分离有机/有机混合物的PVA、CA系列膜及其渗透汽化性能研究(Ⅱ) 成膜条件对渗透汽化性能的影响

用溶剂完全蒸发法制备了PVA复合膜 .经对MTBE/MeOH混合物的分离实验得到了PVA复合膜的最佳成膜条件 ,即 :铸膜液中PVA、马来酸浓度分别为 7%、3% ,热处理温度130～ 15 0℃ .在 1%～ 2 %丙酮水溶液中浸泡处理 4～ 8h ,CTA中空纤维RO膜被改性成渗透汽化膜 .CTA中空纤维膜的分离性能优于其它醋酸纤维素系列膜 ,PVA/CA复合膜的性能优于PVA/PAN复合膜和CA/PAN复合膜     

塔河油田三叠系水平井地质导向技术研究

针对塔河油田三叠系碎屑岩油气藏油层薄、储集层非均质性较强、泥质和低渗致密夹层纵横向变化大的地质特点，在分析、研究不同区块多口水平井资料的基础上，探讨了水平井不同地质情况下斜井段及目的层砂顶的预测和判别技术，以实例阐述了井眼轨迹调整方法，首次形成了一套适合于该地区碎屑岩非均质薄层油气藏水平井录井地质导向技术，在塔河油田水平井钻探开发中取得了显著的成效。     

高水位地基处理实例

介绍高水位(距槽底300)条件下,地基处理的方法、检测及效果验证。为在此条件下进行施工作业提供依据。     

南方沙地开发利用途径的探讨

根据南方风沙化地自然及气候的特点，提出并讨论了在风沙化土地区营造防风固沙林；建立沙地果业基地；发展沙区畜牧水产业；搞好沙地蔬菜生产；完善农防林体系建设，发展沙区工业等开发利用模式。     

氦氢混合工质单级GM型脉管制冷特性实验研究

针对80K温区，在一台单级GM型脉管制冷机中进行了氦氢二元混合工质的实验研究。实验结果表明，采用一定组分的氦氢混合工质可以提高脉管制冷机的制冷性能。     

冷硬树脂砂增韧机理的研究

系统地研究了附加物对冷硬树脂砂的增韧作用，探讨了附加物对冷硬树脂砂的增韧机理     

复杂场景实时目标检测方法

针对含有非平稳背景的复杂场景,提出一种基于空间混合高斯模型的实时目标检测方法.该方法以混合高斯模型作为颜色分布的统计框架,并在空间邻域中利用背景与前景隶属度之间的竞争确定像素的归属,提高了检测准确率;同时通过基于偏差均值的匹配判断和低权重模型的移除提升了混合高斯统计框架的性能,以实时地对运动目标进行准确的检测.实验结果表明,文中方法对非平稳背景有很好的适应能力,在检测准确率和运行效率上均优于其他检测方法.     

Improving human robot collaboration through Force/Torque based learning for object manipulation

Human–Robot Collaboration (HRC) is a term used to describe tasks in which robots and humans work together to achieve a goal. Unlike traditional industrial robots, collaborative robots need to be adaptive; able to alter their approach to better suit the situation and the needs of the human partner. As traditional programming techniques can struggle with the complexity required, an emerging approach is to learn a skill by observing human demonstration and imitating the motions; commonly known as Learning from Demonstration (LfD). In this work, we present a LfD methodology that combines an ensemble machine learning algorithm (i.e. Random Forest (RF)) with stochastic regression, using haptic information captured from human demonstration. The capabilities of the proposed method are evaluated using two collaborative tasks; co-manipulation of an object (where the human provides the guidance but the robot handles the objects weight) and collaborative assembly of simple interlocking parts. The proposed method is shown to be capable of imitation learning; interpreting human actions and producing equivalent robot motion across a diverse range of initial and final conditions. After verifying that ensemble machine learning can be utilised for real robotics problems, we propose a further extension utilising Weighted Random Forest (WRF) that attaches weights to each tree based on its performance. It is then shown that the WRF approach outperforms RF in HRC tasks.