Low-field NMR inversion based on low-rank and sparsity restraint of relaxation spectra

In this paper,we proposed a novel method for low-field nuclear magnetic resonance(NMR)inversion based on low-rank and sparsity restraint(LRSR)of relaxation spectra,with which high quality construction is made possible for one-and two-dimensional low-field and low signal to noise ratio NMR data.In this method,the low-rank and sparsity restraints are introduced into the objective function instead of the smoothing term.The low-rank features in relaxation spectra are extracted to ensure the local characteristics and morphology of spectra.The sparsity and residual term are contributed to the resolution and precision of spectra,with the elimination of the redundant relaxation components.Optimization process of the objective function is designed with alternating direction method of multiples,in which the objective function is decomposed into three subproblems to be independently solved.The optimum solution can be obtained by alternating iteration and updating process.At first,numerical simulations are conducted on synthetic echo data with different signal-to-noise ratios,to optimize the desirable regularization parameters and verify the feasibility and effectiveness of proposed method.Then,NMR experiments on solutions and artificial sandstone samples are conducted and analyzed,which validates the robustness and reliability of the proposed method.The results from simulations and experiments have demonstrated that the suggested method has unique advantages for improving the resolution of relaxation spectra and enhancing the ability of fluid quantitative identification.

Hardware-in-loop adaptive neural control for a tiltable V-tail morphing aircraft

This paper proposes an adaptive neural control(ANC)method for the coupled nonlinear model of a novel type of embedded surface morphing aircraft which has a tiltable V-tail.A nonlinear model with sixdegrees-of-freedom is established.The first-order sliding mode differentiator(FSMD)is applied to the control scheme to avoid the problem of“differential explosion”.Radial basis function neural networks are introduced to estimate the uncertainty and external disturbance of the model,and an ANC controller is proposed based on this design idea.The stability of the proposed ANC controller is proved using Lyapunov theory,and the tracking error of the closed-loop system is semi-globally uniformly bounded.The effectiveness and robustness of the proposed method are verified by numerical simulations and hardware-in-the-loop(HIL)simulations.

Simulation Analyses of Two On-Ramp Lane Arrangements

Ramps are vital pieces of infrastructure connecting city traffic networks to freeways.The performance of a ramp is to some extent determined by the on-ramp lane arrangement.In this paper,our primary aim is to evaluate the performance in terms of travel time and vehicle emissions for two on-ramp lane arrangements:added lane and zip merging.We estimate the travel time and CO_(2) emissions on the basis of the speed,and acceleration of vehicles in accordance with the improved comprehensive modal emission model(CMEM),and then analyse the impacts of traffic volume and heavy goods vehicles(HGVs)on travel time and emissions.The impacts of main road traffic flow on travel time and emissions for the two on-ramp lane arrangements are analysed under scenarios with traffic volumes of 800,1000,1200,1400,1600 and 1800 vehs/h/lane.Meanwhile,the relationships between travel time,emissions and various proportions of HGVs(2%,4%,6%,8%and 10%)for both on-ramp lane arrangements are evaluated as well.We eventually present emission contour charts for the two onramp lane arrangements based on the possible combinations of traffic volumes and HGV percentages.