Centrifuge modelling and dynamic testing of municipal solid waste (MSW) landfills

An investigation into the seismic behaviour of municipal solidwaste (MSW) landfills by dynamic centrifuge testing was undertaken. This paper presents physical modelling of MSW landfills for dynamic centrifuge testing, with regard to the following research areas: 1. amplification characteristics of municipal solid waste; 2. tension induced in geomembranes placed on landfill slopes due to earthquake loading; 3. damage to landfill liners due to liquefaction of foundation soil. A model waste, that has engineering properties similar to MSW, is presented. A model geomembrane that can be used in centrifuge tests is also presented. Results of dynamic centrifuge tests with the model geomembrane showed that an earthquake loading induces additional permanent tension (∼25%) in the geomembrane. © 2006 Taylor & Francis Group, London.

Statistical parametric speech synthesis based on speaker and language factorization

An increasingly common scenario in building speech synthesis and recognition systems is training on inhomogeneous data. This paper proposes a new framework for estimating hidden Markov models on data containing both multiple speakers and multiple languages. The proposed framework, speaker and language factorization, attempts to factorize speaker-/language-specific characteristics in the data and then model them using separate transforms. Language-specific factors in the data are represented by transforms based on cluster mean interpolation with cluster-dependent decision trees. Acoustic variations caused by speaker characteristics are handled by transforms based on constrained maximum-likelihood linear regression. Experimental results on statistical parametric speech synthesis show that the proposed framework enables data from multiple speakers in different languages to be used to: train a synthesis system; synthesize speech in a language using speaker characteristics estimated in a different language; and adapt to a new language. © 2012 IEEE.

Phrase-based statistical language generation using graphical models and active learning

Most previous work on trainable language generation has focused on two paradigms: (a) using a statistical model to rank a set of generated utterances, or (b) using statistics to inform the generation decision process. Both approaches rely on the existence of a handcrafted generator, which limits their scalability to new domains. This paper presents BAGEL, a statistical language generator which uses dynamic Bayesian networks to learn from semantically-aligned data produced by 42 untrained annotators. A human evaluation shows that BAGEL can generate natural and informative utterances from unseen inputs in the information presentation domain. Additionally, generation performance on sparse datasets is improved significantly by using certainty-based active learning, yielding ratings close to the human gold standard with a fraction of the data. © 2010 Association for Computational Linguistics.

Experience using MEMS-based accelerometers in dynamic testing

Measurement of acceleration in dynamic tests is carried out routinely, and in most cases, piezoelectric accelerometers are used at present. However, a new class of instruments based on MEMS technology have become available and are gaining use in many applications due to their small size, low mass and low-cost. This paper describes a centrifuge lateral spreading experiment in which MEMS and piezoelectric accelerometers were placed at similar depths. Good agreement was obtained when the instruments were located in dense sands, but significant differences were observed in loose, liquefiable soils. It was found that the performance of the piezoelectric accelerometer is poor at low frequency, and that the relative phase difference between the piezoelectric and MEMS accelerometer varies significantly at low frequency. © 2010 Taylor & Francis Group, London.

Testing in harsh conditions: Tracking resources on construction sites with machine vision

When tracking resources in large-scale, congested, outdoor construction sites, the cost and time for purchasing, installing and maintaining the position sensors needed to track thousands of materials, and hundreds of equipment and personnel can be significant. To alleviate this problem a novel vision based tracking method that allows each sensor (camera) to monitor the position of multiple entities simultaneously has been proposed. This paper presents the full-scale validation experiments for this method. The validation included testing the method under harsh conditions at a variety of mega-project construction sites. The procedure for collecting data from the sites, the testing procedure, metrics, and results are reported. Full-scale validation demonstrates that the novel vision tracking provides a good solution to track different entities on a large, congested construction site.

Testing of Depth-Encoded Hough Voting for Infrastructure Object Detection

The lack of viable methods to map and label existing infrastructure is one of the engineering grand challenges for the 21st century. For instance, over two thirds of the effort needed to geometrically model even simple infrastructure is spent on manually converting a cloud of points to a 3D model. The result is that few facilities today have a complete record of as-built information and that as-built models are not produced for the vast majority of new construction and retrofit projects. This leads to rework and design changes that can cost up to 10% of the installed costs. Automatically detecting building components could address this challenge. However, existing methods for detecting building components are not view and scale-invariant, or have only been validated in restricted scenarios that require a priori knowledge without considering occlusions. This leads to their constrained applicability in complex civil infrastructure scenes. In this paper, we test a pose-invariant method of labeling existing infrastructure. This method simultaneously detects objects and estimates their poses. It takes advantage of a recent novel formulation for object detection and customizes it to generic civil infrastructure scenes. Our preliminary experiments demonstrate that this method achieves convincing recognition results.

Improving LVCSR system combination using neural network language model cross adaptation

State-of-the-art large vocabulary continuous speech recognition (LVCSR) systems often combine outputs from multiple subsystems developed at different sites. Cross system adaptation can be used as an alternative to direct hypothesis level combination schemes such as ROVER. The standard approach involves only cross adapting acoustic models. To fully exploit the complimentary features among sub-systems, language model (LM) cross adaptation techniques can be used. Previous research on multi-level n-gram LM cross adaptation is extended to further include the cross adaptation of neural network LMs in this paper. Using this improved LM cross adaptation framework, significant error rate gains of 4.0%-7.1% relative were obtained over acoustic model only cross adaptation when combining a range of Chinese LVCSR sub-systems used in the 2010 and 2011 DARPA GALE evaluations. Copyright © 2011 ISCA.

Use of contexts in language model interpolation and adaptation

Language models (LMs) are often constructed by building multiple individual component models that are combined using context independent interpolation weights. By tuning these weights, using either perplexity or discriminative approaches, it is possible to adapt LMs to a particular task. This paper investigates the use of context dependent weighting in both interpolation and test-time adaptation of language models. Depending on the previous word contexts, a discrete history weighting function is used to adjust the contribution from each component model. As this dramatically increases the number of parameters to estimate, robust weight estimation schemes are required. Several approaches are described in this paper. The first approach is based on MAP estimation where interpolation weights of lower order contexts are used as smoothing priors. The second approach uses training data to ensure robust estimation of LM interpolation weights. This can also serve as a smoothing prior for MAP adaptation. A normalized perplexity metric is proposed to handle the bias of the standard perplexity criterion to corpus size. A range of schemes to combine weight information obtained from training data and test data hypotheses are also proposed to improve robustness during context dependent LM adaptation. In addition, a minimum Bayes' risk (MBR) based discriminative training scheme is also proposed. An efficient weighted finite state transducer (WFST) decoding algorithm for context dependent interpolation is also presented. The proposed technique was evaluated using a state-of-the-art Mandarin Chinese broadcast speech transcription task. Character error rate (CER) reductions up to 7.3 relative were obtained as well as consistent perplexity improvements. © 2012 Elsevier Ltd. All rights reserved.

UK-NEES - Distributed hybrid testing between Bristol, Cambridge and Oxford Universities: Connecting structural dynamics labs to a geotechnical centrifuge

Distributed hybrid testing is a natural extension to and builds upon the local hybrid testing technique. Taking advantage of the hybrid nature of the test, it allows a sharing of resources and expertise between researchers from different disciplines by connecting multiple geographically distributed sites for joint testing. As part of the UK-NEES project, a successful series of three-site distributed hybrid tests have been carried out between Bristol, Cambridge and Oxford Universities. The first known multi-site distributed hybrid tests in the UK, they connected via a dedicated fibre network, using custom software, the geotechnical centrifuge at Cambridge to structural components at Bristol and Oxford. These experiments were to prove the connection and useful insights were gained into the issues involved with this distributed environment. A wider aim is towards providing a flexible testing framework to facilitate multi-disciplinary experiments such as the accurate investigation of the influence of foundations on structural systems under seismic and other loading. Time scaling incompatibilities mean true seismic soil structure interaction using a centrifuge at g is not possible, though it is clear that distributed centrifuge testing can be valuable in other problems. Development is continuing to overcome the issues encountered, in order to improve future distributed tests in the UK and beyond.

Flaw sizing in pipes using long-range guided wave testing

The absence of adequate inspection data from difficult-to-access areas on pipelines, such as cased-road crossings, makes determination of fitness for continued service and compliance with increasingly stringent regulatory requirements problematic. Screening for corrosion using long-range guided wave testing is a relatively new inspection technique. The complexity of the possible modes of vibration means the technique can be difficult to implement effectively but this also means that it has great potential for both detecting and characterizing flaws. The ability to determine flaw size would enable the direct application of standard procedures for determining fitness-for-service, such as ASME B31G, RSTRENG, or equivalent for tens of metres of pipeline from a single inspection location. This paper presents a new technique for flaw sizing using guided wave inspection data. The technique has been developed using finite element models and experimentally validated on 6'' Schedule 40 steel pipe. Some basic fitness-for-service assessments have been carried out using the measured values and the maximum allowable operating pressure was accurately determined. © 2011 American Institute of Physics.

Syllable language models for Mandarin speech recognition: exploiting character language models.

Mandarin Chinese is based on characters which are syllabic in nature and morphological in meaning. All spoken languages have syllabiotactic rules which govern the construction of syllables and their allowed sequences. These constraints are not as restrictive as those learned from word sequences, but they can provide additional useful linguistic information. Hence, it is possible to improve speech recognition performance by appropriately combining these two types of constraints. For the Chinese language considered in this paper, character level language models (LMs) can be used as a first level approximation to allowed syllable sequences. To test this idea, word and character level n-gram LMs were trained on 2.8 billion words (equivalent to 4.3 billion characters) of texts from a wide collection of text sources. Both hypothesis and model based combination techniques were investigated to combine word and character level LMs. Significant character error rate reductions up to 7.3% relative were obtained on a state-of-the-art Mandarin Chinese broadcast audio recognition task using an adapted history dependent multi-level LM that performs a log-linearly combination of character and word level LMs. This supports the hypothesis that character or syllable sequence models are useful for improving Mandarin speech recognition performance.

Design and testing of a 250 kW medium-speed Brushless DFIG

This paper presents the design and testing of a 250 kW medium-speed Brushless Doubly-Fed Induction Generator (Brushless DFIG), and its associated power electronics and control systems. The experimental tests confirm the design, and show the system's steady-state and dynamic performance. The medium-speed Brushless DFIG in combination with a simplified two-stage gearbox promises a low-cost low-maintenance and reliable drive train for wind turbine applications.