Growth mechanism and gas-sensing characteristics of organic-additive-free zinc oxide of various shapes

Zinc oxide is widely used in gas sensors, solar cells, and photocatalysts because of its wide bandgap and exciton binding energy of 60 meV in various metal oxides. To use ZnO as a gas sensor, it is necessary to synthesize it with surface defects and a large specific surface area. In this study, hydrothermal synthesis without surfactants was employed to obtain organic-additive-free ZnO. For morphology control, we varied the ratio of the hydroxide ion concentration to the zinc ion concentration. To confirm the growth mechanism of ZnO, we performed X-ray diffraction, scanning electron microscopy, and transmission electron microscopy analyses. Raman spectroscopy and photoluminescence measurements were performed to analyze the surface properties. The Brunauer–Emmett–Teller method and probe stations were used to measure the specific surface area and sensitivity of the gas sensor, respectively. The results confirmed that flower-shaped ZnO is the most suitable gas-sensing material.     

Towards artificial intelligence at scale in the chemical industry

In the Industry 4.0 era, the chemical industry is embracing broad adoption of artificial intelligence (AI) and machine learning (ML) methods. This article provides a holistic view of how the industry is transforming digitally towards AI at scale. First, a historical perspective on how the industry used AI to aid humans in better decision-making is shown. Then state-of-the-art AI research addressing industrial needs on reliability and safety, process optimization, supply chain, material discovery, and reaction engineering is highlighted. Finally, a vision of the plant of the future is illustrated with critical components of AI-ready culture, model life cycle management, and renewed role of humans in chemical manufacturing.     

Coverage optimization algorithm based on VF-CS in mobile sensor network

A random placement of large-scale sensor network in the outdoor environment often causes low coverage.An area coverage optimization algorithm of mobile sensor network (MSN) based on virtual force perturbation and Cuckoo search (VF-CS) was proposed.Firstly,the virtual force of the sensor nodes within the Thiessen polygon was analyzed based on the partitioning of Voronoi diagram of the monitoring area.Secondly,the force of polygon vertices and neighbor nodes was taken as the perturbation factor for updating the node’s location of the Cuckoo search (CS).Finally,the VF-CS guided the node to move so as to achieve the optimal coverage.The simulation results demonstrate that the proposed algorithm has higher coverage and shorter average moving distance of nodes than the Voronoi diagram based algorithms in literatures.     

基于双声表面波器件的引信加速度传感器

In view of the shortcomings of silicon micro acceleration sensor based on piezoresistive effect and capacitance principle, such as temperature drift, low resolution and poor anti-interference ability, a fuze acceleration sensor based on dual SAW devices is proposed. The sensor adopts a dual saw device structure, one is coated with a sensitive film for measurement, the other is an uncoat- ed reference channel for compensation of environmental temperature, pressure, humidity and other factors. The experimental results show that the maximum linear error is only 1.6%, the sensitivity is 54.3Hz/g, and the maximum hysteresis error is less than 1%. Compared with piezoresistive accelerometer and capacitive accelerometer, the linear error of the accelerometer is small, the sensitivi- ty is high, and it has strong anti-interference ability.     

矿用直线振动筛结构故障分析

针对煤矿选煤厂直线振动筛工作过程中出现的结构故障问题,采用有限元仿真与现场测试结合的方法对振动筛出现故障的原因进行了分析研究。介绍了振动筛的主要结构及常见故障,并以某矿选煤厂发生大梁断裂的振动筛为例,对该振动筛进行三维建模和有限元模态分析,分析结果表明:随着模态阶次的增加,振型的变化逐渐由移动转为结构本身的扭曲变形。在该振动筛空载工作状态下,选用本质安全型便携式测振记录仪进行振动测试并处理测试信号,得到该振动筛的工作频率。结合模态分析结果和实际测试结果,判断出空载时频繁运行振动筛是造成其大梁断裂的主要原因。根据故障原因给出维护建议:应当尽量避免振动筛在空载或物料较少的状态下长时间运行;在振动筛投入使用前,应预先了解振动筛的工作状态及可能存在的薄弱环节,尽可能避免结构故障,保证振动筛可靠运行。     

Fault detection and identification for a class of continuous piecewise affine systems with unknown subsystems and partitions

This paper establishes a novel online fault detection and identification strategy for a class of continuous piecewise affine (PWA) systems, namely, bimodal and trimodal PWA systems. The main contributions with respect to the state‐of‐the‐art are the recursive nature of the proposed scheme and the consideration of parametric uncertainties in both partitions and in subsystems parameters. In order to handle this situation, we recast the continuous PWA into its max‐form representation and we exploit the recursive Newton‐Gauss algorithm on a suitable cost function to derive the adaptive laws to estimate online the unknown subsystem parameters, the partitions, and the loss in control authority for the PWA model. The effectiveness of the proposed methodology is verified via simulations applied to the benchmark example of a wheeled mobile robot.     

基于改进奇异值分解滤波和谱峭度的滚动轴承故障诊断

针对含噪信号的有效奇异值个数难以确定的问题，提出了一种改进的奇异值分解降噪方法--奇异值累积法。该方法通过计算奇异值的实际下降值与奇异值平均下降速度累积量的差值，并取该差值最大值点的位置作为有效奇异值的分界点来确定有效奇异值的个数。在此基础上，提出了一种基于奇异值累积法与快速谱峭度的滚动轴承故障诊断方法。采用奇异值累积法对原信号进行降噪处理，然后利用快速谱峭度确定滤波器中心频率及带宽，通过分析频段包络谱中明显的频率成分来诊断故障。该方法可以有效去除信号中的噪声，使得到的峭度值所反映的故障冲击更接近实际情况。对含内圈、外圈故障的滚动轴承实验数据进行分析，实验结果表明，相比快速谱峭度的故障诊断方法，该方法具有更好的故障识别效果。     

乌鲁木齐机场多普勒天气雷达故障统计分析

文章针对乌鲁木齐机场多普勒天气雷达(以下简称ADWR)2009~2019年发生的故障次数、故障分布以及备件更换情况,逐年统计故障率,深入分析了ADWR系统性能,总结出运行维护中的经验,就如何更好地提高雷达业务运行指标提出建议,以提高ADWR雷达的维护、维修能力,建立有针对性的备件管理,为科学的业务运行管理提供依据。     

宁夏红寺堡盆地隐伏古隆起成因机制及其对区域沙漠化的影响

目前对于青藏高原东北缘活动构造研究多集中于活动断裂带的活动性,而对夹持于其间的沉积盆地构造格架及活动性研究甚少。以夹持于烟筒山断裂与牛首山—罗山断裂之间的宁夏红寺堡盆地为研究对象,采用重力资料重处理解译、音频大地电磁测深(AMT)和地震勘探相结合,揭示红寺堡盆地隐伏构造特征,并进一步采用盆山一体化思路分析隐伏构造的成因机制及其对区域沙漠化的控制作用。结果表明:青藏高原在中新世末发生强烈的NE向推挤、扩展和隆升,红寺堡盆地由坳陷盆地转变为挠曲盆地; 在青藏高原NE向扩展的影响下,烟筒山断裂发生强烈的逆冲作用,古生代—中生代基底逆冲于古近系—新近系之上,受构造变形影响的最新地层为中新统彰恩堡组; 红寺堡盆地内的隐伏古隆起呈NW—SE向,与烟筒山构造带具有相同的构造动力学背景,受青藏高原NE向扩展影响形成于中新世末,并且至今仍具有活动性; 隐伏古隆起周缘断裂的活动破坏了地表稳定性、蓄水能力和地表植被,导致区域沙漠化呈现有规律的NW—SE向带状展布。该研究成果对于宁夏红寺堡盆地区域稳定性评价及沙漠化的综合治理具有现实指导意义。     

基于深度学习的传感器优化布置方法

针对传感器优化布置(optimal sensor placement,简称OSP)问题,提出了一种新的使用深度神经网络的解决方案,并以简化的桥梁形状的桁架结构中的振动测试传感器优化为例进行了验证。首先,选择一种传统的传感器优化布置方法,对自动化生成的大量不同的桁架结构分别进行传感器优化布置计算,将所得优化布置结果在进行数据预处理后构建出深度学习方法所需要的训练集与验证集;其次,使用Python语言和深度学习框架TensorFlow设计实现与本研究问题适配的深度神经网络模型并训练;然后,随机生成了新的桁架结构参数;最后,将深度神经网络输出的传感器布置结果和传统方法的计算结果进行了比较,验证了本研究方法的有效性以及在速度上、可移植性与可扩展性方面的性能优势。