Cam profiles play an important part in the performance of cam mechanisms. Syntheses of cam profile designs and dynamics of cam designs are studied at first. Then, a cam profile design optimization model based on the s...Cam profiles play an important part in the performance of cam mechanisms. Syntheses of cam profile designs and dynamics of cam designs are studied at first. Then, a cam profile design optimization model based on the six order classical spline and single DOF(degree of freedom) dynamic model of single-dwell cam mechanisms is developed. And dynamic constraints such as jumps and vibrations of followers are considered. This optimization model, with many advantages such as universalities of applications, conveniences to operations and good performances in improving kinematic and dynamic properties of cam mechanisms, is good except for the discontinuity of jerks at the end knots of cam profiles which will cause vibrations of cam systems. However, the optimization is improved by combining the six order classical spline with general polynomial spline which is the so-called "trade-offs". Finally, improved optimization is proven to have a better performance in designing cam profiles.展开更多
Low-rank matrix recovery is an important problem extensively studied in machine learning, data mining and computer vision communities. A novel method is proposed for low-rank matrix recovery, targeting at higher recov...Low-rank matrix recovery is an important problem extensively studied in machine learning, data mining and computer vision communities. A novel method is proposed for low-rank matrix recovery, targeting at higher recovery accuracy and stronger theoretical guarantee. Specifically, the proposed method is based on a nonconvex optimization model, by solving the low-rank matrix which can be recovered from the noisy observation. To solve the model, an effective algorithm is derived by minimizing over the variables alternately. It is proved theoretically that this algorithm has stronger theoretical guarantee than the existing work. In natural image denoising experiments, the proposed method achieves lower recovery error than the two compared methods. The proposed low-rank matrix recovery method is also applied to solve two real-world problems, i.e., removing noise from verification code and removing watermark from images, in which the images recovered by the proposed method are less noisy than those of the two compared methods.展开更多
文摘Cam profiles play an important part in the performance of cam mechanisms. Syntheses of cam profile designs and dynamics of cam designs are studied at first. Then, a cam profile design optimization model based on the six order classical spline and single DOF(degree of freedom) dynamic model of single-dwell cam mechanisms is developed. And dynamic constraints such as jumps and vibrations of followers are considered. This optimization model, with many advantages such as universalities of applications, conveniences to operations and good performances in improving kinematic and dynamic properties of cam mechanisms, is good except for the discontinuity of jerks at the end knots of cam profiles which will cause vibrations of cam systems. However, the optimization is improved by combining the six order classical spline with general polynomial spline which is the so-called "trade-offs". Finally, improved optimization is proven to have a better performance in designing cam profiles.
基金Projects(61173122,61262032) supported by the National Natural Science Foundation of ChinaProjects(11JJ3067,12JJ2038) supported by the Natural Science Foundation of Hunan Province,China
文摘Low-rank matrix recovery is an important problem extensively studied in machine learning, data mining and computer vision communities. A novel method is proposed for low-rank matrix recovery, targeting at higher recovery accuracy and stronger theoretical guarantee. Specifically, the proposed method is based on a nonconvex optimization model, by solving the low-rank matrix which can be recovered from the noisy observation. To solve the model, an effective algorithm is derived by minimizing over the variables alternately. It is proved theoretically that this algorithm has stronger theoretical guarantee than the existing work. In natural image denoising experiments, the proposed method achieves lower recovery error than the two compared methods. The proposed low-rank matrix recovery method is also applied to solve two real-world problems, i.e., removing noise from verification code and removing watermark from images, in which the images recovered by the proposed method are less noisy than those of the two compared methods.
