Process modeling and optimizing control based on sparse nonuniformly sampled data

In this paper, a process modeling and related optimizing control for nonuniformly sampled （NUS） systems are addressed. By using a proposed nonuniform integration filter and subspace method estimation, an identification method of NUS systems is developed, based on which either an output soft sensor or a hidden state estimator is developed. The optimizing control is implemented by replacing the sparsely-mea- sured/immeasurable variable with the estimated one. Examples of optimizing control problem are given. The proposed optimizing control strategy in the simulation examples is verified to be very effeetive.

Sparse antenna array design methodologies:A review

Designing a sparse array with reduced transmit/receive modules(TRMs)is vital for some applications where the antenna system’s size,weight,allowed operating space,and cost are limited.Sparse arrays exhibit distinct architectures,roughly classified into three categories:Thinned arrays,nonuniformly spaced arrays,and clustered arrays.While numerous advanced synthesis methods have been presented for the three types of sparse arrays in recent years,a comprehensive review of the latest development in sparse array synthesis is lacking.This work aims to fill this gap by thoroughly summarizing these techniques.The study includes synthesis examples to facilitate a comparative analysis of different techniques in terms of both accuracy and efficiency.Thus,this review is intended to assist researchers and engineers in related fields,offering a clear understanding of the development and distinctions among sparse array synthesis techniques.

Process data compression based on recursive identification of nonuniformly sampled systems

A recursive identification method is proposed to obtain continuous-time state-space models in systems with nonuniformly sampled （NUS） data. Due to the nonuniform sampling feature, the time interval from one recursion step to the next varies and the parameter is always updated partially at each step. Furthermore, this identification method is applied to form a combined data compression method in NUS processes. The data to be compressed are first classified with respect to a series of potentially existing （possibly time-varying） models, and then modeled by the NUS identification method. The model parameters are stored instead of the identification output data, which makes the first compression. Subsequently, as the second step, the conventional swinging door trending method is carried out on the data from the first step. Numeric results from simulation as well as practical data are given, showing the effectiveness of the proposed identification method and fold increase of compression ratio achieved by the combined data compression method.

Nanofluid Flows Within Porous Enclosures Using Non-Linear Boussinesq Approximation

In this paper,the Galerkin finite element method(FEM)together with the characteristic-based split(CBS)scheme are applied to study the case of the non-linear Boussinesq approximation within sinusoidal heating inclined enclosures filled with a non-Darcy porous media and nanofluids.The enclosure has an inclination angle and its side-walls have varying sinusoidal temperature distributions.The working fluid is a nanofluid that is consisting of water as a based nanofluid and Al2O3 as nanoparticles.The porous medium is modeled using the Brinkman Forchheimer extended Darcy model.The obtained results are analyzed over wide ranges of the non-linear Boussinesq parameter 0≤ζ≤1,the phase deviation 00≤Φ≤1800,the inclination angle 00≤γ≤900,the nanoparticles volume fraction 0%≤φ≤4%,the amplitude ratio 0≤a≤1 and the Rayleigh number 104≤Ra≤106.The results revealed that the average Nusselt number is enhanced by 0.73%,26.46%and 35.42%at Ra=104,105 and 106,respectively,when the non-linearBoussinesq parameter is varied from 0 to 1.In addition,rate of heat transfer in the case of a non-uniformly heating is higher than that of a uniformly heating.Non-linear Boussinesq parameter rises the flow speed and heat transfer in an enclosure.Phase deviation makes clear changes on the isotherms and heat transfer rate on the right wall of an enclosure.An inclination angle varies the flow speed and it has a slight effect on heat transfer in an enclosure.

Multi-channel sampling theorems for band-limited signals with fractional Fourier transform

Multi-channel sampling for band-limited signals is fundamental in the theory of multi-channel parallel A/D environment and multiplexing wireless communication environment. As the fractional Fourier transform has been found wide applications in signal processing fields, it is necessary to consider the multi-channel sampling theorem based on the fractional Fourier transform. In this paper, the multi-channel sampling theorem for the fractional band-limited signal is firstly proposed, which is the generalization of the well-known sampling theorem for the fractional Fourier transform. Since the periodic nonuniformly sampled signal in the fractional Fourier domain has valuable applications, the reconstruction expression for the periodic nonuniformly sampled signal has been then obtained by using the derived multi-channel sampling theorem and the specific space-shifting and phase-shifting properties of the fractional Fourier transform. Moreover, by designing different fractional Fourier filters, we can obtain reconstruction methods for other sampling strategies.

The Lyapunov exponents of C^1 hyperbolic systems

Let f be a C 1 diffeomorphisim of smooth Riemannian manifold and preserve a hyperbolic ergodic measure μ. We prove that if the Osledec splitting is dominated, then the Lyapunov exponents of μ can be approximated by the exponents of atomic measures on hyperbolic periodic orbits.