基准平面垂直断面法在爆破漏斗试验中的应用

系统阐述了基准平面垂直断面法在爆破漏斗试验中测量爆破漏斗体积的基本原理,并将隧道激光断面仪应用于金厂河矿1 750 m水平15^#采场底部切割巷道爆破漏斗试验爆破漏斗体积测量中。通过与传统体重法等计算法所得漏斗体积分析比较,结果表明基于隧道激光断面仪与3D Mine软件分析的基准平面垂直断面法实用性强、操作方便、结果直观可靠,达到试验预期目的。     

基于电信号特征分析的电机故障诊断技术

介绍了电机故障诊断技术平台的组成,分析了电机常见故障下电信号的特征。利用宜昌和景洪工厂两台电机进行了实际对比测试,宜昌工厂电机定子电流特征频率在49.2Hz时表现出峰值-36.08dB,在50.8Hz时表现峰值则为-37.23dB,表明电机存在转子断条故障;景洪工厂电机在转子特征频率为113.51Hz时,出现峰值-58.47dB,表明电机存在静态偏心故障。经电机现场抽芯检查,验证了电机故障快速诊断平台给出的电机健康状态评估结果,证实了基于电信号特征分析的电机故障诊断技术的准确性。     

Effect of ligand environment of rare-earth ions on temperature measurement performance of SrAO4:xEu3+ (A = Mo and W) phosphors

Molybdate and tungstate with scheelite-type structure are excellent self-luminescent materials, which can be used as ideal hosts for the doping of rare-earth ions. In this study, a series of Eu³⁺-activated SrAO₄ (A = Mo and W) phosphors were successfully synthesized, and their crystal structures, photoluminescence properties, and temperature measurement performance were analyzed in detail. These phosphors were excited by UV light (291 nm and 247 nm, respectively), with clear energy transfer (ET) (MoO₄^2?→Eu³⁺ or WO₄^2?→Eu³⁺). According to fluorescence intensity ratio (FIR) and Judd–Ofelt (J–O) theory, compared to SrWO₄:0.01Eu³⁺ phosphor, SrMoO₄:0.01Eu³⁺ phosphor exhibited better thermal stability, with relatively low Sa value (maximum values were 5.082 %K^?1 and 20.74 %K^?1, respectively), and their Sr values were not significantly different (maximum values were 0.864 %K^?1 and 0.83 %K^?1, respectively). Sa value was negatively correlated to central asymmetry of Eu³⁺, but the optimal Sr value tended to be more suitable for central asymmetry of Eu³⁺. In addition, Eu³⁺ exhibited stronger central asymmetry as well as covalency of Eu–O bond in SrMoO₄. Results reveal that SrMoO₄:xEu³⁺ and SrWO₄:xEu³⁺ can be used for luminescent thermometers.     

Frequency response data-based peak filter design applied to MIMO large-scale high-precision scan stage

A large-scale high-precision scan stage is important equipment in the industrial productions of micro-fabrication such as flat panel display (FPD) lithography systems. Designing controllers for multi-input multi-output (MIMO) systems is time-consuming and needs experience because of the interaction between each axis and many controller tuning parameters. The aim of this study is to develop a peak filter design method based on frequency response data to reduce repetitive disturbance. This data-based approach does not use the model and only uses the frequency response data of the controlled system and the disturbance spectrum calculated from the scanning error data (Contribution 1). The peak filter is designed by convex optimization and satisfies robust stability conditions for six-degree-of-freedom systems (Contribution 2). The control performance of the designed peak filter is experimentally demonstrated with an industrial MIMO large-scale high-precision scan stage in reducing the scanning error of the main stroke of the translation along the $x$-axis (Contribution 3).     

Synthesis and characterizations of highly responsive H2S sensor using p-type Co3O4 nanoparticles/nanorods mixed nanostructures

High-quality p-type semiconducting Co₃O₄ with mixed morphology of nanoparticles/nanorods are synthesized using a hydrothermal route for high response and selective hydrogen sulphide (H₂S) sensor application. XRD and Raman studies revealed the crystal structure and molecular bonding for obtained Co₃O_4, respectively. The nanoparticles/nanorods-like structures were confirmed for Co₃O₄ using FESEM and TEM analysis. The EDS and XPS spectra analysis were carried out for elemental composition and chemical atomic states of Co₃O₄. The Co₃O₄ sensor is investigated for gas sensing properties in dynamic conditions. The sensor exhibited the highest selectivity towards H₂S among various hydrogen-contained gases at 225 °C. The sensor revealed a high response of 357% and 44% for 100 and 10 ppm H₂S gas concentrations, respectively. The Co₃O₄ sensor exhibited a systematic dynamic resistance response for 100–10 ppm range H₂S gas. The excellent dynamic resistance repeatability of the sensor was shown towards 25 ppm H₂S gas. The response of Co₃O₄ sensor was investigated at different operating temperatures and H₂S concentrations. The sensor stability and H₂S sensing mechanism for the Co₃O₄ sensor have been reported. Highly uniform and mixed nanostructures of Co₃O₄ can be the potential sensor material for real-time high-performance H₂S sensor application.     

A numerical study of dynamic behaviors of a unitized regenerative fuel cell during gas purging

The gas purging states affect electricity output and energy storage capacity of unitized regenerative fuel cells. In this study, a model of unitized regenerative fuel cell is established. Cell voltages and operating temperatures influences on the dynamic distribution of thermal fluid during purging process and the discharge of residual liquid water in electrolytic cell mode are investigated. The motivation of the present study is better understanding the gas purging characteristics and its effect on reaction behaviors of unitized regenerative fuel cells. Simulation results reveal a significant influence of purging gas temperature on the water flooding and a great effect of operating voltage on the water diffusion. The operating temperature of electrolytic cell model almost has little effect on purging results at different cell temperature and the same purging gas temperature. When the purging gas temperature is changed, higher temperatures of cell and purging gas facilitate liquid water discharging out from the cell regions. In cell water flooding situation, when having large liquid content, the purging gas has little effects on the water expelling process.     

Shaking table study on seismic behavior of MSE wall with inclined backfill soils reinforced by polymeric geostrips

In this study, the seismic behavior of a mechanically stabilized earth (MSE) wall with inclined backfill is investigated under sinusoidal acceleration excitations using a series of 1-g shaking table tests performed on the MSE model of 150 cm in height reinforced with polymeric geostrips. The effects of the stiffness of the reinforcement and slope angles of the backfill soil on the acceleration amplification factor (RMSA), the lateral displacement of the wall, the surface displacement of the backfill, the distribution of dynamic earth pressure along the height of reinforced wall and the strain distributions on the surfaces of the polymeric geostrips in three planes of the wall are investigated. The experimental results show that the dynamic earth pressure determined by traditional pseudo-static approaches leads to overestimated values. In addition, increasing the inclination angle of backfill soil results in the increase of surface settlement, lateral wall displacements, soil dynamic earth pressures, acceleration amplification factors and strains on the polymeric geostrip materials. The stiffness of the polymeric geostrip material has a negligible effect on the displacement, dynamic earth pressures and failure surface geometry.     

倾斜入射下的单截面平面度绝对检验

针对大尺寸光学平面的直线度的纳米级测量精度需求，提出了倾斜入射下单截面平面度绝对检验方法，实现了对超过相移干涉仪口径的长平晶绝对检验。利用棱镜转向实现倾斜入射角度的精密预标定，棱镜标定角度的精度高于圆光栅和图像分析等方法，可提高测量不确定度到0.0042 μm。对比了常规三面互检绝对检验结果与本方法的差异，在相同尺寸下，直线度误差仅为1.2 nm。在确认标定反射镜位置后，整个倾斜入射的干涉图调整过程将被完全集中到待测长平晶的工作面上，不需要再对反射平晶进行操作。调整长平晶时各个维度的操作互不干涉，可快速简便地得到测量结果。     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

基于音频特征的工程车辆工况识别研究

高效率地使用工程车辆是工程项目管理中节约成本的有效方法，无人监管环境下工程车辆的工况识别，是实现工程车辆高效率使用的有效手段。目前以GPS等技术为核心的车辆智能管理系统未对工程车辆进行工况识别，提出一种基于GRU循环神经网络的工程车辆工况识别方法，通过对工程车辆在不同工况下产生的音频信号进行分析，从中提取Mel倒谱系数作为主要特征，构建GRU循环神经网络模型进行训练和识别。实验结果表明，该方法可以实现对工程车辆工况的有效识别。