Leak detection by inverse transient analysis in an experimental PVC pipe system

Leakage reduction in water supply systems and distribution networks has been an increasingly important issue in the water industry since leaks and ruptures result in major physical and economic losses. Hydraulic transient solvers can be used in the system operational diagnosis, namely for leak detection purposes, due to their capability to describe the dynamic behaviour of the systems and to provide substantial amounts of data. In this research work, the association of hydraulic transient analysis with an optimisation model, through inverse transient analysis (ITA), has been used for leak detection and its location in an experimental facility containing PVC pipes. Observed transient pressure data have been used for testing ITA. A key factor for the success of the leak detection technique used is the accurate calibration of the transient solver, namely adequate boundary conditions and the description of energy dissipation effects since PVC pipes are characterised by a viscoelastic mechanical response. Results have shown that leaks were located with an accuracy between 4-15% of the total length of the pipeline, depending on the discretisation of the system model.

Bayesian analysis of the scatterometer wind retrieval inverse problem:Some new approaches

The retrieval of wind vectors from satellite scatterometer observations is a non-linear inverse problem. A common approach to solving inverse problems is to adopt a Bayesian framework and to infer the posterior distribution of the parameters of interest given the observations by using a likelihood model relating the observations to the parameters, and a prior distribution over the parameters. We show how Gaussian process priors can be used efficiently with a variety of likelihood models, using local forward (observation) models and direct inverse models for the scatterometer. We present an enhanced Markov chain Monte Carlo method to sample from the resulting multimodal posterior distribution. We go on to show how the computational complexity of the inference can be controlled by using a sparse, sequential Bayes algorithm for estimation with Gaussian processes. This helps to overcome the most serious barrier to the use of probabilistic, Gaussian process methods in remote sensing inverse problems, which is the prohibitively large size of the data sets. We contrast the sampling results with the approximations that are found by using the sparse, sequential Bayes algorithm.

Estimação e compensação de não linearidades inerentes aos atuadores dos processos industriais

The oscillations presents in control loops can cause damages in petrochemical industry. Canceling, or even preventing such oscillations, would save up to large amount of dollars. Studies have identified that one of the causes of these oscillations are the nonlinearities present on industrial process actuators. This study has the objective to develop a methodology for removal of the harmful effects of nonlinearities. Will be proposed an parameter estimation method to Hammerstein model, whose nonlinearity is represented by dead-zone or backlash. The estimated parameters will be used to construct inverse models of compensation. A simulated level system was used as a test platform. The valve that controls inflow has a nonlinearity. Results and describing function analysis show an improvement on system response

Ragu: a free tool for the analysis of EEG and MEG event-related scalp field data using global randomization statistics

We present a program (Ragu; Randomization Graphical User interface) for statistical analyses of multichannel event-related EEG and MEG experiments. Based on measures of scalp field differences including all sensors, and using powerful, assumption-free randomization statistics, the program yields robust, physiologically meaningful conclusions based on the entire, untransformed, and unbiased set of measurements. Ragu accommodates up to two within-subject factors and one between-subject factor with multiple levels each. Significance is computed as function of time and can be controlled for type II errors with overall analyses. Results are displayed in an intuitive visual interface that allows further exploration of the findings. A sample analysis of an ERP experiment illustrates the different possibilities offered by Ragu. The aim of Ragu is to maximize statistical power while minimizing the need for a-priori choices of models and parameters (like inverse models or sensors of interest) that interact with and bias statistics.

Autonomous Acquisition of Natural Situated Communication

An important part of human intelligence, both historically and operationally, is our ability to communicate. We learn how to communicate, and maintain our communicative skills, in a society of communicators – a highly effective way to reach and maintain proficiency in this complex skill. Principles that might allow artificial agents to learn language this way are in completely known at present – the multi-dimensional nature of socio-communicative skills are beyond every machine learning framework so far proposed. Our work begins to address the challenge of proposing a way for observation-based machine learning of natural language and communication. Our framework can learn complex communicative skills with minimal up-front knowledge. The system learns by incrementally producing predictive models of causal relationships in observed data, guided by goal-inference and reasoning using forward-inverse models. We present results from two experiments where our S1 agent learns human communication by observing two humans interacting in a realtime TV-style interview, using multimodal communicative gesture and situated language to talk about recycling of various materials and objects. S1 can learn multimodal complex language and multimodal communicative acts, a vocabulary of 100 words forming natural sentences with relatively complex sentence structure, including manual deictic reference and anaphora. S1 is seeded only with high-level information about goals of the interviewer and interviewee, and a small ontology; no grammar or other information is provided to S1 a priori. The agent learns the pragmatics, semantics, and syntax of complex utterances spoken and gestures from scratch, by observing the humans compare and contrast the cost and pollution related to recycling aluminum cans, glass bottles, newspaper, plastic, and wood. After 20 hours of observation S1 can perform an unscripted TV interview with a human, in the same style, without making mistakes.

Análise do grau de proteção do Aquífero Barreiras em perímetros sob fertilização química a partir de dados hidrogeofísicos - área da bacia do Rio Catu-RN

The use of chemical fertilization in arable perimeters provides increased productivity, though it can eventually lead to a qualitative depreciation of groundwater sources, especially if such sources are unconfined in nature. In this context, this thesis presents results from an analysis of the level of natural protection of the Barreiras Aquifer in an area located on the eastern coast of the Rio Grande do Norte State - Brazil. Such an aquifer is clastic in nature and has an unconfined hydraulic character, which clearly makes it susceptible to contamination from surface ground loads with contaminants associated with the leaching of excess fertilizers not absorbed by ground vegetation. The methodology used was based on the use of hydro-geophysical data, particularly inverse models of vertical electrical soundings (VES) and information from well profiles, allowing the acquisition of longitudinal conductance cartographies (S), data in mili-Siemens (mS), and the vulnerability of the aquifer. Such maps were prepared with emphasis to the unsaturated overlying zone, highlighting in particular its thickness and occurrence of clay lithologies. Thus, the longitudinal conductance and aquifer vulnerability reveal areas more susceptible to contamination in the northeast and east-central sections of the study area, with values equal to or less than 10mS and greater than or equal to 0,50, respectively. On the other hand, the southwestern section proved to be less susceptible to contamination, whose longitudinal conductance and vulnerability indices are greater than or equal to 30mS and less than or equal to 0,40, respectively.

Análise do grau de proteção do Aquífero Barreiras em perímetros sob fertilização química a partir de dados hidrogeofísicos - área da bacia do Rio Catu-RN

The use of chemical fertilization in arable perimeters provides increased productivity, though it can eventually lead to a qualitative depreciation of groundwater sources, especially if such sources are unconfined in nature. In this context, this thesis presents results from an analysis of the level of natural protection of the Barreiras Aquifer in an area located on the eastern coast of the Rio Grande do Norte State - Brazil. Such an aquifer is clastic in nature and has an unconfined hydraulic character, which clearly makes it susceptible to contamination from surface ground loads with contaminants associated with the leaching of excess fertilizers not absorbed by ground vegetation. The methodology used was based on the use of hydro-geophysical data, particularly inverse models of vertical electrical soundings (VES) and information from well profiles, allowing the acquisition of longitudinal conductance cartographies (S), data in mili-Siemens (mS), and the vulnerability of the aquifer. Such maps were prepared with emphasis to the unsaturated overlying zone, highlighting in particular its thickness and occurrence of clay lithologies. Thus, the longitudinal conductance and aquifer vulnerability reveal areas more susceptible to contamination in the northeast and east-central sections of the study area, with values equal to or less than 10mS and greater than or equal to 0,50, respectively. On the other hand, the southwestern section proved to be less susceptible to contamination, whose longitudinal conductance and vulnerability indices are greater than or equal to 30mS and less than or equal to 0,40, respectively.

Structure, transports and transformations of the water masses in the Atlantic Subpolar Gyre

We discuss the distributions and transports of the main water masses in the North Atlantic Subpolar Gyre (NASPG) for the mean of the period 2002–2010 (OVIDE sections 2002–2010 every other year), as well as the inter-annual variability of the water mass structure from 1997 (4x and METEOR sections) to 2010. The water mass structure of the NASPG, quantitatively assessed by means of an Optimum MultiParameter analysis (with 14 water masses), was combined with the velocity fields resulting from previous studies using inverse models to obtain the water mass volume transports. We also evaluate the relative contribution to the Atlantic Meridional Overturning Circulation (AMOC) of the main water masses characterizing the NASPG, identifying the water masses that contribute to the AMOC variability. The reduction of the magnitude of the upper limb of the AMOC between 1997 and the 2000s is associated with the reduction in the northward transport of the Central Waters. This reduction of the northward flow of the AMOC is partially compensated by the reduction of the southward flow of the lower limb of the AMOC, associated with the decrease in the transports of Polar Intermediate Water and Subpolar Mode Water (SPMW) in the Irminger Basin. We also decompose the flow over the Reykjanes Ridge from the East North Atlantic Basin to the Irminger Basin (9.4 ± 4.7 Sv) into the contributions of the Central Waters (2.1 ± 1.8 Sv), Labrador Sea Water (LSW, 2.4 ± 2.0 Sv), Subarctic Intermediate Water (SAIW, 4.0 ± 0.5 Sv) and Iceland–Scotland Overflow Water (ISOW, 0.9 ± 0.9 Sv). Once LSW and ISOW cross over the Reykjanes Ridge, favoured by the strong mixing around it, they leave the Irminger Basin through the deep-to-bottom levels. The results also give insights into the water mass transformations within the NASPG, such as the contribution of the Central Waters and SAIW to the formation of the different varieties of SPMW due to air–sea interaction.

Estimação e compensação de não linearidades inerentes aos atuadores dos processos industriais

The oscillations presents in control loops can cause damages in petrochemical industry. Canceling, or even preventing such oscillations, would save up to large amount of dollars. Studies have identified that one of the causes of these oscillations are the nonlinearities present on industrial process actuators. This study has the objective to develop a methodology for removal of the harmful effects of nonlinearities. Will be proposed an parameter estimation method to Hammerstein model, whose nonlinearity is represented by dead-zone or backlash. The estimated parameters will be used to construct inverse models of compensation. A simulated level system was used as a test platform. The valve that controls inflow has a nonlinearity. Results and describing function analysis show an improvement on system response

Models involving two inverse Weibull distributions

In this paper, we look at three models (mixture, competing risk and multiplicative) involving two inverse Weibull distributions. We study the shapes of the density and failure-rate functions and discuss graphical methods to determine if a given data set can be modelled by one of these models. (C) 2001 Elsevier Science Ltd. All rights reserved.

Nonlinear inverse dynamic models of gas sensing systems based on chemical sensor arrays for quantitative measurements

Gas sensing systems based on low-cost chemical sensor arrays are gaining interest for the analysis of multicomponent gas mixtures. These sensors show different problems, e.g., nonlinearities and slow time-response, which can be partially solved by digital signal processing. Our approach is based on building a nonlinear inverse dynamic system. Results for different identification techniques, including artificial neural networks and Wiener series, are compared in terms of measurement accuracy.

Parameter identification of structural dynamic models by inverse statistical analysis

Auf dem Gebiet der Strukturdynamik sind computergestützte Modellvalidierungstechniken inzwischen weit verbreitet. Dabei werden experimentelle Modaldaten, um ein numerisches Modell für weitere Analysen zu korrigieren. Gleichwohl repräsentiert das validierte Modell nur das dynamische Verhalten der getesteten Struktur. In der Realität gibt es wiederum viele Faktoren, die zwangsläufig zu variierenden Ergebnissen von Modaltests führen werden: Sich verändernde Umgebungsbedingungen während eines Tests, leicht unterschiedliche Testaufbauten, ein Test an einer nominell gleichen aber anderen Struktur (z.B. aus der Serienfertigung), etc. Damit eine stochastische Simulation durchgeführt werden kann, muss eine Reihe von Annahmen für die verwendeten Zufallsvariablengetroffen werden. Folglich bedarf es einer inversen Methode, die es ermöglicht ein stochastisches Modell aus experimentellen Modaldaten zu identifizieren. Die Arbeit beschreibt die Entwicklung eines parameter-basierten Ansatzes, um stochastische Simulationsmodelle auf dem Gebiet der Strukturdynamik zu identifizieren. Die entwickelte Methode beruht auf Sensitivitäten erster Ordnung, mit denen Parametermittelwerte und Kovarianzen des numerischen Modells aus stochastischen experimentellen Modaldaten bestimmt werden können.

Evolving graphs: dynamical models, inverse problems and propagation

Applications such as neuroscience, telecommunication, online social networking, transport and retail trading give rise to connectivity patterns that change over time. In this work, we address the resulting need for network models and computational algorithms that deal with dynamic links. We introduce a new class of evolving range-dependent random graphs that gives a tractable framework for modelling and simulation. We develop a spectral algorithm for calibrating a set of edge ranges from a sequence of network snapshots and give a proof of principle illustration on some neuroscience data. We also show how the model can be used computationally and analytically to investigate the scenario where an evolutionary process, such as an epidemic, takes place on an evolving network. This allows us to study the cumulative effect of two distinct types of dynamics.

Inverse scattering approach for massive Thirring models with integrable type-II defects

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A comparison of different choices for the regularization parameter in inverse electrocardiography models

Calculating the potentials on the heart’s epicardial surface from the body surface potentials constitutes one form of inverse problems in electrocardiography (ECG). Since these problems are ill-posed, one approach is to use zero-order Tikhonov regularization, where the squared norms of both the residual and the solution are minimized, with a relative weight determined by the regularization parameter. In this paper, we used three different methods to choose the regularization parameter in the inverse solutions of ECG. The three methods include the L-curve, the generalized cross validation (GCV) and the discrepancy principle (DP). Among them, the GCV method has received less attention in solutions to ECG inverse problems than the other methods. Since the DP approach needs knowledge of norm of noises, we used a model function to estimate the noise. The performance of various methods was compared using a concentric sphere model and a real geometry heart-torso model with a distribution of current dipoles placed inside the heart model as the source. Gaussian measurement noises were added to the body surface potentials. The results show that the three methods all produce good inverse solutions with little noise; but, as the noise increases, the DP approach produces better results than the L-curve and GCV methods, particularly in the real geometry model. Both the GCV and L-curve methods perform well in low to medium noise situations.