Improving the robustness of the iterative solver in state-space modelling of guitar distortion circuitry

Iterative solvers are required for the discrete-time simulation of nonlinear behaviour in analogue distortion circuits. Unfortunately,these methods are often computationally too expensive for realtime simulation. Two methods are presented which attempt to reduce the expense of iterative solvers. This is achieved by applying information that is derived from the specific form of the non linearity.The approach is first explained through the modelling of an asymmetrical diode clipper, and further exemplified by application to the Dallas Rangemaster Treble Booster guitar pedal, which provides an initial perspective of the performance on systems with multiple nonlinearities.

The Type IIb SN 2011dh: Two years of observations and modelling of the lightcurves

We present optical and near-infrared (NIR) photometry and spectroscopy as well as modelling of the lightcurves of the Type IIb supernova (SN) 2011dh. Our extensive dataset, for which we present the observations obtained after day 100, spans two years, and complemented with Spitzer mid-infrared (MIR) data, we use it to build an optical-to-MIR bolometric lightcurve between days 3 and 732. To model the bolometric lightcurve before day 400 we use a grid of hydrodynamical SN models, which allows us to determine the errors in the derived quantities, and a bolometric correction determined with steady-state non-local thermodynamic equilibrium (NLTE) modelling. Using this method we find a helium core mass of 3.1^+0.7<inf>-0.4</inf> M<inf>⊙</inf> for SN 2011dh, consistent within error bars with previous results obtained using the bolometric lightcurve before day 80. We compute bolometric and broad-band lightcurves between days 100 and 500 from spectral steady-state NLTE models, presented and discussed in a companion paper. The preferred 12 M<inf>⊙</inf> (initial mass) model, previously found to agree well with the observed spectra, shows a good overall agreement with the observed lightcurves, although some discrepancies exist. Time-dependent NLTE modelling shows that after day ∼600 a steady-state assumption is no longer valid. The radioactive energy deposition in this phase is likely dominated by the positrons emitted in the decay of ⁵⁶Co, but seems insufficient to reproduce the lightcurves, and what energy source is dominating the emitted flux is unclear. We find an excess in the K and the MIR bands developing between days 100 and 250, during which an increase in the optical decline rate is also observed. A local origin of the excess is suggested by the depth of the He I 20 581 Å absorption. Steady-state NLTE models with a modest dust opacity in the core (τ = 0.44), turned on during this period, reproduce the observed behaviour, but an additional excess in the Spitzer 4.5 μm band remains. Carbon-monoxide (CO) first-overtone band emission is detected at day 206, and possibly at day 89, and assuming the additional excess to bedominated by CO fundamental band emission, we find fundamental to first-overtone band ratios considerably higher than observed in SN 1987A. The profiles of the [O i] 6300 Å and Mg i] 4571 Å lines show a remarkable similarity, suggesting that these lines originate from a common nuclear burning zone (O/Ne/Mg), and using small scale fluctuations in the line profiles we estimate a filling factor of ≲ 0.07 for the emitting material. This paper concludes our extensive observational and modelling work on SN 2011dh. The results from hydrodynamical modelling, steady-state NLTE modelling, and stellar evolutionary progenitor analysis are all consistent, and suggest an initial mass of ∼12 M<inf>⊙</inf> for the progenitor.

Identification of dynamic systems under closed-loop control

This paper introduces a novel modelling framework for identifying dynamic models of systems that are under feedback control. These models are identified under closed-loop conditions and produce a joint representation that includes both the plant and controller models in state space form. The joint plant/controller model is identified using subspace model identification (SMI), which is followed by the separation of the plant model from the identified one. Compared to previous research, this work (i) proposes a new modelling framework for identifying closed-loop systems, (ii) introduces a generic structure to represent the controller and (iii) explains how that the new framework gives rise to a simplified determination of the plant models. In contrast, the use of the conventional modelling approach renders the separation of the plant model a difficult task. The benefits of using the new model method are demonstrated using a number of application studies.

Dynamic Multivariate Statistical Process Control using Subspace Identification

This paper points out a serious flaw in dynamic multivariate statistical process control (MSPC). The principal component analysis of a linear time series model that is employed to capture auto- and cross-correlation in recorded data may produce a considerable number of variables to be analysed. To give a dynamic representation of the data (based on variable correlation) and circumvent the production of a large time-series structure, a linear state space model is used here instead. The paper demonstrates that incorporating a state space model, the number of variables to be analysed dynamically can be considerably reduced, compared to conventional dynamic MSPC techniques.

A hierarchical physical model-based approach to predictive control of a thermal power plant for efficient plant-wide disturbance rejection

Extending the work presented in Prasad et al. (IEEE Proceedings on Control Theory and Applications, 147, 523-37, 2000), this paper reports a hierarchical nonlinear physical model-based control strategy to account for the problems arising due to complex dynamics of drum level and governor valve, and demonstrates its effectiveness in plant-wide disturbance handling. The strategy incorporates a two-level control structure consisting of lower-level conventional PI regulators and a higher-level nonlinear physical model predictive controller (NPMPC) for mainly set-point manoeuvring. The lower-level PI loops help stabilise the unstable drum-boiler dynamics and allow faster governor valve action for power and grid-frequency regulation. The higher-level NPMPC provides an optimal load demand (or set-point) transition by effective handling of plant-wide interactions and system disturbances. The strategy has been tested in a simulation of a 200-MW oil-fired power plant at Ballylumford in Northern Ireland. A novel approach is devized to test the disturbance rejection capability in severe operating conditions. Low frequency disturbances were created by making random changes in radiation heat flow on the boiler-side, while condenser vacuum was fluctuating in a random fashion on the turbine side. In order to simulate high-frequency disturbances, pulse-type load disturbances were made to strike at instants which are not an integral multiple of the NPMPC sampling period. Impressive results have been obtained during both types of system disturbances and extremely high rates of load changes, right across the operating range, These results compared favourably with those from a conventional state-space generalized predictive control (GPC) method designed under similar conditions.

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.

Six ways to leave unemployment

This paper uses a unique Portuguese dataset to examine the effect of access to unemployment benefits (UBs) and their maximum potential duration on escape rates from unemployment. In examining the time profile of transitions out of unemployment, the principal contributions of the paper are twofold. First, it provides a detailed state space of potential outcomes: open-ended employment, fixed-term contracts, part-time work, government-provided jobs, self employment, and labour force withdrawal. Second, it is able to exploit major exogenous discontinuities in the maximum duration of unemployment benefits to identify disincentive effects. While confirming strong disincentive effects, it is shown that use of an aggregate hazard function regression model compounds very different and even contradictory effects of the determinants of unemployment.

Exploiting spatio-temporal constraints for robust 2D pose tracking

We present a Spatio-temporal 2D Models Framework (STMF) for 2D-Pose tracking. Space and time are discretized and a mixture of probabilistic "local models" is learnt associating 2D Shapes and 2D Stick Figures. Those spatio-temporal models generalize well for a particular viewpoint and state of the tracked action but some spatio-temporal discontinuities can appear along a sequence, as a direct consequence of the discretization. To overcome the problem, we propose to apply a Rao-Blackwellized Particle Filter (RBPF) in the 2D-Pose eigenspace, thus interpolating unseen data between view-based clusters. The fitness to the images of the predicted 2D-Poses is evaluated combining our STMF with spatio-temporal constraints. A robust, fast and smooth human motion tracker is obtained by tracking only the few most important dimensions of the state space and by refining deterministically with our STMF.

A Continuous Time Markov Chain Based Sequential Analytical Approach for Composite Power System Reliability Assessment

This paper proposes a continuous time Markov chain (CTMC) based sequential analytical approach for composite generation and transmission systems reliability assessment. The basic idea is to construct a CTMC model for the composite system. Based on this model, sequential analyses are performed. Various kinds of reliability indices can be obtained, including expectation, variance, frequency, duration and probability distribution. In order to reduce the dimension of the state space, traditional CTMC modeling approach is modified by merging all high order contingencies into a single state, which can be calculated by Monte Carlo simulation (MCS). Then a state mergence technique is developed to integrate all normal states to further reduce the dimension of the CTMC model. Moreover, a time discretization method is presented for the CTMC model calculation. Case studies are performed on the RBTS and a modified IEEE 300-bus test system. The results indicate that sequential reliability assessment can be performed by the proposed approach. Comparing with the traditional sequential Monte Carlo simulation method, the proposed method is more efficient, especially in small scale or very reliable power systems.

Space and time resolved rotational state populations and gas temperatures in an inductively coupled hydrogen RF discharge

Gas temperature is of major importance in plasma based surface treatment, since the surface processes are strongly temperature sensitive. The spatial distribution of reactive species responsible for surface modification is also influenced by the gas temperature. Industrial applications of RF plasma reactors require a high degree of homogeneity of the plasma in contact with the substrate. Reliable measurements of spatially resolved gas temperatures are, therefore, of great importance. The gas temperature can be obtained, e.g. by optical emission spectroscopy (OES). Common methods of OES to obtain gas temperatures from analysis of rotational distributions in excited states do not include the population dynamics influenced by cascading processes from higher electronic states. A model was developed to evaluate this effect on the apparent rotational temperature that is observed. Phase resolved OES confirmed the validity of this model. It was found that cascading leads to higher apparent temperatures, but the deviation (similar or equal to 25 K) is relatively small and can be ignored in most cases. This analysis is applied to investigate axially and radially resolved temperature profiles in an inductively coupled hydrogen RF discharge.

Diagnostic based modelling of radio-frequency driven atmospheric pressure plasmas

Diagnostic based modelling (DBM) actively combines complementary advantages of numerical plasma simulations and relatively simple optical emission spectroscopy (OES). DBM is employed to determine absolute atomic oxygen ground state densities in a helium–oxygen radio-frequency driven atmospheric pressure plasma jet. A comparatively simple one-dimensional simulation yields detailed information on electron properties governing the population dynamics of excited states. Important characteristics of the electron dynamics are found to be largely insensitive to details of the chemical composition and to be in very good agreement with space and phase-resolved OES. Benchmarking the time and space resolved simulation allows us to subsequently derive effective excitation rates as the basis for DBM with simple space and time integrated OES. The population dynamics of the upper O 3p 3P (? = 844 nm) atomic oxygen state is governed by direct electron impact excitation, dissociative excitation, radiation losses and collisional induced quenching. Absolute values for atomic oxygen densities are obtained through tracer comparison with the upper Ar 2p1 (? = 750.4 nm) state. The presented results for the atomic oxygen density show excellent quantitative agreement with independent two-photon laser-induced fluorescence measurements.

REDUCED ORDER MODELLING OF THE THERMAL BEHAVIOUR OF AN OFFICE SPACE

Reduced Order Models (ROMs) have proven to be a valid and efficient approach to model the thermal behaviour of building zones. The main issues associated with the use of zonal/lumped models are how to (1) divide the domain (lumps) and (2) evaluate the pa- rameters which characterise the lump-to-lump exchange of energy and momentum. The object of this research is to develop a methodology for the generation of ROMs from CFD models. The lumps of the ROM and their average property values are automatically ex- tracted from the CFD models through user defined constraints. This methodology has been applied to validated CFD models of a zone of the Environmental Research Insti- tute (ERI) Building in University College Cork (UCC). The ROM predicts temperature distribution in the domain with an average error lower than 2%. It is computationally efficient with an execution time of 3.45 seconds. Future steps in this research will be the development of the procedure to automatically extract the parameters which define lump-to-lump energy and momentum exchange. At the moment these parameters are evaluated through the minimisation of a cost function. The ROMs will also be utilised to predict the transient thermal behaviour of the building zone.