Natural frequency of beams with embedded piezoelectric sensors and actuators

A mathematical model is developed to study the natural frequency of beams with embedded piezoelectric sensors and actuators. The piezoelectric sensors/actuators in a non-piezoelectric matrix (host beam) are analyzed as two inhomogeneity problems by using Eshelby’s equivalent inclusion method. The natural frequency of the beam is determined from the variational principle in Rayleigh quotient form, which is expressed as functions of the elastic strain energy and dielectric energy of the piezoelectric sensors/actuators. The Euler-Bernoulli beam theory and Rayleigh-Ritz approximation technique are used in the present analysis. Parametric studies show that the size, volume fraction and location of the piezoelectric inclusions significantly influence the natural frequency of the beam

Bose-Einstein condensation in linear sigma model at Hartree and large N approximation

The BEC of charged pions is investigated in the framework of O(4) linear sigma model. By using Cornwall-Jackiw-Tomboulis formalism, we have derived the gap equations for the effective masses of the mesons at finite temperature and finite isospin density. The BEC is discussed in chiral limit and non-chiral limit at Hartree approximation and also at large N approximation.Comment: 11 pages, 9 figure

Multispectral Deep Neural Networks for Pedestrian Detection

Multispectral pedestrian detection is essential for around-the-clock applications, e.g., surveillance and autonomous driving. We deeply analyze Faster R-CNN for multispectral pedestrian detection task and then model it into a convolutional network (ConvNet) fusion problem. Further, we discover that ConvNet-based pedestrian detectors trained by color or thermal images separately provide complementary information in discriminating human instances. Thus there is a large potential to improve pedestrian detection by using color and thermal images in DNNs simultaneously. We carefully design four ConvNet fusion architectures that integrate two-branch ConvNets on different DNNs stages, all of which yield better performance compared with the baseline detector. Our experimental results on KAIST pedestrian benchmark show that the Halfway Fusion model that performs fusion on the middle-level convolutional features outperforms the baseline method by 11% and yields a missing rate 3.5% lower than the other proposed architectures.Comment: 13 pages, 8 figures, BMVC 2016 ora