple to be implemented and avoid the overfitting caused by the Least Square method. The third RTO drawback discussed in the present thesis is the low frequency of set points update, this problem increases the period in which the process operates at suboptimum conditions. An alternative to handle this problem is proposed in this thesis, by integrating the classic RTO and Self-Optimizing control (SOC) using a new Model Predictive Control strategy. The new approach demonstrates that it is possible to reduce the problem of low frequency of set points updates, improving the economic performance. Finally, the practical aspects of the RTO implementation are carried out in an industrial case study, a Vapor Recompression Distillation (VRD) process located in Paulínea refinery from Petrobras. The conclusions of this study suggest that the model parameters are successfully estimated by the Rotational Discrimination method; the RTO is able to improve the process profit in about 3%, equivalent to 2 million dollars per year; and the integration of SOC and RTO may be an interesting control alternative for the VRD process.