Contraction of monotone phase-coupled oscillators

This paper establishes a global contraction property for networks of phase-coupled oscillators characterized by a monotone coupling function. The contraction measure is a total variation distance. The contraction property determines the asymptotic behavior of the network, which is either finite-time synchronization or asymptotic convergence to a splay state. © 2012 Elsevier B.V. All rights reserved.

Cubically convergent iterations for invariant subspace computation

We propose a Newton-like iteration that evolves on the set of fixed dimensional subspaces of ℝ n and converges locally cubically to the invariant subspaces of a symmetric matrix. This iteration is compared in terms of numerical cost and global behavior with three other methods that display the same property of cubic convergence. Moreover, we consider heuristics that greatly improve the global behavior of the iterations.

Image deconvolution using a Gaussian scale mixtures model to approximate the wavelet sparseness constraint

This paper proposes to use an extended Gaussian Scale Mixtures (GSM) model instead of the conventional ℓ1 norm to approximate the sparseness constraint in the wavelet domain. We combine this new constraint with subband-dependent minimization to formulate an iterative algorithm on two shift-invariant wavelet transforms, the Shannon wavelet transform and dual-tree complex wavelet transform (DTCWT). This extented GSM model introduces spatially varying information into the deconvolution process and thus enables the algorithm to achieve better results with fewer iterations in our experiments. ©2009 IEEE.

Robust modelling of noisy ARMA signals

In this paper methods are developed for enhancement and analysis of autoregressive moving average (ARMA) signals observed in additive noise which can be represented as mixtures of heavy-tailed non-Gaussian sources and a Gaussian background component. Such models find application in systems such as atmospheric communications channels or early sound recordings which are prone to intermittent impulse noise. Markov Chain Monte Carlo (MCMC) simulation techniques are applied to the joint problem of signal extraction, model parameter estimation and detection of impulses within a fully Bayesian framework. The algorithms require only simple linear iterations for all of the unknowns, including the MA parameters, which is in contrast with existing MCMC methods for analysis of noise-free ARMA models. The methods are illustrated using synthetic data and noise-degraded sound recordings.

Probabilistic inference for fast learning in control

A novel framework is provided for very fast model-based reinforcement learning in continuous state and action spaces. It requires probabilistic models that explicitly characterize their levels of condence. Within the framework, exible, non-parametric models are used to describe the world based on previously collected experience. It demonstrates learning on the cart-pole problem in a setting where very limited prior knowledge about the task has been provided. Learning progressed rapidly, and a good policy found after only a small number of iterations.

Efficient block segmentation algorithm for true motion estimation

A block-based motion estimation technique is proposed which permits a less general segmentation performed using an efficient deterministic algorithm. Applied to image pairs from the Flower Garden and Table Tennis sequences, the algorithm successfully localizes motion discontinuities and detects uncovered regions. The algorithm is implemented in C on a Sun Sparcstation 20. The gradient-based motion estimation required 28.8 s CPU time, and 500 iterations of the segmentation algorithm required 32.6 s.

RANS-URANS in axial compressor, a design methodology

Turbomachinery flows are inherently unsteady. Until now during the design process, unsteadiness has been neglected, with resort merely to steady numerical simulations. Despite the assumption involved, the results obtained with steady simulations have been used with success. One of the questions arising in recent years is can unsteady simulations be used to improve the design of turbomachines? In this work the numerical simulation of a multi-stage axial compressor is carried out. Comparison of Reynolds averaged Navier-Stokes (RANS) and unsteady Reynolds averaged Navier-Stokes (URANS) calculation shows that the unsteadiness affects pressure losses and the prediction of stall limit. The unsteady inflow due to the wake passing mainly modifies the losses and whirl angle near the endwalls. The computational cost of the fully unsteady compared with a steady simulation is about four times in terms of mesh dimension and two orders of magnitude as number of iterations. A mixed RANS-URANS solution has been proposed to give the designer the possibility to simulate an unsteady stage embedded in a steady-state simulation. This method has been applied to the simulation of a four-stage axial compressor rig. The mixed RANS-URANS approach has been developed using sliding and mixing planes as interface conditions. The rotor-stator interaction has been captured physically while reducing the computational time and mesh size.

Data-based robust multiobjective optimization of interconnected processes: energy efficiency case study in papermaking.

Reducing energy consumption is a major challenge for "energy-intensive" industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of "optimized" operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method.

Fast L0-based sparse signal recovery

This paper develops an algorithm for finding sparse signals from limited observations of a linear system. We assume an adaptive Gaussian model for sparse signals. This model results in a least square problem with an iteratively reweighted L2 penalty that approximates the L0-norm. We propose a fast algorithm to solve the problem within a continuation framework. In our examples, we show that the correct sparsity map and sparsity level are gradually learnt during the iterations even when the number of observations is reduced, or when observation noise is present. In addition, with the help of sophisticated interscale signal models, the algorithm is able to recover signals to a better accuracy and with reduced number of observations than typical L1-norm and reweighted L1 norm methods. ©2010 IEEE.

Seismic model parameter estimation using least-squares migration

We describe a method for verifying seismic modelling parameters. It is equivalent to performing several iterations of unconstrained least-squares migration (LSM). The approach allows the comparison of modelling/imaging parameter configurations with greater confidence than simply viewing the migrated images. The method is best suited to determining discrete parameters but can be used for continuous parameters albeit with greater computational expense.

Data-based robust multiobjective optimization of interconnected processes: Energy efficiency case study in papermaking

Reducing energy consumption is a major challenge for energy-intensive industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of optimized operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method. © 2006 IEEE.

Polarisation diversity of conformal arrays based on geometric algebra via convex optimisation

A simple and general design procedure is presented for the polarisation diversity of arbitrary conformal arrays; this procedure is based on the mathematical framework of geometric algebra and can be solved optimally using convex optimisation. Aside from being simpler and more direct than other derivations in the literature, this derivation is also entirely general in that it expresses the transformations in terms of rotors in geometric algebra which can easily be formulated for any arbitrary conformal array geometry. Convex optimisation has a number of advantages; solvers are widespread and freely available, the process generally requires a small number of iterations and a wide variety of constraints can be readily incorporated. The study outlines a two-step approach for addressing polarisation diversity in arbitrary conformal arrays: first, the authors obtain the array polarisation patterns using geometric algebra and secondly use a convex optimisation approach to find the optimal weights for the polarisation diversity problem. The versatility of this approach is illustrated via simulations of a 7×10 cylindrical conformal array. © 2012 The Institution of Engineering and Technology.

Blade loading and its application in the mean-line design of low pressure turbines

In order to minimize the number of iterations to a turbine design, reasonable choices of the key parameters must be made at the earliest possible opportunity. The choice of blade loading is of particular concern in the low pressure (LP) turbine of civil aero engines, where the use of high-lift blades is widespread. This paper presents an analytical mean-line design study for a repeating-stage, axial-flow Low Pressure (LP) turbine. The problem of how to measure blade loading is first addressed. The analysis demonstrates that the Zweifel coefficient [1] is not a reasonable gauge of blade loading because it inherently depends on the flow angles. A more appropriate coefficient based on blade circulation is proposed. Without a large set of turbine test data it is not possible to directly evaluate the accuracy of a particular loss correlation. The analysis therefore focuses on the efficiency trends with respect to flow coefficient, stage loading, lift coefficient and Reynolds number. Of the various loss correlations examined, those based on Ainley and Mathieson ([2], [3], [4]) do not produce realistic trends. The profile loss model of Coull and Hodson [5] and the secondary loss models of Craig and Cox [6] and Traupel [7] gave the most reasonable results. The analysis suggests that designs with the highest flow turning are the least sensitive to increases in blade loading. The increase in Reynolds number lapse with loading is also captured, achieving reasonable agreement with experiments. Copyright © 2011 by ASME.

Blade Loading and Its Application in the Mean-Line Design of Low Pressure Turbines

In order to minimize the number of iterations to a turbine design, reasonable choices of the key parameters must be made at the preliminary design stage. The choice of blade loading is of particular concern in the low pressure (LP) turbine of civil aero engines, where the use of high-lift blades is widespread. This paper considers how blade loading should be measured, compares the performance of various loss correlations, and explores the impact of blade lift on performance and lapse rates. To these ends, an analytical design study is presented for a repeating-stage, axial-flow LP turbine. It is demonstrated that the long-established Zweifel lift coefficient (Zweifel, 1945, "The Spacing of Turbomachine Blading, Especially with Large Angular Deflection" Brown Boveri Rev., 32(1), pp. 436-444) is flawed because it does not account for the blade camber. As a result the Zweifel coefficient is only meaningful for a fixed set of flow angles and cannot be used as an absolute measure of blade loading. A lift coefficient based on circulation is instead proposed that accounts for the blade curvature and is independent of the flow angles. Various existing profile and secondary loss correlations are examined for their suitability to preliminary design. A largely qualitative comparison demonstrates that the loss correlations based on Ainley and Mathieson (Ainley and Mathieson, 1957, "A Method of Performance Estimation for Axial-Flow Turbines," ARC Reports and Memoranda No. 2974; Dunham and Came, 1970, "Improvements to the Ainley-Mathieson Method of Turbine Performance Prediction," Trans. ASME: J. Eng. Gas Turbines Power, July, pp. 252-256; Kacker and Okapuu, 1982, "A Mean Line Performance Method for Axial Flow Turbine Efficiency," J. Eng. Power, 104, pp. 111-119). are not realistic, while the profile loss model of Coull and Hodson (Coull and Hodson, 2011, "Predicting the Profile Loss of High-Lift Low Pressure Turbines," J. Turbomach., 134(2), pp. 021002) and the secondary loss model of (Traupel, W, 1977, Thermische Turbomaschinen, Springer-Verlag, Berlin) are arguably the most reasonable. A quantitative comparison with multistage rig data indicates that, together, these methods over-predict lapse rates by around 30%, highlighting the need for improved loss models and a better understanding of the multistage environment. By examining the influence of blade lift across the Smith efficiency chart, the analysis demonstrates that designs with higher flow turning will tend to be less sensitive to increases in blade loading. © 2013 American Society of Mechanical Engineers.