Lattice dynamical wavelet neural networks implemented using particle swarm optimization for spatio-temporal system identification

In this brief, by combining an efficient wavelet representation with a coupled map lattice model, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNNs), is introduced for spatio-temporal system identification. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimization (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the OPP algorithm, significant wavelet neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated wavelet neurons are optimized using a particle swarm optimizer. The resultant network model, obtained in the first stage, however, may be redundant. In the second stage, an orthogonal least squares algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet neurons from the network. An example for a real spatio-temporal system identification problem is presented to demonstrate the performance of the proposed new modeling framework

A unified wavelet-based modelling framework for non-linear system identification: the WANARX model structure

A new unified modelling framework based on the superposition of additive submodels, functional components, and wavelet decompositions is proposed for non-linear system identification. A non-linear model, which is often represented using a multivariate non-linear function, is initially decomposed into a number of functional components via the wellknown analysis of variance (ANOVA) expression, which can be viewed as a special form of the NARX (non-linear autoregressive with exogenous inputs) model for representing dynamic input–output systems. By expanding each functional component using wavelet decompositions including the regular lattice frame decomposition, wavelet series and multiresolution wavelet decompositions, the multivariate non-linear model can then be converted into a linear-in-theparameters problem, which can be solved using least-squares type methods. An efficient model structure determination approach based upon a forward orthogonal least squares (OLS) algorithm, which involves a stepwise orthogonalization of the regressors and a forward selection of the relevant model terms based on the error reduction ratio (ERR), is employed to solve the linear-in-the-parameters problem in the present study. The new modelling structure is referred to as a wavelet-based ANOVA decomposition of the NARX model or simply WANARX model, and can be applied to represent high-order and high dimensional non-linear systems

Dynamical epidemic suppression using stochastic prediction and control

We consider the effects of noise on a model of epidemic outbreaks, where the outbreaks appear. randomly. Using a constructive transition approach that predicts large outbreaks, prior to their occurrence, we derive an adaptive control. scheme that prevents large outbreaks from occurring. The theory inapplicable to a wide range of stochastic processes with underlying deterministic structure.Comment: 14 pages, 6 figure

Service Development Programme: Maximising Life Opportunities for Teenagers. Teenagers' Views and Experiences of Sex and Relationships Education, Sexual Health Services and Family Support Services in Kent - Survey findings for Year 2

This brief report provides findings from data collected in year 2 of a survey of teenagers' views and experiences of sex and relationships education and sexual health services in Kent. The data in year 2 was collected in Autumn 2005, a year after the data collected in year 1. The purpose of this report is to highlight the results in year 2 which differ from the year 1 survey data. It is to be used in conjunction with the report in year 1 entitled "Service Development Programme: Maximising Life Opportunies for Teenagers: Teenagers' Views and Experiences of Sex and Relationships Educatioon, Sexual Health Services and Family Suupport Services in Kent: Survey Findings July 2005". The final report on the survey will consist of findings from further analysis of the data from year 1 and year 2 merged together, available at the end of 2006

Identification of a spatio-temporal model of crystal growth based on boundary curvature

A new method of identifying the spatio-temporal transition rule of crystal growth is introduced based on the connection between growth kinetics and dentritic morphology. Using a modified three-point-method, curvatures of the considered crystal branch are calculated and curvature direction is used to measure growth velocity. A polynomial model is then produced based on a curvature-velocity relationship to represent the spatio-temporal growth process. A very simple simulation example is used initially to clearly explain the methodology. The results of identifying a model from a real crystal growth experiment show that the proposed method can produce a good representation of crystal growth

Identification of the transition rule in a modified cellular automata model: the case of dendritic NH4Br crystal growth

A method of identifying the transition rule, encapsulated in a modified cellular automata (CA) model, is demonstrated using experimentally observed evolution of dendritic crystal growth patterns in NH4Br crystals. The influence of the factors, such as experimental set-up and image pre-processing, colour and size calibrations, on the method of identification are discussed in detail. A noise reduction parameter and the diffusion velocity of the crystal boundary are also considered. The results show that the proposed method can in principle provide a good representation of the dendritic growth anisotropy of any system

MICROCOSM STUDY OF NATURAL ATTENUATION, BIOSTIMULATION, AND BIOAUGMENTATION OF SOILS CONTAMINATED WITH PCBS, DIOXINS, PAHS, AND PETROLEUM HYDROCARBONS

Remediation of weathered petroleum hydrocarbons, polycyclic aromatic hydrocarbons (PAHs), dioxins, and polychlorinated biphenyls (PCBs) through monitored natural attenuation, in-situ biostimulation, and/or bioaugmentation was assessed using laboratory-scale microcosms. These contaminants of interest (COIs) have persisted in Santa Susana Field Laboratory (SSFL) soils for over 40 years in some cases. The objective of this United States Department of Energy (DOE)-funded study was to determine the potential of the aforementioned remediation methods to reduce COI concentrations in soil and estimate potential biodegradation rates of COIs in SSFL soils. Several types of soil microcosms were established: one set of microcosms was run without amendments to estimate natural attenuation rates at the site; biostimulation was tested by addition of nitrogen and phosphorus, rice hulls, and biosurfactant (soya lecithin), another set was augmented with the white-rot fungus Phanerochaete chrysosporium, and gamma-irradiated microcosms served as sterilized controls. Soil samples were collected and analyzed for dioxins, PCBs, PAHs, and extractable fuel hydrocarbons (EFH) after 0, 4, and 8 months of incubation. Soil contamination in the microcosms initially consisted of primarily heavily chlorinated dioxins and PCBs, longer petroleum hydrocarbons (21-40 equivalent carbon chain length), and PAHs with 4-6 aromatic rings. Small decreases in PAH, PCB, and dioxin soil concentrations were observed, but these decreases were not statistically significant. EFH concentrations were inflated at the final sampling event, but they appeared to reduce for two of three soils (Soils A and C) tested at the second sampling event. No COI concentration reductions were statistically significantly during 8 months of incubation. Because petroleum hydrocarbons were primarily longer-chain hydrocarbons in the C21 to C40 EFH range, it is likely that lighter hydrocarbons had been preferentially degraded, leaving the more recalcitrant longer-chain hydrocarbons in the soil. Dioxin concentrations appeared to decrease in some cases, but these reductions were not statistically significant at the 95% confidence level. Larger PAHs (4-6 rings) comprise the majority of residual PAH soil contamination. Given that concentrations of these PAHs have not decreased significantly during this 8-month long study, it is likely that these larger PAH contaminants are somewhat recalcitrant and will take a long time to biodegrade. Similarly, little or no PCB biodegradation was observed which is not surprising because the PCBs are heavily chlorinated, and bacterial biodegradation of these highly chlorinated compounds is reported to occur only under anaerobic conditions. The primary dioxin congener present in soils was octachlorodibenzodioxin (OCDD), which is the heaviest-chlorinated dioxin congener. Like PCBs, this compound requires anaerobic conditions for reductive dechlorination, and these are not present at the site. Total dioxin concentrations decreased in the microcosms amended with Phanerochaete chrysosporium, although this decrease was not statistically significant due to variability of dioxin concentrations measured in the soil. No decrease in tetrachlorodibenzodioxin toxicity equivalence was observed with P. chrysosporium bioaugmentation, and this parameter is important in terms of dioxin toxicity. Soil vapor analyses performed at the site indicate highly aerobic soil conditions. To mimic site conditions as closely as possible, experimental microcosms were maintained incubated in aerobic conditions. Although fungi have been reported to degrade PCBs and dioxins under aerobic conditions, the microcosms augmented with Phanerochaete chrysosporium did not show statistically significant biodegradation of PCBs. Contaminant sequestration in the soil may also have contributed to the lack of observed biodegradation because the COIs at this site are highly weathered. However, even microcosms augmented with a surfactant (soya lecithin), which would be expected to solubilize sequestered COIs, did not show significant biodegradation