has been published in nal form at http://dx.doi.org/10.1002/we.1508. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving. Additional information:Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details.

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The immunogenicity of HLA-A2-restricted T-cell epitopes in the S protein of the Severe acute respiratory syndrome coronavirus (SARS-CoV) and of human coronavirus strain 229e (HCoV-229e) was analyzed for the elicitation of a T-cell immune response in donors who had fully recovered from SARS-CoV infection. We employed online database analysis to compare the differences in the amino acid sequences of the homologous T epitopes of HCoV-229e and SARS-CoV. The identified T-cell epitope peptides were synthesized, and their binding affinities for HLA-A2 were validated and compared in the T2 cell system. The immunogenicity of all these peptides was assessed by using T cells obtained from donors who had fully recovered from SARS-CoV infection and from healthy donors with no history of SARS-CoV infection. HLA-A2 typing by indirect immunofluorescent antibody staining showed that 51.6% of SARS-CoV-infected patients were HLA-A2 positive. Online database analysis and the T2 cell binding test disclosed that the number of HLA-A2-restricted immunogenic epitopes of the S protein of SARS-CoV was decreased or even lost in comparison with the homologous sequences of the S protein of HCoV-229e. Among the peptides used in the study, the affinity of peptides from HCoV-229e (H77 and H881) and peptides from SARS-CoV (S978 and S1203) for binding to HLA-A2 was higher than that of other sequences. The gamma interferon (IFN-␥) release Elispot assay revealed that only SARS-CoV-specific peptides S1203 and S978 induced a high frequency of IFN-␥-secreting T-cell response in HLA-A2؉ donors who had fully recovered from SARS-CoV infection; such a T-cell epitope-specific response was not observed in HLA-A2؉ healthy donors or in HLA-A2 ؊ donors who had been infected with SARS-CoV after full recovery. Thus, T-cell epitopes S1203 and S978 are immunogenic and elicit an overt specific T-cell response in HLA-A2 ؉ SARS-CoV-infected patients.

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Publisher's copyright statement: c 2010 IEEE. 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 to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Additional information:Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract-With an increasing number of wind turbines being erected offshore, there is a need for cost-effective, predictive, and proactive maintenance. A large fraction of wind turbine downtime is due to bearing failures, particularly in the generator and gearbox. One way of assessing impending problems is to install vibration sensors in key positions on these subassemblies. Such equipment can be costly and requires sophisticated software for analysis of the data. An alternative approach, which does not require extra sensors, is investigated in this paper. This involves monitoring the power output of a variable-speed wind turbine generator and processing the data using a wavelet in order to extract the strength of particular frequency components, characteristic of faults. This has been done for doubly fed induction generators (DFIGs), commonly used in modern variable-speed wind turbines. The technique is first validated on a test rig under controlled fault conditions and then is applied to two operational wind turbine DFIGs where generator shaft misalignment was detected. For one of these turbines, the technique detected a problem 3 months before a bearing failure was recorded.

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The paper developed a reasonable and practical method for identifying the useful information from the signal that has been contaminated by noise, so that to provide a feasible tool for vibration analysis. A new concept namely the Singular Entropy (SE) was proposed based on the singular value decomposition technique. With the aid of the SE, a series of investigations were done for discovering the distribution characteristics of noise contaminated and pure signals, and consequently an advanced noise reduction method was developed. The experiments showed that the proposed method was not only applied for dealing with the stationary signals but also applied for dealing with the non-stationary signals. q

Due to constantly varying wind speed, wind turbine (WT) rotor and the other drive train components often operate at variable speeds in order to capture energy from wind as efficiently as possible and therefore generate more electric power. Due to the variable loads and rotational speed, the condition monitoring (CM) signals collected from WTs always contain intra-wave features, which are difficult to extract through performing conventional Time-Frequency Analysis (TFA) because the successful extraction of these intra-wave characteristics requests a locally adaptive signal processing technique. To now, only Empirical Mode Decomposition (EMD) and its extension form can meet such a requirement. However, practice has shown that the EMD and those EMD-based techniques also suffer a number of defects in TFA (e.g. weak robustness of against noise, unidentified ripples, inefficiency in detecting side-band frequencies, etc.). The existence of these issues has significantly limited the extensive application of the EMD family techniques to WT CM. Recently, an alternative TFA method, namely Variational Mode Decomposition (VMD), was proposed to overcome all these issues. The purpose of this paper is to verify the superiorities of the VMD over the EMD and investigate its potential application to the future WT CM.Experiment has shown that the VMD outperforms the EMD not only in noise robustness but also in multi-component signal decomposition, side-band detection, and intra-wave feature extraction.Thus, it has potential as a promising technique for WT CM.