Self Organising Maps for variable selection: Application to human saliva analysed by nuclear magnetic resonance spectroscopy to investigate the effect of an oral healthcare product

SOMs (Self Organising Maps) are derived from the machine learning literature and serve as a valuable method for representing data. In this paper, the use of SOMs as a technique for determining the most significant variables (or markers) in a dataset is described. The method is applied to the NMR spectra of 96 human saliva samples, half of which have been treated with an oral rinse formulation and half of which are controls, and 49 variables consisting of bucketed intensities. In addition, three simulations, two of which consist of the same number of samples and variables as the experimental dataset and a third that contains a much larger number of variables, are described. Two of the simulations contain known discriminatory variables, and the remaining is treated as a null dataset without any specific discriminatory variables added. The described SOM method is contrasted to Partial Least Squares Discriminant Analysis, and a list of the markers determined to be most significant using both approaches was obtained and the differences arising are discussed. A SOM Discrimination Index (SOMDI) is defined, whose magnitude relates to how strongly a variable is considered to be a discriminator. In order to ensure that the model is stable and not dependent on the random starting point of the SOM, one hundred iterations were performed and variables that were consistently of high rank were selected. A variety of approaches for data representation are illustrated, and the main theoretical principles of employing SOMs for determining which variables are most significant are outlined. Software used in this paper was written in-house, allowing greater flexibility over existing packages, and tailored for the specific application in hand.     

基于树形解压缩器的低测试数据量方法

提出一种由异或门按照完全二叉树形状排列而成的树形向量解压缩器。该解压缩器的少数输出端需要由大部分的输入端来确定,而且该结构对其输出值的确定关系类似于扫描链中确定位的分布概率,可有效降低测试数据量。实验结果表明,对于ISCAS’89基准电路,该结构最高将测试数据量压缩了77倍。     

电容分压器的电容及介损测量

对整体状态下 CVT的电容分压器电容及介损的测量方法进行了探索和验证 ,对产生误差的原因进行了分析 ,并对试验电压的选择和监测作了较详细的说明。     

射线溅井测量物理量之间关系以及KALMAN滤波处理

本文从射线统计误差理论出发,以自然伽马测井为例,分析了在射线测井中由于仪器运动测量所产生的纵向分辨率,它与测量有关的诸测量物理这间的关系,以及测量数据的滤波处理原理。     

用于微机械器件和微电子机械系统的关键技术：微机械技术

微机械技术是研究，生产微机械器件和微电子机械诉关键技术，熟练掌握并不容易。本文总结了三个单位为时数年在微机械技术方面的研究成果，重点介绍我们首创的三项技术，即自对准的准三维加工技术，硅三维结构的无掩模腐蚀技术和硅／硅低温直接键合技术，也提到我们开展过的其他几项微机械技术以及所制作的几种微机械器件的参数或结果。     

Indirect adaptive control of nonlinear dynamic systems using self recurrent wavelet neural networks via adaptive learning rates

This paper proposes an indirect adaptive control method using self recurrent wavelet neural networks (SRWNNs) for dynamic systems. The architecture of the SRWNN is a modified model of the wavelet neural network (WNN). However, unlike the WNN, since a mother wavelet layer of the SRWNN is composed of self-feedback neurons, the SRWNN can store the past information of wavelets. In the proposed control architecture, two SRWNNs are used as both an identifier and a controller. The SRWNN identifier approximates dynamic systems and provides the SRWNN controller with information about the system sensitivity. The gradient-descent method using adaptive learning rates (ALRs) is applied to train all weights of the SRWNN. The ALRs are derived from discrete Lyapunov stability theorem, which are applied to guarantee the convergence of the proposed control system. Finally, we perform some simulations to verify the effectiveness of the proposed control scheme.