Composite front maps for improved visibility of dynamic sea-surface features on cloudy SeaWiFS and AVHRR data

Novel techniques have been developed for increasing the value of cloud-affected sequences of Advanced Very High Resolution Radiometer (AVHRR) sea-surface temperature (SST) data and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) ocean colour data for visualising dynamic physical and biological oceanic processes such as fronts, eddies and blooms. The proposed composite front map approach is to combine the location, strength and persistence of all fronts observed over several days into a single map, which allows intuitive interpretation of mesoscale structures. This method achieves a synoptic view without blurring dynamic features, an inherent problem with conventional time-averaging compositing methods. Objective validation confirms a significant improvement in feature visibility on composite maps compared to individual front maps. A further novel aspect is the automated detection of ocean colour fronts, correctly locating 96% of chlorophyll fronts in a test data set. A sizeable data set of 13,000 AVHRR and 1200 SeaWiFS scenes automatically processed using this technique is applied to the study of dynamic processes off the Iberian Peninsula such as mesoscale eddy generation, and many additional applications are identified. Front map animations provide a unique insight into the evolution of upwelling and eddies.

Development and application of a high resolution liquid chromatographic method for the analysis of complex pigment distributions

Ternary and binary gradient systems have been developed for the high-performance liquid chromatographic analysis of complex pigment distributions typical of natural samples. Improved chromatographic resolution reveals significantly more pigment components in extracts from a sediment (Priest Pot, Cumbria, UK), a microbial mat (les Salines de la Trinital, South Catalonia, Spain) and a culture (C. phaeobacteroides) including novel bacteriochlorophyll derivatives. The methods developed are directly suited to LC–MS analysis and the automated acquisition of MS/MS data for pigments.

A Tunable Split Resonator Method for Non-destructive Permittivity Characterization

The split cylinder resonator method is improved for nondestructive and accurate measurement for low permittivity materials at multiple frequency points. The dielectric constants of flat substrate materials are calculated based on a rigorous mode match analysis of the TE/sub 011/ mode. The loss tangent is also approximately calculated. The dielectric properties of two commercial substrates have been measured at multiple frequencies. The results demonstrate that this technology is capable of accurately characterizing the dielectric properties of flat substrate materials versus frequency in a nondestructive way.

Improved fault diagnosis in multivariate systems using regression-based reconstruction

Subspace monitoring has recently been proposed as a condition monitoring tool that requires considerably fewer variables to be analysed compared to dynamic principal component analysis (PCA). This paper analyses subspace monitoring in identifying and isolating fault conditions, which reveals that the existing work suffers from inherent limitations if complex fault senarios arise. Based on the assumption that the fault signature is deterministic while the monitored variables are stochastic, the paper introduces a regression-based reconstruction technique to overcome these limitations. The utility of the proposed fault identification and isolation method is shown using a simulation example and the analysis of experimental data from an industrial reactive distillation unit.

Improved preparations of ionic liquids using microwave irradiation

Controlled, multimode microwave irradiation has been employed in a generic solvent-free process to prepare a wide range of ionic liquids based on nitrogen-containing heterocycles. The developed method offers a flexible, small to large-scale approach to prepare ionic liquids, in either sealed or open vessels, in a faster and greener process than any previously described.

Segmentation of optic disc in retinal images using an improved gradient vector flow algorithm

Image segmentation plays an important role in the analysis of retinal images as the extraction of the optic disk provides important cues for accurate diagnosis of various retinopathic diseases. In recent years, gradient vector flow (GVF) based algorithms have been used successfully to successfully segment a variety of medical imagery. However, due to the compromise of internal and external energy forces within the resulting partial differential equations, these methods can lead to less accurate segmentation results in certain cases. In this paper, we propose the use of a new mean shift-based GVF segmentation algorithm that drives the internal/external energies towards the correct direction. The proposed method incorporates a mean shift operation within the standard GVF cost function to arrive at a more accurate segmentation. Experimental results on a large dataset of retinal images demonstrate that the presented method optimally detects the border of the optic disc.

Helical resonator filters with improved power handling capabilities for space applications

The power-handling capabilities of helical resonator filters for space applications are discussed. Emerging difficulties due to the multipaction effects are highlighted. A method is proposed to increase specified power handling without significantly sacrificing the size/quality factor. Experimental verification is attained by means of a fabricated prototype for which measured filter response and multipaction test results are obtained and presented.

Improved Nonlinear PCA for Process Monitoring Using Support Vector Data Description

Nonlinear principal component analysis (PCA) based on neural networks has drawn significant attention as a monitoring tool for complex nonlinear processes, but there remains a difficulty with determining the optimal network topology. This paper exploits the advantages of the Fast Recursive Algorithm, where the number of nodes, the location of centres, and the weights between the hidden layer and the output layer can be identified simultaneously for the radial basis function (RBF) networks. The topology problem for the nonlinear PCA based on neural networks can thus be solved. Another problem with nonlinear PCA is that the derived nonlinear scores may not be statistically independent or follow a simple parametric distribution. This hinders its applications in process monitoring since the simplicity of applying predetermined probability distribution functions is lost. This paper proposes the use of a support vector data description and shows that transforming the nonlinear principal components into a feature space allows a simple statistical inference. Results from both simulated and industrial data confirm the efficacy of the proposed method for solving nonlinear principal component problems, compared with linear PCA and kernel PCA.

An improved immunoassay for detection of saxitoxin by surface plasmon resonance biosensors

Saxitoxin and its analogs, the causative agents of paralytic shellfish poisoning (PSP), are a worldwide threat to seafood safety. Effective monitoring of potentially contaminated fishing areas as well as screening of seafood samples is necessary to adequately protect the public. While many analytical methods exist for detecting paralytic shellfish toxins (PSTs), each technique has challenges associated with routine use. One recently developed method [1] that overcomes ethical or performance-related issues of other techniques is the surface plasmon resonance (SPR) bioassay. Notwithstanding the advantages of this method, much research remains in optimizing the sensor substrate and assay conditions to create a robust technique for rapid and sensitive measurement of PSTs. This manuscript describes a more rigorous and stable SPR inhibition immunoassay through optimization of the surface chemistry as well as determination of optimum mixture ratios and mixing times. The final system provides rapid substrate formation (18 h saxitoxin conjugation with low reagent consumption), contains a reference channel for each assay, and is capable of triplicate measurements in a single run with detection limits well below the regulatory action level. Published by Elsevier B.V.

Improved conjugate gradient implementation for least squares support vector machines

As a promising method for pattern recognition and function estimation, least squares support vector machines (LS-SVM) express the training in terms of solving a linear system instead of a quadratic programming problem as for conventional support vector machines (SVM). In this paper, by using the information provided by the equality constraint, we transform the minimization problem with a single equality constraint in LS-SVM into an unconstrained minimization problem, then propose reduced formulations for LS-SVM. By introducing this transformation, the times of using conjugate gradient (CG) method, which is a greatly time-consuming step in obtaining the numerical solution, are reduced to one instead of two as proposed by Suykens et al. (1999). The comparison on computational speed of our method with the CG method proposed by Suykens et al. and the first order and second order SMO methods on several benchmark data sets shows a reduction of training time by up to 44%. (C) 2011 Elsevier B.V. All rights reserved.

Improved structure optimization for fuzzy-neural networks

Fuzzy-neural-network-based inference systems are well-known universal approximators which can produce linguistically interpretable results. Unfortunately, their dimensionality can be extremely high due to an excessive number of inputs and rules, which raises the need for overall structure optimization. In the literature, various input selection methods are available, but they are applied separately from rule selection, often without considering the fuzzy structure. This paper proposes an integrated framework to optimize the number of inputs and the number of rules simultaneously. First, a method is developed to select the most significant rules, along with a refinement stage to remove unnecessary correlations. An improved information criterion is then proposed to find an appropriate number of inputs and rules to include in the model, leading to a balanced tradeoff between interpretability and accuracy. Simulation results confirm the efficacy of the proposed method.

Alternative method for producing organic fertiliser from anaerobic digestion liquor and limestone powder:High Shear wet granulation

Generally, the solid and liquid fractions (digestate) from Anaerobic Digestion (AD) energy production are considered as waste. This has a negative impact on the sustainability of AD processes because of the financial outlay required to treat digestate before being discharged into municipal water treatment plants or natural water bodies. The main aim of this research was to investigate feasibility of producing an organic fertiliser using anaerobic digestate and limestone powders as the raw materials employing a high shear granulation process. Two-level factorial experimental design was used to determine the influence of granulation process variables on, the strength, resistance to attrition and yield of the granules. It was concluded from the study that it is technically feasible to produce organic fertiliser granules of acceptable strength and product yield. Increasing the liquid-to-solid ratio during granulation leads to increased granule strength and better product yield. Although the strength of the granules produced was lower than typical strength of commercial synthetic fertiliser granules (about 5 to 7. MPa), this could be improved by mixing the digestate with a polymeric binder or coating the particles post granulation. © 2012 Elsevier B.V.

The development of a surface defect machining method for hard turning processes

Hard turning (HT) is a material removal process employing a combination of a single point cutting tool and high speeds to machine hard ferrous alloys which exhibit hardness values over 45 HRC. In this paper, a surface defect machining (SDM) method for HT is proposed which harnesses the combined advantages of porosity machining and pulsed laser pre-treatment processing. From previous experimental work, this was shown to provide better controllability of the process and improved quality of the machined surface. While the experiments showed promising results, a comprehensive understanding of this new technique could only be achieved through a rigorous, in depth theoretical analysis. Therefore, an assessment of the SDM technique was carried out using both finite element method (FEM) and molecular dynamics (MD) simulations.
FEM modelling was used to compare the conventional HT of AISI 4340 steel (52 HRC) using an Al2O3 insert with the proposed SDM method. The simulations showed very good agreement with the previously published experimental results. Compared to conventional HT, SDM provided favourable machining outcomes, such as reduced shear plane angle, reduced average cutting forces, improved surface roughness, lower residual stresses on the machined surface, reduced tool–chip interface contact length and increased chip flow velocity. Furthermore, a scientific explanation of the improved surface finish was revealed using a state-of-the-art MD simulation model which suggested that during SDM, a combination of both the cutting action and rough polishing action help improve the machined surface finish.

A multiconfigurational time-dependent Hartree-Fock method for excited electronic states. I. General formalism and application to open-shell states

The solution of the time-dependent Schrodinger equation for systems of interacting electrons is generally a prohibitive task, for which approximate methods are necessary. Popular approaches, such as the time-dependent Hartree-Fock (TDHF) approximation and time-dependent density functional theory (TDDFT), are essentially single-configurational schemes. TDHF is by construction incapable of fully accounting for the excited character of the electronic states involved in many physical processes of interest; TDDFT, although exact in principle, is limited by the currently available exchange-correlation functionals. On the other hand, multiconfigurational methods, such as the multiconfigurational time-dependent Hartree-Fock (MCTDHF) approach, provide an accurate description of the excited states and can be systematically improved. However, the computational cost becomes prohibitive as the number of degrees of freedom increases, and thus, at present, the MCTDHF method is only practical for few-electron systems. In this work, we propose an alternative approach which effectively establishes a compromise between efficiency and accuracy, by retaining the smallest possible number of configurations that catches the essential features of the electronic wavefunction. Based on a time-dependent variational principle, we derive the MCTDHF working equation for a multiconfigurational expansion with fixed coefficients and specialise to the case of general open-shell states, which are relevant for many physical processes of interest. (C) 2011 American Institute of Physics. [doi: 10.1063/1.3600397]

Algorithmic method for scraper load-time optimization

Scrapers have established an important position in the earthmoving field as they are independently capable of accomplishing an earthmoving operation. Given that loading a scraper to its capacity does not entail its maximum production, optimizing the scraper’s loading time is an essential prerequisite for successful operations management. The relevant literature addresses the loading time optimization through a graphical method that is founded on the invalid assumption that the hauling time is independent of the load time. To correct this, a new algorithmic optimization method that incorporates the golden section search and the bisection algorithm is proposed. Comparison of the results derived from the proposed and the existing method demonstrates that the latter entails the systematic needless prolongation of the loading stage thus resulting in reduced hourly production and increased cost. Therefore, the proposed method achieves an improved modeling of scraper earthmoving operations and contributes toward a more efficient cost management.