Optimal Bayesian maintenance policy for a gearbox subject to two dependent failure modes

Most maintenance optimization models of gear systems have considered single failure mode. There have been very few papers dealing with multiple failure modes, considering mostly independent failure modes. In this paper, we present an optimal Bayesian control scheme for early fault detection of the gear system with dependent competing risks. The system failures include degradation failure and catastrophic failure. A three‐state continuous‐time–homogeneous hidden Markov model (HMM), namely the model with unobservable healthy and unhealthy states, and an observable failure state, describes the deterioration process of the gear system. The condition monitoring information as well as the age of the system are considered in the proposed optimal Bayesian maintenance policy. The objective is to maximize the long‐run expected average system availability per unit time. The maintenance optimization model is formulated and solved in a semi‐Markov decision process (SMDP) framework. The posterior probability that the system is in the warning state is used for the residual life estimation and Bayesian control chart development. The prediction results show that the mean residual lives obtained in this paper are much closer to the actual values than previously published results. A comparison with the Bayesian control chart based on the previously published HMM and the age‐based replacement policy is given to illustrate the superiority of the proposed approach. The results demonstrate that the Bayesian control scheme with two dependent failure modes can detect the gear fault earlier and improve the availability of the system.     

Synergistic Effect of Aligned Graphene Nanosheets in Graphene Foam for High‐Performance Thermally Conductive Composites

Graphene shows a great potential for high‐performance thermally conductive composite applications because of its extremely high thermal conductivity. However, the graphene‐based polymer composites reported so far only have a limited thermal conductivity, with the highest thermal conductivity enhancement (TCE) per 1 vol% graphene less than 900%. Here, a continuous network of graphene foam (GF), filled with aligned graphene nanosheets (GNs), is shown to be an ideal filler structure for thermally conductive composite materials. Compared to previous reports, a clear thermal percolation is observed at a low graphene loading fraction. The GNs/GF/natural rubber composite shows the highest TCE of 8100% (6.2 vol% graphene loading) ever reported at room temperature, which gives a record‐high TCE per 1 vol% graphene of 1300%. Further analyses reveal a significant synergistic effect between the aligned GNs and 3D interconnected GF, which plays a key role in the formation of a thermal percolation network to remarkably improve the thermal conductivity of the composites. Additionally, the use of this composite for efficient heat dissipation of light‐emitting diode (LED) lamps is demonstrated. These findings provide valuable guidance to design high‐performance graphene‐based thermally conductive materials, and open up the possibility for the use of graphene in high‐power electronic devices.     

带参数摄动的时延分段仿射系统事件触发控制

针对带有参数摄动以及状态时延的网络化分段仿射系统,提出了基于周期事件触发机制的鲁棒控制器设计方法,引入周期事件触发机制来减少通讯网络中信号的传输次数从而降低网络带宽占用。通过构建分段Lyapunov函数,结合李雅普诺夫稳定性定理,将系统的控制器设计问题转换为求线性矩阵不等式可行解问题,实现周期事件触发机制与鲁棒控制器的协同设计。仿真结果说明了该方法的有效性。     

High color purity emission from quantum dots by optimizing the full width at half the maximum and optical density to blue light

In this paper, we got wide color gamut of quantum dot (QD) films by optimizing the spectra width and optical density (OD) of quantum dots. The specific methods to achieve the following: QD R: one layer of color filter R film was coated below the QD R layer. QD G: one layer of yellow‐green film was coated below the QD G film. By a structure optimal design, we got wide color gamut up to 99.2% BT2020 (equal to 132.86% NTSC) in Cd‐based QD and 93.6% BT2020 (equal to 125.35% NTSC) in Cd‐free QD. Furthermore, the gamut of QD display will continue to be improved by continuous refining the structure of QD display.     

基于端到端深度学习的智能车自动转向研究*

为解决由图像直接计算出控制量的端到端深度学习算法中感知器和控制器难以区分的问题,对其网络结构进行了改进。通过预训练一个自编码器,得到良好的道路特征编码后,将编码器作为感知器和和转角预测控制器一起进行端到端的训练。训练结果表明,改进后的自动转向网络模型收敛的更快,预测的角度在测试集上能较好的跟随实际角度变化而变化。利用解码器和特征图反向传播法分别还原出道路图片,可视化了该自动转向模型重点关注的道路特征。     

Two-stream person re-identification with multi-task deep neural networks

Person re-identification (re-id) with images is very useful in video surveillance to find specific targets. However, it is challenging due to the complex variations of human poses, camera viewpoints, lighting, occlusion, resolution, background clutter and so on. The key to tackle this problem is how to represent the body and match these representations among frames. Current methods usually use the features of the whole bodies, and the performance may be reduced because of part invisibility. To solve this problem, we propose a two-stream strategy to use parts and bodies simultaneously. It utilizes a multi-task learning framework with deep neural networks (DNNs). Part detection and body recognition are performed as two tasks, and the features are extracted by two DNNs. The features are connected to multi-task learning to compute the mapping model from features to identifications. With this model, re-id can be achieved. Experimental results on a challenging task show the effectiveness of the proposed method.     

陶瓷材料织构研究的某些进展

根据近年来高技术陶瓷材料织构研究中的一些主要问题，结合工作实践，综合评述织构分析在陶瓷材料研究中的应用及其进展。     

求解大规模水火最优潮流问题的近似牛顿方向解耦算法

分解后计算效率低和解的最优性差一直是困扰大规模水火最优潮流(HTOPF)研究与应用的两个关键问题。针对这些问题,提出了一种求解HTOPF的精确高效的解耦算法。基于近似牛顿方向直接对原问题KKT(Karush-Kuhn-Tucker)条件解耦的思想,将含梯级电厂的HTOPF问题分解为火电问题和水电问题。火电问题分解为单时段最优潮流问题,并进一步划分为多区域子问题;根据水电厂类型的不同将水电问题分解为单个固定水头、单个变化水头水电厂子问题以及梯级水电厂群优化子问题。求解过程中,每个子问题只迭代一次而不用求其最优解,极大地提高了计算效率。仿真计算结果表明:所提算法具有良好的适应性和稳定性,不仅显著减少了内存占用,而且在串行求解时CPU计算时间缩短了3~4倍,在并行计算条件下可获得10~20倍甚至1 000倍以上的加速比,并保证所得最优目标值与准确值之间的误差在10-8以下,确保了分解协调结果的最优性。     

同步荧光结合神经网络同时测定3种抗生素

为了更快速、简便、准确地同时测定多种抗生素混合物,采用同步荧光光谱结合径向基神经网络的方法,对3种氟喹诺酮类抗生素（乳酸环丙沙星、乳酸左氧氟沙星、盐酸左氧氟沙星）的同步荧光光谱进行研究。选择3组分浓度均为1.67ng/mL的混合溶液,测量其3维同步荧光光谱;分别测量39种不同浓度的混合溶液样本的同步荧光光谱;选取其中35种作为训练组,其余4种作为预测组,将训练组样本对应的光谱数据作为输入,建立和训练径向基神经网络;在发射波长与激发波长的差Δλ=194nm条件下,利用训练好的神经网络对预测组中各组分的浓度进行预测,得到3种组分浓度预测的平均相对误差分别达到3.59%,3.47%,3.09%。结果表明,当Δλ设定为194nm时,3种抗生素的同步荧光峰差异最为明显、区分度高,该方法能实现对3种抗生素混合物中各组分的同时测定。这为多种抗生素混合物同时测定提供了一种快速、简便、准确的方法。     

基于PRO/E Wildfire的NC加工二次开发

以NC加工中车削和铣削为例,对Pro／ENGINEER Wildfire系统调用外部刀具库,进行Pro／EN-GINEER Wildfire的二次开发过程,作了较为详细的描述。在Pro／ENGINEER Wildfire二次开发过程中,利用VC＋＋编写动态库链接文件,把相应的外部数据库和动态链接程序,利用Pro／ENGI-NEER Wildfire中的开发工具Pro／TOOLKIT,使它们有机接合起来,这样,可以充分发挥Pro／EN-GINEER Wildfire中开发工具Pro／TOOLKIT的功能,达到提高工作效率的目的。