己二酸和甲醇酯化反应与渗透汽化脱水耦合过程的研究

以己二酸二甲酯的合成为目标反应,选用聚乙烯醇/聚丙烯腈(PVA/PAN)复合膜为渗透汽化膜,比较了蒸馏脱水与渗透汽化脱水对酯化反应转化率的影响.结果表明,在己二酸和甲醇摩尔比为1∶2时,蒸馏脱水与渗透汽化脱水都可将转化率从68%的平衡转化率提高到98%,但渗透汽化脱水方法带出来的甲醇量只有蒸馏脱水法中甲醇量的7%,因而可以大幅度降低因蒸发和提纯甲醇所需要的能耗.     

A New Synthetic Route for 3,3′-Bis(fluorodinitromethyl)difurazanyl Ether (FOF-13) and Its Energetic Properties

A new protocol for preparation of 3,3-bis(fluorodinitromethyl)difurazanyl ether (FOF-13) was developed. It involves (i) nitration of 3,3’-bis(chlorohydroxyminomethyl)difurazanyl ether with N₂O₅/MeCN to give 3,3-bis(chlorodinitromethyl)difurazanyl ether (4), (ii) reduction of 4 with KI/MeOH to obtain potassium salt of 3,3’-bis(dinitromethyl)difurazanyl ether (6) and (iii) fluorination of 6 with XeF₂ in anhydrous acetonitrile to form the desired FOF-13. FOF-13 was fully characterized by IR, ¹³C NMR, ¹⁹F NMR, and elemental analysis. FOF-13 exhibits excellent physicochemical and detonation properties, such as high density (1.91 g cm^?3), good thermal stability, reasonable impact sensitivity (14 J) and friction sensitivity (64%), high measured detonation velocity (8497 m s^?1 at 1.69 g cm^?3). Furthermore, the precursors 4 and 6 were developed for the first time.     

基于神经网络的注塑机注射速度的迭代学习控制

对具有不确定性和干扰项的重复非线性注塑机控制系统,尤其是注射速度的控制,提出基于神经网络的迭代学习控制器,其中迭代学习控制器设计为神经网络控制器,它以前馈方式作用于对象。PD反馈控制器用于使系统达到稳定,同时和前馈的神经网络学习控制器一起使系统达到理想的控制效果。仿真结果表明,该控制器可以随着迭代次数的增加有效减小跟踪误差。     

改进平衡优化器的超声电机无模型自适应控制

为了提高超声电机的控制性能,将基于数据驱动的无模型自适应控制（Model Free Adaptive Control,MFAC）方法应用到超声电机的速度控制中,并针对MFAC存在参数调整困难的问题,提出一种改进的平衡优化器（Improved Equilibrium Optimizer, IEO）算法用于MFAC参数寻优。首先,利用自适应生成概率策略来平衡算法的探索与开发能力;其次,引入折射反向学习策略来扩大解的搜索范围,提高算法的收敛速度,同时采用柯西变异策略来提高算法跳出局部最优的能力;最后,提出一种改进的时间乘以绝对误差积分（Improved Integral Time Absolute Error, IITAE）指标函数用于MFAC的参数寻优。仿真和实验结果表明,与基于原始平衡优化器算法的MFAC相比,基于改进平衡优化器算法的MFAC的稳态误差和调整时间明显减小,系统的控制性能得到显著提高。     

FS-Net: Frequency Statistical Network for Temporal Knowledge Graph Reasoning

Temporal Knowledge Graph (TKG) reasoning has attracted wide attention of researchers. Existing TKG reasoning methods have made great progress through modeling historical information. However, the time variability and unseen entities (relations) are still two major challenges that hinder the further improvement of this field. Moreover, since the structural information and temporal dependencies of the historical subgraph sequence have to be modeled, the traditional embedding-based methods often have high time consumption in the training and predicting processes, which greatly limits the application of the reasoning model in real-world scenarios. To address these issues, in this paper we propose a frequency statistical network for TKG reasoning, namely FS-Net. On the one hand, FS-Net continuously generates time-varying scores for the predictions at the changing timestamps based on the latest short-term historical fact frequency statistics; on the other hand, based on the fact frequency statistics at the current timestamp, FS-Net supplements the historical unseen entities (relations) for the predictions; in particular, FS-Net does not need training and has a very high time efficiency. Plenty of experiments on two TKG benchmark datasets demonstrate that FS-Net outperforms the baseline models.     

苏州市苏蠡路2007-G-31号地块商业步行街设计

通过对苏州市苏蠡路2007-G-31号地块商业步行街方案设计,充分发挥地域优势,精心布局,合理规划,将该市场建成结构清晰、功能完善、文化内涵深厚的新型现代化商业城。     

Robot base frame calibration with a 2D vision system for mobile robotic drilling

The International Journal of Advanced Manufacturing Technology - Mobile robotic drilling for flight control surface assembly at multiple stations demands high positioning accuracy of the equipped...