Depth from defocus is one technology for depth estimation.We estimate particle depth information from two defocused images captured simultaneously by two coaxial cameras with different imaging distances.The images are...Depth from defocus is one technology for depth estimation.We estimate particle depth information from two defocused images captured simultaneously by two coaxial cameras with different imaging distances.The images are processed with the Fourier transform to obtain the characteristic parameter(i.e.,the standard deviation of the relative blur kernel of these two defocused images).First,we theoretically analyze the functional relationship between the object depth and the standard deviation or variation of the relative blur kernel.Then,we verify the relationship experimentally.We analyze the influence of particle size,window size and image noise on the calibration curves using both numerical simulations and experiments.We obtain the depth range and accuracy of this measurement system experimentally.For the verification experiments,we use a sample of glass microbeads and the irregularly-shaped dust particles on a microscope slide.Both of these experiments present a suitable depth measurement result.Finally,we apply the measuring system to the depth estimation of drops from a small anti-fogging spray.The results show that our system and image processing algorithm are robust for different types of particles,facilitating the in-line three-dimensional positioning of particles.展开更多
Scientific workflows have gained the emerging attention in sophisti-cated large-scale scientific problem-solving environments.The pay-per-use model of cloud,its scalability and dynamic deployment enables it suited for ex...Scientific workflows have gained the emerging attention in sophisti-cated large-scale scientific problem-solving environments.The pay-per-use model of cloud,its scalability and dynamic deployment enables it suited for executing scientific workflow applications.Since the cloud is not a utopian environment,failures are inevitable that may result in experiencingfluctuations in the delivered performance.Though a single task failure occurs in workflow based applications,due to its task dependency nature,the reliability of the overall system will be affected drastically.Hence rather than reactive fault-tolerant approaches,proactive measures are vital in scientific workflows.This work puts forth an attempt to con-centrate on the exploration issue of structuring a nature inspired metaheuristics-Intelligent Water Drops Algorithm(IWDA)combined with an efficient machine learning approach-Support Vector Regression(SVR)for task failure prognostica-tion which facilitates proactive fault-tolerance in the scheduling of scientific workflow applications.The failure prediction models in this study have been implemented through SVR-based machine learning approaches and the precision accuracy of prediction is optimized by IWDA and several performance metrics were evaluated on various benchmark workflows.The experimental results prove that the proposed proactive fault-tolerant approach performs better compared with the other existing techniques.展开更多
In this paper,it is shown that the differential reflectivity Z_(DR) is related to drop size distribution(DSD)and the spectral shape variation of different truncated diameters.Z_(DR) is a sensitive function to variatio...In this paper,it is shown that the differential reflectivity Z_(DR) is related to drop size distribution(DSD)and the spectral shape variation of different truncated diameters.Z_(DR) is a sensitive function to variation of DSD of the spectra.The effects of shape variation of DSD on rainfall can be deduced with a(Z_H,Z_(DR))dual-parameter technique,which is effective to improve the accuracy of rainfall measurement and is of the priority of identifying large particles of hydrometeors(especially hailstone)from the rain region.展开更多
基金The authors gratefully acknowledge support from the National Natural Science Foundation of China(51576130,51327803)the Basic Research Program of Major Projects for Aeronautical and Gas Turbines(2017-V-0016-0069)the Educational Development Foundation of Shanghai Municipal Education Commission(14CG46).
文摘Depth from defocus is one technology for depth estimation.We estimate particle depth information from two defocused images captured simultaneously by two coaxial cameras with different imaging distances.The images are processed with the Fourier transform to obtain the characteristic parameter(i.e.,the standard deviation of the relative blur kernel of these two defocused images).First,we theoretically analyze the functional relationship between the object depth and the standard deviation or variation of the relative blur kernel.Then,we verify the relationship experimentally.We analyze the influence of particle size,window size and image noise on the calibration curves using both numerical simulations and experiments.We obtain the depth range and accuracy of this measurement system experimentally.For the verification experiments,we use a sample of glass microbeads and the irregularly-shaped dust particles on a microscope slide.Both of these experiments present a suitable depth measurement result.Finally,we apply the measuring system to the depth estimation of drops from a small anti-fogging spray.The results show that our system and image processing algorithm are robust for different types of particles,facilitating the in-line three-dimensional positioning of particles.
文摘Scientific workflows have gained the emerging attention in sophisti-cated large-scale scientific problem-solving environments.The pay-per-use model of cloud,its scalability and dynamic deployment enables it suited for executing scientific workflow applications.Since the cloud is not a utopian environment,failures are inevitable that may result in experiencingfluctuations in the delivered performance.Though a single task failure occurs in workflow based applications,due to its task dependency nature,the reliability of the overall system will be affected drastically.Hence rather than reactive fault-tolerant approaches,proactive measures are vital in scientific workflows.This work puts forth an attempt to con-centrate on the exploration issue of structuring a nature inspired metaheuristics-Intelligent Water Drops Algorithm(IWDA)combined with an efficient machine learning approach-Support Vector Regression(SVR)for task failure prognostica-tion which facilitates proactive fault-tolerance in the scheduling of scientific workflow applications.The failure prediction models in this study have been implemented through SVR-based machine learning approaches and the precision accuracy of prediction is optimized by IWDA and several performance metrics were evaluated on various benchmark workflows.The experimental results prove that the proposed proactive fault-tolerant approach performs better compared with the other existing techniques.
基金National Disaster Reduction Foundation(96-020-01-07)of China,KZ 951-B1-405 Western Brilliance of the Chinese Academy of Sciences.
文摘In this paper,it is shown that the differential reflectivity Z_(DR) is related to drop size distribution(DSD)and the spectral shape variation of different truncated diameters.Z_(DR) is a sensitive function to variation of DSD of the spectra.The effects of shape variation of DSD on rainfall can be deduced with a(Z_H,Z_(DR))dual-parameter technique,which is effective to improve the accuracy of rainfall measurement and is of the priority of identifying large particles of hydrometeors(especially hailstone)from the rain region.
