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De-interlacing technique based on total variation with spatial-temporal smoothness constraint
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作者 YIN XueMin1,2,3, YUAN JianHua1,2, LU XiaoPeng1,2 & ZOU MouYan1,2 1 Institute of Electronics, Chinese Academy of Sciences, Beijing 100080, China 2 Graduate School, Chinese Academy of Sciences, Beijing 100039, China 3 Jiuquan Satellite Launch Center, Lanzhou 732750, China 《Science in China(Series F)》 2007年第4期561-575,共15页
This paper introduces a new method of converting interlaced video to a progressively scanned video and image, The new method is derived from Bayesian framework with the spatial-temporal smoothness constraint and the M... This paper introduces a new method of converting interlaced video to a progressively scanned video and image, The new method is derived from Bayesian framework with the spatial-temporal smoothness constraint and the MAP is done by minimizing the energy functional, The half-quadratic regularization method is used to solve the corresponding partial differential equations (PDEs), This approach gives the improved results over the conventional de-interlacing methods, Two criteria are proposed in the paper, and they can be used to evaluate the performance of the de-interlacing algorithms, 展开更多
关键词 video processing DE-INTERLACING total variation spatio-temporai smoothness constraint PDES half-quadratic regularization
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Smooth constraint inversion technique in genetic algorithms and its application to surface wave study in the Tibetan Plateau 被引量:3
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作者 吴建平 明跃红 曾融生 《Acta Seismologica Sinica(English Edition)》 EI CSCD 2001年第1期49-57,共9页
Smooth constraint is important in linear inversion, but it is difficult to apply directly to model parameters in genetic algorithms. If the model parameters are smoothed in iteration, the diversity of models will be g... Smooth constraint is important in linear inversion, but it is difficult to apply directly to model parameters in genetic algorithms. If the model parameters are smoothed in iteration, the diversity of models will be greatly suppressed and all the models in population will tend to equal in a few iterations, so the optimal solution meeting requirement can not be obtained. In this paper, an indirect smooth constraint technique is introduced to genetic inversion. In this method, the new models produced in iteration are smoothed, then used as theoretical models in calculation of misfit function, but in process of iteration only the original models are used in order to keep the diversity of models. The technique is effective in inversion of surface wave and receiver function. Using this technique, we invert the phase velocity of Raleigh wave in the Tibetan Plateau, revealing the horizontal variation of S wave velocity structure near the center of the Tibetan Plateau. The results show that the S wave velocity in the north is relatively lower than that in the south. For most paths there is a lower velocity zone with 12-25 km thick at the depth of 15-40 km. The lower velocity zone in upper mantle is located below the depth of 100 km, and the thickness is usually 40-80 km, but for a few paths reach to 100 km thick. Among the area of Ando, Maqi and Ushu stations, there is an obvious lower velocity zone with the lowest velocity of 4.2-4.3 km/s at the depth of 90-230 km. Based on the S wave velocity structures of different paths and former data, we infer that the subduction of the Indian Plate is delimited nearby the Yarlung Zangbo suture zone. 展开更多
关键词 genetic algorithm smooth constraint surface wave S wave velocity structure Tibetan Plateau
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Seismic diagnostics of solar-like oscillating stars 被引量:1
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作者 Ya-Guang Li Ming-Hao Du +5 位作者 Bo-Han Xie Zhi-Jia Tian Shao-Lan Bi Tan-Da Li Ya-Qian Wu Kang Liu 《Research in Astronomy and Astrophysics》 SCIE CAS CSCD 2017年第5期51-64,共14页
High precision and long-lasting Kepler data enabled us to estimate stellar properties with asteroseismology as an accurate tool. We performed asteroseismic analysis on six solar-like stars observed by the Kepler miss... High precision and long-lasting Kepler data enabled us to estimate stellar properties with asteroseismology as an accurate tool. We performed asteroseismic analysis on six solar-like stars observed by the Kepler mission: KIC 6064910, KIC 6766513, KIC 7107778, KIC 10079226, KIC 10147635 and KIC 12069127. The extraction of seismic information includes two parts. First, we obtained two global asteroseismic parameters, mean large separation ?_ν and frequency of maximum power ν_(max),with autocorrelation function and collapsed autocorrelation function. Second, we extracted individual oscillation modes ν_(nl) with low-l degree using a least-squares fit. Stellar grid models were built with Yale Rotating Stellar Evolution Code(YREC) to analyze stellar properties. They covered the range of M = 0.8 ~ 1.8 M_⊙with a step of 0.02 M_⊙ and [Fe/H] =-0.3 ~ 0.4 dex with a step of 0.1 dex.We used a Bayesian approach to estimate stellar fundamental parameters of the six stars, under the constraints of asteroseismic parameters(?_ν, ν_(max)) and non-asteroseismic parameters(Teff, [Fe/H]). We discover that the six targets include five sub-giant stars with 1.2 ~1.5 M_⊙ and one main-sequence star with 1.08 M_⊙, and with ages in the range of 3 ~5 Gyr. 展开更多
关键词 diagnostics stars stellar Bayesian constraints lasting oscillation oscillating giant smoothed
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