Levenberg-Marquardt application to two-phase nonlinear parameter estimation for finned-tube coil evaporators

A procedure for calculation of refrigerant mass flow rate is implemented in the distributed numerical model to simulate the flow in finned-tube coil dry-expansion evaporators, usually found in refrigeration and air-conditioning systems. Two-phase refrigerant flow inside the tubes is assumed to be one-dimensional, unsteady, and homogeneous. In themodel the effects of refrigerant pressure drop and the moisture condensation from the air flowing over the external surface of the tubes are considered. The results obtained are the distributions of refrigerant velocity, temperature and void fraction, tube-wall temperature, air temperature, and absolute humidity. The finite volume method is used to discretize the governing equations. Additionally, given the operation conditions and the geometric parameters, the model allows the calculation of the refrigerant mass flow rate. The value of mass flow rate is computed using the process of parameter estimation with the minimization method of Levenberg-Marquardt minimization. In order to validate the developed model, the obtained results using HFC-134a as a refrigerant are compared with available data from the literature.

Inverse kinematics of a humanoid robot with non-spherical hip: a hybrid algorithm approach

This paper describes an approach to solve the inverse kinematics problem of humanoid robots whose construction shows a small but non negligible offset at the hip which prevents any purely analytical solution to be developed. Knowing that a purely numerical solution is not feasible due to variable efficiency problems, the proposed one first neglects the offset presence in order to obtain an approximate “solution” by means of an analytical algorithm based on screw theory, and then uses it as the initial condition of a numerical refining procedure based on the Levenberg‐Marquardt algorithm. In this way, few iterations are needed for any specified attitude, making it possible to implement the algorithm for real‐time applications. As a way to show the algorithm’s implementation, one case of study is considered throughout the paper, represented by the SILO2 humanoid robot.

The daily and hourly energy consumption and load forecasting using artifitial neural network method: a case study using a set of 93 households in Portugal

It is important to understand and forecast a typical or a particularly household daily consumption in order to design and size suitable renewable energy systems and energy storage. In this research for Short Term Load Forecasting (STLF) it has been used Artificial Neural Networks (ANN) and, despite the consumption unpredictability, it has been shown the possibility to forecast the electricity consumption of a household with certainty. The ANNs are recognized to be a potential methodology for modeling hourly and daily energy consumption and load forecasting. Input variables such as apartment area, numbers of occupants, electrical appliance consumption and Boolean inputs as hourly meter system were considered. Furthermore, the investigation carried out aims to define an ANN architecture and a training algorithm in order to achieve a robust model to be used in forecasting energy consumption in a typical household. It was observed that a feed-forward ANN and the Levenberg-Marquardt algorithm provided a good performance. For this research it was used a database with consumption records, logged in 93 real households, in Lisbon, Portugal, between February 2000 and July 2001, including both weekdays and weekend. The results show that the ANN approach provides a reliable model for forecasting household electric energy consumption and load profile. © 2014 The Author.

A comparison of alpha and gamma spectrometry for environmental natural radioactivity surveys.

An alpha-spectrometry, using automated borate fusion and sequential extraction and exchange chromatography, was used to determine the uranium and thorium based on environmental radioactivity of 20 soil samples. The same set of the samples was analysed using gamma-spectrometry with an HPGe detector. The two data sets were checked for coherence using Z-score and chi2 statistical tests. We show that gamma-spectrometry is a valid alternative to time-consuming alpha-spectrometry for the determination of natural uranium and thorium activity in soil (activity range: 12.5-58.2 Bq/kg). The measured activities were compared with the theoretical activities to ensure secular equilibrium in the 238U and 232Th series. For 226Ra, a special study was made on deconvolution of the 186 keV multiplet with the Levenberg-Marquardt algorithm. Finally, the combined use of Z-score and chi2-tests was found to be a powerful tool for comparing the results obtained with two different methods.

Stiffness based trajectory planning and feedforward based vibration suppression control of parallel robot machines

The dissertation proposes two control strategies, which include the trajectory planning and vibration suppression, for a kinematic redundant serial-parallel robot machine, with the aim of attaining the satisfactory machining performance. For a given prescribed trajectory of the robot's end-effector in the Cartesian space, a set of trajectories in the robot's joint space are generated based on the best stiffness performance of the robot along the prescribed trajectory. To construct the required system-wide analytical stiffness model for the serial-parallel robot machine, a variant of the virtual joint method (VJM) is proposed in the dissertation. The modified method is an evolution of Gosselin's lumped model that can account for the deformations of a flexible link in more directions. The effectiveness of this VJM variant is validated by comparing the computed stiffness results of a flexible link with the those of a matrix structural analysis (MSA) method. The comparison shows that the numerical results from both methods on an individual flexible beam are almost identical, which, in some sense, provides mutual validation. The most prominent advantage of the presented VJM variant compared with the MSA method is that it can be applied in a flexible structure system with complicated kinematics formed in terms of flexible serial links and joints. Moreover, by combining the VJM variant and the virtual work principle, a systemwide analytical stiffness model can be easily obtained for mechanisms with both serial kinematics and parallel kinematics. In the dissertation, a system-wide stiffness model of a kinematic redundant serial-parallel robot machine is constructed based on integration of the VJM variant and the virtual work principle. Numerical results of its stiffness performance are reported. For a kinematic redundant robot, to generate a set of feasible joints' trajectories for a prescribed trajectory of its end-effector, its system-wide stiffness performance is taken as the constraint in the joints trajectory planning in the dissertation. For a prescribed location of the end-effector, the robot permits an infinite number of inverse solutions, which consequently yields infinite kinds of stiffness performance. Therefore, a differential evolution (DE) algorithm in which the positions of redundant joints in the kinematics are taken as input variables was employed to search for the best stiffness performance of the robot. Numerical results of the generated joint trajectories are given for a kinematic redundant serial-parallel robot machine, IWR (Intersector Welding/Cutting Robot), when a particular trajectory of its end-effector has been prescribed. The numerical results show that the joint trajectories generated based on the stiffness optimization are feasible for realization in the control system since they are acceptably smooth. The results imply that the stiffness performance of the robot machine deviates smoothly with respect to the kinematic configuration in the adjacent domain of its best stiffness performance. To suppress the vibration of the robot machine due to varying cutting force during the machining process, this dissertation proposed a feedforward control strategy, which is constructed based on the derived inverse dynamics model of target system. The effectiveness of applying such a feedforward control in the vibration suppression has been validated in a parallel manipulator in the software environment. The experimental study of such a feedforward control has also been included in the dissertation. The difficulties of modelling the actual system due to the unknown components in its dynamics is noticed. As a solution, a back propagation (BP) neural network is proposed for identification of the unknown components of the dynamics model of the target system. To train such a BP neural network, a modified Levenberg-Marquardt algorithm that can utilize an experimental input-output data set of the entire dynamic system is introduced in the dissertation. Validation of the BP neural network and the modified Levenberg- Marquardt algorithm is done, respectively, by a sinusoidal output approximation, a second order system parameters estimation, and a friction model estimation of a parallel manipulator, which represent three different application aspects of this method.

Exact Search Directions for Optimization of Linear and Nonlinear Models Based on Generalized Orthonormal Functions

A novel technique for selecting the poles of orthonormal basis functions (OBF) in Volterra models of any order is presented. It is well-known that the usual large number of parameters required to describe the Volterra kernels can be significantly reduced by representing each kernel using an appropriate basis of orthonormal functions. Such a representation results in the so-called OBF Volterra model, which has a Wiener structure consisting of a linear dynamic generated by the orthonormal basis followed by a nonlinear static mapping given by the Volterra polynomial series. Aiming at optimizing the poles that fully parameterize the orthonormal bases, the exact gradients of the outputs of the orthonormal filters with respect to their poles are computed analytically by using a back-propagation-through-time technique. The expressions relative to the Kautz basis and to generalized orthonormal bases of functions (GOBF) are addressed; the ones related to the Laguerre basis follow straightforwardly as a particular case. The main innovation here is that the dynamic nature of the OBF filters is fully considered in the gradient computations. These gradients provide exact search directions for optimizing the poles of a given orthonormal basis. Such search directions can, in turn, be used as part of an optimization procedure to locate the minimum of a cost-function that takes into account the error of estimation of the system output. The Levenberg-Marquardt algorithm is adopted here as the optimization procedure. Unlike previous related work, the proposed approach relies solely on input-output data measured from the system to be modeled, i.e., no information about the Volterra kernels is required. Examples are presented to illustrate the application of this approach to the modeling of dynamic systems, including a real magnetic levitation system with nonlinear oscillatory behavior.

The influence of ARIMA-GARCH parameters in feed forward neural networks prediction

The objective of this article is to find out the influence of the parameters of the ARIMA-GARCH models in the prediction of artificial neural networks (ANN) of the feed forward type, trained with the Levenberg-Marquardt algorithm, through Monte Carlo simulations. The paper presents a study of the relationship between ANN performance and ARIMA-GARCH model parameters, i.e. the fact that depending on the stationarity and other parameters of the time series, the ANN structure should be selected differently. Neural networks have been widely used to predict time series and their capacity for dealing with non-linearities is a normally outstanding advantage. However, the values of the parameters of the models of generalized autoregressive conditional heteroscedasticity have an influence on ANN prediction performance. The combination of the values of the GARCH parameters with the ARIMA autoregressive terms also implies in ANN performance variation. Combining the parameters of the ARIMA-GARCH models and changing the ANN`s topologies, we used the Theil inequality coefficient to measure the prediction of the feed forward ANN.

A Gradient-Type Method For Real-Time State Estimation Of Water Distribution Networks

Drinking water distribution networks risk exposure to malicious or accidental contamination. Several levels of responses are conceivable. One of them consists to install a sensor network to monitor the system on real time. Once a contamination has been detected, this is also important to take appropriate counter-measures. In the SMaRT-OnlineWDN project, this relies on modeling to predict both hydraulics and water quality. An online model use makes identification of the contaminant source and simulation of the contaminated area possible. The objective of this paper is to present SMaRT-OnlineWDN experience and research results for hydraulic state estimation with sampling frequency of few minutes. A least squares problem with bound constraints is formulated to adjust demand class coefficient to best fit the observed values at a given time. The criterion is a Huber function to limit the influence of outliers. A Tikhonov regularization is introduced for consideration of prior information on the parameter vector. Then the Levenberg-Marquardt algorithm is applied that use derivative information for limiting the number of iterations. Confidence intervals for the state prediction are also given. The results are presented and discussed on real networks in France and Germany.

Previsão de cargas elétricas através de um modelo híbrido de regressão com redes neurais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Microwave breast imaging reconstruction

In this thesis I analyzed the microwave tomography method to recognize breast can- cer. I study how identify the dielectric permittivity, the Helmoltz equation parameter used to model the real physic problem. Through a non linear least squares method I solve a problem of parameters identification; I show the theoric approach and the devel- opment to reach the results. I use the Levenberg-Marquardt algorithm, applied on COMSOL software to multiphysic models; so I do numerical proofs on semplified test problems compared to the specific real problem to solve.

An integrative transcranial magnetic stimulation mapping technique using non-linear curve fitting

The aim of this study is to develop a new simple method for analyzing one-dimensional transcranial magnetic stimulation (TMS) mapping studies in humans. Motor evoked potentials (MEP) were recorded from the abductor pollicis brevis (APB) muscle during stimulation at nine different positions on the scalp along a line passing through the APB hot spot and the vertex. Non-linear curve fitting according to the Levenberg-Marquardt algorithm was performed on the averaged amplitude values obtained at all points to find the best-fitting symmetrical and asymmetrical peak functions. Several peak functions could be fitted to the experimental data. Across all subjects, a symmetric, bell-shaped curve, the complementary error function (erfc) gave the best results. This function is characterized by three parameters giving its amplitude, position, and width. None of the mathematical functions tested with less or more than three parameters fitted better. The amplitude and position parameters of the erfc were highly correlated with the amplitude at the hot spot and with the location of the center of gravity of the TMS curve. In conclusion, non-linear curve fitting is an accurate method for the mathematical characterization of one-dimensional TMS curves. This is the first method that provides information on amplitude, position and width simultaneously.

DCE@urLAB: a dynamic contrast-enhanced MRI pharmacokinetic analysis tool for preclinical data

Background DCE@urLAB is a software application for analysis of dynamic contrast-enhanced magnetic resonance imaging data (DCE-MRI). The tool incorporates a friendly graphical user interface (GUI) to interactively select and analyze a region of interest (ROI) within the image set, taking into account the tissue concentration of the contrast agent (CA) and its effect on pixel intensity. Results Pixel-wise model-based quantitative parameters are estimated by fitting DCE-MRI data to several pharmacokinetic models using the Levenberg-Marquardt algorithm (LMA). DCE@urLAB also includes the semi-quantitative parametric and heuristic analysis approaches commonly used in practice. This software application has been programmed in the Interactive Data Language (IDL) and tested both with publicly available simulated data and preclinical studies from tumor-bearing mouse brains. Conclusions A user-friendly solution for applying pharmacokinetic and non-quantitative analysis DCE-MRI in preclinical studies has been implemented and tested. The proposed tool has been specially designed for easy selection of multi-pixel ROIs. A public release of DCE@urLAB, together with the open source code and sample datasets, is available at http://www.die.upm.es/im/archives/DCEurLAB/ webcite.

Métodos computacionales para visco-hiperelasticidad anisótropa en grandes deformaciones

El comportamiento mecánico de muchos materiales biológicos y poliméricos en grandes deformaciones se puede describir adecuadamente mediante formulaciones isocóricas hiperelásticas y viscoelásticas. Las ecuaciones de comportamiento elástico y viscoelástico y las formulaciones computacionales para materiales incompresibles isótropos en deformaciones finitas están ampliamente desarrolladas en la actualidad. Sin embargo, el desarrollo de modelos anisótropos no lineales y de sus correspondientes formulaciones computacionales sigue siendo un tema de investigación de gran interés. Cuando se consideran grandes deformaciones, existen muchas medidas de deformación disponibles con las que poder formular las ecuaciones de comportamiento. Los modelos en deformaciones cuadráticas facilitan la implementación en códigos de elementos finitos, ya que estas medidas surgen de forma natural en la formulación. No obstante, pueden dificultar la interpretación de los modelos y llevar a resultados pocos realistas. El uso de deformaciones logarítmicas permite el desarrollo de modelos más simples e intuitivos, aunque su formulación computacional debe ser adaptada a las exigencias del programa. Como punto de partida, en esta tesis se demuestra que las deformaciones logarítmicas representan la extensión natural de las deformaciones infinitesimales, tanto axiales como angulares, al campo de las grandes deformaciones. Este hecho permite explicar la simplicidad de las ecuaciones resultantes. Los modelos hiperelásticos predominantes en la actualidad están formulados en invariantes de deformaciones cuadráticas. Estos modelos, ya sean continuos o microestructurales, se caracterizan por tener una forma analítica predefinida. Su expresión definitiva se calcula mediante un ajuste de curvas a datos experimentales. Un modelo que no sigue esta metodología fue desarrollado por Sussman y Bathe. El modelo es sólo válido para isotropía y queda definido por una función de energía interpolada con splines, la cual reproduce los datos experimentales de forma exacta. En esta tesis se presenta su extensión a materiales transversalmente isótropos y ortótropos utilizando deformaciones logarítmicas. Asimismo, se define una nueva propiedad que las funciones de energía anisótropas deben satisfacer para que su convergencia al caso isótropo sea correcta. En visco-hiperelasticidad, aparte de las distintas funciones de energía disponibles, hay dos aproximaciones computational típicas basadas en variables internas. El modelo original de Simó está formulado en tensiones y es válido para materiales anisótropos, aunque sólo es adecuado para pequeñas desviaciones con respecto al equilibrio termodinámico. En cambio, el modelo basado en deformaciones de Reese y Govindjee permite grandes deformaciones no equilibradas pero es, en esencia, isótropo. Las formulaciones anisótropas en este último contexto son microestructurales y emplean el modelo isótropo para cada uno de los constituyentes. En esta tesis se presentan dos formulaciones fenomenológicas viscoelásticas definidas mediante funciones hiperelásticas anisótropas y válidas para grandes desviaciones con respecto al equilibrio termodinámico. El primero de los modelos está basado en la descomposición multiplicativa de Sidoroff y requiere un comportamiento viscoso isótropo. La formulación converge al modelo de Reese y Govindjee en el caso especial de isotropía elástica. El segundo modelo se define a partir de una descomposición multiplicativa inversa. Esta formulación está basada en una descripción co-rotacional del problema, es sustancialmente más compleja y puede dar lugar a tensores constitutivos ligeramente no simétricos. Sin embargo, su rango de aplicación es mucho mayor ya que permite un comportamiento anisótropo tanto elástico como viscoso. Varias simulaciones de elementos finitos muestran la gran versatilidad de estos modelos cuando se combinan con funciones hiperelásticas formadas por splines. ABSTRACT The mechanical behavior of many polymeric and biological materials may be properly modelled be means of isochoric hyperelastic and viscoelastic formulations. These materials may sustain large strains. The viscoelastic computational formulations for isotropic incompressible materials at large strains may be considered well established; for example Ogden’s hyperelastic function and the visco-hyperelastic model of Reese and Govindjee are well known models for isotropy. However, anisotropic models and computational procedures both for hyperelasticity and viscohyperelasticity are still under substantial research. Anisotropic hyperelastic models are typically based on structural invariants obtained from quadratic strain measures. These models may be microstructurallybased or phenomenological continuum formulations, and are characterized by a predefined analytical shape of the stored energy. The actual final expression of the stored energy depends on some material parameters which are obtained from an optimization algorithm, typically the Levenberg-Marquardt algorithm. We present in this work anisotropic spline-based hyperelastic stored energies in which the shape of the stored energy is obtained as part of the procedure and which (exactly in practice) replicates the experimental data. These stored energies are based on invariants obtained from logarithmic strain measures. These strain measures preserve the metric and the physical meaning of the trace and deviator operators and, hence, are interesting and meaningful for anisotropic formulations. Furthermore, the proposed stored energies may be formulated in order to have material-symmetries congruency both from a theoretical and from a numerical point of view, which are new properties that we define in this work. On the other hand, visco-hyperelastic formulations for anisotropic materials are typically based on internal stress-like variables following a procedure used by Sim´o. However, it can be shown that this procedure is not adequate for large deviations from thermodynamic equilibrium. In contrast, a formulation given by Reese and Govindjee is valid for arbitrarily large deviations from thermodynamic equilibrium but not for anisotropic stored energy functions. In this work we present two formulations for visco-hyperelasticity valid for anisotropic stored energies and large deviations from thermodynamic equilibrium. One of the formulations is based on the Sidoroff multiplicative decomposition and converges to the Reese and Govindjee formulation for the case of isotropy. However, the formulation is restricted to isotropy for the viscous component. The second formulation is based on a reversed multiplicative decomposition. This last formulation is substantially more complex and based on a corotational description of the problem. It can also result in a slightly nonsymmetric tangent. However, the formulation allows for anisotropy not only in the equilibrated and non-equilibrated stored energies, but also in the viscous behavior. Some examples show finite element implementation, versatility and interesting characteristics of the models.

Studies of Protein-Protein and Protein-Water Interactions by Small Angle X-Ray Scattering, Terahertz Spectroscopy, ASMOS, And Computer Simulation

The protein folding problem has been one of the most challenging subjects in biological physics due to its complexity. Energy landscape theory based on statistical mechanics provides a thermodynamic interpretation of the protein folding process. We have been working to answer fundamental questions about protein-protein and protein-water interactions, which are very important for describing the energy landscape surface of proteins correctly. At first, we present a new method for computing protein-protein interaction potentials of solvated proteins directly from SAXS data. An ensemble of proteins was modeled by Metropolis Monte Carlo and Molecular Dynamics simulations, and the global X-ray scattering of the whole model ensemble was computed at each snapshot of the simulation. The interaction potential model was optimized and iterated by a Levenberg-Marquardt algorithm. Secondly, we report that terahertz spectroscopy directly probes hydration dynamics around proteins and determines the size of the dynamical hydration shell. We also present the sequence and pH-dependence of the hydration shell and the effect of the hydrophobicity. On the other hand, kinetic terahertz absorption (KITA) spectroscopy is introduced to study the refolding kinetics of ubiquitin and its mutants. KITA results are compared to small angle X-ray scattering, tryptophan fluorescence, and circular dichroism results. We propose that KITA monitors the rearrangement of hydrogen bonding during secondary structure formation. Finally, we present development of the automated single molecule operating system (ASMOS) for a high throughput single molecule detector, which levitates a single protein molecule in a 10 µm diameter droplet by the laser guidance. I also have performed supporting calculations and simulations with my own program codes.

Algoritmos construtivos para treino de redes neuronais b-spline

Dissertação de mest. em Engenharia de Sistemas e Computação - Área de Sistemas de Controlo, Faculdade de Ciências e Tecnologia, Univ.do Algarve, 2001