Performance Analysis of Gradient Neural Network Exploited for Online Time-Varying Matrix Inversion

This technical note presents theoretical analysis and simulation results on the performance of a classic gradient neural network (GNN), which was designed originally for constant matrix inversion but is now exploited for time-varying matrix inversion. Compared to the constant matrix-inversion case, the gradient neural network inverting a time-varying matrix could only approximately approach its time-varying theoretical inverse, instead of converging exactly. In other words, the steady-state error between the GNN solution and the theoretical/exact inverse does not vanish to zero. In this technical note, the upper bound of such an error is estimated firstly. The global exponential convergence rate is then analyzed for such a Hopfield-type neural network when approaching the bound error. Computer-simulation results finally substantiate the performance analysis of this gradient neural network exploited to invert online time-varying matrices.     

Thermal shock of ß-alumina with zirconia additions

Sodium-alumina electrolyte tubes were prepared from spray dried precursor powders with additions of 2, 5, 10 and 15 wt % ZrO₂ made by the sodium zirconate route. The thermal shock behaviour of ring segments cut from the tubes was examined by quenching into water at 0° C. At the 2% ZrO₂ level the dispersed ZrO₂ particle size was low, <0.5m, and the tetragonal phase was retained. For higher weight fractions, particle coarsening during the-alumina sintering schedule was extensive and the large particles transformed to monoclinic on cooling. At the 15% ZrO₂ level the-alumina had a slightly reduced strength and high resistance to thermal shock and to thermal shock damage, quenching into water from 800° C effecting only a 43% reduction in strength. These observations are consistent with the effects of microcracking in the vicinity of second phase ZrO₂ particles.     

细胞分裂素6─BA合成新方法的研究

用医药工业的副产物乙酰基次黄嘌呤（Ⅰ）在弱酸条件下水解，生成次黄嘌呤（Ⅱ），经三氟氧磷在吡啶溶液中缩合，生成６－吡啶盐中间体（Ⅲ），再与苄胺反应，制得高效植物细胞分裂素６－ＢＡ（Ⅳ）。     

SVD-based Preisach hysteresis identification and composite control of piezo actuators

In this paper, the singular value decomposition (SVD) based identification and compensation of the hysteretic phenomenon in piezo actuators are addressed using a Preisach model. First, this paper presents an SVD-based least squares algorithm and a revision approach of the identification through updating the SVD. With the identified parameters and a log of the memory curve, a Preisach-based inversion compensator is constructed which is complemented with a feedback controller to address the inevitable and residual modeling errors. Experimental results are furnished for both the identification and compensation approaches. The Preisach-based feedforward controller significantly improves the tracking performance and reduces the root-mean-square (RMS) tracking error of a PID controller by 76.7% and 89% at 1 Hz and 25 Hz, respectively. With the proposed composite controller, the percent-RMS errors at 1 Hz and 25 Hz are reduced to 0.035% and 0.31%, respectively.     

电力电容器的相控技术

讨论分析了电力电容器连续相控（ＴｈｙｒｉｓｔｏｒＣｏｎｔｒｏｌｌｅｄＣａｐａｃｉｔｏｒ）ＴＣＣ新技术。其中主要阐崐述了ＴＣＣ技术的工作原理、波形分析结果和谐波抑制方法。     

基于边界表示的曲面实体造型系统的数据结构

实体造型是ＣＡＤ系统的核心模块，而二次曲面在工程中常用来描述轴，孔及圆角等形状特征。讨论了在微机上实现的一个基于边界表示的实体模型的数据结构，并针对二次曲面的特点，实现了对自封闭曲面的无二义性拓扩定义，为顺利进行曲面的拼合运算打下了基础。     

电动缸式钢包长水口机械手的技术改造

介绍了宝钢5号连铸机钢包长水口机械手的设备构成及技术性能,阐述设备长时间运行所存在的问题,针对问题提出了设备改进的措施。     

氧化锰矿直接浸出制备硫酸锰的几个影响因素

根据云南建水锰矿一年多来的生产实践，就氧化锰矿直接浸出制备硫酸锰工业生产的几个影响因素，进行了探讨并提出了提高浸出回收率的措施。     

A general stochastic clustering method for automatic cluster discovery

Finding clusters in data is a challenging problem. Given a dataset, we usually do not know the number of natural clusters hidden in the dataset. The problem is exacerbated when there is little or no additional information except the data itself. This paper proposes a general stochastic clustering method that is a simplification of nature-inspired ant-based clustering approach. It begins with a basic solution and then performs stochastic search to incrementally improve the solution until the underlying clusters emerge, resulting in automatic cluster discovery in datasets. This method differs from several recent methods in that it does not require users to input the number of clusters and it makes no explicit assumption about the underlying distribution of a dataset. Our experimental results show that the proposed method performs better than several existing methods in terms of clustering accuracy and efficiency in majority of the datasets used in this study. Our theoretical analysis shows that the proposed method has linear time and space complexities, and our empirical study shows that it can accurately and efficiently discover clusters in large datasets in which many existing methods fail to run.