确定维修规则所涉及的构筑物、系统和设备的范围是核电厂建立维修规则体系并开展监测的基础。结合VVER机组系统设备特点和改进型以可靠性为中心的维修分析方法,对VVER机组维修规则范围筛选的技术方法进行了研究,制定了维修规则范围筛选...确定维修规则所涉及的构筑物、系统和设备的范围是核电厂建立维修规则体系并开展监测的基础。结合VVER机组系统设备特点和改进型以可靠性为中心的维修分析方法,对VVER机组维修规则范围筛选的技术方法进行了研究,制定了维修规则范围筛选的技术流程和实施方法,以在满足10CFR 50.65 "Requirements for monitoring the effectiveness of maintenance at nuclear power plants"文件要求的情况下完成维修规则范围内构筑物、系统和设备的筛选工作。展开更多
Snowfall in the Tianshan Mountains in China is frequent during winter;thus,avalanches have become a severe issue in snow-covered areas.Accumulation and metamorphosis,as well as hydrothermal exchanges with the environm...Snowfall in the Tianshan Mountains in China is frequent during winter;thus,avalanches have become a severe issue in snow-covered areas.Accumulation and metamorphosis,as well as hydrothermal exchanges with the environment,considerably affect the stability of snow on slopes.Therefore,a hydrothermal model of snow cover and its underlying surfaces must be developed on the basis of meteorological data to predict and help manage avalanches.This study adopted the conceptual model of snow as a porous medium and quantitatively analysed its internal physical processes on the basis of the thermal exchanges amongst its components.The effects of local meteorological factors on snow structure and the redistribution of energy and mass inside the snow cover in the Tianshan Mountains were simulated.Simulation results showed that deformation as a result of overlying snow and sublimation of snow cover at the bottom is the main cause of density variation in the vertical profile of snow cover.Temperature drives water movement in snow.The low-density area of the bottom snow is the result of temperature gradient.The simulation results of the long-term snow internal mass distribution obtained by the method established in this study are highly consistent with the actual observed trend of variation.Such consistency indicates an accurate simulation of the physical characteristics of snow cover in small and microscale metamorphism in the Tianshan Mountains during the stable period.展开更多
Motion deblurring is a basic problem in the field of image processing and analysis. This paper proposes a new method of single image blind deblurring which can be significant to kernel estimation and non-blind deconvo...Motion deblurring is a basic problem in the field of image processing and analysis. This paper proposes a new method of single image blind deblurring which can be significant to kernel estimation and non-blind deconvolution. Experiments show that the details of the image destroy the structure of the kernel, especially when the blur kernel is large. So we extract the image structure with salient edges by the method based on RTV. In addition, the traditional method for motion blur kernel estimation based on sparse priors is conducive to gain a sparse blur kernel. But these priors do not ensure the continuity of blur kernel and sometimes induce noisy estimated results. Therefore we propose the kernel refinement method based on L0 to overcome the above shortcomings. In terms of non-blind deconvolution we adopt the L1/L2 regularization term. Compared with the traditional method, the method based on L1/L2 norm has better adaptability to image structure, and the constructed energy functional can better describe the sharp image. For this model, an effective algorithm is presented based on alternating minimization algorithm.展开更多
文摘确定维修规则所涉及的构筑物、系统和设备的范围是核电厂建立维修规则体系并开展监测的基础。结合VVER机组系统设备特点和改进型以可靠性为中心的维修分析方法,对VVER机组维修规则范围筛选的技术方法进行了研究,制定了维修规则范围筛选的技术流程和实施方法,以在满足10CFR 50.65 "Requirements for monitoring the effectiveness of maintenance at nuclear power plants"文件要求的情况下完成维修规则范围内构筑物、系统和设备的筛选工作。
基金supported by the 13th Five-year Informatization Plan of the Chinese Academy of Sciences,Grant No.XXH13506 and XXH13505-220Data sharing fundamental program for Construction of the National Science Technology Infrastructure Platform(Grant No.Y719H71006)。
文摘Snowfall in the Tianshan Mountains in China is frequent during winter;thus,avalanches have become a severe issue in snow-covered areas.Accumulation and metamorphosis,as well as hydrothermal exchanges with the environment,considerably affect the stability of snow on slopes.Therefore,a hydrothermal model of snow cover and its underlying surfaces must be developed on the basis of meteorological data to predict and help manage avalanches.This study adopted the conceptual model of snow as a porous medium and quantitatively analysed its internal physical processes on the basis of the thermal exchanges amongst its components.The effects of local meteorological factors on snow structure and the redistribution of energy and mass inside the snow cover in the Tianshan Mountains were simulated.Simulation results showed that deformation as a result of overlying snow and sublimation of snow cover at the bottom is the main cause of density variation in the vertical profile of snow cover.Temperature drives water movement in snow.The low-density area of the bottom snow is the result of temperature gradient.The simulation results of the long-term snow internal mass distribution obtained by the method established in this study are highly consistent with the actual observed trend of variation.Such consistency indicates an accurate simulation of the physical characteristics of snow cover in small and microscale metamorphism in the Tianshan Mountains during the stable period.
基金Partially Supported by National Natural Science Foundation of China(No.61173102)
文摘Motion deblurring is a basic problem in the field of image processing and analysis. This paper proposes a new method of single image blind deblurring which can be significant to kernel estimation and non-blind deconvolution. Experiments show that the details of the image destroy the structure of the kernel, especially when the blur kernel is large. So we extract the image structure with salient edges by the method based on RTV. In addition, the traditional method for motion blur kernel estimation based on sparse priors is conducive to gain a sparse blur kernel. But these priors do not ensure the continuity of blur kernel and sometimes induce noisy estimated results. Therefore we propose the kernel refinement method based on L0 to overcome the above shortcomings. In terms of non-blind deconvolution we adopt the L1/L2 regularization term. Compared with the traditional method, the method based on L1/L2 norm has better adaptability to image structure, and the constructed energy functional can better describe the sharp image. For this model, an effective algorithm is presented based on alternating minimization algorithm.
