Analysis of operating limits and combustion state regulation for low-calorific value gases in industrial burners

The effective and efficient utilization of low-calorific value (LCV) gases has gained increasing attention in scientific research and industrial fields. In this study, the combustion characteristics of three LCV gases in practical devices are analyzed by using a nonadiabatic perfectly stirred reactor model. The complete steady-state solution in the temperature-residence time parameter space is obtained with arc-length continuation. The stable operation region is quantified by the eigenvalue analysis. The transition of solution curves is quantified with heat loss coefficient. Five key system parameters are systematically investigated on their effects on stability limits. With the combustion performance being quantified by a combustion state index, a combustion state regulation method is proposed to find the optimal regulation path of system parameters. Active subspace method is further applied to shorten the regulation step by identifying the active direction. The proposed method and findings are useful for optimal regulation of burning LCV gases in industrial burners.     

Adaptive GLR-, Rao- and Wald-based CFAR detectors for a subspace signal embedded in structured Gaussian interference

The problem of detecting a subspace signal embedded in subspace Gaussian interference and thermal noise is studied in this paper. In this problem, both the signal-independent and signal-dependent interferences are assumed to be present, therefore the overall interference subspace covers the signal subspace. The approach of this paper extends previous works involving either of those two kinds of interferences. A set of secondary data containing only interference plus noise is employed to estimate the interference covariance matrix and the noise power. Three new detectors are designed via the generalized likelihood ratio (GLR), Rao and Wald tests, respectively. Their probabilities of false alarms (PFAs) and detections are analytically derived. The PFAs show that the new detectors have the constant false alarm rate (CFAR) property against the interference and noise. Numerical results show that the new detectors outperform their counterparts for the studied problem. Furthermore, the new detectors are less sensitive to the secondary data size and to the mismatched subspace signal than some other detectors, such as the GLR detector (GLRD), the adaptive matched filter (AMF), the adaptive subspace detector (ASD), etc.     

Observers for linearly unobservable nonlinear systems

We provide a method for constructing local observers for some nonlinear systems around a critical point where the linearization is not observable or not detectable. Two examples are provided to illustrate the results of the paper.     

一种基于子空间分解的IIR信道盲辩识算法

本文证明了对IIR信道输出进行过采样 (采样率是输入码率的整数倍 )可以转化成单输入多输出的多信道模型 ,并且不同的信道有相同的AR系数 .基于这一特性本文提出一种基于子空间分解的信道参数盲辩识方法 ,即不同信道的MA系数可以由输出信号的噪声子空间唯一确定 ,而AR系数则可以通过求解YW方程得到     

OFDM系统信道盲估计的一种新算法

利用循环前缀（CP）引入的信息冗余，在利用接受信号的二阶统计特性实现正交频分复用（OFDM）系统信道盲估计的子空间算法基础上，采用矩阵变换的方法，改善信息序列的非周期相关函数，推导出信道估计的新算法。     

Numerical experiments of preconditioned Krylov subspace methods solving the dense non-symmetric systems arising from BEM

Discretization of boundary integral equations leads, in general, to fully populated non-symmetric linear systems of equations. An inherent drawback of boundary element method (BEM) is that, the non-symmetric dense linear systems must be solved. For large-scale problems, the direct methods require expensive computational cost and therefore the iterative methods are perhaps more preferable. This paper studies the comparative performances of preconditioned Krylov subspace solvers as bi-conjugate gradient (Bi-CG), generalized minimal residual (GMRES), conjugate gradient squared (CGS), quasi-minimal residual (QMR) and bi-conjugate gradient stabilized (Bi-CGStab) for the solution of dense non-symmetric systems. Several general preconditioners are also considered and assessed. The results of numerical experiments suggest that the preconditioned Krylov subspace methods are effective approaches solving the large-scale dense non-symmetric linear systems arising from BEM.     

Subspace distance analysis with application to adaptive Bayesian algorithm for face recognition

We propose subspace distance measures to analyze the similarity between intrapersonal face subspaces, which characterize the variations between face images of the same individual. We call the conventional intrapersonal subspace average intrapersonal subspace (AIS) because the image differences often come from a large number of persons. An intrapersonal subspace is referred to as specific intrapersonal subspace (SIS) if the image differences are from just one person. We demonstrate that SIS varies significantly from person to person, and most SISs are not similar to AIS. Based on these observations, we introduce the maximum a posteriori (MAP) adaptation to the problem of SIS estimation, and apply it to the Bayesian face recognition algorithm. Experimental results show that the adaptive Bayesian algorithm outperforms the non-adaptive Bayesian algorithm as well as Eigenface and Fisherface methods if a small number of adaptation images are available.     

基于特征选择的集成分类器抗噪性能分析

特征选择有助于增强集成分类器成员间的随机差异性,从而提高泛化精度。研究了随机子空间法（RandomSub-space）和旋转森林法（RotationForest）两种基于特征选择的集成分类器构造算法,分析讨论了两算法特征选择的方式与随机差异程度之间的关系。通过对UCI数据集引入噪声,比较两者在噪声环境下的分类精度。实验结果表明：当噪声增加及特征关联度下降时,基本学习算法及噪声程度对集成效果均有影响,当噪声增强到一定程度后。集成效果和单分类器的性能趋于一致。     

完全判别分析

针对现有的线性判别分析算法中存在的降维舍弃空间的判别信息丢失问题,以及秩空间和零空间的判别信息难以兼顾的问题,本文提出了一种完全判别分析算法.算法通过新构建一个子空间以及其中的判别矩阵,实现了可以充分使用全空间的判别信息;且新空间的维数较低,算法流程简单,计算代价较小.相关实验结果证实了本文算法较传统判别分析算法有更好的性能和效率.