Appraisal of PD6493:1991 fracture assessment procedures

PD6493:1991 fracture assessment have been performed for a range of large-scale fracture mechanics tests conducted at TWI in the past. These tests cover several material groups, including pressure vessel steels, pipeline steels, stainless steels and aluminium alloys, including parent material and weldments. Ninety-two wide plate and pressure vessel tests have been assessed, following Levels 1, 2 and 3 PD6493:1991 procedures. In total, over 400 assessments have been performed, examining many features of the fracture assessment procedure including toughness input, proof testing, residual stress assumptions and stress state (tension, bending and biaxial). In all cases the large scale tests have been assessed as one would actual structures: i.e., based on lower bound toughness values obtained from small scale fracture toughness specimens.

Cross-Lingual spoken language understanding from unaligned data using discriminative classification models and machine translation

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.