奥钢联板坯连铸计算机系统升级研究

某钢厂板坯连铸过程控制系统采用的奥钢联(VAI)技术,运行数年来,由于模型不透明,维护和调整困难,迫切需要升级改造。改造后的计算机系统充分考虑现有资源和需求,能够达到升级改造的目的。     

Ф16mm、Ф18mm钼棒的研制

采用等静压制法，压制钼圆棒坯，经中频高温烧结，制得尺寸适合、用户满意的钼电极棒。     

泡沫铝的声频内耗

研究了泡沫Ａｌ的声频内耗，结果表明：泡沫Ａｌ的内耗值比普通Ａｌ大一个量级以上，且随测量频率的增加而减小。出现了正常振幅效应。用平面波动方程，推导了泡沫Ａｌ的内耗与频率，孔隙率和孔径的关系，较好地解释了实验结果。     

基于平移不变小波变换的多普勒速度声纳输出信号处理

针对水下航行器组合导航系统所使用的多普勒速度声纳输出信号的特点,采用了平移不变小波变换的多分辨率分析的方法,利用Daubechies小波函数,采用了半软阈值去噪的方法,能够在低信噪比的情况下准确地估计多普勒声纳的频移,从含有噪声的多普勒输出信号中提取出原始信号.通过仿真实验表明:采用4层小波分解,算法计算量减小,有效地...     

卷绕铅酸电池泡沫铅负极电化学行为的研究

以泡沫铅作为负极集流体制备了卷绕VRLA电池.采用计时电流法、循环伏安曲线、电化学阻抗谱和充放电实验研究了泡沫铅负极的电化学行为,结果表明泡沫铅负极的真实表面积比铅箔负极的大,因此泡沫铅负极具有较低的过电势,并且不论是在怎样的放电状态下,泡沫铅负极的电化学反应电阻较小;与铅箔负极相比,在10、5和2小时率放电状态下,泡沫铅负极的质量比容量分别增加25.9%,30.0%和48.2%.此外,SEM观察显示,泡沫铅负极表面活性物质为更加细小的晶体颗粒和具有更高的孔率.     

西门子平坦度检测系统在冷轧中的应用

结合西门子平坦度检测系统在冷轧厂的应用实例，通过对其检测原理的分析，从系统的硬件构成到软件实现，较详细地介绍了西门子新一代平坦度检测系统。最后对该检测系统的特点进行了总评。     

Study of Co-Ce coating and surface on pasted nickel electrodes substrate

The process of electroplating Co-Ce alloys on the nickel foam framework surface can improve electro-conductivity for active materials and nickelsubstrate interface. The results of inductive coupled plasma emission spectrometer (ICP), cyclic voltammetry (CV), scanning electron microscopy (SEM), X-ray diffraction (XRD) and electron probe microanalysis (EPMA) indicate that the Co-Ce coating chemical content of rare earth Ce 0.19wt.%-0.28wt.% can not only alter the microstructure of electroplating coating, but also accelerate the oxidation reaction of Co and improve its transfer rate of electric current conductivity to the active material particles. The grads-like distributing electro-conductive network of CoOOH is formed on the nickel substrate surface, which improves reversibility of pasted nickel electrode. The charging receptivity is improved by Co-Ce coating on the pasted nickel electrode substrate, and its specific discharging capacity is improved by 50%.     

Real-time transient stability status prediction using cost-sensitive extreme learning machine

Real-time transient stability status prediction (RTSSP) is very important to maintain the safety and stability of electrical power systems, where any unstable contingency will be likely to cause large-scale blackout. Most of machine learning methods used for RTSSP attempt to attain a low classification error, which implies that the misclassification costs of different categories are the same. However, misclassifying an unstable case as stable one usually leads to much higher costs than misclassifying a stable case as unstable one. In this paper, a new RTSSP method based on cost-sensitive extreme learning machine (CELM) is proposed, which recognizes the RTSSP as a cost-sensitive classification problem. The CELM is constructed pursuing the minimum misclassification costs, and its detailed implementation procedures for RSSTP are also researched in this work. The proposed method is implemented on the New England 39-bus electrical power system. Compared with three cost-blind methods (ELM, SVM and DT) and two cost-sensitive methods (cost-sensitive DT, cost-sensitive SVM), the simulation results have proved that the lower total misclassification costs and false dismissal rate with low computational complexity can be achieved by the proposed method, which meets the demands for the computation speed and the reliability of RTSSP.