22,169 research outputs found
Vibration based damage detection in a wind turbine blade based on autoregressive coefficients
Peer reviewedPreprin
Fuzzy Interval-Valued Multi Criteria Based Decision Making for Ranking Features in Multi-Modal 3D Face Recognition
Soodamani Ramalingam, 'Fuzzy interval-valued multi criteria based decision making for ranking features in multi-modal 3D face recognition', Fuzzy Sets and Systems, In Press version available online 13 June 2017. This is an Open Access paper, made available under the Creative Commons license CC BY 4.0 https://creativecommons.org/licenses/by/4.0/This paper describes an application of multi-criteria decision making (MCDM) for multi-modal fusion of features in a 3D face recognition system. A decision making process is outlined that is based on the performance of multi-modal features in a face recognition task involving a set of 3D face databases. In particular, the fuzzy interval valued MCDM technique called TOPSIS is applied for ranking and deciding on the best choice of multi-modal features at the decision stage. It provides a formal mechanism of benchmarking their performances against a set of criteria. The technique demonstrates its ability in scaling up the multi-modal features.Peer reviewedProo
Dynamics of episodic transient correlations in currency exchange rate returns and their predictability
We study the dynamics of the linear and non-linear serial dependencies in
financial time series in a rolling window framework. In particular, we focus on
the detection of episodes of statistically significant two- and three-point
correlations in the returns of several leading currency exchange rates that
could offer some potential for their predictability. We employ a rolling window
approach in order to capture the correlation dynamics for different window
lengths and analyze the distributions of periods with statistically significant
correlations. We find that for sufficiently large window lengths these
distributions fit well to power-law behavior. We also measure the
predictability itself by a hit rate, i.e. the rate of consistency between the
signs of the actual returns and their predictions, obtained from a simple
correlation-based predictor. It is found that during these relatively brief
periods the returns are predictable to a certain degree and the predictability
depends on the selection of the window length.Comment: 19 pages, 8 figure
Data-Driven Computing in Dynamics
We formulate extensions to Data Driven Computing for both distance minimizing
and entropy maximizing schemes to incorporate time integration. Previous works
focused on formulating both types of solvers in the presence of static
equilibrium constraints. Here formulations assign data points a variable
relevance depending on distance to the solution and on maximum-entropy
weighting, with distance minimizing schemes discussed as a special case. The
resulting schemes consist of the minimization of a suitably-defined free energy
over phase space subject to compatibility and a time-discretized momentum
conservation constraint. The present selected numerical tests that establish
the convergence properties of both types of Data Driven solvers and solutions.Comment: arXiv admin note: substantial text overlap with arXiv:1702.0157
Insights into the behaviour of systems biology models from dynamic sensitivity and identifiability analysis: a case study of an NF-kB signaling pathway
Mathematical modelling offers a variety of useful techniques to help in understanding the intrinsic behaviour of complex signal transduction networks. From the system engineering point of view, the dynamics of metabolic and signal transduction models can always be described by nonlinear ordinary differential equations (ODEs) following mass balance principles. Based on the state-space formulation, many methods from the area of automatic control can conveniently be applied to the modelling, analysis and design of cell networks. In the present study, dynamic sensitivity analysis is performed on a model of the IB-NF-B signal pathway system. Univariate analysis of the Euclidean-form overall sensitivities shows that only 8 out of the 64 parameters in the model have major influence on the nuclear NF-B oscillations. The sensitivity matrix is then used to address correlation analysis, identifiability assessment and measurement set selection within the framework of least squares estimation and multivariate analysis. It is shown that certain pairs of parameters are exactly or highly correlated to each other in terms of their effects on the measured variables. The experimental design strategy provides guidance on which proteins should best be considered for measurement such that the unknown parameters can be estimated with the best statistical precision. The whole analysis scheme we describe provides efficient parameter estimation techniques for complex cell networks
Exploring the potential of 3D Zernike descriptors and SVM for protein\u2013protein interface prediction
Abstract Background The correct determination of proteinâprotein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task. Results In this work we investigate the potential of a novel local surface descriptor based on 3D Zernike moments for the interface prediction task. Descriptors invariant to roto-translations are extracted from circular patches of the protein surface enriched with physico-chemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. Support Vector Machines are used as a classifier to distinguish interface local surface patches from non-interface ones. The proposed method was validated on 16 classes of proteins extracted from the ProteinâProtein Docking Benchmark 5.0 and compared to other state-of-the-art protein interface predictors (SPPIDER, PrISE and NPS-HomPPI). Conclusions The 3D Zernike descriptors are able to capture the similarity among patterns of physico-chemical and biochemical properties mapped on the protein surface arising from the various spatial arrangements of the underlying residues, and their usage can be easily extended to other sets of amino acid properties. The results suggest that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction task, and that optimality strongly depends on the class of proteins whose interface we want to characterise. We postulate that different protein classes should be treated separately and that it is necessary to identify an optimal set of features for each protein class
The role of statistical methodology in simulation
statistical methods;simulation;operations research
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