基于改进SOA-VMD的磁声发射信号去噪算法

磁声发射（MAE）是铁磁性材料磁化过程中产生的声发射信号，在构件应力检测和微观损伤检测中有着广泛的应用。针对MAE信号非稳态、复杂性、衰减性等特点，提出海鸥算法结合变分模态分解（SOA-VMD）的去噪方法，为克服海鸥算法求解过程中易陷入局部最优解问题，利用柯西变异算子产生随机迭代过程，使改进算法即柯西变异海欧算法（CVSOA）跳出早熟收敛。采用以幅值谱熵为适应度函数，优化VMD算法中分解模态个数K和二次惩戒因子α两个参数，将含噪声的MAE信号进行VMD分解重构。经仿真信号和实际检测信号分析表明，改进后的CVSOA-VMD算法全局寻优能力和去噪性能优于传统的SOA-VMD算法，降噪后的MAE信号特征值对于不同应力下均方根、偏斜度特征值的重复性更好，可靠性更高。     

基于VMD和改进TCN的短期光伏发电功率预测

光伏发电功率存在波动性,且光伏出力易受各种气象特征影响,传统TCN网络容易过度强化空间特性而弱化个体特性。针对上述问题,文中提出一种基于VMD和改进TCN的短期光伏发电功率预测模型。通过VMD将原始光伏发电功率时间序列分解为若干不同频率的模态分量,将各个模态分量以及相对应的气象数据输入至改进TCN网络进行建模学习。利用中心频率法确定VMD的最优分解模态分解个数。在传统TCN预测模型的基础上,使用DropBlock正则化取代Dropout正则化以达到抑制卷积层中信息协同的效果,并引入注意力机制自主挖掘并突出关键气象输入特征的影响,量化各气象因素对光伏发电的影响,从而提高预测精度。以江苏省某光伏电站真实数据为例进行仿真实验,结果表明所提预测方法的RMSE为0.62 MW,MAPE为2.03%。     

基于VMD和BSA-KELM的高陡边坡位移预测模型研究

边坡位移的时间序列曲线存在复杂的非线性特性,传统的预测模型精度不足以满足预测要求。为此提出了基于变分模态分解的鸟群优化-核极限学习机的预测模型,并用于河北省某水泥厂的边坡位移预测。该方法首先采用VMD把边坡位移序列分解为一系列的有限带宽的子序列,再对各子序列分别采用相空间重构并用核极限学习机预测,采用鸟群算法优化相空间重构的嵌入维度和KELM中惩罚系数和核参数三个数值,以取得最优预测模型。最后将各个子序列预测值叠加,得到边坡位移的最终预测值。结果表明：和KELM、BSA-KELM、EEMD-BSA-KELM模型相比,基于VMD的BSA-KELM预测精度更高,为边坡位移的预测提供一种有效的方法。     

Production of the Carboxylate Reductase from Nocardia otitidiscaviarum in a Soluble,Active Form for in vitro Applications

Accessing aldehydes from carboxylate moieties is often a challenging task. In this regard, carboxylate reductases (CARs) are promising catalysts provided by nature that are able to accomplish this task in just one step, avoiding over-reduction to the alcohol product. However, the heterologous expression of CARs can be quite difficult due to the excessive formation of insoluble protein, thus hindering further characterization and application of the enzyme. Here, the heterologous production of the carboxylate reductase from Nocardia otitidiscaviarum (NoCAR) was optimized by a combination of i) optimized cultivation conditions, ii) post-translational modification with a phosphopantetheinyl transferase and iii) selection of an appropriate expression strain. Especially, the selection of Escherichia coli tuner cells as host had a strong effect on the final 110-fold increase in the specific activity of NoCAR. This highly active NoCAR was used to reduce sodium benzoate to benzaldehyde, and it was successfully assembled with an in vitro regeneration of ATP and NADPH, being capable of reducing about 30 mM sodium benzoate with high selectivity in only 2 h of reaction.     

A globally convergent BFGS method for pseudo-monotone variational inequality problems

In this paper, we propose a globally convergent BFGS method to solve Variational Inequality Problems (VIPs). In fact, a globalization technique on the basis of the hyperplane projection method is applied to the BFGS method. The technique, which is independent of any merit function, is applicable for pseudo-monotone problems. The proposed method applies the BFGS direction and tries to reduce the distance of iterates to the solution set. This property, called Fejer monotonicity of iterates with respect to the solution set, is the basis of the convergence analysis. The method applied to pseudo-monotone VIP is globally convergent in the sense that subproblems always have unique solutions, and the sequence of iterates converges to a solution to the problem without any regularity assumption. Finally, some numerical simulations are included to evaluate the efficiency of the proposed algorithm.     

Semirigid inclusions in elastic bodies: Mechanical interplay and optimal control

The paper concerns an analysis of an equilibrium problem for 2D elastic body with two semirigid inclusions. It is assumed that inclusions have a joint point, and we investigate a junction problem for these inclusions. The existence of solutions is proved, and different equivalent formulations of the problem are proposed. We investigate a convergence to infinity of a rigidity parameter of the semirigid inclusion. It is proved that in the limit, we obtain an equilibrium problem for the elastic body with a rigid inclusion and a semirigid one. A parameter identification problem is investigated. In particular, the existence of a solution to a suitable optimal control problem is proved.     

耐蚀合金中TiN析出机制

摘要：TiN颗粒尺寸及其分布对耐蚀合金性能有明显的影响，因此有必要对TiN在铸坯中的分布及其析出行为进行研究。采用扫描电镜(SEM)、金相显微镜（OM）观察了TiN夹杂物在铸坯中的分布、尺寸及其形貌；基于热力学和动力学理论分析了耐蚀合金铸坯中TiN夹杂物的析出时机及其尺寸，结合试验结果和理论计算明确了TiN夹杂物在凝固后铸坯中的位置和尺寸与析出时机的关系，为控制TiN夹杂物提供理论指导。结果表明，冶炼过程中析出的TiN夹杂物尺寸较大，在凝固过程中被枝晶吞没，位于铸坯枝晶内和等轴晶内；微观偏析计算结果表明，在凝固分数为0.55时，TiN开始析出，最开始析出TiN夹杂物的逐渐长大，长大后的TiN易于被二次枝晶吞没，最终位于铸坯中的枝晶间和等轴晶内，后期析出的TiN则在枝晶间和等轴晶间。固相中析出的TiN夹杂物长大较慢，尺寸细小，最终位于奥氏体晶界。     

304不锈钢不同精炼渣碱度下夹杂物的演变

 为了提高304不锈钢的产品质量，结合生产实际，利用热力学计算和扫描电镜能谱分析方法, 研究了硅脱氧条件下，LF精炼渣碱度对304不锈钢在LF精炼、连铸过程夹杂物变化规律的影响。试验结果表明,随着冶炼过程进行，全氧质量分数和夹杂物数量依次减小。304不锈钢采用1.75高碱度炉渣，可以得到较低的全氧质量分数和夹杂物数量，但是夹杂物中Al2O3质量分数高，夹杂物熔点高。采用1.53低碱度炉渣，钢液中全氧质量分数较采用高碱度炉渣高，但是夹杂物中CaO、Al2O3质量分数相对较低，SiO2和MnO质量分数较高，夹杂物熔点低。针对304不锈钢产品可以采用不同的生产工艺路线来满足产品的不同需求。     

国内外帘线钢盘条夹杂物检验方法的介绍

围绕国内外帘线钢盘条的夹杂物检验方法，对国内外4种主要典型帘线钢盘条夹杂物检验方法进行了详细介绍及比较，了解了4种检验方法的技术特点，总结了帘线钢盘条夹杂物检验方法的核心要点，尤其对这4种检验方法中所涉及的试样取样面积、夹杂物的类型名称、光学显微镜下不同类型夹杂物的特征形貌进行了识别，阐明了做好帘线钢盘条夹杂物检验和质量控制的价值和意义。     

基于VMD瞬时能量与GA- RBF的滚动轴承故障诊断

针对滚动轴承早期故障的有效识别,提出了一种基于VMD瞬时能量与GA优化的RBF神经网络的滚动轴承故障诊断方法,可以有效对滚动故障做出诊断。首先,VMD将滚动轴承振动信号进行分解成合适数目的本证模态函数;其次,计算本证模态函数分量的瞬时能量并组成特征向量;最后,将特征向量输入到GA优化的RBF神经网络实现轴承故障识别。通过滚动轴承故障诊断实验对该方法进行验证。结果表明,该方法识别滚动轴承故障的准确率为96.43%,较默认参数的RBF神经网络和EEMD瞬时能量与GA-RBF神经网络有明显的提高,证明了所提方法的可行性。