This paper analyzes the geometric quantities that remain unchanged during parallel mapping （i.e., mapping from a reference curved surface to a parallel surface with identical normal direction）. The second gradient o...This paper analyzes the geometric quantities that remain unchanged during parallel mapping （i.e., mapping from a reference curved surface to a parallel surface with identical normal direction）. The second gradient operator, the second class of integral theorems, the Gauss-curvature-based integral theorems, and the core property of parallel mapping are used to derive a series of parallel mapping invariants or geometrically conserved quantities. These include not only local mapping invariants but also global mapping invafiants found to exist both in a curved surface and along curves on the curved surface. The parallel mapping invariants are used to identify important transformations between the reference surface and parallel surfaces. These mapping invariants and transformations have potential applications in geometry, physics, biomechanics, and mechanics in which various dynamic processes occur along or between parallel surfaces.展开更多
For the characteristics of intra prediction algorithms, the data dependence and parallelism between intra prediction models are first analyzed. This paper proposes a parallelization method based on dynamic reconfigura...For the characteristics of intra prediction algorithms, the data dependence and parallelism between intra prediction models are first analyzed. This paper proposes a parallelization method based on dynamic reconfigurable array processors provided by the project team, and uses data level parallel(DLP) algorithms in multi-core units. The experimental results show that Y-component of peak signal to noise ratio(Y-PSNR) is improved about 10 dB and the time is saved 63% compared with high-efficiency video coding(HEVC) test model HM10.0. This method can effectively reduce codec time of the video and reduce computational complexity.展开更多
A high-performance predictor for critical unstable generators(CUGs) of power systems is presented in this paper. The predictor is driven by the Map Reduce based parallelized neural networks. Specifically, a group of b...A high-performance predictor for critical unstable generators(CUGs) of power systems is presented in this paper. The predictor is driven by the Map Reduce based parallelized neural networks. Specifically, a group of back propagation neural networks(BPNNs), fed by massive response trajectories data, are efficiently organized and concurrently trained in Hadoop to identify dynamic behavior of individual generator. Rather than simply classifying global stability of power systems, the presented approach is able to distinguish unstable generators accurately with a few cycles of synchronized trajectories after fault clearing,enabling more in-depth emergency awareness based on wide-area implementation. In addition, the technique is of rich scalability due to Hadoop framework, which can be deployed in the control centers as a high-performance computing infrastructure for real-time instability alert.Numerical examples are studied using NPCC 48-machines test system and a realistic power system of China.展开更多
基金Supported by the National Natural Science Foundation of China(Nos.10572076 and 10872114)the Natural Science Foundation of Jiangsu Province,China (No.BK2008370)
文摘This paper analyzes the geometric quantities that remain unchanged during parallel mapping （i.e., mapping from a reference curved surface to a parallel surface with identical normal direction）. The second gradient operator, the second class of integral theorems, the Gauss-curvature-based integral theorems, and the core property of parallel mapping are used to derive a series of parallel mapping invariants or geometrically conserved quantities. These include not only local mapping invariants but also global mapping invafiants found to exist both in a curved surface and along curves on the curved surface. The parallel mapping invariants are used to identify important transformations between the reference surface and parallel surfaces. These mapping invariants and transformations have potential applications in geometry, physics, biomechanics, and mechanics in which various dynamic processes occur along or between parallel surfaces.
基金Supported by the National Natural Science Foundation of China(No.61772417,61634004,61602377,61272120)the Shaanxi Provincial Co-ordination Innovation Project of Science and Technology(No.2016KTZDGY02-04-02)the Shaanxi Provincial key R&D plan(No.2017GY-060)
文摘For the characteristics of intra prediction algorithms, the data dependence and parallelism between intra prediction models are first analyzed. This paper proposes a parallelization method based on dynamic reconfigurable array processors provided by the project team, and uses data level parallel(DLP) algorithms in multi-core units. The experimental results show that Y-component of peak signal to noise ratio(Y-PSNR) is improved about 10 dB and the time is saved 63% compared with high-efficiency video coding(HEVC) test model HM10.0. This method can effectively reduce codec time of the video and reduce computational complexity.
文摘A high-performance predictor for critical unstable generators(CUGs) of power systems is presented in this paper. The predictor is driven by the Map Reduce based parallelized neural networks. Specifically, a group of back propagation neural networks(BPNNs), fed by massive response trajectories data, are efficiently organized and concurrently trained in Hadoop to identify dynamic behavior of individual generator. Rather than simply classifying global stability of power systems, the presented approach is able to distinguish unstable generators accurately with a few cycles of synchronized trajectories after fault clearing,enabling more in-depth emergency awareness based on wide-area implementation. In addition, the technique is of rich scalability due to Hadoop framework, which can be deployed in the control centers as a high-performance computing infrastructure for real-time instability alert.Numerical examples are studied using NPCC 48-machines test system and a realistic power system of China.
