Numerical Simulation Study of Vibration Characteristics of Cantilever Traffic Signal Support Structure under Wind Environment

Computational fluid dynamics(CFD)and the finite element method(FEM)are used to investigate the wind-driven dynamic response of cantilever traffic signal support structures as a whole.By building a finite element model with the same scale as the actual structure and performing modal analysis,a preliminary understanding of the dynamic properties of the structure is obtained.Based on the two-way fluid-structure coupling calculation method,the wind vibration response of the structure under different incoming flow conditions is calculated,and the vibration characteristics of the structure are analyzed through the displacement time course data of the structure in the crosswind direction and along-wind direction.The results show that the maximum response of the structure increases gradually with the increase of wind speed under 90°wind direction angle,showing a vibration dispersion state,and the vibration response characteristics are following the vibration phenomenon of galloping;under 270°wind direction angle,the maximum displacement response of the structure occurs at the lower wind speed of 5 and 6m/s,and the vibration generated by the structure is vortex vibration at this time;the displacement response of the structure in along-wind direction increaseswith the increase of wind speed.The along-wind displacement response of the structure will increase with increasing wind speed,and the effective wind area and shape characteristics of the structurewill also affect the vibration response of the structure.

Robust signal recovery algorithm for structured perturbation compressive sensing

It is understood that the sparse signal recovery with a standard compressive sensing（CS） strategy requires the measurement matrix known as a priori. The measurement matrix is, however, often perturbed in a practical application.In order to handle such a case, an optimization problem by exploiting the sparsity characteristics of both the perturbations and signals is formulated. An algorithm named as the sparse perturbation signal recovery algorithm（SPSRA） is then proposed to solve the formulated optimization problem. The analytical results show that our SPSRA can simultaneously recover the signal and perturbation vectors by an alternative iteration way, while the convergence of the SPSRA is also analytically given and guaranteed. Moreover, the support patterns of the sparse signal and structured perturbation shown are the same and can be exploited to improve the estimation accuracy and reduce the computation complexity of the algorithm. The numerical simulation results verify the effectiveness of analytical ones.

Authenticating GNSS civilian signals:a survey

Civilian services of Global Navigation Satellite System are threatened by spoofng attacks since it is hard to determine the authenticity of a navigation signal with a detailed structure open to the public.Signal authentication efectively protects the security of the signal by attaching unforgeable information to one or several elements of the signal.Receivers can verify the authenticity of the signal by extracting and validating this information.Developing good signal authentication schemes requires understanding possible spoofng modes,signal element specialty,and performance evaluation methods.This paper is an overview of navigation signal authentication,where the theories and reported approaches are described in detail.A design/performance matrix that demonstrates the advantages and defects of the signal element and its authentication design is summarized.Recommendations are proposed to improve the robustness,security,efciency,and implementation hardness for future designs of navigation signal authentication.

Knowledge-based potentials in bioinformatics: From a physicist's viewpoint

Biological raw data are growing exponentially, providing a large amount of information on what life is. It is believed that potential functions and the rules governing protein behaviors can be revealed from analysis on known native structures of proteins. Many knowledge-based potentials for proteins have been proposed. Contrary to most existing review articles which mainly describe technical details and applications of various potential models, the main foci for the discussion here are ideas and concepts involving the construction of potentials, including the relation between free energy and energy, the additivity of potentials of mean force and some key issues in potential construction. Sequence analysis is briefly viewed from an energetic viewpoint.

Super-Resolution Image Reconstruction Based on an Improved Maximum a Posteriori Algorithm

A maximum a posteriori（ MAP） algorithm is proposed to improve the accuracy of super resolution（ SR） reconstruction in traditional methods. The algorithm applies both joints image registration and SR reconstruction in the framework,but separates them in the process of iteratiion. Firstly,we estimate the shifting parameters through two lowresolution（ LR） images and use the parameters to reconstruct initial HR images. Then,we update the shifting parameters using HR images. The aforementioned steps are repeated until the ideal HR images are obtained. The metrics such as PSNR and SSIM are used to fully evaluate the quality of the reconstructed image. Experimental results indicate that the proposed method can enhance image resolution efficiently.

Convex Reconstruction of Structured Matrix Signals from Linear Measurements:Theoretical Results

The problem of reconstructing n-by-n structured matrix signal X=(x1,...,xn)via convex optimization is investigated,where each column xj is a vector of s-sparsity and all columns have the same l1-norm value.In this paper,the convex programming problem was solved with noise-free or noisy measurements.The uniform sufficient conditions were established which are very close to necessary conditions and non-uniform conditions were also discussed.In addition,stronger conditions were investigated to guarantee the reconstructed signal’s support stability,sign stability and approximation-error robustness.Moreover,with the convex geometric approach in random measurement setting,one of the critical ingredients in this contribution is to estimate the related widths’bounds in case of Gaussian and non-Gaussian distributions.These bounds were explicitly controlled by signal’s structural parameters r and s which determined matrix signal’s column-wise sparsity and l1-column-flatness respectively.This paper provides a relatively complete theory on column-wise sparse and l1-column-flat matrix signal reconstruction,as well as a heuristic foundation for dealing with more complicated high-order tensor signals in,e.g.,statistical big data analysis and related data-intensive applications.