Polyamorphism in aluminate liquids

McMillan, P. F., Wilson, M., Wilding, M. C. (2003). Polyamorphism in aluminate liquids. Journal of Physics: Condensed Matter, 15 (36), 6105-6121 RAE2008

Power system parameters forecasting using Hilbert-Huang transform and machine learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. A part from introduction and references the paper is organized as follows. The second section presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learningbased algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.

Beam-target double-spin asymmetry A{LT} in charged pion production from deep inelastic scattering on a transversely polarized {3}He target at 1.4

We report the first measurement of the double-spin asymmetry A{LT} for charged pion electroproduction in semi-inclusive deep-inelastic electron scattering on a transversely polarized {3}He target. The kinematics focused on the valence quark region, 0.16distribution function g{1T}{q} and therefore provide access to quark spin-orbit correlations. Our results indicate a positive azimuthal asymmetry for π{-} production on {3}He and the neutron, while our π{+} asymmetries are consistent with zero.

Artificial neural networks in modelling osmotic dehydration of foods

Artificial neural network (ANN) models for water loss (WL) and solid gain (SG) were evaluated as potential alternative to multiple linear regression (MLR) for osmotic dehydration of apple, banana and potato. The radial basis function (RBF) network with a Gaussian function was used in this study. The RBF employed the orthogonal least square learning method. When predictions of experimental data from MLR and ANN were compared, an agreement was found for ANN models than MLR models for SG than WL. The regression coefficient for determination (R2) for SG in MLR models was 0.31, and for ANN was 0.91. The R2 in MLR for WL was 0.89, whereas ANN was 0.84.Osmotic dehydration experiments found that the amount of WL and SG occurred in the following descending order: Golden Delicious apple > Cox apple > potato > banana. The effect of temperature and concentration of osmotic solution on WL and SG of the plant materials followed a descending order as: 55 > 40 > 32.2C and 70 > 60 > 50 > 40%, respectively.

Automatic classification of field-collected dinoflagellates by artificial neural network

Automatic taxonomic categorisation of 23 species of dinoflagellates was demonstrated using field-collected specimens. These dinoflagellates have been responsible for the majority of toxic and noxious phytoplankton blooms which have occurred in the coastal waters of the European Union in recent years and make severe impact on the aquaculture industry. The performance by human 'expert' ecologists/taxonomists in identifying these species was compared to that achieved by 2 artificial neural network classifiers (multilayer perceptron and radial basis function networks) and 2 other statistical techniques, k-Nearest Neighbour and Quadratic Discriminant Analysis. The neural network classifiers outperform the classical statistical techniques. Over extended trials, the human experts averaged 85% while the radial basis network achieved a best performance of 83%, the multilayer perceptron 66%, k-Nearest Neighbour 60%, and the Quadratic Discriminant Analysis 56%.

Analysis of classification accuracy for pre-filtered multichannel remote sensing data

Noise is one of the main factors degrading the quality of original multichannel remote sensing data and its presence influences classification efficiency, object detection, etc. Thus, pre-filtering is often used to remove noise and improve the solving of final tasks of multichannel remote sensing. Recent studies indicate that a classical model of additive noise is not adequate enough for images formed by modern multichannel sensors operating in visible and infrared bands. However, this fact is often ignored by researchers designing noise removal methods and algorithms. Because of this, we focus on the classification of multichannel remote sensing images in the case of signal-dependent noise present in component images. Three approaches to filtering of multichannel images for the considered noise model are analysed, all based on discrete cosine transform in blocks. The study is carried out not only in terms of conventional efficiency metrics used in filtering (MSE) but also in terms of multichannel data classification accuracy (probability of correct classification, confusion matrix). The proposed classification system combines the pre-processing stage where a DCT-based filter processes the blocks of the multichannel remote sensing image and the classification stage. Two modern classifiers are employed, radial basis function neural network and support vector machines. Simulations are carried out for three-channel image of Landsat TM sensor. Different cases of learning are considered: using noise-free samples of the test multichannel image, the noisy multichannel image and the pre-filtered one. It is shown that the use of the pre-filtered image for training produces better classification in comparison to the case of learning for the noisy image. It is demonstrated that the best results for both groups of quantitative criteria are provided if a proposed 3D discrete cosine transform filter equipped by variance stabilizing transform is applied. The classification results obtained for data pre-filtered in different ways are in agreement for both considered classifiers. Comparison of classifier performance is carried out as well. The radial basis neural network classifier is less sensitive to noise in original images, but after pre-filtering the performance of both classifiers is approximately the same.

Wave height analysis from 10 years of observations in the Norwegian Sea

Large waves pose risks to ships, offshore structures, coastal infrastructure and ecosystems. This paper analyses 10 years of in-situ measurements of significant wave height (Hs) and maximum wave height (Hmax) from the ocean weather ship Polarfront in the Norwegian Sea. During the period 2000 to 2009, surface elevation was recorded every 0.59 s during sampling periods of 30 min. The Hmax observations scale linearly with Hs on average. A widely-used empirical Weibull distribution is found to estimate average values of Hmax/H s and Hmax better than a Rayleigh distribution, but tends to underestimate both for all but the smallest waves. In this paper we propose a modified Rayleigh distribution which compensates for the heterogeneity of the observed dataset: the distribution is fitted to the whole dataset and improves the estimate of the largest waves. Over the 10-year period, the Weibull distribution approximates the observed Hs and Hmax well, and an exponential function can be used to predict the probability distribution function of the ratio Hmax/Hs. However, the Weibull distribution tends to underestimate the occurrence of extremely large values of Hs and Hmax. The persistence of Hs and Hmax in winter is also examined. Wave fields with Hs > 12 m and Hmax > 16 m do not last longer than 3 h. Low-to-moderate wave heights that persist for more than 12 h dominate the relationship of the wave field with the winter NAO index over 2000–2009. In contrast, the inter-annual variability of wave fields with Hs > 5.5 m or Hmax > 8.5 m and wave fields persisting over ~2.5 days is not associated with the winter NAO index.

A Hybrid Forward Algorithm for RBF Neural Network Construction

This paper proposes a novel hybrid forward algorithm (HFA) for the construction of radial basis function (RBF) neural networks with tunable nodes. The main objective is to efficiently and effectively produce a parsimonious RBF neural network that generalizes well. In this study, it is achieved through simultaneous network structure determination and parameter optimization on the continuous parameter space. This is a mixed integer hard problem and the proposed HFA tackles this problem using an integrated analytic framework, leading to significantly improved network performance and reduced memory usage for the network construction. The computational complexity analysis confirms the efficiency of the proposed algorithm, and the simulation results demonstrate its effectiveness

Solvation effects on equilibria: Triazoles and N-methyl piperidinol

We have performed calculations of the solvation effects on a number of equilibrium constants in water using a recently proposed hybrid quantum classical scheme in which the liquid environment is modelled using classical solvent molecules and the solute electronic structure is computed using modern quantum chemical methods. The liquid phase space is sampled from a fully classical simulation. We find that solvation effects on both triazole tautomeric equilibrium constants and piperidinol conformational equilibrium constants can be interpreted in terms of subtle differences in the local environment which can be seen in probability densities and radial distribution functions. Lower level calculations were performed for comparison and we conclude that the solvation thermodynamics can be predicted from a good classical model of solvent and solute molecules, but the implicit models that we tried are less successful.

Development of complex classical force fields through force matching to ab initio data: Application to a room-temperature ionic liquid

Recent experimental neutron diffraction data and ab initio molecular dynamics simulation of the ionic liquid dimethylimidazolium chloride ([dmim]Cl) have provided a structural description of the system at the molecular level. However, partial radial distribution functions calculated from the latter, when compared to previous classical simulation results, highlight some limitations in the structural description offered by force fieldbased simulations. With the availability of ab initio data it is possible to improve the classical description of [dmim]Cl by using the force matching approach, and the strategy for fitting complex force fields in their original functional form is discussed. A self-consistent optimization method for the generation of classical potentials of general functional form is presented and applied, and a force field that better reproduces the observed first principles forces is obtained. When used in simulation, it predicts structural data which reproduces more faithfully that observed in the ab initio studies. Some possible refinements to the technique, its application, and the general suitability of common potential energy functions used within many ionic liquid force fields are discussed.

Energy analysis of hyperthermal hydrogen atoms generated through surface neutralisation of ions

Hydrogen ions (H+, H-2(+) and H-3(+)) are produced in a magnetically confined inductively coupled radio frequency plasma. Ions are accelerated in the plasma boundary sheath potential, of several hundred volts, in front of a biased metal electrode immersed in the plasma. Backscattered hyperthermal hydrogen atoms are investigated by optical emission spectroscopy and an energy-resolved mass spectrometer. Ionisation of fast neutrals through electron stripping of atoms in the plasma allows energy analysis of the resulting ions. Thereby, the energy distribution function of the hyperthermal atoms can be deduced. The energy spectra can be explained as a superposition of individual spectra of the various ion species. The measured spectra also shows contributions of negative ions created at the electrode surface. In addition to experimental measurements, simulations of the neutral flux of backscattered atoms are carried out.

Prospects of phase resolved optical emission spectroscopy as a powerful diagnostic tool for RF-discharges

Phase resolved optical emission spectroscopy (PROES) bears considerable potential for diagnostics of RF discharges that give detailed insight of spatial and temporal variations of excitation processes. Based on phase and space resolved measurements of the population dynamics of excited states several diagnostic techniques have been developed. Results for a hydrogen capacitively coupled RF (CCRF) discharge are discussed as an example. The gas temperature, the degree of dissociation and the temporally and spatially resolved electron energy distribution function (EEDF) of energetic electrons (>12eV) are measured. Furthermore, the pulsed electron impact excitation during the field reversal phase, typical for hydrogen CCRF discharges, is exploited for measurements of atomic and molecular data like lifetimes of excited states, coefficients for radiationless collisional de-excitation (quenching coefficients), and cascading processes from higher electronic states.

A novel continuous forward algorithm for RBF neural modelling

A continuous forward algorithm (CFA) is proposed for nonlinear modelling and identification using radial basis function (RBF) neural networks. The problem considered here is simultaneous network construction and parameter optimization, well-known to be a mixed integer hard one. The proposed algorithm performs these two tasks within an integrated analytic framework, and offers two important advantages. First, the model performance can be significantly improved through continuous parameter optimization. Secondly, the neural representation can be built without generating and storing all candidate regressors, leading to significantly reduced memory usage and computational complexity. Computational complexity analysis and simulation results confirm the effectiveness.

Application of static charge transfer within an ionic-liquid force field and its effect on structure and dynamics

The effects of linear scaling of the atomic charges of a reference potential on the structure, dynamics, and energetics of the ionic liquid 1,3-dimethylimidazolium chloride are investigated. Diffusion coefficients that span over four orders of magnitude are observed between the original model and a scaled model in which the ionic charges are +/- 0.5 e. While the three-dimensional structure of the liquid is less affected, the partial radial distribution functions change markedly-with the positive result that for ionic charges of +/- 0.7 e, an excellent agreement is observed with ab initio molecular dynamics data. Cohesive energy densities calculated from these partial-charge models are also in better agreement with those calculated from the ab initio data. We postulate that ionic-liquid models in which the ionic charges are assumed to be +/- 1 e overestimate the intermolecular attractions between ions, which results in overstructuring, slow dynamics, and increased cohesive energy densities. The use of scaled-charge sets may be of benefit in the simulation of these systems-especially when looking at properties beyond liquid structure-thus providing on alternative to computationally expensive polarisable force fields.

Characterization of a high-density, direct-current reflex discharge plasma source operating in Ar and N-2

The characterization of a direct current, low-pressure, and high-density reflex discharge plasma source operating in argon and in nitrogen, over a range of pressures 1.0-10(-2) mbar, discharge currents 20-200 mA, and magnetic fields 0-120 G, and its parametric characterization is presented. Both external parameters, such as the breakdown potential and the discharge voltage-current characteristic, and internal parameters, like the charge carrier's temperature and density, plasma potential, floating potential, and electron energy distribution function, were measured. The electron energy distribution functions are bi-Maxwellian, but some structure is observed in these functions in nitrogen plasmas. There is experimental evidence for the existence of three groups of electrons within this reflex discharge plasma. Due to the enhanced hollow cathode effect by the magnetic trapping of electrons, the density of the cold group of electrons is as high as 10(18) m(-3), and the temperature is as low as a few tenths of an electron volt. The bulk plasma density scales with the dissipated power. Another important feature of this reflex plasma source is its high degree of uniformity, while the discharge bulk region is free of electric field. (C) 2002 American Institute of Physics.