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1.
为进一步做好称重式降水传感器技术装备保障工作,使观测数据更加准确,文章以DSC3型称重式降水传感器为例,依据其结构及工作原理,向收集容器注入不同水位,控制流量模拟不同雨强对称重式降水传感器进行现场校准。结果表明:校准称重式降水传感器与收集容器水位无关,不同的降水强度其误差稳定不变,校准后所得误差可作为系统误差,较翻斗式雨量传感器更加精准可靠。 相似文献
2.
Shraddha Parate Vikas Kumar Danishuddin Jong Chan Hong Keun Woo Lee 《International journal of molecular sciences》2021,22(10)
Heparanase (Hpse) is an endo-β-D-glucuronidase capable of cleaving heparan sulfate side chains. Its upregulated expression is implicated in tumor growth, metastasis and angiogenesis, thus making it an attractive target in cancer therapeutics. Currently, a few small molecule inhibitors have been reported to inhibit Hpse, with promising oral administration and pharmacokinetic (PK) properties. In the present study, a ligand-based pharmacophore model was generated from a dataset of well-known active small molecule Hpse inhibitors which were observed to display favorable PK properties. The compounds from the InterBioScreen database of natural (69,034) and synthetic (195,469) molecules were first filtered for their drug-likeness and the pharmacophore model was used to screen the drug-like database. The compounds acquired from screening were subjected to molecular docking with Heparanase, where two molecules used in pharmacophore generation were used as reference. From the docking analysis, 33 compounds displayed higher docking scores than the reference and favorable interactions with the catalytic residues. Complex interactions were further evaluated by molecular dynamics simulations to assess their stability over a period of 50 ns. Furthermore, the binding free energies of the 33 compounds revealed 2 natural and 2 synthetic compounds, with better binding affinities than reference molecules, and were, therefore, deemed as hits. The hit compounds presented from this in silico investigation could act as potent Heparanase inhibitors and further serve as lead scaffolds to develop compounds targeting Heparanase upregulation in cancer. 相似文献
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针对综采工作面液压支架跟机自动化过程中移架动作存在的丢架、推移不到位等问题,提出了基于遗传算法(GA)与BP神经网络组合模型的控制方法。通过建立BP神经网络控制器为主体的反馈控制,将支架的运动参数输入模型,神经网络控制器计算实际输出与理想输出之间误差,判别是否需要回调控制,并添加遗传算法来优化更新模型的各层阈值和权值,从而得到网络模型的最优解,最终由执行部分来完成输出动作。组合网络模型具有良好的非线性特性,可以更好的满足非线性环境,利用神经网络的预测值与实际输出的差值来得到拟合曲线。通过对BP神经网络模型、GA模型、GA-BP组合模型的均方误差(MSE)分析,判断出GA-BP组合模型具有更快的训练速度和更高的预测准确率。相比较于单一的BP神经网络模型和GA模型,GA-BP组合模型可以很大程度地提高液压支架跟机过程中的推移精度,从而更好地适应综采工作面的环境和设备变化。基于对模型稳定性的分析,绘制组合网络的适应度曲线,种群在第5次迭代后趋于收敛,在第5次到第15次迭代的适应度值就已基本达到稳定,在迭代第15次后种群已达到最优参数集且恒定不变。采用上述方案的液压支架电液控制系统能够自主感知设备各项运动参数的变化,实现支架自身的静态调整和动态演化,可为综采工作面无人化建设提供技术支撑。 相似文献
5.
Aspheric optics has been widely employed in some high-tech industries for its superiority. In order to achieve the ultra-precision machining of these aspheric surfaces, the large optical ultra-precision grinding machine becomes crucial because it determines the efficiency of the whole process. As the key functional unit in ultra-precision machine tool, the hydrostatic guideways are commonly adopted for the excellent performance. However, the motion errors of hydrostatic guideways have a direct influence on the accuracy of the machined workpiece, and some analysis approaches have been reported correspondingly. Although the existed analysis models do work, their imperfections also can be easily captured. Accordingly, a novel analysis model with less imperfection is deserved to be developed. In this paper, the kinematic theory is utilized to establish the quasi-static analysis model for motion errors in closed hydrostatic guideways. Through the large ultra-precision grinding machine designed by our research group, the consistently good agreement between the predicted results and the measured experimental data are obtained. Furthermore, it is found that the motion accuracy is more sensitive to the profile error of the guide rail bearing the external load rather than that of the other guide rail in the closed hydrostatic guideway. The presented research is supposed to be valuable to the peer designers. 相似文献
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双臂机器人系统是当前机器人领域的研究热点,特别是随着单臂机器人在操作能力、控制等方面的局限性不断凸显,最近的研究集中在拥有协调操作能力的冗余双臂机器人。对双臂操作进行分类,然后从双臂协调运动方式、双臂协调控制问题、感知传感器、模仿学习、人机交互五个方面进行分析;综述从运动学、动力学现状入手,分析了双臂协调控制与单臂控制方式在约束关系、运动规划、协调控制方式等方面的不同与发展,结合感知传感器、模仿学习等方法在双臂协调控制中的应用;对人机协作中的交互方式进行了分析,并对双臂机器人的未来研究方向进行了展望。 相似文献
8.
分析了混合型有源滤波器HAPF(hybrid active power filter)在高压直流系统应用时的稳定性问题,其中HAPF采用H桥级联变换器与LC滤波器组合的方案,对高压直流系统的特征谐波电流进行滤波。为了分析在电网阻抗变化时HAPF出现的谐波谐振现象,对HAPF进行了阻抗建模,基于阻抗模型的分析结果,采用了具有延时补偿功能的谐振调节器对阻抗特性进行修正,并从提高稳定性角度提出了相应的控制参数设计方法。阻抗模型分析结果表明,控制系统延时产生的负阻尼是引发稳定性问题的主要原因,采用改进后的控制方法可以将延时产生的负阻尼修正为正阻尼,能够有效避免谐波谐振。在PSCAD中进行仿真建模,并通过阻抗扫描验证了有源滤波器的阻抗特性。仿真案例证实了控制方法的有效性。 相似文献
9.
Makoto Yamaguchi 《应用聚合物科学杂志》2021,138(14):50172
Perfluorosulfonic acid ionomer membranes have been widely used as proton conducting membranes in various electrochemical processes such as polymer electrolyte fuel cells and water electrolysis. While their thermal stability has been studied by thermogravimetry and analysis of low molecular weight products, their decomposition mechanism is little understood. In this study a newly developed methodology of thermal desorption and pyrolysis in combination with direct analysis in real time mass spectrometry is applied for Nafion membrane. An ambient ionization source and a high-resolution time-of-flight mass spectrometer enabled unambiguous assignment of gaseous products. Thermal decomposition is initiated by side chain detachment above 350°C, which leaves carbonyls on the main chain at the locations of the side chains. Perfluoroalkanes are released above 400°C by main chain scission and their further decomposition products dominate above 500 °C. DFT calculation of reaction energies and barrier heights of model compounds support proposed decomposition reactions. 相似文献
10.
Xiaonan Lu Irmak Sargin John D. Vienna 《Journal of the American Ceramic Society》2021,104(11):5636-5647
Nepheline precipitation in nuclear waste glasses during vitrification can be detrimental due to the negative effect on chemical durability often associated with its formation. Developing models to accurately predict nepheline precipitation from compositions is important for increasing waste loading since existing models can be overly conservative. In this study, an expanded dataset of 955 glasses, including 352 high-level waste glasses, was compiled from literature data. Previously developed submixture models were refitted using the new dataset, where a misclassification rate of 7.8% was achieved. In addition, nine machine learning (ML) algorithms (k-nearest neighbor, Gaussian process regression, artificial neural network, support vector machine, decision tree, etc.) were applied to evaluate their ability to predict nepheline precipitation from glass compositions. Model accuracy, precision, recall/sensitivity, and F1 scores were systemically compared between different ML algorithms and modeling protocols. Model prediction with an accuracy of ~0.9 (misclassification rate of ~10%) was observed for different algorithms under certain protocols. This study evaluated various ML models to predict nepheline precipitation in waste glasses, highlighting the importance of data preparation and modeling protocol, and their effect on model stability and reproducibility. The results provide insights into applying ML to predict glass properties and suggest areas for future research on modeling nepheline precipitation. 相似文献