Probabilistic wind power forecasts with an inverse power curve transformation and censored regression

Forecasting wind power is an important part of a successful integration of wind power into the power grid. Forecasts with lead times longer than 6 h are generally made by using statistical methods to post-process forecasts from numerical weather prediction systems. Two major problems that complicate this approach are the non-linear relationship between wind speed and power production and the limited range of power production between zero and nominal power of the turbine. In practice, these problems are often tackled by using non-linear non-parametric regression models. However, such an approach ignores valuable and readily available information: the power curve of the turbine's manufacturer. Much of the non-linearity can be directly accounted for by transforming the observed power production into wind speed via the inverse power curve so that simpler linear regression models can be used. Furthermore, the fact that the transformed power production has a limited range can be taken care of by employing censored regression models. In this study, we evaluate quantile forecasts from a range of methods: (i) using parametric and non-parametric models, (ii) with and without the proposed inverse power curve transformation and (iii) with and without censoring. The results show that with our inverse (power-to-wind) transformation, simpler linear regression models with censoring perform equally or better than non-linear models with or without the frequently used wind-to-power transformation.

Improving an inverse model of sheet metal forming by neural network based regression

An inverse model for a sheet meta l forming process aims to determine the initial parameter levels required to form the final formed shape. This is a difficult problem that is usually approached by traditional methods such as finite element analysis. Formulating the problem as a classification problem makes it possible to use well established classification algorithms, such as decision trees. Classification is, however, generally based on a winner-takes-all approach when associating the output value with the corresponding class. On the other hand, when formulating the problem as a regression task, all the output values are combined to produce the corresponding class value. For a multi-class problem, this may result in very different associations compared with classification between the output of the model and the corresponding class. Such formulation makes it possible to use well known regression algorithms, such as neural networks. In this paper, we develop a neural network based inverse model of a sheet forming process, and compare its performance with that of a linear model. Both models are used in two modes, classification mode and a function estimation mode, to investigate the advantage of re-formulating the problem as a function estimation. This results in large improvements in the recognition rate of set-up parameters of a sheet metal forming process for both models, with a neural network model achieving much more accurate parameter recognition than a linear model.

A significant inverse relationship between concentrations of plasma hoocysteine and phospholipid docosahexaenoic acid in healthy male subjects

The aim of this study was to investigate the possibility of a relationship between plasma homocysteine (Hcy) and phospholipid FA (PUFA) in healthy Australian males. One hundred thirty six healthy male subjects aged 20–55 yr were recruited from the Melbourne metropolitan area. Each volunteer completed a semiquantitative food frequency questionnaire and gave a blood sample. Plasma Hcy concentrations were determined by an established HPLC method; the plasma phospholipid FA were determined by standard methods. Plasma Hcy concentration was significantly negatively correlated with plasma phospholipid concentration of the PUFA 20∶5n−3 (r=−0.226, P=0.009), 22∶5n−3 (r=−0.182, P=0.036), 22∶6n−3 (r=−0.286, P=0.001), total n−3 (r=−0.270, P=0.002) and the ratio n−3/n−6 PUFA (r=−0.265, P=0.002), and significantly positively correlated with 20∶4n−6 (r=0.180, P=0.037). In the partial correlation analysis, after controlling for serum vitamin B12 and folate concentration, plasma Hcy was significantly negatively correlated with the plasma phospholipid concentration of 22∶6n−3 (r=−0.205, P=0.019), total n−3 (r=−0.182, P=0.038) and the ratio n−3/n−6 PUFA (r=−0.174, P=0.048). Evidence indicates that an increased concentration of n−3 PUFA in tissues has a beneficial effect on cardiovascular health. Our findings provide further evidence that increased consumption of dietary n−3 PUFA increases the concentration of n−3 PUFA in plasma phospholipid, which is associated with a protective effect on cardiovascular diseases and lower plasma Hcy levels. The mechanism that might explain the association between plasma 22∶6n−3 and Hcy levels is not clear.

The inverse relationship between number of steps per day and obesity in a population-based sample - the AusDiab study

Background:
Physical activity (PA) is inversely associated with obesity but the effect has been difficult to quantify using questionnaires. In particular, the shape of the association has not yet been well described. Pedometers provide an opportunity to better characterize the association.

Methods:
Residents of households over the age of 25 years in randomly selected census districts in Tasmania were eligible to participate in the AusDiab cross-sectional survey conducted in 1999–2000. 1848 completed the AusDiab survey and 1126 of these (609 women and 517 men) wore a pedometer for 2-weekdays. Questionnaire data on recent PA, TV time and other factors were obtained. The outcomes were waist circumference (in cm) and body mass index (BMI) (kg/m2).

Results:
Increasing daily steps were associated with a decline in the obesity measures. The logarithmic nature of the associations was indicated by a sharper decline for those with lower daily steps. For example, an additional 2000 steps for those taking only 2000 steps per day was associated with a reduction of 2.8 (95% confidence interval (CI): 2.1,4.4) cm in waist circumference among men (for women; 2.2 (95% CI: 0.6, 3.9 cm)) with a baseline of only 2000, steps compared to a 0.7 (95% CI 0.3, 1.1) cm reduction (for women; 0.6 (95% CI: 0.2, 1.0)) for those already walking 10 000 steps daily. In the multivariable analysis, clearer associations were detected for PA and these obesity measures using daily step number rather than PA time by questionnaire.

Interpretation:
Pedometer measures of activity indicate that the inverse association between recent PA and obesity is logarithmic in form with the greatest impact for a given arithmetic step number increase seen at lower levels of baseline activity. The findings from this study need to be examined in prospective settings.

Vesselness features and the inverse compositional AAM for robust face recognition using thermal IR

Over the course of the last decade, infrared (IR) and particularly thermal IR imaging based face recognition has emerged as a promising complement to conventional, visible spectrum based approaches which continue to struggle when applied in the real world. While inherently insensitive to visible spectrum illumination changes, IR images introduce specific challenges of their own, most notably sensitivity to factors which affect facial heat emission patterns, e.g. emotional state, ambient temperature, and alcohol intake. In addition, facial expression and pose changes are more difficult to correct in IR images because they are less rich in high frequency detail which is an important cue for fitting any deformable model. In this paper we describe a novel method which addresses these major challenges. Specifically, to normalize for pose and facial expression changes we generate a synthetic frontal image of a face in a canonical, neutral facial expression from an image of the face in an arbitrary pose and facial expression. This is achieved by piecewise affine warping which follows active appearance model (AAM) fitting. This is the first publication which explores the use of an AAM on thermal IR images; we propose a pre-processing step which enhances detail in thermal images, making AAM convergence faster and more accurate. To overcome the problem of thermal IR image sensitivity to the exact pattern of facial temperature emissions we describe a representation based on reliable anatomical features. In contrast to previous approaches, our representation is not binary; rather, our method accounts for the reliability of the extracted features. This makes the proposed representation much more robust both to pose and scale changes. The effectiveness of the proposed approach is demonstrated on the largest public database of thermal IR images of faces on which it achieved 100% identification rate, significantly outperforming previously described methods

The need to revise the current methods to measure and assess static recrystallization behavior

Heterogeneous deformation developed during "static recrystallization (SRX) tests" poses serious questions about the validity of the conventional methods to measure softening fraction. The challenges to measure SRX and verify a proposed kinetic model of SRX are discussed and a least square technique is utilized to quantify the error in a proposed SRX kinetic model. This technique relies on an existing computational-experimental multi-layer formulation to account for the heterogeneity during the post interruption hot torsion deformation. The kinetics of static recrystallization for a type 304 austenitic stainless steel deformed at 900 °C and strain rate of 0.01s^-1 is characterized implementing the formulation. Minimizing the error between the measured and calculated torque-twist data, the parameters of the kinetic model and the flow behavior during the second hit are evaluated and compared with those obtained based on a conventional technique. Typical static recrystallization distributions in the test sample will be presented. It has been found that the major differences between the conventional and the presented technique results are due to the heterogeneous recrystallization in the cylindrical core of the specimen where the material is still partially recrystallized at the onset of the second hit deformation. For the investigated experimental conditions, the core is confined in the first two-thirds of the gauge radius, when the holding time is shorter than 50 s and the maximum pre-strain is about 0.5.

Computational inverse analysis of static recrystallization kinetics

© 2015 Published by Elsevier Ltd. All rights reserved. Accurate static recrystallization (SRX) models are necessary to improve the properties of austenitic steels by thermo-mechanical operations. This relies heavily on a careful and accurate analysis of "the interrupted test data" and conversion of the heterogeneous deformation data to the flow stress. A "computational-experimental inverse method" was presented and implemented here to analyze the SRX test data, which takes into account the heterogeneous softening of the post-interruption test sample. Conventional and "inverse" methods were used to identify the SRX kinetics for a model austenitic steel deformed at 1273 K (with a strain rate of 1 s-1) using the hot torsion test assess the merits of each method. Typical "static recrystallization distribution maps" in the test sample indicated that, at the onset of the second pass deformation with less than a critical holding time and a given pre-strain, a "partially-recrystallized zone" existed in the cylindrical core of the specimen near its center line. For the investigated scenario, the core was confined in the first half of the gauge radius when the holding time and the maximum pre strain were below 29 s and 0.5, respectively. For maximum pre strains smaller than 0.2, the specimen did not fully recrystallize, even at the gauge surface after holding for 50 s. Under such conditions, the conventional methods produced significant error.

Applying inverse just-noticeable-differences of velocity to position data for haptic data reduction

ust-Noticeable-Differences (JND) as a dead-band in perceptual analysis has been widely used for more than a decade. This technique has been employed for data reduction in hap tic data transmission systems by several researchers. In fact, researchers use two different JND coefficients that are JNDV and JNDF for velocity and force data respectively. For position data, they usually rely on the resolution of hap tic display device to omit data that are unperceivable to human. In this paper, pruning undesirable position data that are produced by the vibration of the device or subject and/or noise in transmission line is addressed. It is shown that using inverse JNDV for position data can prune undesirable position data. Comparison of the results of the proposed method in this paper with several well known filters and some available methods proposed by other researchers is performed. It is shown that combination of JNDV could provide lower error with desirable curve smoothness, and as little as possible computation effort and complexity. It also has been shown that this method reduces much more data rather than using forward-JNDV.

Inverse relaxation effect of azo-dye molecules: The role of the film anisotropy

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Inverse Modeling of GPS Multipath for Snow Depth Estimation-Part II: Application and Validation

GPS multipath reflectometry (GPS-MR) is a technique that uses geodetic quality GPS receivers to estimate snow depth. The accuracy and precision of GPS-MR retrievals are evaluated at three different sites: grasslands, alpine, and forested. The assessment yields a correlation of 0.98 and an rms error of 6-8 cm for observed snow depths of up to 2.5 m. GPS-MR underestimates in situ snow depth by 10%-15% at these three sites, although the validation methods do not measure the same footprint as GPS-MR.

Computing inverse optical flow

[EN] We propose four algorithms for computing the inverse optical flow between two images. We assume that the forward optical flow has already been obtained and we need to estimate the flow in the backward direction. The forward and backward flows can be related through a warping formula, which allows us to propose very efficient algorithms. These are presented in increasing order of complexity. The proposed methods provide high accuracy with low memory requirements and low running times.In general, the processing reduces to one or two image passes. Typically, when objects move in a sequence, some regions may appear or disappear. Finding the inverse flows in these situations is difficult and, in some cases, it is not possible to obtain a correct solution. Our algorithms deal with occlusions very easy and reliably. On the other hand, disocclusions have to be overcome as a post-processing step. We propose three approaches for filling disocclusions. In the experimental results, we use standard synthetic sequences to study the performance of the proposed methods, and show that they yield very accurate solutions. We also analyze the performance of the filling strategies. 

Tools and methods for the quality assurance of force platforms in human movement analysis

The human movement analysis (HMA) aims to measure the abilities of a subject to stand or to walk. In the field of HMA, tests are daily performed in research laboratories, hospitals and clinics, aiming to diagnose a disease, distinguish between disease entities, monitor the progress of a treatment and predict the outcome of an intervention [Brand and Crowninshield, 1981; Brand, 1987; Baker, 2006]. To achieve these purposes, clinicians and researchers use measurement devices, like force platforms, stereophotogrammetric systems, accelerometers, baropodometric insoles, etc. This thesis focus on the force platform (FP) and in particular on the quality assessment of the FP data. The principal objective of our work was the design and the experimental validation of a portable system for the in situ calibration of FPs. The thesis is structured as follows: Chapter 1. Description of the physical principles used for the functioning of a FP: how these principles are used to create force transducers, such as strain gauges and piezoelectrics transducers. Then, description of the two category of FPs, three- and six-component, the signals acquisition (hardware structure), and the signals calibration. Finally, a brief description of the use of FPs in HMA, for balance or gait analysis. Chapter 2. Description of the inverse dynamics, the most common method used in the field of HMA. This method uses the signals measured by a FP to estimate kinetic quantities, such as joint forces and moments. The measures of these variables can not be taken directly, unless very invasive techniques; consequently these variables can only be estimated using indirect techniques, as the inverse dynamics. Finally, a brief description of the sources of error, present in the gait analysis. Chapter 3. State of the art in the FP calibration. The selected literature is divided in sections, each section describes: systems for the periodic control of the FP accuracy; systems for the error reduction in the FP signals; systems and procedures for the construction of a FP. In particular is detailed described a calibration system designed by our group, based on the theoretical method proposed by ?. This system was the “starting point” for the new system presented in this thesis. Chapter 4. Description of the new system, divided in its parts: 1) the algorithm; 2) the device; and 3) the calibration procedure, for the correct performing of the calibration process. The algorithm characteristics were optimized by a simulation approach, the results are here presented. In addiction, the different versions of the device are described. Chapter 5. Experimental validation of the new system, achieved by testing it on 4 commercial FPs. The effectiveness of the calibration was verified by measuring, before and after calibration, the accuracy of the FPs in measuring the center of pressure of an applied force. The new system can estimate local and global calibration matrices; by local and global calibration matrices, the non–linearity of the FPs was quantified and locally compensated. Further, a non–linear calibration is proposed. This calibration compensates the non– linear effect in the FP functioning, due to the bending of its upper plate. The experimental results are presented. Chapter 6. Influence of the FP calibration on the estimation of kinetic quantities, with the inverse dynamics approach. Chapter 7. The conclusions of this thesis are presented: need of a calibration of FPs and consequential enhancement in the kinetic data quality. Appendix: Calibration of the LC used in the presented system. Different calibration set–up of a 3D force transducer are presented, and is proposed the optimal set–up, with particular attention to the compensation of non–linearities. The optimal set–up is verified by experimental results.

Bayesian Analysis of Linear Inverse Problems with Applications in Economics and Finance

In my PhD thesis I propose a Bayesian nonparametric estimation method for structural econometric models where the functional parameter of interest describes the economic agent's behavior. The structural parameter is characterized as the solution of a functional equation, or by using more technical words, as the solution of an inverse problem that can be either ill-posed or well-posed. From a Bayesian point of view, the parameter of interest is a random function and the solution to the inference problem is the posterior distribution of this parameter. A regular version of the posterior distribution in functional spaces is characterized. However, the infinite dimension of the considered spaces causes a problem of non continuity of the solution and then a problem of inconsistency, from a frequentist point of view, of the posterior distribution (i.e. problem of ill-posedness). The contribution of this essay is to propose new methods to deal with this problem of ill-posedness. The first one consists in adopting a Tikhonov regularization scheme in the construction of the posterior distribution so that I end up with a new object that I call regularized posterior distribution and that I guess it is solution of the inverse problem. The second approach consists in specifying a prior distribution on the parameter of interest of the g-prior type. Then, I detect a class of models for which the prior distribution is able to correct for the ill-posedness also in infinite dimensional problems. I study asymptotic properties of these proposed solutions and I prove that, under some regularity condition satisfied by the true value of the parameter of interest, they are consistent in a "frequentist" sense. Once I have set the general theory, I apply my bayesian nonparametric methodology to different estimation problems. First, I apply this estimator to deconvolution and to hazard rate, density and regression estimation. Then, I consider the estimation of an Instrumental Regression that is useful in micro-econometrics when we have to deal with problems of endogeneity. Finally, I develop an application in finance: I get the bayesian estimator for the equilibrium asset pricing functional by using the Euler equation defined in the Lucas'(1978) tree-type models.

Inverse problems in classical and quantum physics

The subject of this thesis is in the area of Applied Mathematics known as Inverse Problems. Inverse problems are those where a set of measured data is analysed in order to get as much information as possible on a model which is assumed to represent a system in the real world. We study two inverse problems in the fields of classical and quantum physics: QCD condensates from tau-decay data and the inverse conductivity problem. Despite a concentrated effort by physicists extending over many years, an understanding of QCD from first principles continues to be elusive. Fortunately, data continues to appear which provide a rather direct probe of the inner workings of the strong interactions. We use a functional method which allows us to extract within rather general assumptions phenomenological parameters of QCD (the condensates) from a comparison of the time-like experimental data with asymptotic space-like results from theory. The price to be paid for the generality of assumptions is relatively large errors in the values of the extracted parameters. Although we do not claim that our method is superior to other approaches, we hope that our results lend additional confidence to the numerical results obtained with the help of methods based on QCD sum rules. EIT is a technology developed to image the electrical conductivity distribution of a conductive medium. The technique works by performing simultaneous measurements of direct or alternating electric currents and voltages on the boundary of an object. These are the data used by an image reconstruction algorithm to determine the electrical conductivity distribution within the object. In this thesis, two approaches of EIT image reconstruction are proposed. The first is based on reformulating the inverse problem in terms of integral equations. This method uses only a single set of measurements for the reconstruction. The second approach is an algorithm based on linearisation which uses more then one set of measurements. A promising result is that one can qualitatively reconstruct the conductivity inside the cross-section of a human chest. Even though the human volunteer is neither two-dimensional nor circular, such reconstructions can be useful in medical applications: monitoring for lung problems such as accumulating fluid or a collapsed lung and noninvasive monitoring of heart function and blood flow.

Inverse size exclusion chromatography (ISEC)

In this work, an improved protocol for inverse size-exclusion chromatography (ISEC) was established to assess important pore structural data of porous silicas as stationary phases in packed chromatographic columns. After the validity of the values generated by ISEC was checked by comparison with data obtained from traditional methods like nitrogen sorption at 77 K (Study A), the method could be successfully employed as valuable tool at the development of bonded poly(methacrylate)-coated silicas, while traditional methods generate partially incorrect pore structural information (Study B). Study A: Different mesoporous silicas were converted by a pseudomorphical transition into ordered MCM-41-type silica while maintaining the particle-size and -shape. The essential parameters like specific surface area, average pore diameter and specific pore volume, the pore connectivity from ISEC remained nearly the same which was reflected by the same course of the theoretical plate height vs. linear velocity curves. Study B: In the development of bonded poly(methacrylate)-coated silicas for the reversed phase separation of biopolymers, ISEC was the only method to generate valid pore structural information of the polymer-coated materials. Synthesis procedures were developed to obtain reproducibly covalently bonded poly(methacrylate) coatings with good thermal stability on different base materials, employing as well particulate and monolithic materials.