Robust design optimization (RDO) of large-scale engineering systems is computationally intensive and requires significant CPU time. Considerable computational effort still required within conventional meta-model assisted RDO frameworks. The primary objective this article to minimize further the requirements by developing a global two-layered approximation based technique. in inner layer approxi...

We study invariant random subgroups (IRSs) of semidirect products G = AoΓ. In particular, we characterize all IRSs of parabolic subgroups of SLd(R), and show that all ergodic IRSs of Rdo SLd(R) are either of the form RdoK for some IRS of SLd(R), or are induced from IRSs of Λo SL(Λ), where Λ < Rd is a lattice.

A H.264/AVC encoder can incorporate many coding schemes, such as rate-distortion optimization (RDO), into its design to improve its compression performance, dramatically raising computational complexity. With the H.264/AVC RDO encoder, computation time is primarily spent calculating the rate-distortion cost in choosing the optimal coding mode for both inter and intra coding modes. Parallel comp...

The ORC technology is subject to manifold sources of uncertainty that can have a severe impact on the thermodynamic and economic efficiency plant components, particularly when system operated at off-design conditions. In this contribution we focus development turbines with stable performance under uncertainty: novel multi-fidelity robust design optimization (RDO) strategy used first nozzle an t...

A permanent magnet synchronous motor (PMSM) is a crucial device for power conversion in an energy system. The cogging torque of the PMSM output characteristic, robustness which affects operational reliability Therefore, robust design optimization (RDO) has aroused widespread concern. There are several challenges designing PMSM. In particular, some parameters contain repetitive units, and finite...

This 6.1 project is part of a long-term effort to identify and solve the major challenges of parameterizing the impacts of physical processes on atmospheric predictions extending out to seasonal time scales. Achievement of this goal will represent a significant step towards the development of an operational global earth system model targeted by the national Earth System Prediction CapabilityRes...

Optimization plays a pivotal role in computer graphics and vision. Learning-based optimization algorithms have emerged as powerful technique for solving problems with robustness accuracy because it learns gradients from data without calculating the Jacobian Hessian matrices. The key aspect of is least-squares method, which formulates general parametrized model unconstrained optimizations makes ...

Prediction in HEVC exploits the redundant information fame to improve compression efficiency. The computational complexity of prediction is comparatively high as it recursively calculates depth by comparing rate-distortion optimization cost (RDO) exhaustively. deep learning technology has shown a good mark this area compared traditional signal processing because its content-based analysis and a...