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.     

结构化加权稀疏低秩恢复算法在人脸识别中的应用

针对训练样本或测试样本存在污损的情况，提出一种结构化加权稀疏低秩恢复算法（structured and weighted-sparse low rank representation，SWLRR）。SWLRR对低秩表示进行加权稀疏约束和结构化约束，使得低秩表示系数更加趋近于块对角结构，进而可获得具有判别性的低秩表示。SWLRR将训练样本恢复成干净训练样本后，再根据原始训练样本和恢复后的训练样本学习到低秩投影矩阵，把测试样本投影到相应的低秩子空间，即可有效地去除测试样本中的污损部分。在几个人脸数据库上的实验结果验证了SWLRR在不同情况下的有效性和鲁棒性。     

Subspace Clustering for Situation Assessment in Aquatic Drones: A Sensitivity Analysis for State-Model Improvement

Abstract

In this paper, we propose the use of subspace clustering to detect the states of dynamical systems from sequences of observations. In particular, we generate sparse and interpretable models that relate the states of aquatic drones involved in autonomous water monitoring to the properties (e.g., statistical distribution) of data collected by drone sensors. The subspace clustering algorithm used is called SubCMedians. A quantitative experimental analysis is performed to investigate the connections between i) learning parameters and performance, ii) noise in the data and performance. The clustering obtained with this analysis outperforms those generated by previous approaches.     

子空间夹角结合时间序列分析判定反应终点

拉曼光谱经过基线校正处理后,运用子空间模式识别原理,反应物水杨酸、醋酸酐以及催化剂氨基磺酸的拉曼光谱作为子空间,各个反应时刻的拉曼光谱作为被关注向量,求取被关注与子空间夹角,通过时间序列分析模型对空间向量夹角变动分析体系内组分变化情况设经验阀值δ(ti)小于0.5判定为反应终点,此时水杨酸的转化率为97.8%。     

基于随机子空间结合稳定图的拱桥模态参数识别方法

为了避免目前常用的结构模态参数识别方法容易出现虚假模态等缺陷,提出了一种将随机子空间法与稳定图法相结合的模态参数识别方法。通过随机子空间法有效地从环境激励的结构响应中获取模态参数,通过稳定图方法确定系统阶次。通过对稳定图方法的改进,避免了虚假模态的出现,进而提高了随机子空间方法的识别精度,并在一钢管混凝土拱桥上对这种新方法进行了验证。结果表明,该方法具有良好的识别效果。     

桥梁风洞试验模态参数识别的随机子空间方法

模态参数识别是桥梁风洞试验中的一项重要内容,发展了基于参考点、无需输出协方差估计的识别振动系统模态参数的随机子空间方法,其识别精度和可靠性通过数值算例验证。对状态空间理论中的稳定图作了全面的阐释。根据苏通大桥和苏拉马都大桥主梁三自由度节段模型风洞试验采集到的位移信号,采用随机子空间方法识别了侧弯、竖弯和扭转模态频率和阻尼比,并且与随机搜索方法识别结果进行对比。分析结果表明,随机子空间方法和随机搜索方法识别结果非常吻合,本文发展的随机子空间方法是识别风洞试验桥梁模态参数的一种有效和实用方法。     

抑制局部放电信号中周期性窄带干扰的子空间重构方法

为有效提取局部放电信号,提出一种基于子空间重构的窄带干扰抑制方法。对局部放电信号数据形成的HANKEL矩阵进行奇异值分解,将信号划分为窄带干扰子空间和局部放电信号子空间。利用子空间数据重构窄带干扰波形,然后与原始数据相减,得到抑制干扰后的局部放电信号。该方法能在保证局部放电信号失真较小的同时有效去除窄带干扰,抗随机干扰能力较强。仿真和实测信号的处理结果验证了其有效性。