A Novel System for Reducing Turbo-Lag by Injection of Compressed Gas into the Exhaust Manifold

A key challenge in achieving good transient performance of highly boosted engines is the difficulty of accelerating the turbocharger from low air flow conditions (“turbo lag”). Multi-stage turbocharging, electric turbocharger assistance, electric compressors and hybrid powertrains are helpful in the mitigation of this deficit, but these technologies add significant cost and integration effort. Air-assist systems have the potential to be more cost-effective. Injecting compressed air into the intake manifold has received considerable attention, but the performance improvement offered by this concept is severely constrained by the compressor surge limit. The literature describes many schemes for generating the compressed gas, often involving significant mechanical complexity and/or cost. In this paper we demonstrate a novel exhaust assist system in which a reservoir is charged during braking. Experiments have been conducted using a 2.0 litre light-duty Diesel engine equipped with exhaust gas recirculation (EGR) and variable geometry turbine (VGT) coupled to an AC transient dynamometer, which was controlled to mimic engine load during in-gear braking and acceleration. The experimental results confirm that the proposed system reduces the time to torque during the 3rd gear tip-in by around 60%. Such a significant improvement was possible due to the increased acceleration of turbocharger immediately after the tip-in. Injecting the compressed gas into the exhaust manifold circumvents the problem of compressor surge and is the key enabler of the superior performance of the proposed concept.

A novel system for reducing turbo-lag by injection of compressed gas into the exhaust manifold

A key challenge in achieving good transient performance of highly boosted engines is the difficulty of accelerating the turbocharger from low air flow conditions (turbo lag). Multi-stage turbocharging, electric turbocharger assistance, electric compressors and hybrid powertrains are helpful in the mitigation of this deficit, but these technologies add significant cost and integration effort. Air-assist systems have the potential to be more cost-effective. Injecting compressed air into the intake manifold has received considerable attention, but the performance improvement offered by this concept is severely constrained by the compressor surge limit. The literature describes many schemes for generating the compressed gas, often involving significant mechanical complexity and/or cost. In this paper we demonstrate a novel exhaust assist system in which a reservoir is charged during braking. Experiments have been conducted using a 2.0 litre light-duty Diesel engine equipped with exhaust gas recirculation (EGR) and variable geometry turbine (VGT) coupled to an AC transient dynamometer, which was controlled to mimic engine load during in-gear braking and acceleration. The experimental results confirm that the proposed system reduces the time to torque during the 3rd gear tip-in by around 60%. Such a significant improvement was possible due to the increased acceleration of turbocharger immediately after the tip-in. Injecting the compressed gas into the exhaust manifold circumvents the problem of compressor surge and is the key enabler of the superior performance of the proposed concept. Copyright © 2013 SAE International.

Experimental investigation of a control method for SI-HCCI-SI transition in a multi-cylinder gasoline engine

In HCCI engines, the Air/Fuel Ratio (AFR) and Residual Gas Fraction (RGF) are difficult to control during the SI-HCCI-SI transition, and this may result in incomplete combustion and/or high pressure raise rates. As a result, there may be undesirably high engine load fluctuations. The objectives of this work are to further understand this process and develop control methods to minimize these load fluctuations. This paper presents data on instantaneous AFR and RGF measurements, both taken by novel experimental techniques. The data provides an insight into the cyclic AFR and RGF fluctuations during the switch. These results suggest that the relatively slow change in the intake Manifold Air Pressure (MAP) and actuation time of the Variable Valve Timing (VVT) are the main causes of undesired AFR and RGF fluctuations, and hence an unacceptable Net IMEP (NIMEP) fluctuation. We also found large cylinder-to-cylinder AFR variations during the transition. Therefore, besides throttle opening control and VVT shifting, cyclic and individual cylinder fuel injection control is necessary to achieve a smooth transition. The control method was developed and implemented in a test engine, and the result was a considerably reduced NIMEP fluctuation during the mode switch. The instantaneous AFR and RGF measurements could furthermore be adopted to develop more sophisticated control methods for SI-HCCI-SI transitions. © 2010 SAE International.

On-line learning of mutually orthogonal subspaces for face recognition by image sets.

We address the problem of face recognition by matching image sets. Each set of face images is represented by a subspace (or linear manifold) and recognition is carried out by subspace-to-subspace matching. In this paper, 1) a new discriminative method that maximises orthogonality between subspaces is proposed. The method improves the discrimination power of the subspace angle based face recognition method by maximizing the angles between different classes. 2) We propose a method for on-line updating the discriminative subspaces as a mechanism for continuously improving recognition accuracy. 3) A further enhancement called locally orthogonal subspace method is presented to maximise the orthogonality between competing classes. Experiments using 700 face image sets have shown that the proposed method outperforms relevant prior art and effectively boosts its accuracy by online learning. It is shown that the method for online learning delivers the same solution as the batch computation at far lower computational cost and the locally orthogonal method exhibits improved accuracy. We also demonstrate the merit of the proposed face recognition method on portal scenarios of multiple biometric grand challenge.

Face recognition from video

In spite of over two decades of intense research, illumination and pose invariance remain prohibitively challenging aspects of face recognition for most practical applications. The objective of this work is to recognize faces using video sequences both for training and recognition input, in a realistic, unconstrained setup in which lighting, pose and user motion pattern have a wide variability and face images are of low resolution. In particular there are three areas of novelty: (i) we show how a photometric model of image formation can be combined with a statistical model of generic face appearance variation, learnt offline, to generalize in the presence of extreme illumination changes; (ii) we use the smoothness of geodesically local appearance manifold structure and a robust same-identity likelihood to achieve invariance to unseen head poses; and (iii) we introduce an accurate video sequence "reillumination" algorithm to achieve robustness to face motion patterns in video. We describe a fully automatic recognition system based on the proposed method and an extensive evaluation on 171 individuals and over 1300 video sequences with extreme illumination, pose and head motion variation. On this challenging data set our system consistently demonstrated a nearly perfect recognition rate (over 99.7%), significantly outperforming state-of-the-art commercial software and methods from the literature. © Springer-Verlag Berlin Heidelberg 2006.

Gaussian mixture modeling with Gaussian process latent variable models

Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristics of the data. Recently, the Gaussian Process Latent Variable Model (GPLVM) has successfully been used to find low dimensional manifolds in a variety of complex data. The GPLVM consists of a set of points in a low dimensional latent space, and a stochastic map to the observed space. We show how it can be interpreted as a density model in the observed space. However, the GPLVM is not trained as a density model and therefore yields bad density estimates. We propose a new training strategy and obtain improved generalisation performance and better density estimates in comparative evaluations on several benchmark data sets. © 2010 Springer-Verlag.

UEGO based measurement of EGR rate and residual gas fraction

A technique is presented for measuring the exhaust gas recirculation (EGR) and residual gas fraction (RGF) using a fast UEGO based O2 measurement of the manifold or in-cylinder gases, and of the exhaust gases. The technique has some advantages over the more common CO2-based method. In the case of an RGF measurement, fuel interference must be eliminated and special fuelling arrangements are is required. It is shown how a UEGO-based measurement, though sensitive to reactive species in the exhaust (such as H 2), as a system reports EGR/ RGF rates faithfully. Preliminary tests showed that EGR and RGF measurements using the O2 approach agreed well with CO2-based measurements. © 2011 SAE International.

Warped Mixtures for Nonparametric Cluster Shapes

A mixture of Gaussians fit to a single curved or heavy-tailed cluster will report that the data contains many clusters. To produce more appropriate clusterings, we introduce a model which warps a latent mixture of Gaussians to produce nonparametric cluster shapes. The possibly low-dimensional latent mixture model allows us to summarize the properties of the high-dimensional clusters (or density manifolds) describing the data. The number of manifolds, as well as the shape and dimension of each manifold is automatically inferred. We derive a simple inference scheme for this model which analytically integrates out both the mixture parameters and the warping function. We show that our model is effective for density estimation, performs better than infinite Gaussian mixture models at recovering the true number of clusters, and produces interpretable summaries of high-dimensional datasets.

Achieving robust face recognition from video by combining a weak photometric model and a learnt generic face invariant

In spite of over two decades of intense research, illumination and pose invariance remain prohibitively challenging aspects of face recognition for most practical applications. The objective of this work is to recognize faces using video sequences both for training and recognition input, in a realistic, unconstrained setup in which lighting, pose and user motion pattern have a wide variability and face images are of low resolution. The central contribution is an illumination invariant, which we show to be suitable for recognition from video of loosely constrained head motion. In particular there are three contributions: (i) we show how a photometric model of image formation can be combined with a statistical model of generic face appearance variation to exploit the proposed invariant and generalize in the presence of extreme illumination changes; (ii) we introduce a video sequence re-illumination algorithm to achieve fine alignment of two video sequences; and (iii) we use the smoothness of geodesically local appearance manifold structure and a robust same-identity likelihood to achieve robustness to unseen head poses. We describe a fully automatic recognition system based on the proposed method and an extensive evaluation on 323 individuals and 1474 video sequences with extreme illumination, pose and head motion variation. Our system consistently achieved a nearly perfect recognition rate (over 99.7% on all four databases). © 2012 Elsevier Ltd All rights reserved.

Liquid transport facilitated by channels in Bacillus subtilis biofilms.

Many bacteria on earth exist in surface-attached communities known as biofilms. These films are responsible for manifold problems, including hospital-acquired infections and biofouling, but they can also be beneficial. Biofilm growth depends on the transport of nutrients and waste, for which diffusion is thought to be the main source of transport. However, diffusion is ineffective for transport over large distances and thus should limit growth. Nevertheless, biofilms can grow to be very large. Here we report the presence of a remarkable network of well-defined channels that form in wild-type Bacillus subtilis biofilms and provide a system for enhanced transport. We observe that these channels have high permeability to liquid flow and facilitate the transport of liquid through the biofilm. In addition, we find that spatial variations in evaporative flux from the surface of these biofilms provide a driving force for the flow of liquid in the channels. These channels offer a remarkably simple system for liquid transport, and their discovery provides insight into the physiology and growth of biofilms.

UEGO based measurement of EGR rate and residual gas fraction

A technique is presented for measuring the exhaust gas recirculation (EGR) and residual gas fraction (RGF) using a fast UEGO based O2 measurement of the manifold or in-cylinder gases, and of the exhaust gases. The technique has some advantages over the more common CO2-based method. In the case of an RGF measurement, fuel interference must be eliminated and special fuelling arrangements are is required. It is shown how a UEGO-based measurement, though sensitive to reactive species in the exhaust (such as H 2), as a system reports EGR/ RGF rates faithfully. Preliminary tests showed that EGR and RGF measurements using the O2 approach agreed well with CO2-based measurements. Copyright © 2011 SAE International.