A controller for a miniature intra-aortic ventricular assist device.

Two control algorithms have been developed for a minimally invasive axial-flow ventricular assist device (VAD) for placement in the descending aorta. The purpose of the device is to offload the left ventricle and to augment lower body perfusion in patients with moderate congestive heart failure. The VAD consists of an intra-aortic impeller with a built-in permanent magnet rotor and an extra-aortic stator. The control algorithms, which use pressure readings upstream and downstream of the VAD to determine the pump status, have been tested in a mock circulatory system under two conditions, namely with or without afterload sensitivity. The results give an insight into controller design for an intra-aortic blood pump working in series with the heart.

Integrated avalanche diode for 600 v Trench IGBT over-voltage protection

Avalanche multiplication has been one of the major destructive failure mechanisms in IGBTs; in order to avoid operating an IGBT under abnormal conditions, it is desirable to develop peripheral protecting circuits monolithically integrated without compromising the operation and performance of the IGBT. In this paper, a monolithically integrated avalanche diode (D av) for 600V Trench IGBT over-voltage protection is proposed. The mix-mode transient simulation proves the clamping capability of the D av when the IGBT is experiencing over-voltage stress in unclamped inductive switching (UIS) test. The spread of avalanche energy, which prevents hot-spot formation, through the help of the avalanche diode feeding back a large fraction of the avalanche current to a gate resistance (R G) is also explained. © 2011 IEEE.

盘qu属鱼类口吸盘的形态分化及其意义

具大、小吸盘的两个类群是盘qu鱼类在演化进程中分化成的两个自然类群, 它们为适应不同的栖息环境, 吸盘的微观结构和须的长短等进一步分化, 分化途径在两个类群各不相同。图版2参9

Statistical user simulation with a hidden agenda

Recent work in the area of probabilistic user simulation for training statistical dialogue managers has investigated a new agenda-based user model and presented preliminary experiments with a handcrafted model parameter set. Training the model on dialogue data is an important next step, but non-trivial since the user agenda states are not observable in data and the space of possible states and state transitions is intractably large. This paper presents a summary-space mapping which greatly reduces the number of state transitions and introduces a tree-based method for representing the space of possible agenda state sequences. Treating the user agenda as a hidden variable, the forward/backward algorithm can then be successfully applied to iteratively estimate the model parameters on dialogue data. © 2007 Association for Computational Linguistics.

On-line policy optimisation of spoken dialogue systems via live interaction with human subjects

Statistical dialogue models have required a large number of dialogues to optimise the dialogue policy, relying on the use of a simulated user. This results in a mismatch between training and live conditions, and significant development costs for the simulator thereby mitigating many of the claimed benefits of such models. Recent work on Gaussian process reinforcement learning, has shown that learning can be substantially accelerated. This paper reports on an experiment to learn a policy for a real-world task directly from human interaction using rewards provided by users. It shows that a usable policy can be learnt in just a few hundred dialogues without needing a user simulator and, using a learning strategy that reduces the risk of taking bad actions. The paper also investigates adaptation behaviour when the system continues learning for several thousand dialogues and highlights the need for robustness to noisy rewards. © 2011 IEEE.