CHARACTERIZATION OF GAMMA-IRRADIATED CRYSTALLINE POLYMER .3. THERMAL-BEHAVIOR OF GAMMA-IRRADIATED POLYAMIDE-1010

Thermal behaviour of gamma-irradiated plain PA1010 and PA1010 containing different amounts of difunctional cross-linking agent BMI was investigated. In DSC endo- and exotherm, it was found that during irradiation, the presence of BMI markedly changes the melting and crystallisation characteristics of PA1010. A supposition that the network of BMI-containing specimens is rather loose in structure was proposed to explain the discrepancy in thermal behaviour between these two kinds of specimens. The supposition was further ascertained by the less brittleness in mechanical property of specimens containing BMI. Besides, the complexity of the thermal behaviour of gamma-irradiated PA1010 was discussed and attributed mainly to the increase in sigma-e, the fold surface free energy of chain fold crystals.

Neural network retrieval of ocean surface parameters from SSM/I data

A new algorithm based on the multiparameter neural network is proposed to retrieve wind speed (WS), sea surface temperature (SST), sea surface air temperature, and relative humidity ( RH) simultaneously over the global oceans from Special Sensor Microwave Imager (SSM/I) observations. The retrieved geophysical parameters are used to estimate the surface latent heat flux and sensible heat flux using a bulk method over the global oceans. The neural network is trained and validated with the matchups of SSM/I overpasses and National Data Buoy Center buoys under both clear and cloudy weather conditions. In addition, the data acquired by the 85.5-GHz channels of SSM/I are used as the input variables of the neural network to improve its performance. The root-mean-square (rms) errors between the estimated WS, SST, sea surface air temperature, and RH from SSM/I observations and the buoy measurements are 1.48 m s(-1), 1.54 degrees C, 1.47 degrees C, and 7.85, respectively. The rms errors between the estimated latent and sensible heat fluxes from SSM/I observations and the Xisha Island ( in the South China Sea) measurements are 3.21 and 30.54 W m(-2), whereas those between the SSM/ I estimates and the buoy data are 4.9 and 37.85 W m(-2), respectively. Both of these errors ( those for WS, SST, and sea surface air temperature, in particular) are smaller than those by previous retrieval algorithms of SSM/ I observations over the global oceans. Unlike previous methods, the present algorithm is capable of producing near-real-time estimates of surface latent and sensible heat fluxes for the global oceans from SSM/I data.

基于循环抑制CPG模型控制的蛇形机器人三维运动

具有三维运动能力和独特的节律运动方式,使生物蛇能在复杂的地形环境中生存.大多数动物节律运动是由中央模式发生器(Centralpatterngenerator,CPG)控制的.以此为理论依据,首次以循环抑制建模机理构建蛇形机器人组合关节运动控制的CPG模型.证明该模型是节律输出型CPG中微分方程维数最少的.采用单向激励方式连接该类CPG构建蛇形机器人三维运动神经网络控制体系,给出该CPG网络产生振荡输出的必要条件.应用蛇形机器人动力学模型仿真得到控制三维运动的CPG神经网络参数,利用该CPG网络的输出使“勘查者”成功实现三维运动.该结果为建立未探明的生物蛇神经网络模型提供了一种全新的方法.