Process monitoring approach using fast moving window PCA

This paper introduces a fast algorithm for moving window principal component analysis (MWPCA) which will adapt a principal component model. This incorporates the concept of recursive adaptation within a moving window to (i) adapt the mean and variance of the process variables, (ii) adapt the correlation matrix, and (iii) adjust the PCA model by recomputing the decomposition. This paper shows that the new algorithm is computationally faster than conventional moving window techniques, if the window size exceeds 3 times the number of variables, and is not affected by the window size. A further contribution is the introduction of an N-step-ahead horizon into the process monitoring. This implies that the PCA model, identified N-steps earlier, is used to analyze the current observation. For monitoring complex chemical systems, this work shows that the use of the horizon improves the ability to detect slowly developing drifts.

Energetic costs of parasitism in the Cape ground squirrel Xerus inauris

Parasites have been suggested to influence many aspects of host behaviour. Some of these effects may be mediated via their impact on host energy budgets. This impact may include effects on both energy intake and absorption as well as components of expenditure, including resting metabolic rate (RMR) and activity (e.g. grooming). Despite their potential importance, the energy costs of parasitism have seldom been directly quantified in a field setting. Here we pharmacologically treated female Cape ground squirrels (Xerus inauris) with anti-parasite drugs and measured the change in body composition, the daily energy expenditure (DEE) using doubly labelled water, the RMR by respirometry and the proportions of time spent looking for food, feeding, moving and grooming. Post-treatment animals gained an average 19 g of fat or approximately 25 kJ d(-1). DEE averaged 382 kJ d-1 prior to and 375 kJ d-1 post treatment (p> 0.05). RMR averaged 174 kJ d-1 prior to and 217 kJ d-1 post treatment (p

An Antennas and Propagation Approach to Improving Physical Layer Performance in Wireless Body Area Networks

A combined antennas and propagation study has been undertaken with a view to directly improving link conditions for wireless body area networks. Using tissue-equivalent numerical and experimental phantoms representative of muscle tissue at 2.45 GHz, we show that the node to node [S-21] path gain performance of a new wearable integrated antenna (WIA) is up to 9 dB better than a conventional compact Printed-F antenna, both of which are suitable for integration with wireless node circuitry. Overall, the WIA performed extremely well with a measured radiation efficiency of 38% and an impedance bandwidth of 24%. Further benefits were also obtained using spatial diversity, with the WIA providing up to 7.7 dB of diversity gain for maximal ratio combining. The results also show that correlation was lower for a multipath environment leading to higher diversity gain. Furthermore, a diversity implementation with the new antenna gave up to 18 dB better performance in terms of mean power level and there was a significant improvement in level crossing rates and average fade durations when moving from a single-branch to a two-branch diversity system.

Moving window kernel PCA for adaptive monitoring of nonlinear processes

This paper discusses the monitoring of complex nonlinear and time-varying processes. Kernel principal component analysis (KPCA) has gained significant attention as a monitoring tool for nonlinear systems in recent years but relies on a fixed model that cannot be employed for time-varying systems. The contribution of this article is the development of a numerically efficient and memory saving moving window KPCA (MWKPCA) monitoring approach. The proposed technique incorporates an up- and downdating procedure to adapt (i) the data mean and covariance matrix in the feature space and (ii) approximates the eigenvalues and eigenvectors of the Gram matrix. The article shows that the proposed MWKPCA algorithm has a computation complexity of O(N2), whilst batch techniques, e.g. the Lanczos method, are of O(N3). Including the adaptation of the number of retained components and an l-step ahead application of the MWKPCA monitoring model, the paper finally demonstrates the utility of the proposed technique using a simulated nonlinear time-varying system and recorded data from an industrial distillation column.

Maximum likelihood estimation of higher-order integer-valued autoregressive processes

In this article, we extend the earlier work of Freeland and McCabe [Journal of time Series Analysis (2004) Vol. 25, pp. 701–722] and develop a general framework for maximum likelihood (ML) analysis of higher-order integer-valued autoregressive processes. Our exposition includes the case where the innovation sequence has a Poisson distribution and the thinning is binomial. A recursive representation of the transition probability of the model is proposed. Based on this transition probability, we derive expressions for the score function and the Fisher information matrix, which form the basis for ML estimation and inference. Similar to the results in Freeland and McCabe (2004), we show that the score function and the Fisher information matrix can be neatly represented as conditional expectations. Using the INAR(2) speci?cation with binomial thinning and Poisson innovations, we examine both the asymptotic e?ciency and ?nite sample properties of the ML estimator in relation to the widely used conditional least
squares (CLS) and Yule–Walker (YW) estimators. We conclude that, if the Poisson assumption can be justi?ed, there are substantial gains to be had from using ML especially when the thinning parameters are large.

Maximum likelihood estimation of higher-order integer-valued autoregressive processes

Corrigendum Vol. 30, Issue 2, 259, Article first published online: 15 MAR 2009 to correct the order of authors names: Bu R., K. Hadri, and B. McCabe.