In this paper, an improved high-order model-free adaptive iterative control (IHOMFAILC) method for a class of nonlinear discrete-time systems is proposed based on the compact format dynamic linearization method. This adds differential tracking error in criteria function to compensate effect random disturbance. Meanwhile, estimation algorithm used estimate value pseudo partial derivative (PPD), ...

Quantum entanglement is a particularly useful characterization of topological orders which lack conventional order parameters. In this work, we study the in topologically ordered states between two arbitrary spatial regions, using distinct mixed-state measures: so-called computable cross-norm or realignment (CCNR) negativity, and more well-known partial-transpose (PT) negativity. We first gener...

The purpose of the research presented in this paper has been to characterize flexibility in lean mixed model assembly lines through exploring mechanisms used to achieve flexibility. The study combines a literature review and a multiple case study in two manufacturing companies. Scenarios of volume, mix and operation flexibility, as well as flexibility to introduce or remove products were studie...

Uncertainty lower bounds for parameter estimations associated with a unitary family of mixed-state density matrices are obtained by embedding the space in Hilbert square-root matrices. In Hilbert-space setup measure uncertainty is given skew information second kind, while bound Wigner-Yanase conjugate observable. Higher-order corrections to determined higher-order quantum moments; expressions t...

The use of the high-order Flux Reconstruction (FR) spatial discretization scheme for LES on unstructured meshes is investigated. Simulations of the compressible Taylor-Green vortex at Re = 1600 demonstrate that the FR scheme has low numerical dissipation and accurately reproduces the turbulent energy cascade at low resolution, making it ideal for high-order LES. To permit the use of subgrid-sca...

The traditional k-prototypes algorithm is well versed in clustering data with mixed numeric and categorical attributes, while it is limited to complete data. In order to handle incomplete data set with missing values, an improved k-prototypes algorithm is proposed in this paper, which employs a new dissimilarity measure for incomplete data set with mixed numeric and categorical attributes and a...