Clinical Detection and Feature Analysis on Neuro Signals

Research on neuro signals is challenging and significative in modern natural science. By clinical experiment, signals from three main nerves (median nerve, radial nerve and ulnar nerve) are successfully detected and recorded without any infection. Further analysis on their features under different movements, their mechanics and correlations in dominating actions are also performed. The original discovery and first-hand materials make it possible for developing practical neuro-prosthesis.     

新试验水池数据采集系统的实现

上海船舶运输科学研究所2号船舶拖曳试验水池的数据采集系统已老化,随目前试验要求的不断提高,需要一个与新改造的试验水池能够配套的精确测量和可视化的采集系统。改造后的新系统选用National Instruments公司数据采集卡,不间断采集阻力、扭矩等参数,利用通信传输采集车速和转速数据;用VB编程工具开发数据采集系统的数据处理和可视化操作界面。本系统已成功应用于2号船舶拖曳试验水池,取得了可观的经济效益。     

Optimization for PID controller of cryogenic ground support equipment based on cooperative random learning particle swarm optimization

Cryogenic ground support equipment (CGSE) is an important part of a famous particle physics experiment — AMS-02. In this paper a design method which optimizes PID parameters of CGSE control system via the particle swarm optimization (PSO) algorithm is presented. Firstly, an improved version of the original PSO, cooperative random learning particle swarm optimization (CRPSO), is put forward to enhance the performance of the conventional PSO. Secondly, the way of finding PID coefficient will be studied by using this algorithm. Finally, the experimental results and practical works demonstrate that the CRPSO-PID controller achieves a good performance.     

Control system design and test for cryogenic ground support equipment

The cryogenic ground support equipment (CGSE) is an important part of Alpha Magnetic Spectrometer-02 (AMS-02) experiment which is a particle physics experiment. CGSE is used to cool down the superconducting magnet of AMS-02 to its operating temperature of 1.8K and to fill the magnet helium tank with superfluid helium. This paper introduces the control system of CGSE and presents the performance of the system.     

RSOFCPN: CONTROL SYSTEM STRUCTURE AND ALGORITHM DESIGN

IntroductionThe learning approaches of neural network canusually be distinguished into three classes:su-pervised learning,unsupervised learning and re-inforcement learning.The supervised learningscheme has the highest efficiency but requiresthe learning systems to provide with desired out-puts.Obviously,it is very difficultin many cas-es.Unsupervised learning methods constructin-ternal models at capture regularities in their in-puts without receiving any additional informa-tion.Therefore,its …