Application of auto-associative neural networks to transient fault detection in an IC engine

The tailpipe emissions from automotive engines have been subject to steadily reducing legislative limits. This reduction has been achieved through the addition of sub-systems to the basic four-stroke engine which thereby increases its complexity. To ensure the entire system functions correctly, each system and / or sub-systems needs to be continuously monitored for the presence of any faults or malfunctions. This is a requirement detailed within the On-Board Diagnostic (OBD) legislation. To date, a physical model approach has been adopted by me automotive industry for the monitoring requirement of OBD legislation. However, this approach has restrictions from the available knowledge base and computational load required. A neural network technique incorporating Multivariant Statistical Process Control (MSPC) has been proposed as an alternative method of building interrelationships between the measured variables and monitoring the correct operation of the engine. Building upon earlier work for steady state fault detection, this paper details the use of non-linear models based on an Auto-associate Neural Network (ANN) for fault detection under transient engine operation. The theory and use of the technique is shown in this paper with the application to the detection of air leaks within the inlet manifold system of a modern gasoline engine whilst operated on a pseudo-drive cycle. Copyright © 2007 by ASME.

Neural network based control for synchronous generators

In this paper, a Radial Basis Function neural network based AVR is proposed. A control strategy which generates local linear models from a global neural model on-line is used to derive controller feedback gains based on the Generalised Minimum Variance technique. Testing is carried out on a micromachine system which enables evaluation of practical implementation of the scheme. Constraints imposed by gathering training data, computational load, and memory requirements for the training algorithm are addressed.

A fast method for fuzzy neural modeling and refinement

In the identification of complex dynamic systems using fuzzy neural networks, one of the main issues is the curse of dimensionality, which makes it difficult to retain a large number of system inputs or to consider a large number of fuzzy sets. Moreover, due to the correlations, not all possible network inputs or regression vectors in the network are necessary and adding them simply increases the model complexity and deteriorates the network generalisation performance. In this paper, the problem is solved by first proposing a fast algorithm for selection of network terms, and then introducing a refinement procedure to tackle the correlation issue. Simulation results show the efficacy of the method.

Adult neural stem cells expressing IL-10 confer potent immunomodulation and remyelination in experimental autoimmune encephalitis

Adult neural stem cells (aNSCs) derived from the subventricular zone of the brain show therapeutic effects in EAE, an animal model of the chronic inflammatory neurodegenerative disease MS; however, the beneficial effects are modest. One critical weakness of aNSC therapy may be an insufficient antiinflammatory effect. Here, we demonstrate that i.v. or i.c.v. injection of aNSCs engineered to secrete IL-10 (IL-10–aNSCs), a potent immunoregulatory cytokine, induced more profound functional and pathological recovery from ongoing EAE than that with control aNSCs. IL-10–aNSCs exhibited enhanced antiinflammatory effects in the periphery and inflammatory foci in the CNS compared with control aNSCs, more effectively reducing myelin damage, a hallmark of MS. When compared with mice treated with control aNSCs, those treated with IL-10–aNSCs demonstrated differentiation of transplanted cells into greater numbers of oligodendrocytes and neurons but fewer astrocytes, thus enhancing exogenous remyelination and neuron/axonal growth. Finally, IL-10–aNSCs converted a hostile environment to one supportive of neurons/oligodendrocytes, thereby promoting endogenous remyelination. Thus, aNSCs engineered to express IL-10 show enhanced ability to induce immune suppression, remyelination, and neuronal repair and may represent a novel approach that can substantially improve the efficacy of neural stem cell–based therapy in EAE/MS.

Persistent measles virus infection of mouse neural cells lacking known human entry receptors

Aims: Infection of the mouse central nervous system with wild type (WT) and vaccine strains of measles virus (MV) results in lack of clinical signs and limited antigen detection. It is considered that cell entry receptors for these viruses are not present on murine neural cells and infection is restricted at cell entry.

Methods: To examine this hypothesis, virus antigen and caspase 3 expression (for apoptosis) was compared in primary mixed, neural cell cultures infected in vitro or prepared from mice infected intracerebrally with WT, vaccine or rodent neuroadapted viruses. Viral RNA levels were examined in mouse brain by nested and real-time reverse transcriptase polymerase chain reaction.

Results: WT and vaccine strains were demonstrated for the first time to infect murine oligodendrocytes in addition to neurones despite a lack of the known MV cell receptors. Unexpectedly, the percentage of cells positive for viral antigen was higher for WT MV than neuroadapted virus in both in vitro and ex vivo cultures. In the latter the percentage of positive cells increased with time after mouse infection. Viral RNA (total and mRNA) was detected in brain for up to 20 days, while cultures were negative for caspase 3 in WT and vaccine virus infections.

Conclusions: WT and vaccine MV strains can use an endogenous cell entry receptor(s) or alternative virus uptake mechanism in murine neural cells. However, viral replication occurs at a low level and is associated with limited apoptosis. WT MV mouse infection may provide a model for the initial stages of persistent MV human central nervous system infections.

Freeze-dried, mucoadhesive system for vaginal delivery of the HIV microbicide, dapivirine: Optimisation by an artificial neural network

Dapivirine mucoadhesive gels and freeze-dried tablets were prepared using a 3 x 3 x 2 factorial design. An artificial neural network (ANN) with multi-layer perception was used to investigate the effect of hydroxypropyl-methylcellulose (HPMC): polyvinylpyrrolidone (PVP) ratio (XI), mucoadhesive concentration (X2) and delivery system (gel or freeze-dried mucoadhesive tablet, X3) on response variables; cumulative release of dapivirine at 24 h (Q(24)), mucoadhesive force (F-max) and zero-rate viscosity. Optimisation was performed by minimising the error between the experimental and predicted values of responses by ANN. The method was validated using check point analysis by preparing six formulations of gels and their corresponding freeze-dried tablets randomly selected from within the design space of contour plots. Experimental and predicted values of response variables were not significantly different (p > 0.05, two-sided paired t-test). For gels, Q(24) values were higher than their corresponding freeze-dried tablets. F-max values for freeze-dried tablets were significantly different (2-4 times greater, p > 0.05, two-sided paired t-test) compared to equivalent gets. Freeze-dried tablets having lower values for X1 and higher values for X2 components offered the best compromise between effective dapivirine release, mucoadhesion and viscosity such that increased vaginal residence time was likely to be achieved. (C) 2009 Elsevier B.V. All rights reserved.

Reduced Nitro-oxidative Stress and Neural Cell Death Suggests a Protective Role for Microglial Cells in TNFα−/− Mice in Ischemic Retinopathy

Purpose. Neovascularization occurs in response to tissue ischemia and growth factor stimulation. In ischemic retinopathies, however, new vessels fail to restore the hypoxic tissue; instead, they infiltrate the transparent vitreous. In a model of oxygen-induced retinopathy (OIR), TNFa and iNOS, upregulated in response to tissue ischemia, are cytotoxic and inhibit vascular repair. The aim of this study was to investigate the mechanism for this effect.

Methods. Wild-type C57/BL6 (WT) and TNFa-/- mice were subjected to OIR by exposure to 75% oxygen (postnatal days 7–12). The retinas were removed during the hypoxic phase of the model. Retinal cell death was determined by TUNEL staining, and the microglial cells were quantified after Z-series capture with a confocal microscope. In situ peroxynitrite and superoxide were measured by using the fluorescent dyes DCF and DHE. iNOS, nitrotyrosine, and arginase were analyzed by real-time PCR, Western blot analysis, and activity determined by radiolabeled arginine conversion. Astrocyte coverage was examined after GFAP immunostaining.

Results. The TNFa-/- animals displayed a significant reduction in TUNEL-positive apoptotic cells in the inner nuclear layer of the avascular retina compared with that in the WT control mice. The reduction coincided with enhanced astrocytic survival and an increase in microglial cells actively engaged in phagocytosing apoptotic debris that displayed low ROS, RNS, and NO production and high arginase activity.

Conclusions. Collectively, the results suggest that improved vascular recovery in the absence of TNFa is associated with enhanced astrocyte survival and that both phenomena are dependent on preservation of microglial cells that display an anti-inflammatory phenotype during the early ischemic phase of OIR.

Short, medium and long term load forecasting model and virtual load forecaster based on radial basis neural networks

Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications. However, they are not typically used in medium and long term load forecasting (MLTLF) electric power models because of the difficulties associated with collecting and processing the necessary data. Virtual instrument (VI) techniques can be applied to electric power load forecasting but this is rarely reported in the literature. In this paper, we investigate the modelling and design of a VI for short, medium and long term load forecasting using ANNs. Three ANN models were built for STLF of electric power. These networks were trained using historical load data and also considering weather data which is known to have a significant affect of the use of electric power (such as wind speed, precipitation, atmospheric pressure, temperature and humidity). In order to do this a V-shape temperature processing model is proposed. With regards MLTLF, a model was developed using radial basis function neural networks (RBFNN). Results indicate that the forecasting model based on the RBFNN has a high accuracy and stability. Finally, a virtual load forecaster which integrates the VI and the RBFNN is presented.