Adding constrained discontinuities to Gaussian process models of wind fields

Gaussian Processes provide good prior models for spatial data, but can be too smooth. In many physical situations there are discontinuities along bounding surfaces, for example fronts in near-surface wind fields. We describe a modelling method for such a constrained discontinuity and demonstrate how to infer the model parameters in wind fields with MCMC sampling.

Techniques to improve forecasting models: applications to energy demand and price

This thesis is a study of three techniques to improve performance of some standard fore-casting models, application to the energy demand and prices. We focus on forecasting demand and price one-day ahead. First, the wavelet transform was used as a pre-processing procedure with two approaches: multicomponent-forecasts and direct-forecasts. We have empirically compared these approaches and found that the former consistently outperformed the latter. Second, adaptive models were introduced to continuously update model parameters in the testing period by combining ?lters with standard forecasting methods. Among these adaptive models, the adaptive LR-GARCH model was proposed for the fi?rst time in the thesis. Third, with regard to noise distributions of the dependent variables in the forecasting models, we used either Gaussian or Student-t distributions. This thesis proposed a novel algorithm to infer parameters of Student-t noise models. The method is an extension of earlier work for models that are linear in parameters to the non-linear multilayer perceptron. Therefore, the proposed method broadens the range of models that can use a Student-t noise distribution. Because these techniques cannot stand alone, they must be combined with prediction models to improve their performance. We combined these techniques with some standard forecasting models: multilayer perceptron, radial basis functions, linear regression, and linear regression with GARCH. These techniques and forecasting models were applied to two datasets from the UK energy markets: daily electricity demand (which is stationary) and gas forward prices (non-stationary). The results showed that these techniques provided good improvement to prediction performance.

Why are MD simulated protein folding times wrong?

The question of significant deviations of protein folding times simulated using molecular dynamics from experimental values is investigated. It is shown that in the framework of Markov State Model (MSM) describing the conformational dynamics of peptides and proteins, the folding time is very sensitive to the simulation model parameters, such as forcefield and temperature. Using two peptides as examples, we show that the deviations in the folding times can reach an order of magnitude for modest variations of the molecular model. We, therefore, conclude that the folding rate values obtained in molecular dynamics simulations have to be treated with care.

Elastic and inelastic scattering of fast neutrons in fusion reactor materials

In this work, the angular distributions for elastic and. inelastic scattering of fast neutrons in fusion .reactor materials have been studied. Lithium and lead material are likely to be common components of fusion reactor wall configuration design. The measurements were performed using an associated particle time-of- flight technique. The 14 and 14.44 Mev neutrons were produced by the T(d,n} 4He reaction with deuterons being accelerated in a 150kev SAMES type J accelerator at ASTON and in.the 3. Mev DYNAMITRON at the Joint Radiation Centre, Birmingham respectively. The associated alpha-particles and fast. neutrons were detected.by means of a plastic scintillator mounted on a fast focused photomultiplier tube. The samples used were extended flat plates of thicknesses up to 0.9 mean-free-path for Lithium and 1.562 mean-free-path for Lead. The differential elastic scattering cross-sections were measured for 14 Mev neutrons for various thicknesses of Lithium and Lead in the angular range from zero to; 90º. In addition, the angular distributions of elastically scattered 14,.44 Mev .neutrons from Lithium samples were studied in the same angular range. Inelastic scattering to the 4.63 Mev state in 7Li and the 2.6 Mev state, and 4.1 Mev state in 208Pb have:been :measured.The results are compared to ENDF/B-IV data files and to previous measurements. For the Lead samples the differential neutron scattering:cross-sections for discrete 3 Mev ranges and the angular distributions were measured. The increase in effective cross-section due to multiple scattering effects,as the sample thickness increased:was found to be predicted by the empirical .relation ....... A good fit to the exoerimental data was obtained using the universal constant............ The differential elastic scattering cross-section data for thin samples of Lithium and Lead were analyzed in terms of optical model calculations using the. computer code. RAROMP. Parameter search procedures produced good fits to the·cross-sections. For the case of thick samples of Lithium and Lead, the measured angular distributions of :the scattered neutrons were compared to the predictions of the continuous slowing down model.

Bayesian error bars for regression

Regression problems are concerned with predicting the values of one or more continuous quantities, given the values of a number of input variables. For virtually every application of regression, however, it is also important to have an indication of the uncertainty in the predictions. Such uncertainties are expressed in terms of the error bars, which specify the standard deviation of the distribution of predictions about the mean. Accurate estimate of error bars is of practical importance especially when safety and reliability is an issue. The Bayesian view of regression leads naturally to two contributions to the error bars. The first arises from the intrinsic noise on the target data, while the second comes from the uncertainty in the values of the model parameters which manifests itself in the finite width of the posterior distribution over the space of these parameters. The Hessian matrix which involves the second derivatives of the error function with respect to the weights is needed for implementing the Bayesian formalism in general and estimating the error bars in particular. A study of different methods for evaluating this matrix is given with special emphasis on the outer product approximation method. The contribution of the uncertainty in model parameters to the error bars is a finite data size effect, which becomes negligible as the number of data points in the training set increases. A study of this contribution is given in relation to the distribution of data in input space. It is shown that the addition of data points to the training set can only reduce the local magnitude of the error bars or leave it unchanged. Using the asymptotic limit of an infinite data set, it is shown that the error bars have an approximate relation to the density of data in input space.

A study of the scattering of fast neutrons in large samples

In the present work, the elastic scattering of fast neutrons from iron and concrete samples were studied at incident neutron energies of 14.0 and 14.4 Mev, using a neutron spectrometer based on the associated particle time-of-flight technique. These samples were chosen because of their importance in the design of fusion reactor shielding and construction. Using the S.A.M.E.S. accelerator and the 3 M v Dynamitron accelerator at the Radiation Centre, 14.0 and 14.4 Mev neutrons were produced by the T(d, n)4He reaction at incident deuteron energies of 140 keV and 900 keV mass III ions respectively. The time of origin of the neutron was determined by detecting the associated alpha particles. The samples used were extended flat plates of thicknesses up to 1.73 mean free paths for iron and 2.3 mean free paths for concrete. The associated alpha particles and fast neutrons were detected by means of a plastic scintillator mounted on a fast focused photomultiplier tube. The differential neutron elastic scattering cross-sections were measured for 14 Mev neutrons in various thicknesses of iron and concrete in the angular range from zero to 90°. In addition, the angular distributions of 14.4 Mev neutrons after passing through extended samples of iron were measured at several scattering angles in the same angular range. The measurements obtained for the thin sample of iron were compared with the results of Coon et al. The differential cross-sections for the thin iron sample were also analyzed on the optical model using the computer code RAROMP. For the concrete sample, the angular distribution of the thin sample was compared with the cross-sections calculated from the major constituent elements of concrete, and with the predicted values of the optical model for those elements. No published data could be found to compare with the results of the concrete differential cross-sections. In the case of thick samples of iron and concrete, the number of scattered neutrons were compared with a phenomological calculation based on the continuous slowing down model. The variation of measured cross-sections with sample thickness were found to follow the empirical relation σ = σ0 eαx. By using the universal constant "K", good fits were obtained to the experimental data. In parallel with the work at 14.0 and 14.4 Mev, an associated particle time-of-flight spectrometer was investigated which used the 2H(d,n)3He reaction for 3.02 Mev neutron energy at the incident deuteron energy of 1 Mev.

Modeling high-speed network traffic with truncated α-stable processes

It has been reported that high-speed communication network traffic exhibits both long-range dependence (LRD) and burstiness, which posed new challenges in network engineering. While many models have been studied in capturing the traffic LRD, they are not capable of capturing efficiently the traffic impulsiveness. It is desirable to develop a model that can capture both LRD and burstiness. In this letter, we propose a truncated a-stable LRD process model for this purpose, which can characterize both LRD and burstiness accurately. A procedure is developed further to estimate the model parameters from real traffic. Simulations demonstrate that our proposed model has a higher accuracy compared to existing models and is flexible in capturing the characteristics of high-speed network traffic. © 2012 Springer-Verlag GmbH.

Quantitative analysis of multi-spectral fundus images

We have developed a new technique for extracting histological parameters from multi-spectral images of the ocular fundus. The new method uses a Monte Carlo simulation of the reflectance of the fundus to model how the spectral reflectance of the tissue varies with differing tissue histology. The model is parameterised by the concentrations of the five main absorbers found in the fundus: retinal haemoglobins, choroidal haemoglobins, choroidal melanin, RPE melanin and macular pigment. These parameters are shown to give rise to distinct variations in the tissue colouration. We use the results of the Monte Carlo simulations to construct an inverse model which maps tissue colouration onto the model parameters. This allows the concentration and distribution of the five main absorbers to be determined from suitable multi-spectral images. We propose the use of "image quotients" to allow this information to be extracted from uncalibrated image data. The filters used to acquire the images are selected to ensure a one-to-one mapping between model parameters and image quotients. To recover five model parameters uniquely, images must be acquired in six distinct spectral bands. Theoretical investigations suggest that retinal haemoglobins and macular pigment can be recovered with RMS errors of less than 10%. We present parametric maps showing the variation of these parameters across the posterior pole of the fundus. The results are in agreement with known tissue histology for normal healthy subjects. We also present an early result which suggests that, with further development, the technique could be used to successfully detect retinal haemorrhages.

Adsorption of dimethyl ether (DME) on zeolite molecular sieves

In recent years there has been growing interest in the use of dimethyl ether (DME) as an alternative fuel. In this study, the adsorption of DME on molecular sieves 4Å (Mol4A) and 5Å (Mol5A) has been experimentally investigated using the volumetric adsorption method. Data on the adsorption isotherms, heats of adsorption, and adsorption kinetic have been obtained and used to draw conclusions and compare the performance of the two adsorbents. Within the conditions considered, the adsorption capacity of Mol5A was found to be around eight times higher than the capacity of Mol4A. Low temperature adsorption and thermal pre-treatment of the adsorbents in vacuum were observed to be favourable for increased adsorption capacity. The adsorption isotherms for both adsorbent were fitted to the Freundlich model and the corresponding model parameters are proposed. The adsorption kinetic analysis suggest that the DME adsorption on Mol5A is controlled by intracrystalline diffusion resistance, while on Mol4A it is mainly controlled by surface layering resistance with the diffusion only taking place at the start of adsorption and for a very limited short time. The heats of adsorption were calculated by a calorimetric method based on direct temperature measurements inside the adsorption cell. Isosteric heats, calculated by the thermodynamic approach (Clasius-Clapeyron equation), have consistently shown lower values. The maximum heat of adsorption was found to be 25.9kJmol-1 and 20.1kJmol-1 on Mol4A and Mol5A, respectively; thus indicating a physisorption type of interactions. © 2014 Elsevier B.V.

Hierarchical approach to project planning:the case of a petroleum pipeline construction

Projects exposed to an uncertain environment must be adapted to deal with the effective integration of various planning elements and the optimization of project parameters. Time, cost, and quality are the prime objectives of a project that need to be optimized to fulfill the owner's goal. In an uncertain environment, there exist many other conflicting objectives that may also need to be optimized. These objectives are characterized by varying degrees of conflict. Moreover, an uncertain environment also causes several changes in the project plan throughout its life, demanding that the project plan be totally flexible. Goal programming (GP), a multiple criteria decision making technique, offers a good solution for this project planning problem. There the planning problem is considered from the owner's perspective, which leads to classifying the project up to the activity level. GP is applied separately at each level, and the formulated models are integrated through information flow. The flexibility and adaptability of the models lies in the ease of updating the model parameters at the required level through changing priorities and/or constraints and transmitting the information to other levels. The hierarchical model automatically provides integration among various element of planning. The proposed methodology is applied in this paper to plan a petroleum pipeline construction project, and its effectiveness is demonstrated.

Electrografting of poly (carbazole-co-acrylamide) onto highly oriented pyroltic graphite. A cyclovoltammetric, atomic force microscopic and ellipsometric study

The electrocopolymerization of carbazole and acrylamide on highly oriented pyrolytic graphite (HOPG) from ACN solutions via cyclovoltammetry (CV) was studied in order to evaluate the possibility to deposit uniform and thin but pinhole-free and still reactive coatings onto graphite-like substrates. The morphology of the coatings was investigated using atomic force microscopy and the coating thicknesses and optical parameters were measured using ellipsometry. It was found that under the chosen conditions thin (coating thickness hf>180 nm) and relatively smooth (root mean square surface roughness RMS<150 nm) P(Cz-co-AAm)-coatings exhibiting a uniform globuoidal morphology can be deposited onto graphite. From a certain coating thickness (hf>50 nm) no pinholes could be detected. It was found that the thickness of the deposited coatings increases almost linearly with increasing number of CV-cycles while keeping all other experimental parameters (scan rate and comonomer concentration ratio) constant. No influence of the comonomer concentration ratio on the film thickness and coating appearance could be observed, however, at quite low initial concentrations. However, the CV-scanning rate has quite a significant influence on the thickness of the deposited coatings. Higher scan rates (100 mV/s) result in thin (hf≈22 nm) coatings whereas at lower scan rates (<50 mV/s) coatings with thicknesses of approximately 50 nm were obtained. The optical coating parameters (the refractive index n and extinction coefficient k) seem to be independent of the deposition parameters and therefore averaged values of n̄=1.54±0.03 and k̄=0.08±0.03 were obtained.

Probabilistic control for uncertain systems

In this paper a new framework has been applied to the design of controllers which encompasses nonlinearity, hysteresis and arbitrary density functions of forward models and inverse controllers. Using mixture density networks, the probabilistic models of both the forward and inverse dynamics are estimated such that they are dependent on the state and the control input. The optimal control strategy is then derived which minimizes uncertainty of the closed loop system. In the absence of reliable plant models, the proposed control algorithm incorporates uncertainties in model parameters, observations, and latent processes. The local stability of the closed loop system has been established. The efficacy of the control algorithm is demonstrated on two nonlinear stochastic control examples with additive and multiplicative noise.

Characterization of viscoplastic yielding of asphalt concrete

A temperature and strain rate dependent yield surface model was proposed to characterize the viscoplastic yielding of asphalt concrete. Laboratory tests were conducted on specimens that have two binders, two air void contents, and three aging periods. Strain decomposition was performed to obtain viscoplastic strain and stress-pseudostrain curves were constructed to determine the model parameters accurately and efficiently. Results indicate that a stiffer asphalt concrete has greater cohesion and strain hardening amplitude, both of which decline as temperature increases or strain rate decreases. The temperature and strain rate factors of the yield surface can be accurately determined solely by the peak stress of the strength tests. © 2013 Elsevier Ltd. All rights reserved.

Parameter Identification of a Fed-Batch Cultivation of S. Cerevisiae using Genetic Algorithms

Fermentation processes as objects of modelling and high-quality control are characterized with interdependence and time-varying of process variables that lead to non-linear models with a very complex structure. This is why the conventional optimization methods cannot lead to a satisfied solution. As an alternative, genetic algorithms, like the stochastic global optimization method, can be applied to overcome these limitations. The application of genetic algorithms is a precondition for robustness and reaching of a global minimum that makes them eligible and more workable for parameter identification of fermentation models. Different types of genetic algorithms, namely simple, modified and multi-population ones, have been applied and compared for estimation of nonlinear dynamic model parameters of fed-batch cultivation of S. cerevisiae.

Improved stochastic optimization of railway timetables

We present a general model to find the best allocation of a limited amount of supplements (extra minutes added to a timetable in order to reduce delays) on a set of interfering railway lines. By the best allocation, we mean the solution under which the weighted sum of expected delays is minimal. Our aim is to finely adjust an already existing and well-functioning timetable. We model this inherently stochastic optimization problem by using two-stage recourse models from stochastic programming, building upon earlier research from the literature. We present an improved formulation, allowing for an efficient solution using a standard algorithm for recourse models. We show that our model may be solved using any of the following theoretical frameworks: linear programming, stochastic programming and convex non-linear programming, and present a comparison of these approaches based on a real-life case study. Finally, we introduce stochastic dependency into the model, and present a statistical technique to estimate the model parameters from empirical data.