Optimising the feedback loop of a millimetre-wave absolute photoacoustic power meter using H-infinity control synthesis

An H-infinity control strategy has been developed for the design of controllers used in feedback controlled electrical substitution measurements (FCESM). The methodology has the potential to provide substantial improvements in both response time and resolution of a millimetre-wave absolute photoacoustic power meter.

Estimation of distances in virtual environments using size constancy

It is reported in the literature that distances from the observer are underestimated more in virtual environments (VEs) than in physical world conditions. On the other hand estimation of size in VEs is quite accurate and follows a size-constancy law when rich cues are present. This study investigates how estimation of distance in a CAVETM environment is affected by poor and rich cue conditions, subject experience, and environmental learning when the position of the objects is estimated using an experimental paradigm that exploits size constancy. A group of 18 healthy participants was asked to move a virtual sphere controlled using the wand joystick to the position where they thought a previously-displayed virtual cube (stimulus) had appeared. Real-size physical models of the virtual objects were also presented to the participants as a reference of real physical distance during the trials. An accurate estimation of distance implied that the participants assessed the relative size of sphere and cube correctly. The cube appeared at depths between 0.6 m and 3 m, measured along the depth direction of the CAVE. The task was carried out in two environments: a poor cue one with limited background cues, and a rich cue one with textured background surfaces. It was found that distances were underestimated in both poor and rich cue conditions, with greater underestimation in the poor cue environment. The analysis also indicated that factors such as subject experience and environmental learning were not influential. However, least square fitting of Stevens’ power law indicated a high degree of accuracy during the estimation of object locations. This accuracy was higher than in other studies which were not based on a size-estimation paradigm. Thus as indirect result, this study appears to show that accuracy when estimating egocentric distances may be increased using an experimental method that provides information on the relative size of the objects used.

Gas-phase kinetics of chlorosilylene reactions II. ClSiH + C2H4: absolute rate measurements and quantum chemical and RRKM calculations for the prototype pi addition reaction

Time-resolved studies of chlorosilylene, ClSiH, generated by the 193 nm laser flash photolysis of 1-chloro-1-silacyclopent-3-ene, are carried out to obtain rate constants for its bimolecular reaction with ethene, C2H4, in the gas-phase. The reaction is studied over the pressure range 0.13-13.3 kPa (with added SF6) at five temperatures in the range 296-562 K. The second order rate constants, obtained by extrapolation to the high pressure limits at each temperature, fitted the Arrhenius equation: log(k(infinity)/cm(3) molecule(-1) s(-1))=(-10.55 +/- 0.10) + (3.86 +/- 0.70) kJ mol(-1)/RT ln10. The Arrhenius parameters correspond to a loose transition state and the rate constant at room temperature is 43% of that for SiH2 + C2H4, showing that the deactivating effect of Cl-for-H substitution in the silylene is not large. Quantum chemical calculations of the potential energy surface for this reaction at the G3MP2//B3LYP level show that, as well as 1-chlorosilirane, ethylchlorosilylene is a viable product. The calculations reveal how the added effect of the Cl atom on the divalent state stabilisation of ClSiH influences the course of this reaction. RRKM calculations of the reaction pressure dependence suggest that ethylchlorosilylene should be the main product. The results are compared and contrasted with those of SiH2 and SiCl2 with C2H4.

Genetic least squares for system identification

The recursive least-squares algorithm with a forgetting factor has been extensively applied and studied for the on-line parameter estimation of linear dynamic systems. This paper explores the use of genetic algorithms to improve the performance of the recursive least-squares algorithm in the parameter estimation of time-varying systems. Simulation results show that the hybrid recursive algorithm (GARLS), combining recursive least-squares with genetic algorithms, can achieve better results than the standard recursive least-squares algorithm using only a forgetting factor.

Self-tuning proportional, integral and derivative controller based on genetic algorithm least squares

A self-tuning proportional, integral and derivative control scheme based on genetic algorithms (GAs) is proposed and applied to the control of a real industrial plant. This paper explores the improvement in the parameter estimator, which is an essential part of an adaptive controller, through the hybridization of recursive least-squares algorithms by making use of GAs and the possibility of the application of GAs to the control of industrial processes. Both the simulation results and the experiments on a real plant show that the proposed scheme can be applied effectively.

System identification using partitioned least squares

A novel partitioned least squares (PLS) algorithm is presented, in which estimates from several simple system models are combined by means of a Bayesian methodology of pooling partial knowledge. The method has the added advantage that, when the simple models are of a similar structure, it lends itself directly to parallel processing procedures, thereby speeding up the entire parameter estimation process by several factors.

Gas-phase kinetics of chlorosilylene reactions. I. ClSiH + Me3SiH: absolute rate measurements and theoretical calculations for prototype Si−H insertion reactions

Time-resolved studies of chlorosilylene, CISiH, generated by the 193 nm laser flash photolysis of 1-chloro-1-silacyclopent-3-ene, have been carried out to obtain rate constants for its bimolecular reaction with trimethylsilane, Me3SiH, in the gas phase. The reaction was studied at total pressures up to 100 torr (with and without added SF6) over the temperature range 297-407 K. The rate constants were found to be pressure independent and gave the following Arrhenius equation: log(k/cm(3) molecule(-1) s(-1)) = (-13.97 +/- 0.25) + (12.57 +/- 1.64) kJ mol(-1)/RT In 10. The Arrhenius parameters are consistent with a mechanism involving an intermediate complex, whose rearrangement is the rate-determining step. Quantum chemical calculations of the potential energy surface for this reaction and also the reactions of CISiH with SiH4 and the other methylsilanes support this conclusion. Comparisons of both experiment and theory with the analogous Si-H insertion processes of SiH2 and SiMe2 show that the main factor causing the lower reactivity of ClSiH is the secondary energy barrier. The calculations also show the existence of a novel intramolecular H-atom exchange process in the complex of ClSiH with MeSiH3.

Nonlinear model structure design and construction using orthogonal least squares and D-optimality design

A very efficient learning algorithm for model subset selection is introduced based on a new composite cost function that simultaneously optimizes the model approximation ability and model robustness and adequacy. The derived model parameters are estimated via forward orthogonal least squares, but the model subset selection cost function includes a D-optimality design criterion that maximizes the determinant of the design matrix of the subset to ensure the model robustness, adequacy, and parsimony of the final model. The proposed approach is based on the forward orthogonal least square (OLS) algorithm, such that new D-optimality-based cost function is constructed based on the orthogonalization process to gain computational advantages and hence to maintain the inherent advantage of computational efficiency associated with the conventional forward OLS approach. Illustrative examples are included to demonstrate the effectiveness of the new approach.

Nonlinear model structure detection using optimum experimental design and orthogonal least squares

A very efficient learning algorithm for model subset selection is introduced based on a new composite cost function that simultaneously optimizes the model approximation ability and model adequacy. The derived model parameters are estimated via forward orthogonal least squares, but the subset selection cost function includes an A-optimality design criterion to minimize the variance of the parameter estimates that ensures the adequacy and parsimony of the final model. An illustrative example is included to demonstrate the effectiveness of the new approach.

Variable selection algorithm for the construction of MIMO operating point dependent neurofuzzy networks

An input variable selection procedure is introduced for the identification and construction of multi-input multi-output (MIMO) neurofuzzy operating point dependent models. The algorithm is an extension of a forward modified Gram-Schmidt orthogonal least squares procedure for a linear model structure which is modified to accommodate nonlinear system modeling by incorporating piecewise locally linear model fitting. The proposed input nodes selection procedure effectively tackles the problem of the curse of dimensionality associated with lattice-based modeling algorithms such as radial basis function neurofuzzy networks, enabling the resulting neurofuzzy operating point dependent model to be widely applied in control and estimation. Some numerical examples are given to demonstrate the effectiveness of the proposed construction algorithm.

The Prony fitting method for digital deconvolution problem

The Prony fitting theory is applied in this paper to solve the deconvolution problem. There are two cases in deconvolution in which unstable solution is easy to appear. They are: (1)the frequency band of known kernel is more narraw than that of the unknown kernel; (2) there exists noise. These two cases are studied thoroughly and the effectiveness of Prony fitting method is showed. Finally, this method is simulated in computer. The simulation results are compared with those obtained by using FFT method directly.