Bundle adjustment is a camera and point refinement technique in a 3D scene reconstruction pipeline. The camera parameters and the 3D points are refined by minimizing the difference between computed projection and obse...Bundle adjustment is a camera and point refinement technique in a 3D scene reconstruction pipeline. The camera parameters and the 3D points are refined by minimizing the difference between computed projection and observed projection of the image points formulated as a non-linear least-square problem. Levenberg-Marquardt method is used to solve the non-linear least-square problem. Solving the non-linear least-square problem is computationally expensive, proportional to the number of cameras, points, and projections. In this paper, we implement the Bundle Adjustment (BA) algorithm and analyze techniques to improve algorithmic performance by reducing the mean square error. We investigate using an additional radial distortion camera parameter in the BA algorithm and demonstrate better convergence of the mean square error. We also demonstrate the use of explicitly computed analytical derivatives. In addition, we implement the BA algorithm on GPUs using the CUDA parallel programming model to reduce the computational time burden of the BA algorithm. CUDA Streams, atomic operations, and cuBLAS library in the CUDA programming model are proposed, implemented, and demonstrated to improve the performance of the BA algorithm. Our implementation has demonstrated better convergence of the BA algorithm and achieved a speedup of up to 16× on the use of the BA algorithm on various datasets.展开更多
In the present work, the classical Bethe–Weizs?cker(BW) mass formula with five energy terms is revisited and updated. We use the least-squares adjustments on the binding energy of 2497 different nuclides from the las...In the present work, the classical Bethe–Weizs?cker(BW) mass formula with five energy terms is revisited and updated. We use the least-squares adjustments on the binding energy of 2497 different nuclides from the last update of the atomic mass evaluation,AME2016 published in March 2017, to provide a new set of energy coefficients of the mass formula. The obtained set of formula coefficients allowed us to reproduce most of the experimental values of the binding energies for each nucleus with A ≥50. The comparison between the binding energies provided with updated mass formula and those of AME2016 on the one hand, and those of previous works,on the other hand, yields relative errors that oscillate between less than 0.05% and 1.5%. The revisited BW formula is in very good agreement with the experimental data.展开更多
In the paper, we briefly introduce the development and present situation of earthquake insurance in China and foreign countries, and the evaluation of earthquake losses on the basis of seismic risk and structural vuln...In the paper, we briefly introduce the development and present situation of earthquake insurance in China and foreign countries, and the evaluation of earthquake losses on the basis of seismic risk and structural vulnerability analyses. The emphasis is given to the probabilistic density function of earthquake loss adjustment for a single building under the given insurance policy and the overall variance of estimated earthquake losses aggregated from various locations with keen interest in the insurance industries. The correlation coefficient for the damages among single structures in the United States is also introduced to interpret the risk of loss concentration in the earthquake insurance. The paper provides a scientific basis for adjusting earthquake loss and premium rate, and it also provides a useful reference for the application and expansion of earthquake insurance in China.展开更多
文摘Bundle adjustment is a camera and point refinement technique in a 3D scene reconstruction pipeline. The camera parameters and the 3D points are refined by minimizing the difference between computed projection and observed projection of the image points formulated as a non-linear least-square problem. Levenberg-Marquardt method is used to solve the non-linear least-square problem. Solving the non-linear least-square problem is computationally expensive, proportional to the number of cameras, points, and projections. In this paper, we implement the Bundle Adjustment (BA) algorithm and analyze techniques to improve algorithmic performance by reducing the mean square error. We investigate using an additional radial distortion camera parameter in the BA algorithm and demonstrate better convergence of the mean square error. We also demonstrate the use of explicitly computed analytical derivatives. In addition, we implement the BA algorithm on GPUs using the CUDA parallel programming model to reduce the computational time burden of the BA algorithm. CUDA Streams, atomic operations, and cuBLAS library in the CUDA programming model are proposed, implemented, and demonstrated to improve the performance of the BA algorithm. Our implementation has demonstrated better convergence of the BA algorithm and achieved a speedup of up to 16× on the use of the BA algorithm on various datasets.
文摘In the present work, the classical Bethe–Weizs?cker(BW) mass formula with five energy terms is revisited and updated. We use the least-squares adjustments on the binding energy of 2497 different nuclides from the last update of the atomic mass evaluation,AME2016 published in March 2017, to provide a new set of energy coefficients of the mass formula. The obtained set of formula coefficients allowed us to reproduce most of the experimental values of the binding energies for each nucleus with A ≥50. The comparison between the binding energies provided with updated mass formula and those of AME2016 on the one hand, and those of previous works,on the other hand, yields relative errors that oscillate between less than 0.05% and 1.5%. The revisited BW formula is in very good agreement with the experimental data.
基金Key research project of China Earthquake Administration (0303008).
文摘In the paper, we briefly introduce the development and present situation of earthquake insurance in China and foreign countries, and the evaluation of earthquake losses on the basis of seismic risk and structural vulnerability analyses. The emphasis is given to the probabilistic density function of earthquake loss adjustment for a single building under the given insurance policy and the overall variance of estimated earthquake losses aggregated from various locations with keen interest in the insurance industries. The correlation coefficient for the damages among single structures in the United States is also introduced to interpret the risk of loss concentration in the earthquake insurance. The paper provides a scientific basis for adjusting earthquake loss and premium rate, and it also provides a useful reference for the application and expansion of earthquake insurance in China.
