937 resultados para Lingual wire
The effects of a trip wire and unsteadiness on a high speed highly loaded low-pressure turbine blade
Resumo:
This paper presents the effect of a single spanwise 2D wire upon the downstream position of boundary layer transition under steady and unsteady inflow conditions. The study is carried out on a high turning, high-speed, low pressure turbine (LPT) profile designed to take account of the unsteady flow conditions. The experiments were carried out in a transonic cascade wind tunnel to which a rotating bar system had been added. The range of Reynolds and Mach numbers studied includes realistic LPT engine conditions and extends up to the transonic regime. Losses are measured to quantify the influence of the roughness with and without wake passing. Time resolved measurements such as hot wire boundary layer surveys and surface unsteady pressure are used to explain the state of the boundary layer. The results suggest that the effect of roughness on boundary layer transition is a stability governed phenomena, even at high Mach numbers. The combination of the effect of the roughness elements with the inviscid Kelvin-Helmholtz instability responsible for the rolling up of the separated shear layer (Stieger [1]) is also examined. Wake traverses using pneumatic probes downstream of the cascade reveal that the use of roughness elements reduces the profile losses up to exit Mach numbers of 0.8. This occurs with both steady and unsteady inflow conditions.
Resumo:
This paper investigates several approaches to bootstrapping a new spoken language understanding (SLU) component in a target language given a large dataset of semantically-annotated utterances in some other source language. The aim is to reduce the cost associated with porting a spoken dialogue system from one language to another by minimising the amount of data required in the target language. Since word-level semantic annotations are costly, Semantic Tuple Classifiers (STCs) are used in conjunction with statistical machine translation models both of which are trained from unaligned data to further reduce development time. The paper presents experiments in which a French SLU component in the tourist information domain is bootstrapped from English data. Results show that training STCs on automatically translated data produced the best performance for predicting the utterance's dialogue act type, however individual slot/value pairs are best predicted by training STCs on the source language and using them to decode translated utterances. © 2010 ISCA.
Resumo:
Model combination wire ropes with different covering materials were prepared and worked out specification for the prototype. A table model hand operated wire rope twisting machine was also developed for this. Prototype combination wire rope was twisted in collaboration with M/s South India Wire Ropes Ltd., Alwaye. Specification details, properties and field performance of the prototype studied are reported.
Resumo:
The prototype combination wire rope (Cift-CWR 1) developed for deep sea trawling was further studied for improvement, optimisation of efficiency and standardization. A series of improved prototype combination wire ropes (Cift-CWR 2 to 6) were twisted and evaluated their mechanical properties and reported in this paper with recommendations for a standard 17mm dia combination wire rope of 6S (7C+8+1 Scr) + 6 Crs(6+1+1 Crc) construction.
Resumo:
Tensile and extension properties of standard Cift-CWR and imported combination wire ropes from Japan, Norway and Denmark are studied and the analysis is presented in the paper. Tensile and chemical properties of steel wire, tensile and abrasive properties of PP covering, effect of twist on material at different stages are worked out and reported.
Resumo:
Hidden Markov model (HMM)-based speech synthesis systems possess several advantages over concatenative synthesis systems. One such advantage is the relative ease with which HMM-based systems are adapted to speakers not present in the training dataset. Speaker adaptation methods used in the field of HMM-based automatic speech recognition (ASR) are adopted for this task. In the case of unsupervised speaker adaptation, previous work has used a supplementary set of acoustic models to estimate the transcription of the adaptation data. This paper first presents an approach to the unsupervised speaker adaptation task for HMM-based speech synthesis models which avoids the need for such supplementary acoustic models. This is achieved by defining a mapping between HMM-based synthesis models and ASR-style models, via a two-pass decision tree construction process. Second, it is shown that this mapping also enables unsupervised adaptation of HMM-based speech synthesis models without the need to perform linguistic analysis of the estimated transcription of the adaptation data. Third, this paper demonstrates how this technique lends itself to the task of unsupervised cross-lingual adaptation of HMM-based speech synthesis models, and explains the advantages of such an approach. Finally, listener evaluations reveal that the proposed unsupervised adaptation methods deliver performance approaching that of supervised adaptation.