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At present, almost all the cocking work is completed in smoke-filled kitchen, which is a time-consuming and laborious work. However, with the development of automatic and robotics technology, how to apply the relevant technology to the field of cooking has become increasingly urgent. The purpose of this paper is to apply quick-return mechanisms to automatic cooking robot, so as to obtain the realization of the movement of reversing pot. And the main three parts of the study are: the analysis of several typical quick-return mechanisms and the description of their possibility in realizing the movement of reversing pot; dynamic simulation analysis of some kinds of mechanisms, which can cover the basic requirements of the movement of reversing pot, is done so as to determine their functions in realizing the movement of reversing pot and their different characteristics; through taking the typical Sharper(quick-return swing guide) agency as an example and using Pro/E and Adams to realize the dynamic simulation and analysis, the key parameters of such bodies is selected and can provide the basis for the further study of automatic cooking robot.

The evoked spike discharges of a neuron depend critically on the recent history of its electrical activity. A well-known example is the phenomenon of spike-frequency adaptation that is a commonly observed property of neurons. In this paper, using a leaky integrate-and-fire model that includes an adaptation current, we propose an event-driven strategy to simulate integrate-and-fire models with spike-frequency adaptation. Such approach is more precise than traditional clock-driven numerical integration approach because the timing of spikes is treated exactly. In experiments, using event-driven and clock-driven strategies we simulated the adaptation time course of single neuron and the random network with spike-timing dependent plasticity, the results indicate that (1) the temporal precision of spiking events impacts on neuronal dynamics of single as well as network in the different simulation strategies and (2) the simulation time scales linearly with the total number of spiking events in the event-driven simulation strategies.

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