A distortion identification technique is presented based on Hilbert-Huang transform to identify distortion model and distortion frequency of distorted real-world image sequences. The distortion model is identified sim...A distortion identification technique is presented based on Hilbert-Huang transform to identify distortion model and distortion frequency of distorted real-world image sequences. The distortion model is identified simply based on Hilbert marginal spectral analysis after empirical mode decomposing. And distortion frequency is identified by analyzing the occurrence frequency of instantaneous frequency components of every intrinsic mode functions. Rational digital frequency filter with suitable cutoff frequency is designed to remove undesired fluctuations based on identification results. Experimental results show that this technique can identify distortion model and distortion frequency of displacement sequence accurately and efficiently. Based on identification results, distorted image sequence can be stabilized effectively.展开更多
A degradation model with a random failure threshold is presented for the assessment of reliability by the Bayesian approach. This model is different from others in that the degradation process is proceeding under pre-...A degradation model with a random failure threshold is presented for the assessment of reliability by the Bayesian approach. This model is different from others in that the degradation process is proceeding under pre-specified periodical calibrations. And here a random threshold distribution instead of a constant threshold which is difficult to determine in practice is used. The system reliability is defined as the probability that the degradation signals do not exceed the random threshold. Based on the posterior distribution estimates of degradation performance, two models for Bayesian reliability assessments are presented in terms of the degradation performance and the distribution of random failure threshold. The methods proposed in this paper are very useful and practical for multi-stage system with uncertain failure threshold. This study perfects the degradation modeling approaches and plays an important role in the remaining useful life estimation and maintenance decision making.展开更多
基金Supported by the President Fund of Graduate University, Chinese Academy of Sciences.
文摘A distortion identification technique is presented based on Hilbert-Huang transform to identify distortion model and distortion frequency of distorted real-world image sequences. The distortion model is identified simply based on Hilbert marginal spectral analysis after empirical mode decomposing. And distortion frequency is identified by analyzing the occurrence frequency of instantaneous frequency components of every intrinsic mode functions. Rational digital frequency filter with suitable cutoff frequency is designed to remove undesired fluctuations based on identification results. Experimental results show that this technique can identify distortion model and distortion frequency of displacement sequence accurately and efficiently. Based on identification results, distorted image sequence can be stabilized effectively.
基金the National Natural Science Foundation of China(No.71371031)
文摘A degradation model with a random failure threshold is presented for the assessment of reliability by the Bayesian approach. This model is different from others in that the degradation process is proceeding under pre-specified periodical calibrations. And here a random threshold distribution instead of a constant threshold which is difficult to determine in practice is used. The system reliability is defined as the probability that the degradation signals do not exceed the random threshold. Based on the posterior distribution estimates of degradation performance, two models for Bayesian reliability assessments are presented in terms of the degradation performance and the distribution of random failure threshold. The methods proposed in this paper are very useful and practical for multi-stage system with uncertain failure threshold. This study perfects the degradation modeling approaches and plays an important role in the remaining useful life estimation and maintenance decision making.
