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.

Expressive visual text-to-speech using active appearance models

This paper presents a complete system for expressive visual text-to-speech (VTTS), which is capable of producing expressive output, in the form of a 'talking head', given an input text and a set of continuous expression weights. The face is modeled using an active appearance model (AAM), and several extensions are proposed which make it more applicable to the task of VTTS. The model allows for normalization with respect to both pose and blink state which significantly reduces artifacts in the resulting synthesized sequences. We demonstrate quantitative improvements in terms of reconstruction error over a million frames, as well as in large-scale user studies, comparing the output of different systems. © 2013 IEEE.

Kernelized log linear models for continuous speech recognition

Large margin criteria and discriminative models are two effective improvements for HMM-based speech recognition. This paper proposed a large margin trained log linear model with kernels for CSR. To avoid explicitly computing in the high dimensional feature space and to achieve the nonlinear decision boundaries, a kernel based training and decoding framework is proposed in this work. To make the system robust to noise a kernel adaptation scheme is also presented. Previous work in this area is extended in two directions. First, most kernels for CSR focus on measuring the similarity between two observation sequences. The proposed joint kernels defined a similarity between two observation-label sequence pairs on the sentence level. Second, this paper addresses how to efficiently employ kernels in large margin training and decoding with lattices. To the best of our knowledge, this is the first attempt at using large margin kernel-based log linear models for CSR. The model is evaluated on a noise corrupted continuous digit task: AURORA 2.0. © 2013 IEEE.

An overview of the ILSP unit selection text-to-speech synthesis system

This paper presents an overview of the Text-to-Speech synthesis system developed at the Institute for Language and Speech Processing (ILSP). It focuses on the key issues regarding the design of the system components. The system currently fully supports three languages (Greek, English, Bulgarian) and is designed in such a way to be as language and speaker independent as possible. Also, experimental results are presented which show that the system produces high quality synthetic speech in terms of naturalness and intelligibility. The system was recently ranked among the first three systems worldwide in terms of achieved quality for the English language, at the international Blizzard Challenge 2013 workshop. © 2014 Springer International Publishing.