The superlattice structure and self-adaptive performance of C–Ti/MoS2 composite coatings

To improve the self-adaptability of MoS₂ coating in different environments, the coatings were doped with functional C and Ti by unbalanced magnetron sputtering system. The clear superlattice structure with minimal modulation period was investigated by High Resolution Transmission Electron Microscope (HRTEM). The co-doped coatings have better mechanical properties due to the special structure and the formation of C–Mo, Ti–S and Ti–O bonds, and better lubrication performance in both high humidity and vacuum than those of the single-doped ones. The doped Ti not only facilitates the formation of the MoS₂ (002) basal plane, but also improves the oxidation resistance of the composite film. The degree of friction-induced graphitization on the wear tracks and the quality of transfer films on the wear scars are key factors affecting the lubrication performance of the composite film. In the high-humidity environment, the reasonable doping elements can promote the formation the high-quality transfer film by interacting with H₂O water molecules, which will benefit the lubrication of the coating better. Our findings deepen the understanding of MoS₂ composite coating and provide a new solution for improving the self-adaptability of the coating.     

寒冷地区某既有办公建筑围护结构优化改造研究

既有公共建筑能耗中,围护结构节能潜力巨大。以兰州某既有办公建筑为例,采用DeST-C软件模拟围护结构不同材料厚度、窗型,得出系列负荷,通过对数据分析、比较,得出如下结论:外墙外保温适宜材料为50mm的挤塑板,最大热负荷节能率为45.66%;屋面适宜保温材料为65mm的挤塑板,最大热负荷节能率为4.25%;外窗各个朝向全部更换玻璃,推荐选用内张膜中空玻璃(双膜),最大热负荷节能率为7.36%;单独更换南向外窗材料,推荐选用真空镀膜复合中空玻璃,最大热负荷节能率为1.78%。     

继电保护中多路模拟量采集系统的设计

介绍了一种基于高性能浮点DSP芯片TMS320C32、CPLD芯片XC95288和A/D采样芯片AD976组成的多路采集系统的工作原理以及设计方法。通过对第一路施加特殊的电压量,在CCS开发环境下读取采样缓冲区的值,并利用Matlab对采样数据进行了全波傅氏变换。此外,该系统已在继电保护中得到广泛应用,实践表明,该系统能较好地解决多路模拟量的采集,并确保了采样数据的安全可靠性。     

Robot learning through task identification

The operation of an autonomous mobile robot in a semi-structured environment is a complex, usually non-linear and partly unpredictable process. Lacking a theory of robot–environment interaction that allows the design of robot control code based on theoretical analysis, roboticists still have to resort to trial-and-error methods in mobile robotics.The RobotMODIC project aims to develop a theoretical understanding of a robot’s interaction with its environment, and uses system identification techniques to identify the system robot–task–environment. In this paper, we present two practical examples of the RobotMODIC process: mobile robot self-localisation and mobile robot training to achieve door traversal.In both examples, a transparent mathematical function is obtained that maps inputs–sensory perception in both cases–to output — location and steering velocity respectively. Analysis of the obtained models reveals further information about the way in which a task is achieved, the relevance of individual sensors, possible ways of obtaining more parsimonious models, etc.     

γ射线含水仪远传功能在油井的应用研究

根据目前含水仪无法远距离传输数据的情况设计出系统硬件,着重指出通过RS485实现数据的远传以及串口通讯设计。     

On the Cutoff Wavelength of Rectangular-Shaped Microshield Line by Edge Element Method

In this paper, the cutoff characteristic of rectangular-shaped microshield transmission line has been analysed by edge element method. Dependence of cutoff wavelengths on the thickness of metallic signal strip, dielectric constant of dielectric substrate and the width of the rectangular-shaped ground conductor are presented in tabular form. Numerical results in this paper have important values in design of rectangular-shaped microshield lines in microwave and millimeter wave integrated circuits.     

基于网络断层扫描的传感器网络链路丢包研究

文中提出了一种基于网络断层扫描的无线传感器网络链路丢包率测量方法,通过边界节点的丢包率来估计传感器内部链路丢包的情况。根据无线传感器网络数据聚合的特点,提出了网络逻辑拓扑和链路报文丢包模型,在将链路报文丢失率推测问题形式化为MLE问题的基础上,用引入修正因子的μ的EM算法来推测链路报文丢失率。NS2仿真结果证明,μ-EM算法推测的链路报文丢失率与预设值更加接近,特别是在大规模网络中μ-EM算法在精确度和有效性方面均明显优于EM算法。     

融合外部属性的短时交通流预测研究

针对现有交通流量预测算法大多仅考虑常态下的预测,而未考虑天气属性、周围地理属性对预测结果的影响,提出一种融合外部属性的组合预测模型（A-STIGCN）。首先,将外部属性作为路网中路段的属性,同时对路段的属性和交通特征进行建模,得到增强的特征向量。其次,采用图小波变换和自适应矩阵分别提取交通流局部和全局空间特征信息,并借助门控循环单元（GRU）对时间信息的长时记忆能力以提取其时间特性。最后,通过注意力机制来捕获时空动态变化性进行交通流预测。采用深圳出租车轨迹数据、对应天气数据以及POI数据进行预测,研究结果表明：A-STIGCN组合模型预测效果优于传统线性模型及变体模型,与未引入注意力机制的ASTGCN模型相比,MAE降低了约0.131,精度提高了0.068,与未引入外部因素的TGCN模型对比分析,MAPE降低了约0.637%,精度提高了0.079,从而更好地为交通管理提供指导意见。     

基于区间箱粒子多伯努利滤波器的传感器控制策略

多目标跟踪中的传感器控制本质上是一个最优非线性控制问题, 其在理论分析和计算上极具挑战性. 本文基于区间不确定性推理, 利用箱粒子多伯努利滤波器提出了一种基于信息测度的传感器控制策略. 首先, 本文利用箱粒子实现多伯努利滤波器, 并通过一组带有权值的箱粒子来表征多目标后验概率密度函数. 其次, 利用箱粒子的高斯分布假设, 将多伯努利密度近似为高斯混合. 随后, 选择柯西施瓦兹(Cauchy-Schwarz, CS) 散度作为评价函数, 并详细推导了两个高斯混合之间的CS散度的求解公式, 以此为基础提出相应的传感器控制策略. 此外, 作为一种对比方案, 利用蒙特卡罗方法, 本文还给出了通过对箱粒子进行混合均匀采样, 进而通过点粒子求解CS散度的递推公式, 并提出了相应的控制策略. 最后, 仿真实验验证了所提算法的有效性.