Material removal profile prediction and experimental validation for obliquely axial ultrasonic vibration-assisted polishing of K9 optical glass

K9 optical glass is one of the typical components in optical systems. However, because of its poor fracture resistance, it is difficult to polish it with ultra-precision and high-efficiency and without any surface damage simultaneously. The emergence of the obliquely axial ultrasonic vibration-assisted polishing (UVAP) method can solve this problem which encounters in polishing efficiency and shape accuracy. However, due to the unclear material removal profile (MRP) mechanism, obliquely axial UVAP is not widely used in the processing field. This paper introduces the obliquely axial UVAP method in research processes, mainly focusing on the fixed point MRP analysis of the obliquely axial UVAP. Based on Hertz's contact theory, polishing pressure, the length of the semi-long axis (LLA) and the length of the semi-short axis (LSA) of the contact area are calculated under ultrasonic vibration conditions. Meanwhile, the relative linear velocity distribution of the oblique polishing tool in the instantaneous contact area is modeled by mathematical geometry method. A novel model of the MRP distribution for obliquely axial UVAP is proposed following the Preston equation. Subsequently, a series of polishing experiments were carried out to verify this model. The results show that the numerical model has good agreement with the experimental results on MRP, LLA, LSA, material removal depth and material removal rate (MRR). In addition, the material removal capability can be significantly improved by larger ultrasonic amplitude and larger oblique angle. This model not only more clearly elucidates the processing mechanism of obliquely axial UVAP, but also provides theoretical support for the polishing of free-form optical lenses.     

煤矿锚杆机除尘马达改进

针对煤矿进口锚杆机除尘马达频繁出现故障的情况,通过排查除尘装置的安装方式和监测马达工作时的压力状态,查找故障原因,并制定解决方案。通过马达外接单向阀,可满足马达进油口最低安全压力限制的要求,解决了马达频繁损坏的问题,保障了设备工作的稳定性,提高了工作效率。     

Stem Photosynthesis—A Key Element of Grass Pea (Lathyrus sativus L.) Acclimatisation to Salinity

Grass pea (Lathyrus sativus) is a leguminous plant of outstanding tolerance to abiotic stress. The aim of the presented study was to describe the mechanism of grass pea (Lathyrus sativus L.) photosynthetic apparatus acclimatisation strategies to salinity stress. The seedlings were cultivated in a hydroponic system in media containing various concentrations of NaCl (0, 50, and 100 mM), imitating none, moderate, and severe salinity, respectively, for three weeks. In order to characterise the function and structure of the photosynthetic apparatus, Chl a fluorescence, gas exchange measurements, proteome analysis, and Fourier-transform infrared spectroscopy (FT-IR) analysis were done inter alia. Significant differences in the response of the leaf and stem photosynthetic apparatus to severe salt stress were observed. Leaves became the place of harmful ion (Na⁺) accumulation, and the efficiency of their carboxylation decreased sharply. In turn, in stems, the reconstruction of the photosynthetic apparatus (antenna and photosystem complexes) activated alternative electron transport pathways, leading to effective ATP synthesis, which is required for the efficient translocation of Na⁺ to leaves. These changes enabled efficient stem carboxylation and made them the main source of assimilates. The observed changes indicate the high plasticity of grass pea photosynthetic apparatus, providing an effective mechanism of tolerance to salinity stress.     

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).     

Influence of accelerated curing on the compressive strength of polymer-modified concrete

In recent building practice, rapid construction is one of the principal requisites. Furthermore, in designing concrete structures, compressive strength is the most significant of all parameters. While 3-d and 7-d compressive strength reflects the strengths at early phases, the ultimate strength is paramount. An effort has been made in this study to develop mathematical models for predicting compressive strength of concrete incorporating ethylene vinyl acetate (EVA) at the later phases. Kolmogorov-Smirnov (KS) goodness-of-fit test was used to examine distribution of the data. The compressive strength of EVA-modified concrete was studied by incorporating various concentrations of EVA as an admixture and by testing at ages of 28, 56, 90, 120, 210, and 365 d. An accelerated compressive strength at 3.5 hours was considered as a reference strength on the basis of which all the specified strengths were predicted by means of linear regression fit. Based on the results of KS goodness-of-fit test, it was concluded that KS test statistics value (D) in each case was lower than the critical value 0.521 for a significance level of 0.05, which demonstrated that the data was normally distributed. Based on the results of compressive strength test, it was concluded that the strength of EVA-modified specimens increased at all ages and the optimum dosage of EVA was achieved at 16% concentration. Furthermore, it was concluded that predicted compressive strength values lies within a 6% difference from the actual strength values for all the mixes, which indicates the practicability of the regression equations. This research work may help in understanding the role of EVA as a viable material in polymer-based cement composites.     

基于参数预标定的长行程PMLSM无传感器控制

为了解决长行程定子不连续永磁直线同步电机存在的因无法全程安装位置传感器和不同动子和定子之间的电磁参数不固定所造成的控制性能下降的难题,提出一种在每一段定子内先进行参数标定,再进行速度控制的控制系统设计。首先,在动子进入过程中,对电机进行电磁参数标定,根据标定参数对控制器参数进行调整,以达到更好的控制效果。然后,使用无位置传感器控制系统使动子快速达到设定速度值并稳定运行。实验结果表明:动子进入过程参数标定精度分别为0.002Wb和0.000 4H;无位置传感器控制中位置估计精度为0.63mm,速度收敛时间为0.45s,稳态误差为0.02m/s。基本满足永磁直线同步电机用于长行程运输的控制快速性、稳定性等要求。     

遗传算法优化的线性二次型调节器控制下3-UPS并联机器人稳定性分析

通过对3-UPS并联机器人进行动力学建模与状态空间分析,基于遗传算法(GA)优化的线性二次型调节器(LQR)控制方法对其进行控制仿真,结合多体系统振动理论,并运用MATLAB软件和ADAMS软件联合仿真,分别对该并联机器人在动平台施加外部载荷、定平台施加随机振动,以及动、定平台施加复合干扰3种情况下机器人的末端稳定性进行分析。结果表明:GA优化的LQR控制下,机器人动平台质心振动的衰减时间和振动幅度都明显减小,且共振峰也明显降低,但是高频振动处的减振效果不明显;机器人动平台质心x和y方向的共振频率均保持在5 Hz左右,而z方向的共振频率却有明显的后移现象,逐渐向高频(10 Hz)靠近。该研究为分析并联机器人稳定性与工作精度提供了一定的理论基础。     

Isolation and identification of soil bacteria resistant to surfactants in washing detergents

Linear alkylbenzene sulfonate (LAS) and polyoxyethylene lauryl ether (POLE) are the major surfactants in washing detergents. In the present study, we isolated surfactant-resistant bacteria from soil samples collected from a sports ground and a farm field. The samples were treated with 2.0% LAS or POLE at 25°C for 30 min and cultivated on agar plates at 25°C for several days, after which manifold bacterial colonies were isolated. Thereafter, we tested the ability of each bacterial isolate to resist the antibacterial activity of the surfactant. Ten LAS-resistant strains were isolated, and all were found to be Gram-negative bacteria such as Enterobacter and Pseudomonas. On the other hand, 18 POLE-resistant strains were isolated, of which 14 were Gram-positive bacteria including Bacillus and Microbacterium. Notably, one POLE-resistant strain was identified as Bacillus cereus, a potential causative agent for foodborne illness. The genera of LAS- and POLE-resistant bacteria did not overlap. Therefore, the combination of LAS and POLE could be more effective to eliminate soil bacteria from clothes and/or daily necessities.     

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。     

马尔可夫跳变线性系统最优控制的研究现状与进展

马尔可夫跳变线性系统（MJLS）是一种具有多个模态的随机系统，系统在各个模态之间的跳变转移由一组马尔可夫链来决定。MJLS模型因其在表示过程中可以产生突变而更能精确的描述实际工程应用中的系统。近年来，MJLS的最优控制问题成为了研究的热点，动态规划、极大值原理以及线性矩阵不等式等成为了解决此类问题的主流方法。本文对MJLS最优控制领域的研究现状进行了综述。分别对一般情况下、带有噪声的情况下、带有时滞的情况下以及某些特定情况下的MLJS最优控制问题的国内外研究现状进行论述。最后进行了总结并提出MJLS最优控制领域未来值得关注的研究方向。