An agent-based control mechanism for WFQ in IP networks

This paper presents a new packet scheduling scheme called agent-based WFQ to control and maintain QoS parameters in virtual private networks (VPNs) within the confines of adaptive networks. Future networks are expected to be open heterogeneous environments consisting of more than one network operator. In this adaptive environment, agents act on behalf of users or third-party operators to obtain the best service for their clients and maintain those services through the modification of the scheduling scheme in routers and switches spanning the VPN. In agent-based WFQ, an agent on the router monitors the accumulated queuing delay for each service. In order to control and to keep the end-to-end delay within the bounds, the weights for services are adjusted dynamically by agents on the routers spanning the VPN. If there is an increase or decrease in queuing delay of a service, an agent on a downstream router informs the upstream routers to adjust the weights of their queues. This keeps the end-to-end delay of services within the specified bounds and offers better QoS compared to VPNs using static WFQ. This paper also describes the algorithm for agent-based WFQ, and presents simulation results. (C) 2003 Elsevier Science Ltd. All rights reserved.

Fault reconstruction in linear dynamic systems using multivariate statistics

Treasure et al. (2004) recently proposed a new sub space-monitoring technique, based on the N4SID algorithm, within the multivariate statistical process control framework. This dynamic-monitoring method requires considerably fewer variables to be analysed when compared with dynamic principal component analysis (PCA). The contribution charts and variable reconstruction, traditionally employed for static PCA, are analysed in a dynamic context. The contribution charts and variable reconstruction may be affected by the ratio of the number of retained components to the total number of analysed variables. Particular problems arise if this ratio is large and a new reconstruction chart is introduced to overcome these. The utility of such a dynamic contribution chart and variable reconstruction is shown in a simulation and by application to industrial data from a distillation unit.

Improved reliability in diagnosing faults using multivariate statistics

This paper analyses multivariate statistical techniques for identifying and isolating abnormal process behaviour. These techniques include contribution charts and variable reconstructions that relate to the application of principal component analysis (PCA). The analysis reveals firstly that contribution charts produce variable contributions which are linearly dependent and may lead to an incorrect diagnosis, if the number of principal components retained is close to the number of recorded process variables. The analysis secondly yields that variable reconstruction affects the geometry of the PCA decomposition. The paper further introduces an improved variable reconstruction method for identifying multiple sensor and process faults and for isolating their influence upon the recorded process variables. It is shown that this can accommodate the effect of reconstruction, i.e. changes in the covariance matrix of the sensor readings and correctly re-defining the PCA-based monitoring statistics and their confidence limits. (c) 2006 Elsevier Ltd. All rights reserved.

Learning soft computing control strategies in a modular neural network architecture

Modelling and control of nonlinear dynamical systems is a challenging problem since the dynamics of such systems change over their parameter space. Conventional methodologies for designing nonlinear control laws, such as gain scheduling, are effective because the designer partitions the overall complex control into a number of simpler sub-tasks. This paper describes a new genetic algorithm based method for the design of a modular neural network (MNN) control architecture that learns such partitions of an overall complex control task. Here a chromosome represents both the structure and parameters of an individual neural network in the MNN controller and a hierarchical fuzzy approach is used to select the chromosomes required to accomplish a given control task. This new strategy is applied to the end-point tracking of a single-link flexible manipulator modelled from experimental data. Results show that the MNN controller is simple to design and produces superior performance compared to a single neural network (SNN) controller which is theoretically capable of achieving the desired trajectory. (C) 2003 Elsevier Ltd. All rights reserved.

Surrogate-Based Optimization Using Multifidelity Models with Variable Parameterization and Corrected Space Mapping

Surrogate-based-optimization methods provide a means to achieve high-fidelity design optimization at reduced computational cost by using a high-fidelity model in combination with lower-fidelity models that are less expensive to evaluate. This paper presents a provably convergent trust-region model-management methodology for variableparameterization design models: that is, models for which the design parameters are defined over different spaces. Corrected space mapping is introduced as a method to map between the variable-parameterization design spaces. It is then used with a sequential-quadratic-programming-like trust-region method for two aerospace-related design optimization problems. Results for a wing design problem and a flapping-flight problem show that the method outperforms direct optimization in the high-fidelity space. On the wing design problem, the new method achieves 76% savings in high-fidelity function calls. On a bat-flight design problem, it achieves approximately 45% time savings, although it converges to a different local minimum than did the benchmark.

Multiple Input Governor Control for a Diesel Generating Set

The paper presents a multiple input single output fuzzy logic governor algorithm that can be used to improve the transient response of a diesel generating set, when supplying an islanded load. The proposed governor uses the traditional speed input in addition to voltage and power factor to modify the fuelling requirements during various load disturbances. The use of fuzzy logic control allows the use of PID type structures that can provide variable gain strategies to account for non-linearities in the system. Fuzzy logic also provides a means of processing other input information by linguistic reasoning and a logical control output to aid the governor action during transient disturbance. The test results were obtained using a 50 kVA naturally aspirated diesel generator testing facility. Both real and reactive load tests were conducted. The complex load test results demonstrate that, by using additional inputs to the governor algorithm, enhanced generator transient speed recovery response can be obtained.

SDSS J150722.30+523039.8: a cataclysmic variable formed directly from a detached white dwarf/brown dwarf binary?

We present high-speed, three-colour photometry of the eclipsing cataclysmic variable SDSS J150722.30+523039.8 (hereafter SDSS J1507). This system has an orbital period of 66.61 min, placing it below the observed `period minimum' for cataclysmic variables. We determine the system parameters via a parametrized model of the eclipse fitted to the observed lightcurve by ?2 minimization. We obtain a mass ratio of q = 0.0623 +/- 0.0007 and an orbital inclination . The primary mass is Mw = 0.90 +/- 0.01Msolar. The secondary mass and radius are found to be Mr = 0.056 +/- 0.001Msolar and Rr = 0.096 +/- 0.001Rsolar, respectively. We find a distance to the system of 160 +/- 10pc. The secondary star in SDSS J1507 has a mass substantially below the hydrogen burning limit, making it the second confirmed substellar donor in a cataclysmic variable. The very short orbital period of SDSS J1507 is readily explained if the secondary star is nuclearly evolved, or if SDSS J1507 formed directly from a detached white dwarf/brown dwarf binary. Given the lack of any visible contribution from the secondary star, the very low secondary mass and the low HeI ?6678/Ha emission-line ratio, we argue that SDSS J1507 probably formed directly from a detached white dwarf/brown dwarf binary. If confirmed, SDSS J1507 will be the first such system identified. The implications for binary star evolution, the brown dwarf desert and the common envelope phase are discussed.

The component masses of the cataclysmic variable V347 Puppis

We present time-resolved spectroscopy and photometry of the double-lined eclipsing cataclysmic variable V347 Pup ( = LB 1800). There is evidence of irradiation on the inner hemisphere of the secondary star, which we correct for using a model to give a secondary-star radial velocity of K R = 198 +/- 5 km s(-1). The rotational velocity of the secondary star in V347 Pup is found to be v sin i = 131 +/- 5 km s(-1) and the system inclination is i = 84degrees.0 +/- 2degrees.3. From these parameters we obtain masses of M-1 = 0.63 +/- 0.04 M for the white dwarf primary and M-2 = 0.52 +/- 0.06 M for the M0.5V secondary star, giving a mass ratio of q = 0.83 +/- 0.05. On the basis of the component masses, and the spectral type and radius of the secondary star in V347 Pup, we find tentative evidence for an evolved companion. V347 Pup shows many of the characteristics of the SW Sex stars, exhibiting single-peaked emission lines, high-velocity S-wave components and phase-offsets in the radial velocity curve. We find spiral arms in the accretion disc of V347 Pup and measure the disc radius to be close to the maximum allowed in a pressureless disc.

Plant-wide predictive control for a thermal power plant based on a physical plant model

A constrained non-linear, physical model-based, predictive control (NPMPC) strategy is developed for improved plant-wide control of a thermal power plant. The strategy makes use of successive linearisation and recursive state estimation using extended Kalman filtering to obtain a linear state-space model. The linear model and a quadratic programming routine are used to design a constrained long-range predictive controller One special feature is the careful selection of a specific set of plant model parameters for online estimation, to account for time-varying system characteristics resulting from major system disturbances and ageing. These parameters act as nonstationary stochastic states and help to provide sufficient degrees-of-freedom to obtain unbiased estimates of controlled outputs. A 14th order non-linear plant model, simulating the dominant characteristics of a 200 MW oil-fired pou er plant has been used to test the NPMPC algorithm. The control strategy gives impressive simulation results, during large system disturbances and extremely high rate of load changes, right across the operating range. These results compare favourably to those obtained with the state-space GPC method designed under similar conditions.

Convergence and tracking analysis of a variable normalised LMF (XE-NLMF) algorithm

The least-mean-fourth (LMF) algorithm is known for its fast convergence and lower steady state error, especially in sub-Gaussian noise environments. Recent work on normalised versions of the LMF algorithm has further enhanced its stability and performance in both Gaussian and sub-Gaussian noise environments. For example, the recently developed normalised LMF (XE-NLMF) algorithm is normalised by the mixed signal and error powers, and weighted by a fixed mixed-power parameter. Unfortunately, this algorithm depends on the selection of this mixing parameter. In this work, a time-varying mixed-power parameter technique is introduced to overcome this dependency. A convergence analysis, transient analysis, and steady-state behaviour of the proposed algorithm are derived and verified through simulations. An enhancement in performance is obtained through the use of this technique in two different scenarios. Moreover, the tracking analysis of the proposed algorithm is carried out in the presence of two sources of nonstationarities: (1) carrier frequency offset between transmitter and receiver and (2) random variations in the environment. Close agreement between analysis and simulation results is obtained. The results show that, unlike in the stationary case, the steady-state excess mean-square error is not a monotonically increasing function of the step size. (c) 2007 Elsevier B.V. All rights reserved.

Implementation of European standards of care for cystic fibrosis - Control and treatment of infection

Background: Several guidelines oil infection control and treatment of infection exist for cystic fibrosis (CF) caregivers, although the extent of implementation is variable.

The Role of Process Parameters in Determining Wall Thickness Distribution in Plug-Assisted Thermoforming

This article describes the results of a comprehensive investigation to determine the link between process parameters and observed wall thickness output for the plug-assisted thermoforming process. The overall objective of the work was to systematically investigate the process parameters that may be adjusted during production to control the wall thickness distribution of parts manufactured by plug-assisted thermoforming. The parameters investigated were the sheet temperature, plug temperature, plug speed, plug displacement, plug shape, and air pressure. As well as quantifying the effects of each parameter on the wall thickness distribution, a further aim of the work was to improve the understanding of the physical mechanisms of deformation of the sheet during the different stages of the process. The process parameters shown to have the greatest effect on experimentally determined wall thickness distribution were the plug displacement, sheet temperature, plug temperature, and plug shape. It is proposed that during the plug-assisted thermoforming of polystyrene the temperature dependent friction between the plug and sheet surface was the most important factor in determining product wall thickness distribution, whereas heat transfer was shown to play a less important role. POLYM. ENG. SCI., 2010. © 2010 Society of Plastics Engineers

Controllable Gaussian-Qubit Interface for Extremal Quantum State Engineering

We study state engineering through bilinear interactions between two remote qubits and two-mode Gaussian light fields. The attainable two-qubit states span the entire physically allowed region in the entanglement-versus-global-purity plane. Two-mode Gaussian states with maximal entanglement at fixed global and marginal entropies produce maximally entangled two-qubit states in the corresponding entropic diagram. We show that a small set of parameters characterizing extremally entangled two-mode Gaussian states is sufficient to control the engineering of extremally entangled two-qubit states, which can be realized in realistic matter-light scenarios.

The exponentiated convex variable step-size (ECVSS) algorithm

For some time there is a large interest in variable step-size methods for adaptive filtering. Recently, a few stochastic gradient algorithms have been proposed, which are based on cost functions that have exponential dependence on the chosen error. However, we have experienced that the cost function based on exponential of the squared error does not always satisfactorily converge. In this paper we modify this cost function in order to improve the convergence of exponentiated cost function and the novel ECVSS (exponentiated convex variable step-size) stochastic gradient algorithm is obtained. The proposed technique has attractive properties in both stationary and abrupt-change situations. (C) 2010 Elsevier B.V. All rights reserved.