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Validation methodology for distribution-based degradation model 被引量:1
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作者 Yunxia Chen Zhiguo Zeng Rui Kang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第4期553-559,共7页
Distribution-based degradation models (or graphical approach in some literature) occur in a wide range of applications. However, few of existing methods have taken the validation of the built model into consideratio... Distribution-based degradation models (or graphical approach in some literature) occur in a wide range of applications. However, few of existing methods have taken the validation of the built model into consideration. A validation methodology for distribution-based models is proposed in this paper. Since the model can be expressed as consisting of assumptions of model structures and embedded model parameters, the proposed methodology carries out the validation from these two aspects. By using appropriate statistical techniques, the rationality of degradation distributions, suitability of fitted models and validity of degradation models are validated respectively. A new statistical technique based on control limits is also proposed, which can be implemented in the validation of degradation models' validity. The case study on degradation modeling of an actual accelerometer shows that the proposed methodology is an effective solution to the validation problem of distribution-based de qradation models. 展开更多
关键词 degradation model distribution-based degradationmodel graphical method model validation control limits.
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Modeling and inferring 2.1D sketch with mixed Markov random field
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作者 Anlong Ming Yu Zhou Tianfu Wu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2017年第2期361-373,共13页
This paper presents a method of computing a 2.1D sketch (i.e., layered image representation) from a single image with mixed Markov random field (MRF) under the Bayesian framework. Our model consists of three layers: t... This paper presents a method of computing a 2.1D sketch (i.e., layered image representation) from a single image with mixed Markov random field (MRF) under the Bayesian framework. Our model consists of three layers: the input image layer, the graphical representation layer of the computed 2D atomic regions and 3-degree junctions (such as T or arrow junctions), and the 2.1D sketch layer. There are two types of vertices in the graphical representation of the 2D entities: (i) regions, which act as the vertices found in traditional MRF, and (ii) address variables assigned to the terminators decomposed from the 3-degree junctions, which are a new type of vertices for the mixed MRF. We formulate the inference problem as computing the 2.1D sketch from the 2D graphical representation under the Bayesian framework, which consists of two components: (i) region layering/coloring based on the Swendsen-Wang cuts algorithm, which infers partial occluding order of regions, and (ii) address variable assignments based on Gibbs sampling, which completes the open bonds of the terminators of the 3-degree junctions. The proposed method is tested on the D-Order dataset, the Berkeley segmentation dataset and the Stanford 3D dataset. The experimental results show the efficiency and robustness of our approach. © 2017 Beijing Institute of Aerospace Information. 展开更多
关键词 Graphic methods Image segmentation Inference engines Markov processes Structural frames
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