An adaptive neural-wavelet model for short term load forecasting

This paper proposed a novel model for short term load forecast in the competitive electricity market. The prior electricity demand data are treated as time series. The forecast model is based on wavelet multi-resolution decomposition by autocorrelation shell representation and neural networks (multilayer perceptrons, or MLPs) modeling of wavelet coefficients. To minimize the influence of noisy low level coefficients, we applied the practical Bayesian method Automatic Relevance Determination (ARD) model to choose the size of MLPs, which are then trained to provide forecasts. The individual wavelet domain forecasts are recombined to form the accurate overall forecast. The proposed method is tested using Queensland electricity demand data from the Australian National Electricity Market. (C) 2001 Elsevier Science B.V. All rights reserved.

Supervisory control of wastewater treatment plants by combining principal component analysis and fuzzy c-means clustering

In this paper a methodology for integrated multivariate monitoring and control of biological wastewater treatment plants during extreme events is presented. To monitor the process, on-line dynamic principal component analysis (PCA) is performed on the process data to extract the principal components that represent the underlying mechanisms of the process. Fuzzy c-means (FCM) clustering is used to classify the operational state. Performing clustering on scores from PCA solves computational problems as well as increases robustness due to noise attenuation. The class-membership information from FCM is used to derive adequate control set points for the local control loops. The methodology is illustrated by a simulation study of a biological wastewater treatment plant, on which disturbances of various types are imposed. The results show that the methodology can be used to determine and co-ordinate control actions in order to shift the control objective and improve the effluent quality.

Ethanol-related adaptive changes and physical dependence in rats after exposure to ethanol

Adaptive changes that occur after chronic exposure to ethanol are an important component in the development of physical dependence. We have focused our research on ethanol-induced changes in the expression of several genes that may be important in adaptation. In this article, we describe adaptive changes at the level of the N-methyl-D-aspartate receptor, in the protein expression and activity of the Egr transcription factors, and in the expression of a novel gene of unknown function. (C) 2001 Elsevier Science Inc. All rights reserved.

The effects of time delays in adaptive phase measurements

It is not possible to make measurements of the phase of an optical mode using linear optics without introducing an extra phase uncertainty. This extra phase variance is quite large for heterodyne measurements, however it is possible to reduce it to the theoretical limit of log (n) over bar (4 (n) over bar (2)) using adaptive measurements. These measurements are quite sensitive to experimental inaccuracies, especially time delays and inefficient detectors. Here it is shown that the minimum introduced phase variance when there is a time delay of tau is tau/(8 (n) over bar). This result is verified numerically, showing that the phase variance introduced approaches this limit for most of the adaptive schemes using the best final phase estimate. The main exception is the adaptive mark II scheme with simplified feedback, which is extremely sensitive to time delays. The extra phase variance due to time delays is considered for the mark I case with simplified feedback, verifying the tau /2 result obtained by Wiseman and Killip both by a more rigorous analytic technique and numerically.

A mixture model-based approach to the clustering of microarray expression data

Motivation: This paper introduces the software EMMIX-GENE that has been developed for the specific purpose of a model-based approach to the clustering of microarray expression data, in particular, of tissue samples on a very large number of genes. The latter is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. A feasible approach is provided by first selecting a subset of the genes relevant for the clustering of the tissue samples by fitting mixtures of t distributions to rank the genes in order of increasing size of the likelihood ratio statistic for the test of one versus two components in the mixture model. The imposition of a threshold on the likelihood ratio statistic used in conjunction with a threshold on the size of a cluster allows the selection of a relevant set of genes. However, even this reduced set of genes will usually be too large for a normal mixture model to be fitted directly to the tissues, and so the use of mixtures of factor analyzers is exploited to reduce effectively the dimension of the feature space of genes. Results: The usefulness of the EMMIX-GENE approach for the clustering of tissue samples is demonstrated on two well-known data sets on colon and leukaemia tissues. For both data sets, relevant subsets of the genes are able to be selected that reveal interesting clusterings of the tissues that are either consistent with the external classification of the tissues or with background and biological knowledge of these sets.

Adaptive multiscale principal components analysis for online monitoring of wastewater treatment

Fault detection and isolation (FDI) are important steps in the monitoring and supervision of industrial processes. Biological wastewater treatment (WWT) plants are difficult to model, and hence to monitor, because of the complexity of the biological reactions and because plant influent and disturbances are highly variable and/or unmeasured. Multivariate statistical models have been developed for a wide variety of situations over the past few decades, proving successful in many applications. In this paper we develop a new monitoring algorithm based on Principal Components Analysis (PCA). It can be seen equivalently as making Multiscale PCA (MSPCA) adaptive, or as a multiscale decomposition of adaptive PCA. Adaptive Multiscale PCA (AdMSPCA) exploits the changing multivariate relationships between variables at different time-scales. Adaptation of scale PCA models over time permits them to follow the evolution of the process, inputs or disturbances. Performance of AdMSPCA and adaptive PCA on a real WWT data set is compared and contrasted. The most significant difference observed was the ability of AdMSPCA to adapt to a much wider range of changes. This was mainly due to the flexibility afforded by allowing each scale model to adapt whenever it did not signal an abnormal event at that scale. Relative detection speeds were examined only summarily, but seemed to depend on the characteristics of the faults/disturbances. The results of the algorithms were similar for sudden changes, but AdMSPCA appeared more sensitive to slower changes.

The development and psychometric properties of a measure of social and adaptive functioning for children and adolescents

Developed, piloted, and examined the psychometric properties of the Child and Adolescent Social and Adaptive Functioning Scale (CASAFS), a self-report measure designed to examine the social functioning of young people in the areas of school performance, peer relationships, family relationships, and home duties/self-care. The findings of confirmatory and exploratory factor analysis support a 4-factor solution consistent with the hypothesized domains. Fit indexes suggested that the 4-correlated factor model represented a satisfactory solution for the data, with the covariation between factors being satisfactorily explained by a single, higher order factor reflecting social and adaptive functioning in general. The internal consistency and 12-month test-retest reliability of the total scale was acceptable. A significant, negative correlation was found between the CASAFS and a measure of depressive symptoms, showing that high levels of social functioning are associated with low levels of depression. Significant differences in CASAFS total and subscale scores were found between clinically depressed adolescents and a matched sample of nonclinical controls. Adolescents who reported elevated but subclinical levels of depression also reported lower levels of social functioning in comparison to nonclinical controls.

Was there a second adaptive radiation of giant tortoises in the Indian Ocean? Using mitochondrial DNA to investigate speciation and biogeography of Aldabrachelys (Reptilia, Testudinidae)

A radiation of five species of giant tortoises (Cylindraspis ) existed in the southwest Indian Ocean, on the Mascarene islands, and another (of Aldabrachelys ) has been postulated on small islands north of Madagascar, from where at least eight nominal species have been named and up to five have been recently recognized. Of 37 specimens of Madagascan and small-island Aldabrachelys investigated by us, 23 yielded significant portions of a 428-base-pair (bp) fragment of mitochondrial (cytochrome b and tRNA-Glu), including type material of seven nominal species (A. arnoldi, A. dussumieri, A. hololissa, A. daudinii, A. sumierei, A. ponderosa and A. gouffei ). These and nearly all the remaining specimens, including 15 additional captive individuals sequenced previously, show little variation. Thirty-three exhibit no differences and the remainder diverge by only 1-4 bp (0.23-0.93%). This contrasts with more widely accepted tortoise species which show much greater inter- and intraspecific differences. The non-Madagascan material examined may therefore only represent a single species and all specimens may come from Aldabra where the common haplotype is known to occur. The present study provides no evidence against the Madagascan origin for Aldabra tortoises suggested by a previous molecular phylogenetic analysis, the direction of marine currents and phylogeography of other reptiles in the area. Ancient mitochondrial DNA from the extinct subfossil A. grandidieri of Madagascar differs at 25 sites (5.8%) from all other Aldabrachelys samples examined here.

Model-based clustering in gene expression microarrays: An application to breast cancer data

In microarray studies, the application of clustering techniques is often used to derive meaningful insights into the data. In the past, hierarchical methods have been the primary clustering tool employed to perform this task. The hierarchical algorithms have been mainly applied heuristically to these cluster analysis problems. Further, a major limitation of these methods is their inability to determine the number of clusters. Thus there is a need for a model-based approach to these. clustering problems. To this end, McLachlan et al. [7] developed a mixture model-based algorithm (EMMIX-GENE) for the clustering of tissue samples. To further investigate the EMMIX-GENE procedure as a model-based -approach, we present a case study involving the application of EMMIX-GENE to the breast cancer data as studied recently in van 't Veer et al. [10]. Our analysis considers the problem of clustering the tissue samples on the basis of the genes which is a non-standard problem because the number of genes greatly exceed the number of tissue samples. We demonstrate how EMMIX-GENE can be useful in reducing the initial set of genes down to a more computationally manageable size. The results from this analysis also emphasise the difficulty associated with the task of separating two tissue groups on the basis of a particular subset of genes. These results also shed light on why supervised methods have such a high misallocation error rate for the breast cancer data.

A new wavelet-based adaptive method for solving population balance equations

A new wavelet-based adaptive framework for solving population balance equations (PBEs) is proposed in this work. The technique is general, powerful and efficient without the need for prior assumptions about the characteristics of the processes. Because there are steeply varying number densities across a size range, a new strategy is developed to select the optimal order of resolution and the collocation points based on an interpolating wavelet transform (IWT). The proposed technique has been tested for size-independent agglomeration, agglomeration with a linear summation kernel and agglomeration with a nonlinear kernel. In all cases, the predicted and analytical particle size distributions (PSDs) are in excellent agreement. Further work on the solution of the general population balance equations with nucleation, growth and agglomeration and the solution of steady-state population balance equations will be presented in this framework. (C) 2002 Elsevier Science B.V. All rights reserved.

Automated image analysis of lung branching morphogenesis from microscopic images of fetal rat explants

Background: Regulating mechanisms of branching morphogenesis of fetal lung rat explants have been an essential tool for molecular research. This work presents a new methodology to accurately quantify the epithelial, outer contour and peripheral airway buds of lung explants during cellular development from microscopic images. Methods: The outer contour was defined using an adaptive and multi-scale threshold algorithm whose level was automatically calculated based on an entropy maximization criterion. The inner lung epithelial was defined by a clustering procedure that groups small image regions according to the minimum description length principle and local statistical properties. Finally, the number of peripheral buds were counted as the skeleton branched ends from a skeletonized image of the lung inner epithelial. Results: The time for lung branching morphometric analysis was reduced in 98% in contrast to the manual method. Best results were obtained in the first two days of cellular development, with lesser standard deviations. Non-significant differences were found between the automatic and manual results in all culture days. Conclusions: The proposed method introduces a series of advantages related to its intuitive use and accuracy, making the technique suitable to images with different lightning characteristics and allowing a reliable comparison between different researchers.