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.     

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.     

Field-Effect Chiral Anomaly Devices with Dirac Semimetal

Charge-based field-effect transistors (FETs) greatly suffer from unavoidable carrier scattering and heat dissipation. Analogous to valley degree of freedom in semiconductors, chiral anomaly current in Weyl/Dirac semimetals is theoretically predicted to be nearly nondissipative over long distances, but still lacks experimental ways to efficiently control its transport. Here, field-effect chirality devices are demonstrated with Dirac semimetal PtSe₂, in which its Fermi level is close to the Dirac point in the conduction band owing to intrinsic defects. The chiral anomaly is further corroborated by the planar Hall effect and nonlocal valley transport measurement, which can also be effectively modulated by external fields, showing robust nonlocal valley transport with micrometer diffusion length. Similar to charge-based FETs, the chiral conductivity in PtSe₂ devices can be modulated by electrostatic gating with an ON/OFF ratio of more than 10³. Basic logic functions in the devices are also demonstrated with electric and magnetic fields as input signals.     

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。     

卫生纸机压榨部运行参数及能效关系分析与优化

通过对纸机压榨部压力和压榨脱水原理的分析，得到出压榨部纸幅干度可作为压榨部的能耗指标，同时确定压榨线压力和纸机车速是压榨部能耗的主要影响因素。结合纸机实际生产测试并记录压榨脱水数据，绘制测试数据曲线图并拟合压榨线压力、纸机车速和出压榨部纸幅干度三者之间的函数关系，最终确定压榨线压力和纸机车速的最优取值范围。通过对测试数据的分析和拟合，控制压榨线压力在90 kN/m、纸机车速在1075 m/min时，纸幅质量可满足正常生产的要求，还可以节约2.1%的电能，实现卫生纸机压榨部的能耗优化。     

A fuzzy logic PI control with feedforward compensation for hydrogen pressure in vehicular fuel cell system

In a vehicular fuel cell system, alternative load and frequent purge action can lead to anode pressure varies with the hydrogen mass flow fluctuation. It's crucial to control the pressure difference between anode and cathode within a reasonable range to avoid adverse phenomena such as membrane failure, reactant starvation, or even water management fault. In this paper, an improved proportional integrative (PI) controller by the fuzzy logic technique that considers the engineer experience and knowledge on the hydrogen supply system behavior is proposed for hydrogen pressure control, in which the PI parameters are tuned by a fuzzy decision process. Furthermore, load current and purge action regarded as input disturbances are applied for feedforward compensation to reduce the pressure response hysteresis. A hydrogen supply subsystem based on the proportional valve is modeled, and corresponding parameters are determined by analyzing the response time and steady pressure fluctuation. The performance of the conventional PI controller, the fuzzy logic PI (FLPI) controller and fuzzy logic PI with feedforward (FLPIF) controller is validated. The presented results indicated that the FLPI controller significantly improves the dynamic response of hydrogen pressure compared to the PI controller, and the FLPIF controller can further reduce overshoot caused by disturbance. Finally, the proposed FLPIF controller is implemented on a rapid prototype platform of the hydrogen supply subsystem and an actual fuel cell system, exhibiting satisfactory performance.     

基于伏安电子舌的枸杞产地快速辨识

提出一种基于希尔伯特—黄变换(HHT)—线性判别分析(LDA)的枸杞产地电子舌辨识方法。以宁夏、新疆、甘肃、青海4个产地的枸杞为试验材料,采用伏安电子舌采集不同产地枸杞的"指纹图谱"信息,利用集合经验模态分解(EEMD)对电子舌原始信号进行多尺度分解得到一组本征模态函数(IMF),分别求取其奇异谱熵和Hilbert边际谱作为特征向量。在该基础上,利用LDA建立枸杞产地非线性组合预测模型。试验结果表明,HHTLDA与分别采用特征点提取(FPE)、主成分分析(PCA)和离散小波变换(DWT)的算法相比,具有更好的分类效果。对未知产地枸杞的总体分类精度和kappa系数分别达到98%和0.973,均表明该模型具有较好的鉴别效果。     

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。