Sampled-data filtering with error covariance assignment

Copyright [2001] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.We consider the sampled-data filtering problem by proposing a new performance criterion in terms of the estimation error covariance. An innovation approach to sampled-data filtering is presented. First, the definition of the estimation covariance e for a sampled-data system is given, then the sampled-data filtering problem is reduced to the Kalman filter design problem for a fictitious discrete-time system, and finally, an effective method is developed to design discrete-time Kalman filters in such a way that the resulting sampled-data estimation covariance achieves a prescribed value. We derive both the existence conditions and the explicit expression of the desired filters and provide an illustrative numerical example to demonstrate the directness and flexibility of the present design metho

Online adaptive learning of continuous-density hidden Markov models based on multiple-stream prior evolution and posterior pooling

We introduce a new adaptive Bayesian learning framework, called multiple-stream prior evolution and posterior pooling, for online adaptation of the continuous density hidden Markov model (CDHMM) parameters. Among three architectures we proposed for this framework, we study in detail a specific two stream system where linear transformations are applied to the mean vectors of the CDHMMs to control the evolution of their prior distribution. This new stream of prior distribution can be combined with another stream of prior distribution evolved without any constraints applied. In a series of speaker adaptation experiments on the task of continuous Mandarin speech recognition, we show that the new adaptation algorithm achieves a similar fast-adaptation performance as that of the incremental maximum likelihood linear regression (MLLR) in the case of small amount of adaptation data, while maintains the good asymptotic convergence property as that of our previously proposed quasi-Bayes adaptation algorithms.published_or_final_versio

Irrelevant variability normalization in learning HMM state tying from data based on phonetic decision-tree

We propose to apply the concept of irrelevant variability normalization to the general problem of learning structure from data. Because of the problems of a diversified training data set and/or possible acoustic mismatches between training and testing conditions, the structure learned from the training data by using a maximum likelihood training method will not necessarily generalize well on mismatched tasks. We apply the above concept to the structural learning problem of phonetic decision-tree based hidden Markov model (HMM) state tying. We present a new method that integrates a linear-transformation based normalization mechanism into the decision-tree construction process to make the learned structure have a better modeling capability and generalizability. The viability and efficacy of the proposed method are confirmed in a series of experiments for continuous speech recognition of Mandarin Chinese.published_or_final_versio

Experimental and analytical study of the shear transfer in composite shallow cellular floor beams

This research investigated the longitudinal shear transfer mechanism in composite shallow cellular floor beams. The shear transfer mechanism is different with the headed shear studs used in composite construction. The shear resisting properties and behaviour of the shear transfer mechanism has not been studied previously. Experimental and analytical studies were carried out with the aims of improving and optimizing the design details, and advancing the method of shear connection in shallow floor beam construction. The composite shallow cellular floor beam investigated in this research is a new type of beam fabricated by welding two highly asymmetric cellular tees along the web. The shear connections of this type of composite beam are formed by the web openings, which transfer longitudinal shear force. Four types of these shear connections were studied: concrete-infill-only, tie-bar, ducting and web-welded-stud shear connections. In total, 24 push-out tests were performed in two test series to investigate the load-slip behaviour and shear resistance of the shear connections under direct shear force. The failure mechanisms of the two forms of shear connections were extensively studied, which lead to the development of a design method for the composite action. The concrete infill element passing through the web opening is subject to a complex three-dimensional stress state, and it is difficult to analyse it using the mathematical model rather than empirical formula. Finite Element Analysis of the concrete-infill-only shear connection was performed with a parametric study to further verify the design method that has been developed. Two flexural tests were carried out on a full-scale composite shallow cellular floor beam with a solid slab. The shear connections investigated in the flexural tests were: concrete-infill-only and tie-bar shear connections passed through the web. The behaviour and performance of the shear connections in the flexural tests were compared with those in the push-out tests. The degree of shear connection of the two flexural tests was determined in the back analysis using plastic theory with measured material properties. Based on the findings of the push-out tests and flexural tests, two design methods of deflection check and moment resistance were developed for composite shallow cellular floor beams at the serviceability limit state and the ultimate limit state respectively. The deflection check design method is based on the uncracked section properties of the composite beam. The moment resistance design method developed in this thesis is compatible with the design methods of BS5950 and Eurocode 4 (EC4)

Efficient ML training of CDHMM parameters based on prior evolution,posterior intervention and feedback

We present an efficient maximum likelihood (ML) training procedure for Gaussian mixture continuous density hidden Markov model (CDHMM) parameters. This procedure is proposed using the concept of approximate prior evolution, posterior intervention and feedback (PEPIF). In a series of experiments for training CDHMMs for a continuous Mandarin Chinese speech recognition task, the new PEPIF procedure achieves a 4-fold speed-up in terms of user CPU time over that of the Baum-Welch algorithm in producing models of given likelihood or recognition accuracy.published_or_final_versio

High-Resolution Analysis of the Efficiency, Heritability, and Editing Outcomes of CRISPR/Cas9-Induced Modifications of NCED4 in Lettuce (Lactuca sativa).

CRISPR/Cas9 is a transformative tool for making targeted genetic alterations. In plants, high mutation efficiencies have been reported in primary transformants. However, many of the mutations analyzed were somatic and therefore not heritable. To provide more insights into the efficiency of creating stable homozygous mutants using CRISPR/Cas9, we targeted LsNCED4 (9-cis-EPOXYCAROTENOID DIOXYGENASE4), a gene conditioning thermoinhibition of seed germination in lettuce. Three constructs, each capable of expressing Cas9 and a single gRNA targeting different sites in LsNCED4, were stably transformed into lettuce (Lactuca sativa) cvs. Salinas and Cobham Green. Analysis of 47 primary transformants (T1) and 368 T2 plants by deep amplicon sequencing revealed that 57% of T1 plants contained events at the target site: 28% of plants had germline mutations in one allele indicative of an early editing event (mono-allelic), 8% of plants had germline mutations in both alleles indicative of two early editing events (bi-allelic), and the remaining 21% of plants had multiple low frequency mutations indicative of late events (chimeric plants). Editing efficiency was similar in both genotypes, while the different gRNAs varied in efficiency. Amplicon sequencing of 20 T1 and more than 100 T2 plants for each of the three gRNAs showed that repair outcomes were not random, but reproducible and characteristic for each gRNA. Knockouts of NCED4 resulted in large increases in the maximum temperature for seed germination, with seeds of both cultivars capable of germinating >70% at 37°. Knockouts of NCED4 provide a whole-plant selectable phenotype that has minimal pleiotropic consequences. Targeting NCED4 in a co-editing strategy could therefore be used to enrich for germline-edited events simply by germinating seeds at high temperature

Design of a five-axis ultra-precision micro-milling machine—UltraMill. Part 2: Integrated dynamic modelling, design optimisation and analysis

Using computer models to predict the dynamic performance of ultra-precision machine tools can help manufacturers to substantially reduce the lead time and cost of developing new machines. However, the use of electronic drives on such machines is becoming widespread, the machine dynamic performance depending not only on the mechanical structure and components but also on the control system and electronic drives. Bench-top ultra-precision machine tools are highly desirable for the micro-manufacturing of high-accuracy micro-mechanical components. However, the development is still at the nascent stage and hence lacks standardised guidelines. Part 2 of this two-part paper proposes an integrated approach, which permits analysis and optimisation of the entire machine dynamic performance at the early design stage. Based on the proposed approach, the modelling and simulation process of a novel five-axis bench-top ultra-precision micro-milling machine tool—UltraMill—is presented. The modelling and simulation cover the dynamics of the machine structure, the moving components, the control system and the machining process and are used to predict the entire machine performance of two typical configurations

The impact of supply chain integration on performance: A contingency and configuration approach

This study extends the developing body of literature on supply chain integration (SCI), which is the degree to which a manufacturer strategically collaborates with its supply chain partners and collaboratively manages intra- and inter-organizational processes, in order to achieve effective and efficient flows of products and services, information, money and decisions, to provide maximum value to the customer. The previous research is inconsistent in its findings about the relationship between SCI and performance. We attribute this inconsistency to incomplete definitions of SCI, in particular, the tendency to focus on customer and supplier integration only, excluding the important central link of internal integration. We study the relationship between three dimensions of SCI, operational and business performance, from both a contingency and a configuration perspective. In applying the contingency approach, hierarchical regression was used to determine the impact of individual SCI dimensions (customer, supplier and internal integration) and their interactions on performance. In the configuration approach, cluster analysis was used to develop patterns of SCI, which were analyzed in terms of SCI strength and balance. Analysis of variance was used to examine the relationship between SCI pattern and performance. The findings of both the contingency and configuration approach indicated that SCI was related to both operational and business performance. Furthermore, the results indicated that internal and customer integration were more strongly related to improving performance than supplier integration

Probing the BCS-BEC crossover with photons in a nonlinear optical fiber

We propose a scheme where strongly correlated photons generated inside a hollow-core one-dimensional fiber filled with two cold atomic species can be used to simulate the BCS-BEC crossover. We first show how stationary light-matter excitations (polaritons) in the system can realize an optically tunable two component Bose-Hubbard model, and then analyze the optical parameters regime necessary to generate an effective Fermi-Hubbard model of photons exhibiting Cooper pairing. The characteristic correlated phases of the system can be efficiently observed due to the {\it in situ} accessibility of the photon correlations with standard optical technology.Comment: 4 and bit pages, 4 figures, comments welcom

Assessment of China's virtual air pollution transport embodied in trade by using a consumption-based emission inventory

Substantial anthropogenic emissions from China have resulted in serious air pollution, and this has generated considerable academic and public concern. The physical transport of air pollutants in the atmosphere has been extensively investigated; however, understanding the mechanisms how the pollutant was transferred through economic and trade activities remains a challenge. For the first time, we quantified and tracked China's air pollutant emission flows embodied in interprovincial trade, using a multiregional input - output model framework. Trade relative emissions for four key air pollutants (primary fine particle matter, sulfur dioxide, nitrogen oxides and non-methane volatile organic compounds) were assessed for 2007 in each Chinese province. We found that emissions were significantly redistributed among provinces owing to interprovincial trade. Large amounts of emissions were embodied in the imports of eastern regions from northern and central regions, and these were determined by differences in regional economic status and environmental policy. It is suggested that measures should be introduced to reduce air pollution by integrating cross-regional consumers and producers within national agreements to encourage efficiency improvement in the supply chain and optimize consumption structure internationally. The consumption-based air pollutant emission inventory developed in this work can be further used to attribute pollution to various economic activities and final demand types with the aid of air quality models