Hybrid probabilistic wind power forecasting using temporally local Gaussian process

The demand for sustainable development has resulted in a rapid growth in wind power worldwide. Despite various approaches have been proposed to improve the accuracy and to overcome the uncertainties associated with traditional methods, the stochastic and variable nature of wind still remains the most challenging issue in accurately forecasting wind power. This paper presents a hybrid deterministic-probabilistic method where a temporally local ‘moving window’ technique is used in Gaussian Process to examine estimated forecasting errors. This temporally local Gaussian Process employs less measurement data while faster and better predicts wind power at two wind farms, one in the USA and the other in Ireland. Statistical analysis on the results shows that the method can substantially reduce the forecasting error while more likely generate Gaussian-distributed residuals, particularly for short-term forecast horizons due to its capability to handle the time-varying characteristics of wind power.

Special condition wind power forecasting based on Gaussian process and similar historical data

Due to the variability of wind power, it is imperative to accurately and timely forecast the wind generation to enhance the flexibility and reliability of the operation and control of real-time power. Special events such as ramps, spikes are hard to predict with traditional methods using solely recently measured data. In this paper, a new Gaussian Process model with hybrid training data taken from both the local time and historic dataset is proposed and applied to make short-term predictions from 10 minutes to one hour ahead. A key idea is that the similar pattern data in history are properly selected and embedded in Gaussian Process model to make predictions. The results of the proposed algorithms are compared to those of standard Gaussian Process model and the persistence model. It is shown that the proposed method not only reduces magnitude error but also phase error.

Performance assessment of cascade control loopswith non-Gaussian disturbances using entropy information

Cascade control is one of the routinely used control strategies in industrial processes because it can dramatically improve the performance of single-loop control, reducing both the maximum deviation and the integral error of the disturbance response. Currently, many control performance assessment methods of cascade control loops are developed based on the assumption that all the disturbances are subject to Gaussian distribution. However, in the practical condition, several disturbance sources occur in the manipulated variable or the upstream exhibits nonlinear behaviors. In this paper, a general and effective index of the performance assessment of the cascade control system subjected to the unknown disturbance distribution is proposed. Like the minimum variance control (MVC) design, the output variances of the primary and the secondary loops are decomposed into a cascade-invariant and a cascade-dependent term, but the estimated ARMA model for the cascade control loop based on the minimum entropy, instead of the minimum mean squares error, is developed for non-Gaussian disturbances. Unlike the MVC index, an innovative control performance index is given based on the information theory and the minimum entropy criterion. The index is informative and in agreement with the expected control knowledge. To elucidate wide applicability and effectiveness of the minimum entropy cascade control index, a simulation problem and a cascade control case of an oil refinery are applied. The comparison with MVC based cascade control is also included.

BAR domains as sensors of membrane curvature:the amphiphysin BAR structure

The BAR (Bin/amphiphysin/Rvs) domain is the most conserved feature in amphiphysins from yeast to human and is also found in endophilins and nadrins. We solved the structure of the Drosophila amphiphysin BAR domain. It is a crescent-shaped dimer that binds preferentially to highly curved negatively charged membranes. With its N-terminal amphipathic helix and BAR domain (N-BAR), amphiphysin can drive membrane curvature in vitro and in vivo. The structure is similar to that of arfaptin2, which we find also binds and tubulates membranes. From this, we predict that BAR domains are in many protein families, including sorting nexins, centaurins, and oligophrenins. The universal and minimal BAR domain is a dimerization, membrane-binding, and curvature-sensing module.

Curvature of clathrin-coated pits driven by epsin

Clathrin-mediated endocytosis involves cargo selection and membrane budding into vesicles with the aid of a protein coat. Formation of invaginated pits on the plasma membrane and subsequent budding of vesicles is an energetically demanding process that involves the cooperation of clathrin with many different proteins. Here we investigate the role of the brain-enriched protein epsin 1 in this process. Epsin is targeted to areas of endocytosis by binding the membrane lipid phosphatidylinositol-4,5-bisphosphate (PtdIns(4,5)P(2)). We show here that epsin 1 directly modifies membrane curvature on binding to PtdIns(4,5)P(2) in conjunction with clathrin polymerization. We have discovered that formation of an amphipathic alpha-helix in epsin is coupled to PtdIns(4,5)P(2) binding. Mutation of residues on the hydrophobic region of this helix abolishes the ability to curve membranes. We propose that this helix is inserted into one leaflet of the lipid bilayer, inducing curvature. On lipid monolayers epsin alone is sufficient to facilitate the formation of clathrin-coated invaginations.

Discrete Gaussian Sampling for Low-Power Devices

Thesis (Master's)--University of Washington, 2015

Positive radial solutions of the Dirichlet problem for the Minkowski-Curvature equation in a ball

We study the existence and multiplicity of positive radial solutions of the Dirichlet problem for the Minkowski-curvature equation { -div(del upsilon/root 1-vertical bar del upsilon vertical bar(2)) in B-R, upsilon=0 on partial derivative B-R,B- where B-R is a ball in R-N (N >= 2). According to the behaviour off = f (r, s) near s = 0, we prove the existence of either one, two or three positive solutions. All results are obtained by reduction to an equivalent non-singular one-dimensional problem, to which variational methods can be applied in a standard way.

Machine learning Vasicek model calibration with Gaussian processes

In this article, we calibrate the Vasicek interest rate model under the risk neutral measure by learning the model parameters using Gaussian processes for machine learning regression. The calibration is done by maximizing the likelihood of zero coupon bond log prices, using mean and covariance functions computed analytically, as well as likelihood derivatives with respect to the parameters. The maximization method used is the conjugate gradients. The only prices needed for calibration are zero coupon bond prices and the parameters are directly obtained in the arbitrage free risk neutral measure.

Fatigue life assessment of endodontic instruments subjected to multi planar curvature

This present study aimed to investigate the fatigue life of unused (new) endodontic instruments made of NiTi with control memory by Coltene™ and subjected to the multi curvature of a mandibular first molar root canal. Additionally, the instrument‟s structural behaviour was analysed through non-linear finite element analysis (FEA). The fatigue life of twelve Hyflex™ CM files was assessed while were forced to adopt a stance with multiple radius of curvature, similar to the ones usually found in a mandibular first molar root canal; nine of them were subjected to Pecking motion, a relative movement of axial type. To achieve this, it was designed an experimental setup with the aim of timing the instruments until fracture while worked inside a stainless steel mandibular first molar model with relative axial motion to simulate the pecking motion. Additionally, the model‟s root canal multi-curvature was confirmed by radiography. The non-linear finite element analysis was conducted using the computer aided design software package SolidWorks™ Simulation, in order to define the imposed displacement required by the FEA, it was necessary to model an endodontic instrument with simplified geometry using SolidWorks™ and subsequently analyse the geometry of the root canal CAD model. The experimental results shown that the instruments subjected to pecking motion displayed higher fatigue life values and higher lengths of fractured tips than those with only rotational relative movement. The finite element non-linear analyses shown, for identical conditions, maximum values for the first principal stress lower than the yield strength of the material and those were located in similar positions to the instrument‟s fracture location determined by the experimental testing results.

A Test of New Aggregate Damand Curvature Properties.

In This Paper We Present and Implement an Econometric Test of Both Negative Semi-Definiteness of the Matrix of Compensated Price Effects and of the Negative Quasi-Definiteness of the Matrix of Uncompensated Price Effects. This Test Allows Us to Evaluate Two Alternative Characterizations of Aggregate Demand Systems: the First, That They Behave Like Individual Demand Fuctions, and the Second, That They Respect the Properties Implied by the Assumptions Proposed by Hidebrand (1983) Or Grandmont (1984).

Testing Mean-Variance Efficiency in CAPM with Possibly Non-Gaussian Errors : An Exact Simulation-Based Approach

In this paper we propose exact likelihood-based mean-variance efficiency tests of the market portfolio in the context of Capital Asset Pricing Model (CAPM), allowing for a wide class of error distributions which include normality as a special case. These tests are developed in the frame-work of multivariate linear regressions (MLR). It is well known however that despite their simple statistical structure, standard asymptotically justified MLR-based tests are unreliable. In financial econometrics, exact tests have been proposed for a few specific hypotheses [Jobson and Korkie (Journal of Financial Economics, 1982), MacKinlay (Journal of Financial Economics, 1987), Gib-bons, Ross and Shanken (Econometrica, 1989), Zhou (Journal of Finance 1993)], most of which depend on normality. For the gaussian model, our tests correspond to Gibbons, Ross and Shanken’s mean-variance efficiency tests. In non-gaussian contexts, we reconsider mean-variance efficiency tests allowing for multivariate Student-t and gaussian mixture errors. Our framework allows to cast more evidence on whether the normality assumption is too restrictive when testing the CAPM. We also propose exact multivariate diagnostic checks (including tests for multivariate GARCH and mul-tivariate generalization of the well known variance ratio tests) and goodness of fit tests as well as a set estimate for the intervening nuisance parameters. Our results [over five-year subperiods] show the following: (i) multivariate normality is rejected in most subperiods, (ii) residual checks reveal no significant departures from the multivariate i.i.d. assumption, and (iii) mean-variance efficiency tests of the market portfolio is not rejected as frequently once it is allowed for the possibility of non-normal errors.

Essays on numerically efficient inference in nonlinear and non-Gaussian state space models, and commodity market analysis.

The first two articles build procedures to simulate vector of univariate states and estimate parameters in nonlinear and non Gaussian state space models. We propose state space speci fications that offer more flexibility in modeling dynamic relationship with latent variables. Our procedures are extension of the HESSIAN method of McCausland[2012]. Thus, they use approximation of the posterior density of the vector of states that allow to : simulate directly from the state vector posterior distribution, to simulate the states vector in one bloc and jointly with the vector of parameters, and to not allow data augmentation. These properties allow to build posterior simulators with very high relative numerical efficiency. Generic, they open a new path in nonlinear and non Gaussian state space analysis with limited contribution of the modeler. The third article is an essay in commodity market analysis. Private firms coexist with farmers' cooperatives in commodity markets in subsaharan african countries. The private firms have the biggest market share while some theoretical models predict they disappearance once confronted to farmers cooperatives. Elsewhere, some empirical studies and observations link cooperative incidence in a region with interpersonal trust, and thus to farmers trust toward cooperatives. We propose a model that sustain these empirical facts. A model where the cooperative reputation is a leading factor determining the market equilibrium of a price competition between a cooperative and a private firm