Human figure segmentation using independent component analysis

In this paper, we present a Statistical Shape Model for Human Figure Segmentation in gait sequences. Point Distribution Models (PDM) generally use Principal Component analysis (PCA) to describe the main directions of variation in the training set. However, PCA assumes a number of restrictions on the data that do not always hold. In this work, we explore the potential of Independent Component Analysis (ICA) as an alternative shape decomposition to the PDM-based Human Figure Segmentation. The shape model obtained enables accurate estimation of human figures despite segmentation errors in the input silhouettes and has really good convergence qualities.

Automatic decomposition and efficient semi-structured meshing of complex solids

In this paper, a novel approach to automatically sub-divide a complex geometry and apply an efficient mesh is presented. Following the identification and removal of thin-sheet regions from an arbitrary solid using the thick/thin decomposition approach developed by Robinson et al. [1], the technique here employs shape metrics generated using local sizing measures to identify long-slender regions within the thick body. A series of algorithms automatically partition the thick region into a non-manifold assembly of long-slender and complex sub-regions. A structured anisotropic mesh is applied to the thin-sheet and long-slender bodies, and the remaining complex bodies are filled with unstructured isotropic tetrahedra. The resulting semi-structured mesh possesses significantly fewer degrees of freedom than the equivalent unstructured mesh, demonstrating the effectiveness of the approach. The accuracy of the efficient meshes generated for a complex geometry is verified via a study that compares the results of a modal analysis with the results of an equivalent analysis on a dense tetrahedral mesh.

Can we trust mass spectrometry for determination of arsenic peptides in plants:comparison of LC-ICP-MS and LC-ES-MS/ICP-MS with XANES/EXAFS in analysis of Thunbergia alata

The weakest step in the analytical procedure for speciation analysis is extraction from a biological material into an aqueous solution which undergoes HPLC separation and then simultaneous online detection by elemental and molecular mass spectrometry (ICP-MS/ES-MS). This paper describes a study to determine the speciation of arsenic and, in particular, the arsenite phytochelatin complexes in the root from an ornamental garden plant Thunbergia alata exposed to 1 mg As L(-1) as arsenate. The approach of formic acid extraction followed by HPLC-ES-MS/ICP-MS identified different As(III)-PC complexes in the extract of this plant and made their quantification via sulfur (m/z 32) and arsenic (m/z 75) possible. Although sulfur sensitivity could be significantly increased when xenon was used as collision gas in ICP-qMS, or when HR-ICP-MS was used in medium resolution, the As:S ratio gave misleading results in the identification of As(III)-PC complexes due to the relatively low resolution of the chromatography system in relation to the variety of As-peptides in plants. Hence only the parallel use of ES-MS/ICP-MS was able to prove the occurrence of such arsenite phytochelatin complexes. Between 55 and 64% of the arsenic was bound to the sulfur of peptides mainly as As(III)(PC(2))(2), As(III)(PC(3)) and As(III)(PC(4)). XANES (X-ray absorption near-edge spectroscopy) measurement, using the freshly exposed plant root directly, confirmed that most of the arsenic is trivalent and binds to S of peptides (53% As-S) while 38% occurred as arsenite and only 9% unchanged as arsenate. EXAFS data confirmed that As-S and As-O bonds occur in the plants. This study confirms, for the first time, that As-peptides can be extracted by formic acid and chromatographically separated on a reversed-phase column without significant decomposition or de-novo synthesis during the extraction step.

Epitaxial Magnetic Oxide Nanocrystals Via Phase Decomposition of Bismuth Perovskite Precursors

The phase instability of bismuth perovskite (BiMO₃), where M is a ferromagnetic cation, is exploited to create self-assembled magnetic oxide nanocrystal arrays on oxide supports. Conditions during pulsed laser deposition are tuned so as to induce complete breakdown of the perovskite precursor into bismuth oxide (Bi₂ O₃ ) and metal oxide (M-O_x ) pockets. Subsequent cooling in vacuum volatizes the Bi₂ O₃ leaving behind an array of monodisperse nanocrystals. In situ reflective high energy electron diffraction beam is exploited to monitor the synthesis in real-time. Analysis of the patterns confi rms the phase separation and volatization process. Successful synthesis of M-O_x, where M = Mn, Fe, Co, and Cr, is shown using this template-free facile approach. Detailed magnetic characterization of nanocrystals is carried out to reveal the functionalities such as magnetic anisotropy as well as larger than bulk moments, as expected in these oxide nanostructures.

Stability Analysis and Implementation of a Decentralized Formation Control Strategy for Unmanned Vehicles

This paper presents a new methodology for solving the multi-vehicle formation control problem. It employs a unique extension-decomposition-aggregation scheme to transform the overall complex formation control problem into a group of subproblems, which work via boundary interactions or disturbances. Thus, it is proved that the overall formation system is exponentially stable in the sense of Lyapunov, if all the individual augmented subsystems (IASs) are stable. Linear matrix inequality-based H8 control methodology is employed to design the decentralized formation controllers to reject the impact of the formation changes being treated as boundary disturbances and guarantee the stability of all the IASs, consequently maintaining the stability of the overall formation system. Simulation studies are performed to verify the stability, performance, and effectiveness of the proposed strategy.

Decentralised formation control and stability analysis for cooperative multi-vehicle manoeuvre

Multi-vehicle cooperative formation control problem is an important and typical topic of research on multi-agent system. This paper presents a formation stability conjecture to conceive a new methodology for solving the decentralised multi-vehicle formation control problem. It employs the “extension-decomposition-aggregation” scheme to transform the complex multi-agent control problem into a group of sub-problems which is able to be solved conveniently. Based on this methodology, it is proved that if all the individual augmented subsystems can be stabilised by using any approach, the overall formation system is not only asymptotically but also exponentially stable in the sense of Lyapunov within a neighbourhood of the desired formation. Simulation study on 6-DOF aerial vehicles (Aerosonde UAVs) has been performed to verify the achieved formation stability result. The proposed multi-vehicle formation control strategy can be conveniently extended to other cooperative control problems of multi-agent systems.

Multi-Dimensional Deprivation in Ireland Among 9-Year-Olds in Ireland:An Analysis of the Growing Up in Ireland Survey’

In this paper we make use of the 9-year old wave of the Growing Up in Ireland study to analyse multidimensional deprivation in Ireland. The Alkire and Foster adjusted head count ratio approach (AHCR; 2007, 2011a, 2011b) applied here constitutes a significant improvement on union and intersection approaches and allows for the decomposition of multidimensional poverty in terms of dimensions and sub-groups. The approach involves a censoring of data such that deprivations count only for those above the specified multidimensional threshold leading to a stronger set of interrelationships between deprivation dimensions. Our analysis shows that the composition of the adjusted head ratio is influenced by a range of socio-economic factors. For less-favoured socio-economic groups dimensions relating to material deprivation are disproportionately represented while for the more advantaged groups, those relating to behavioral and emotional issues and social interaction play a greater role. Notwithstanding such variation in composition, our analysis showed that the AHCR varied systematically across categories of household type, and the social class, education and age group of the primary care giver. Furthermore, these variables combined in a cumulative manner. The most systematic variation was in relation to the head count of those above the multidimensional threshold rather than intensity, conditional on being above that cut-off point. Without seeking to arbitrate on the relative value of composite indices versus disaggregated profiles, our analysis demonstrates that there is much to be gained from adopting an approach with clearly understood axiomatic properties. Doing so allows one to evaluate the consequences of the measurement strategy employed for the understanding of levels of multidimensional deprivation, the nature of such deprivation profiles and socio-economic risk patterns. Ultimately it permits an informed assessment of the strengths and weaknesses of the particular choices made.

Formation stability analysis of unmanned multi-vehicles under interconnection topologies

In this paper, the overall formation stability of unmanned multi-vehicle is mathematically presented under interconnection topologies. A novel definition of formation error is first given and followed by the proposed formation stability hypothesis. Based on this hypothesis, a unique extension-decomposition-aggregation scheme is then employed to support the stability analysis for the overall multi-vehicle formation under a mesh topology. It is proved that the overall formation control system consisting of N number of nonlinear vehicles is not only asymptotically, but also exponentially stable in the sense of Lyapunov within a neighbourhood of the desired formation. This technique is shown to be applicable for a mesh topology but is equally applicable for other topologies. Simulation study of the formation manoeuvre of multiple Aerosonde UAVs, in 3D-space, is finally carried out verifying the achieved formation stability result.

Using Brain Data for Sentiment Analysis

We present the results of exploratory experiments using lexical valence extracted from brain using electroencephalography (EEG) for sentiment analysis. We selected 78 English words (36 for training and 42 for testing), presented as stimuli to 3 English native speakers. EEG signals were recorded from the subjects while they performed a mental imaging task for each word stimulus. Wavelet decomposition was employed to extract EEG features from the time-frequency domain. The extracted features were used as inputs to a sparse multinomial logistic regression (SMLR) classifier for valence classification, after univariate ANOVA feature selection. After mapping EEG signals to sentiment valences, we exploited the lexical polarity extracted from brain data for the prediction of the valence of 12 sentences taken from the SemEval-2007 shared task, and compared it against existing lexical resources.

Development of a Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) Cell for the In Situ Analysis of Co-Electrolysis in a Solid Oxide Cell

Co-electrolysis of carbon dioxide and steam has been shown to be an efficient way to produce syngas, however further optimisation requires detailed understanding of the complex reactions, transport processes and degradation mechanisms occurring in the solid oxide cell (SOC) during operation. Whilst electrochemical measurements are currently conducted in situ, many analytical techniques can only be used ex situ and may even be destructive to the cell (e.g. SEM imaging of microstructure). In order to fully understand and characterise co-electrolysis, in situ monitoring of the reactants, products and SOC is necessary. Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) is ideal for in situ monitoring of co-electrolysis as both gaseous and adsorbed CO and CO2 species can be detected, however it has previously not been used for this purpose. The challenges of designing an experimental rig which allows optical access alongside electrochemical measurements at high temperature and operates in a dual atmosphere are discussed. The rig developed has thus far been used for symmetric cell testing at temperatures from 450[degree]C to 600[degree]C. Under a CO atmosphere, significant changes in spectra were observed even over a simple Au|10Sc1CeSZ|Au SOC. The changes relate to a combination of CO oxidation, the water gas shift reaction and carbonate formation and decomposition processes, with the dominant process being both potential and temperature dependent.

The photocatalytic decomposition of microcystin-LR using selected titanium dioxide materials

Microcystins (cyclic heptapeptides) produced by a number of freshwater cyanobacteria are a potential cause for concern in potable water supplies due to their acute and chronic toxicity. TiO₂ photocatalysis is a promising technology for removal of these toxins from drinking water. It is, however, necessary to have a sufficient knowledge of how the catalyst materials cause the degradation of the toxins through the photocatalytic process. The present study reports microcystin degradation products of the photocatalytic oxidation by using a number of commercial TiO₂ powder (P25, PC50, PC500 and UV100) and granular (KO1, KO3, TiCat-C, TiCat-S) materials, so aiding the mechanistic understanding of this process. Liquid chromatography-mass spectrometry analysis demonstrated that the major destruction pathway of microcystin for all the catalysts tested followed almost the same pathway, indicating the physical properties of the catalysts had little effects on the degradation pathway of microcystin-LR. 

Mechanistic studies of the photocatalytic oxidation of microcystin-LR: An investigation of byproducts of the decomposition process

Microcystins (cyclic heptapeptides) are produced by a number of freshwater cyanobacteria and cause concern in potable water supplies due to their acute and chronic toxicity. The present study reports the structural characterization of the degradation products of the photocatalytic oxidation of microcystin-LR, so aiding the mechanistic understanding of this process. TiO₂ photocatalysis is a promising technology for removal of these toxins from drinking water. However, before it can be adopted in any practical application it is necessary to have a sufficient knowledge of degradation byproducts and their potential toxicity. Liquid chromatography-mass spectrometry analysis demonstrated that the major destruction pathway of microcystin appears to be initiated via three mechanisms: UV irradiation, hydroxyl radical attack, and oxidation. UV irradiation caused geometrical isomerization of microcystin converting the (4E), (6E) of the Adda configuration to (4E), 6(Z) or 4(Z), 6(E). Hydroxyl radical attack on the conjugated diene structure of Adda moiety produced dihyroxylated products. Further oxidation cleaved the hydroxylated 4-5 and/or 6-7 bond of Adda to form aldehyde or ketone peptide residues, which then were oxidized into the corresponding carboxylic acids. Photocatalysis also hydrolyzed the peptide bond on the ring structure of microcystin to form linear structures although this appeared to be a minor pathway.

Interface management for automating finite element analysis workflows

This paper outlines the importance of robust interface management for facilitating finite element analysis workflows. Topological equivalences between analysis model representations are identified and maintained in an editable and accessible manner. The model and its interfaces are automatically represented using an analysis-specific cellular decomposition of the design space. Rework of boundary conditions following changes to the design geometry or the analysis idealization can be minimized by tracking interface dependencies. Utilizing this information with the Simulation Intent specified by an analyst, automated decisions can be made to process the interface information required to rebuild analysis models. Through this work automated boundary condition application is realized within multi-component, multi-resolution and multi-fidelity analysis workflows.

Loss-of-Main Monitoring and Detection for Distributed Generations Using Dynamic Principal Component Analysis

In this paper, our previous work on Principal Component Analysis (PCA) based fault detection method is extended to the dynamic monitoring and detection of loss-of-main in power systems using wide-area synchrophasor measurements. In the previous work, a static PCA model was built and verified to be capable of detecting and extracting system faulty events; however the false alarm rate is high. To address this problem, this paper uses a well-known ‘time lag shift’ method to include dynamic behavior of the PCA model based on the synchronized measurements from Phasor Measurement Units (PMU), which is named as the Dynamic Principal Component Analysis (DPCA). Compared with the static PCA approach as well as the traditional passive mechanisms of loss-of-main detection, the proposed DPCA procedure describes how the synchrophasors are linearly
auto- and cross-correlated, based on conducting the singular value decomposition on the augmented time lagged synchrophasor matrix. Similar to the static PCA method, two statistics, namely T2 and Q with confidence limits are calculated to form intuitive charts for engineers or operators to monitor the loss-of-main situation in real time. The effectiveness of the proposed methodology is evaluated on the loss-of-main monitoring of a real system, where the historic data are recorded from PMUs installed in several locations in the UK/Ireland power system.