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.

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.

Identification and control of Hammerstein systems via wireless networks

This paper investigates a systematic approach for the identification and control of Hammerstein systems over a physical IEEE 802.11b wireless channel.

Identification and output tracking control of Hammerstein systems through wireless networks

his paper investigates the identification and output tracking control of a class of Hammerstein systems through a wireless network within an integrated framework and the statistic characteristics of the wireless network are modelled using the inverse Gaussian cumulative distribution function. In the proposed framework, a new networked identification algorithm is proposed to compensate for the influence of the wireless network delays so as to acquire the more precise Hammerstein system model. Then, the identified model together with the model-based approach is used to design an output tracking controller. Mean square stability conditions are given using linear matrix inequalities (LMIs) and the optimal controller gains can be obtained by solving the corresponding optimization problem expressed using LMIs. Illustrative numerical simulation examples are given to demonstrate the effectiveness of our proposed method.

Identification of tone duration, line length and letter position: An experimental approach to timing and working memory deficits in schizophrenia.

Patients with schizophrenia display numerous cognitive deficits, including problems in working memory, time estimation, and absolute identification of stimuli. Research in these fields has traditionally been conducted independently. We examined these cognitive processes using tasks that are structurally similar and that yield rich error data. Relative to healthy control participants (n = 20), patients with schizophrenia (n = 20) were impaired on a duration identification task and a probed-recall memory task but not on a line-length identification task. These findings do not support the notion of a global impairment in absolute identification in schizophrenia. However, the authors suggest that some aspect of temporal information processing is indeed disturbed in schizophrenia.

A two-stage algorithm for identification of nonlinear dynamic systems

This paper investigates the two-stage stepwise identification for a class of nonlinear dynamic systems that can be described by linear-in-the-parameters models, and the model has to be built from a very large pool of basis functions or model terms. The main objective is to improve the compactness of the model that is obtained by the forward stepwise methods, while retaining the computational efficiency. The proposed algorithm first generates an initial model using a forward stepwise procedure. The significance of each selected term is then reviewed at the second stage and all insignificant ones are replaced, resulting in an optimised compact model with significantly improved performance. The main contribution of this paper is that these two stages are performed within a well-defined regression context, leading to significantly reduced computational complexity. The efficiency of the algorithm is confirmed by the computational complexity analysis, and its effectiveness is demonstrated by the simulation results.

A Fast Nonlinear Model Identification Method

The identification of nonlinear dynamic systems using linear-in-the-parameters models is studied. A fast recursive algorithm (FRA) is proposed to select both the model structure and to estimate the model parameters. Unlike orthogonal least squares (OLS) method, FRA solves the least-squares problem recursively over the model order without requiring matrix decomposition. The computational complexity of both algorithms is analyzed, along with their numerical stability. The new method is shown to require much less computational effort and is also numerically more stable than OLS.

Elevated beta1,4-galactosyltransferase I in highly metastatic human lung cancer cells. Identification of E1AF as important transcription activator.

The elevated levels of beta1,4-galactosyltransferase I (GalT I; EC 2.4.1.38) are detected in highly metastatic lung cancer PGBE1 cells compared with its less metastatic partner PGLH7 cells. Decreasing the GalT I surface expression by small interfering RNA or interfering with the surface of GalT I function by mutation inhibited cell adhesion on laminin, the invasive potential in vitro, and tyrosine phosphorylation of focal adhesion kinase. The mechanism by which GalT I activity is up-regulated in highly metastatic cells remains unclear. To investigate the regulation of GalT I expression, we cloned the 5'-region flanking the transcription start point of the GalT I gene (-1653 to +52). Cotransfection of the GalT I promoter/luciferase reporter and the Ets family protein E1AF expression plasmid increased the luciferase reporter activity in a dose-dependent manner. By deletion and mutation analyses, we identified an Ets-binding site between nucleotides -205 and -200 in the GalT I promoter that was critical for responsiveness to E1AF. It was identified that E1AF could bind to and activate the GalT I promoter by electrophoretic mobility shift assay in PGLH7 cells and COS1 cells. A stronger affinity of E1AF for DNA has contributed to the elevated expression of GalT I in PGBE1 cells. Stable transfection of the E1AF expression plasmid resulted in increased GalT I expression in PGLH7 cells, and stable transfectants migrated faster than control cells. Meanwhile, the content of the beta1,4-Gal branch on the cell surface was increased in stably transfected PGLH7 cells. GalT I expression can also be induced by epidermal growth factor and dominant active Ras, JNK1, and ERK1. These data suggest an essential role for E1AF in the activation of the human GalT I gene in highly metastatic lung cancer cells.