Mitigating ground effect on mini quadcopters with model reference adaptive control

Mitigating ground effect becomes a big challenge for autonomous aerial vehicles when they are flying in close proximity to the ground. This paper aims to develop a precise model of ground effect on mini quadcopters, provide an advanced control algorithm to counter the model uncertainty and, as a result, improves the command tracking performance when the vehicle is in the ground effect region. The mathematical model of ground effect has been established through a series of experiments and validated by a flight test. The experiments show that the total thrust generated by rotors increases linearly as the vehicle gets closer to the ground, which is different from the commonly-used ground effect model for a single rotor vehicle. In addition, the model switches from a piecewise linear to a quadratic function when the rotor to rotor distance is increased. A control architecture that utilizes the model reference adaptive controller (MRAC) has also been designed, where MRAC is added to the altitude loop. The performance of the proposed control algorithm has been evaluated through a set of flight tests on a mini quadcopter platform and compared with a traditional proportional–integral–derivative (PID) controller. The results demonstrate that MRAC dramatically improves the tracking performance of altitude command and can reduce the rise time by 80 % under the ground effect

Electronic Interface Reconstruction at Polar-Nonpolar Mott Insulator Heterojunctions

We report on a theoretical study of the electronic interface reconstruction (EIR) induced by polarity discontinuity at a heterojunction between a polar and a nonpolar Mott insulators, and of the two-dimensional strongly-correlated electron systems (2DSCESs) which accompany the reconstruction. We derive an expression for the minimum number of polar layers required to drive the EIR, and discuss key parameters of the heterojunction system which control 2DSCES properties. The role of strong correlations in enhancing confinement at the interface is emphasized.Comment: 7 pages, 6 figures, some typos correcte

Direction-Projection-Permutation for High Dimensional Hypothesis Tests

Motivated by the prevalence of high dimensional low sample size datasets in modern statistical applications, we propose a general nonparametric framework, Direction-Projection-Permutation (DiProPerm), for testing high dimensional hypotheses. The method is aimed at rigorous testing of whether lower dimensional visual differences are statistically significant. Theoretical analysis under the non-classical asymptotic regime of dimension going to infinity for fixed sample size reveals that certain natural variations of DiProPerm can have very different behaviors. An empirical power study both confirms the theoretical results and suggests DiProPerm is a powerful test in many settings. Finally DiProPerm is applied to a high dimensional gene expression dataset

Microwave Instability Near Transition Energy

Monte Carlo simulation for the microwave instability agrees with analytic calculation solving the Vlasov equation, provided that bunch shape distortion due to coupling is taken into account. 9 refs., 2 figs

Scaling Behavior of the Activated Conductivity in a Quantum Hall Liquid

We propose a scaling model for the universal longitudinal conductivity near the mobility edge for the integer quantum Hall liquid. We fit our model with available experimental data on exponentially activated conductance near the Landau level tails in the integer quantum Hall regime. We obtain quantitative agreement between our scaling model and the experimental data over a wide temperature and magnetic field range.Comment: 9 pages, Latex, 2 figures (available upon request), #phd0

Data augmentation and semi-supervised learning for deep neural networks-based text classifier

User feedback is essential for understanding user needs. In this paper, we use free-text obtained from a survey on sleep-related issues to build a deep neural networks-based text classifier. However, to train the deep neural networks model, a lot of labelled data is needed. To reduce manual data labelling, we propose a method which is a combination of data augmentation and pseudo-labelling: data augmentation is applied to labelled data to increase the size of the initial train set and then the trained model is used to annotate unlabelled data with pseudo-labels. The result shows that the model with the data augmentation achieves macro-averaged f1 score of 65.2% while using 4,300 training data, whereas the model without data augmentation achieves macro-averaged f1 score of 68.2% with around 14,000 training data. Furthermore, with the combination of pseudo-labelling, the model achieves macro-averaged f1 score of 62.7% with only using 1,400 training data with labels. In other words, with the proposed method we can reduce the amount of labelled data for training while achieving relatively good performance