@@ -79,4 +79,8 @@ We acquired a rapidly preserved human surgical sample from the temporal lobe of
This paper presents a balance control technique for a novel wheel-legged robot. We first derive a dynamic model of the robot and then apply a linear feedback controller based on output regulation and linear quadratic regulator (LQR) methods to maintain the standing of the robot on the ground without moving backward and forward mightily. To take into account nonlinearities of the model and obtain a large domain of stability, a nonlinear controller based on the interconnection and damping assignment - passivity-based control (IDA-PBC) method is exploited to control the robot in more general scenarios. Physical experiments are performed with various control tasks. Experimental results demonstrate that the proposed linear output regulator can maintain the standing of the robot, while the proposed nonlinear controller can balance the robot under an initial starting angle far away from the equilibrium point, or under a changing robot height.
## Very Deep Convolutional Networks for Large-Scale Image Recognition
In this work we investigate the effect of the convolutional network *depth on its accuracy* in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of *increasing depth using an architecture with very small (3x3) convolution filters*, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to *16-19 weight layers*. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that *our representations generalise well* to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of *deep visual representations* in computer vision.
