A novel dead-time vector approach to analysis of DC link current inPWM inverter drives

An analysis on the DC link current of space-vector PWM AC drives using novel dead-time vector (DTV) concept has been presented. By employing the DTV, a bi-value function to quantify the polarities of three phase winding currents, inverter output voltages and DC link current at dead time state can be expressed in close vectored forms. Thus, the entire drive system is considered as a whole to investigate the characteristics of DC link current at both normal and dead time states during steady and transient operations, increasingly, a phenomenon of negative spikes on DC link current caused by dead time effect is newly observed and highlighted. This phenomenon can be accurately predicted and mathematically characterized by using the proposed DTV approach. Both computer simulation and experimental results have been used for verification.published_or_final_versio

A unified analysis of DC link current in space-vector PWM drives

A unified analysis of the DC link current in space-vector PWM drives, comprising induction motor, inverter and space-vector PWM scheme, is presented. The DC link current is investigated in a unified way, whether the inverter is operating at normal or dead-time switching modes. The key is to introduce a dead-time vector, which is mathematically similar to the well-known space voltage vector, to express the DC link current and inverter output voltages into a unified form. Moreover, the formation of positive and negative spikes on the DC link current is discussed. In particular, the occurrence of negative spikes is mathematically formulated. The proposed unified approach is verified by means of both computer simulation and experimental results. The occurrence of positive and negative spikes is also verified by using circuit-oriented and device-oriented computer simulations as well as experimental results.published_or_final_versio

Switching characteristics and efficiency improvement with auxiliaryresonant snubber based soft-switching inverters

The auxiliary resonant snubber inverter (RSI) has demonstrated superiority in reduction of switching losses and dv/dt. It was found that the overall inverter system efficiency might not be improved if the resonant current was not controlled in accordance with the load current. This paper proposes an improved control scheme to minimize the operation of auxiliary circuit for efficiency improvement. The principle of this control scheme is to vary the resonant current with variable timing control based on the load current magnitude and to disable the auxiliary circuit operation when diode freewheeling occurs after switching. To illustrate the operation of this control scheme, this paper compares the switching characteristics and inverter power loss of hard-switching inverter, RSI with fixed-timing and variable-timing control and RSI with the proposed control scheme. Experimental results fully agree with the analyses and prove that the proposed control scheme is suitable for RSI to achieve better operating conditions.published_or_final_versio

A computational strategy for the search of regulatory small RNAs in Actinobacillus pleuropneumoniae.

Bacterial regulatory small RNAs (sRNAs) play important roles in gene regulation and are frequently connected to the expression of virulence factors in diverse bacteria. Only a few sRNAs have been described for Pasteurellaceae pathogens and no in-depth analysis of sRNAs has been described for Actinobacillus pleuropneumoniae, the causative agent of porcine pleuropneumonia, responsible for considerable losses in the swine industry. To search for sRNAs in A. pleuropneumoniae, we developed a strategy for the computational analysis of the bacterial genome by using four algorithms with different approaches, followed by experimental validation. The coding strand and expression of 17 out of 23 RNA candidates were confirmed by Northern blotting, RT-PCR, and RNA sequencing. Among them, two are likely riboswitches, three are housekeeping regulatory RNAs, two are the widely studied GcvB and 6S sRNAs, and 10 are putative novel trans-acting sRNAs, never before described for any bacteria. The latter group has several potential mRNA targets, many of which are involved with virulence, stress resistance, or metabolism, and connect the sRNAs in a complex gene regulatory network. The sRNAs identified are well conserved among the Pasteurellaceae that are evolutionarily closer to A. pleuropneumoniae and/or share the same host. Our results show that the combination of newly developed computational programs can be successfully utilized for the discovery of novel sRNAs and indicate an intricate system of gene regulation through sRNAs in A. pleuropneumoniae and in other Pasteurellaceae, thus providing clues for novel aspects of virulence that will be explored in further studies

Coating for fabrics with nano tube-panis and the properties of conducting and shielding microwave

2005-2006 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Data assimilation predictive GAN (DA-PredGAN) applied to a spatio-temporal compartmental model in epidemiology

We propose a novel use of generative adversarial networks (GANs) (i) to make predictions in time (PredGAN) and (ii) to assimilate measurements (DA-PredGAN). In the latter case, we take advantage of the natural adjoint-like properties of generative models and the ability to simulate forwards and backwards in time. GANs have received much attention recently, after achieving excellent results for their generation of realistic-looking images. We wish to explore how this property translates to new applications in computational modelling and to exploit the adjoint-like properties for efficient data assimilation. We apply these methods to a compartmental model in epidemiology that is able to model space and time variations, and that mimics the spread of COVID-19 in an idealised town. To do this, the GAN is set within a reduced-order model, which uses a low-dimensional space for the spatial distribution of the simulation states. Then the GAN learns the evolution of the low-dimensional states over time. The results show that the proposed methods can accurately predict the evolution of the high-fidelity numerical simulation, and can efficiently assimilate observed data and determine the corresponding model parameters

Learning Optimal Deep Projection of 18^{18}F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes

Several diseases of parkinsonian syndromes present similar symptoms at early stage and no objective widely used diagnostic methods have been approved until now. Positron emission tomography (PET) with $^{18}$F-FDG was shown to be able to assess early neuronal dysfunction of synucleinopathies and tauopathies. Tensor factorization (TF) based approaches have been applied to identify characteristic metabolic patterns for differential diagnosis. However, these conventional dimension-reduction strategies assume linear or multi-linear relationships inside data, and are therefore insufficient to distinguish nonlinear metabolic differences between various parkinsonian syndromes. In this paper, we propose a Deep Projection Neural Network (DPNN) to identify characteristic metabolic pattern for early differential diagnosis of parkinsonian syndromes. We draw our inspiration from the existing TF methods. The network consists of a (i) compression part: which uses a deep network to learn optimal 2D projections of 3D scans, and a (ii) classification part: which maps the 2D projections to labels. The compression part can be pre-trained using surplus unlabelled datasets. Also, as the classification part operates on these 2D projections, it can be trained end-to-end effectively with limited labelled data, in contrast to 3D approaches. We show that DPNN is more effective in comparison to existing state-of-the-art and plausible baselines.Comment: 8 pages, 3 figures, conference, MICCAI DLMIA, 201

Intraspecies diversity of SARS-like coronaviruses in Rhinolophus sinicus and its implications for the origin of SARS coronaviruses in humans

The Chinese rufous horseshoe bat (Rhinolophus sinicus) has been suggested to carry the direct ancestor of severe acute respiratory syndrome (SARS) coronavirus (SCoV), and the diversity of SARS-like CoVs (SLCoV) within this Rhinolophus species is therefore worth investigating. Here, we demonstrate the remarkable diversity of SLCoVs in R. sinicus and identify a strain with the same pattern of phylogenetic incongruence (i.e. an indication of recombination) as reported previously in another SLCoV strain. Moreover, this strain possesses a distinctive 579 nt deletion in the nsp3 region that was also found in a human SCoV from the late-phase epidemic. Phylogenetic analysis of the Orf1 region suggested that the human SCoVs are phylogenetically closer to SLCoVs in R. sinicus than to SLCoVs in other Rhinolophus species. These findings reveal a closer evolutionary linkage between SCoV in humans and SLCoVs in R. sinicus, defining the scope of surveillance to search for the direct ancestor of human SCoVs. © 2010 SGM.published_or_final_versio

Experimental Zika Virus Infection in the Pregnant Common Marmoset Induces Spontaneous Fetal Loss and Neurodevelopmental Abnormalities.

During its most recent outbreak across the Americas, Zika virus (ZIKV) was surprisingly shown to cause fetal loss and congenital malformations in acutely and chronically infected pregnant women. However, understanding the underlying pathogenesis of ZIKV congenital disease has been hampered by a lack of relevant in vivo experimental models. Here we present a candidate New World monkey model of ZIKV infection in pregnant marmosets that faithfully recapitulates human disease. ZIKV inoculation at the human-equivalent of early gestation caused an asymptomatic seroconversion, induction of type I/II interferon-associated genes and proinflammatory cytokines, and persistent viremia and viruria. Spontaneous pregnancy loss was observed 16-18 days post-infection, with extensive active placental viral replication and fetal neurocellular disorganization similar to that seen in humans. These findings underscore the key role of the placenta as a conduit for fetal infection, and demonstrate the utility of marmosets as a highly relevant model for studying congenital ZIKV disease and pregnancy loss