A refined algorithm for finite-difference synthesis of cam profiles

The present paper deals with the synthesis of a cam profile when the prescribed acceleration pattern is given in numerical form. It is primarily an extension of previous work by the author with a view to upgrading the numerical accuracy of the synthesized cam profile, hence broadening the scope of application to a general class of high-speed cams. The technique is to differentiate the Stirling interpolation formula or the Gregory-Newton interpolation formula, thereby refining the prescribed initial acceleration data so that it will be correct to differences of high order. The effectiveness and the accuracy of this technique is demonstrated by a numerical example.     

面向边缘计算的嵌入式FPGA卷积神经网络构建方法

当前,高计算消耗的应用和服务逐渐从集中式云计算中心向网络边缘的嵌入式环境迁移,FPGA因其灵活性和高能效特性,使其在边缘计算的嵌入式系统中得到广泛的应用.传统的FPGA卷积神经网络构造方法存在设计周期长和优化空间小等缺点,无法有效探索硬件加速器的设计空间,在网络边缘的的嵌入式环境下尤为明显.针对该问题,提出一种面向边缘计算的嵌入式FPGA平台卷积神经网络通用的构建方法.通过设计卷积神经网络函数中的网络层间可复用的加速器核心,以少量硬件资源实现性能优化的卷积神经网络硬件;通过拓展设计、缓存优化及数据流优化等技术,实现HLS设计优化;利用该方法在嵌入式FPGA平台上构建相应卷积神经网络,实验结果表明:优化后的网络模型在与Xeon E5-1620 CPU和GTX Titan GPU相比时,在功耗与性能方面具有一定优势,适合应用于边缘计算环境中.     

塔里木盆地巴什托超高压裂缝性油藏改善开发效果对策研究

针对巴什托超高压裂缝性油藏在开发过程中出现的含水上升快、产量下降快、底水能量弱等特点,在精细地质研究的基础上,对储层特征和油井见水规律进行了研究,建立了5种油井见水模式,对油藏实施的转抽、堵水、提液等措施进行了总结分析,为下一步提高开发效果提出了切实可行的技术方案。     

A novel multi scale predictor for comlpex time series

为解决非线性复杂时间序列在线预测问题,提出了一种基于过程神经网络模型的在线预测方法. 首先,在历史数据的基础上建立双并联离散过程神经网络模型;然后,根据在线更新的数据样本,采用递推极限学习算法对过程神经网络隐层到输出层的权值进行相应的更新;最后,应用权值更新后的过程神经网络模型对时间序列进行预测. 文中给出了具体的过程神经网络学习算法与权值更新机制,并以混沌时间序列与液体火箭发动机的状态预测为例对方法进行了验证. 研究结果表明:该方法在预测精度和适应能力上较单一的离线模型有显著提高,可以为非线性复杂时间序列在线预测问题提供一种有效的解决方法.

     

二次生长法合成沸石膜技术的研究进展

沸石悬浮液的制备和在栽体上涂晶种是二次生长法合成沸石膜中的两个关键因素,尤其涂种过程有利于控制膜的厚度和定向生长,引领着二次生长法合成沸石膜技术的发展方向.对二次生长法合成沸石膜过程中常用涂种方法,如浸涂法、直接打磨法、喷涂法、旋转涂膜法、静电吸附法和脉冲激光沉积法进行了详细的评述.另外,也对几个新的涂种方法,如真空法和电泳沉积法进行了简要介绍.     

Specific S100 Proteins Bind Tumor Necrosis Factor and Inhibit Its Activity

Tumor necrosis factor (TNF) inhibitors (anti-TNFs) represent a cornerstone of the treatment of various immune-mediated inflammatory diseases and are among the most commercially successful therapeutic agents. Knowledge of TNF binding partners is critical for identification of the factors able to affect clinical efficacy of the anti-TNFs. Here, we report that among eighteen representatives of the multifunctional S100 protein family, only S100A11, S100A12 and S100A13 interact with the soluble form of TNF (sTNF) in vitro. The lowest equilibrium dissociation constants (K_d) for the complexes with monomeric sTNF determined using surface plasmon resonance spectroscopy range from 2 nM to 28 nM. The apparent K_d values for the complexes of multimeric sTNF with S100A11/A12 estimated from fluorimetric titrations are 0.1–0.3 µM. S100A12/A13 suppress the cytotoxic activity of sTNF against Huh-7 cells, as evidenced by the MTT assay. Structural modeling indicates that the sTNF-S100 interactions may interfere with the sTNF recognition by the therapeutic anti-TNFs. Bioinformatics analysis reveals dysregulation of TNF and S100A11/A12/A13 in numerous disorders. Overall, we have shown a novel potential regulatory role of the extracellular forms of specific S100 proteins that may affect the efficacy of anti-TNF treatment in various diseases.