Blind Channel Identification Based on Noisy Observation by Stochastic Approximation Method

A stochastic approximation algorithm for estimating multichannel coefficients is proposed, and the estimate is proved to converge to the true parameters a.s. up-to a constant scaling factor. The estimate is updated after receiving each new observation, so the output data need not be collected in advance. The input signal is allowed to be dependent and the observation is allowed to be corrupted by noise, but no noise statistics are used in the estimation algorithm.     

The Problem of Aliasing in Identifying Finite Parameter Continuous Time Stochastic Models

This note exposits the problem of aliasing in identifying finite parameter continuous time stochastic models, including econometric models, on the basis of discrete data. The identification problem for continuous time vector autoregressive models is characterised as an inverse problem involving a certain block triangular matrix, facilitating the derivation of an improved sufficient condition for the restrictions the parameters must satisfy in order that they be identified on the basis of equispaced discrete data. Sufficient conditions already exist in the literature but these conditions are not sharp and rule out plausible time series behaviour.     

Fastiterative methods for least squares estimations

Least squares estimations have been used extensively in many applications, e.g. system identification and signal prediction. When the stochastic process is stationary, the least squares estimators can be found by solving a Toeplitz or near-Toeplitz matrix system depending on the knowledge of the data statistics. In this paper, we employ the preconditioned conjugate gradient method with circulant preconditioners to solve such systems. Our proposed circulant preconditioners are derived from the spectral property of the given stationary process. In the case where the spectral density functions() of the process is known, we prove that ifs() is a positive continuous function, then the spectrum of the preconditioned system will be clustered around 1 and the method converges superlinearly. However, if the statistics of the process is unknown, then we prove that with probability 1, the spectrum of the preconditioned system is still clustered around 1 provided that large data samples are taken. For finite impulse response (FIR) system identification problems, our numerical results show that annth order least squares estimator can usually be obtained inO(n logn) operations whenO(n) data samples are used. Finally, we remark that our algorithm can be modified to suit the applications of recursive least squares computations with the proper use of sliding window method arising in signal processing applications.Research supported in part by HKRGC grant no. 221600070, ONR contract no. N00014-90-J-1695 and DOE grant no. DE-FG03-87ER25037.     

基于改进克隆选择算法的统一混沌系统同步控制与参数辨识

提出一种改进的克隆选择算法用于解决混沌系统的参数辨识问题.该算法利用抗体的高频变异和受体编辑两种机制有效平衡算法的全局探索与局部开发，并引入向精英抗体学习策略进一步提高算法的收敛质量.对10个优化问题的实验表明：所提出算法在求解精度、收敛速度以及稳定性方面具有更好的性能.以参数未知统一混沌系统的同步控制为研究对象，合理设计同步控制器，并对同步系统的稳定性进行理论分析.通过对同步比例因子的设置，实现统一混沌系统的完全同步、反同步、投影同步等多种同步方式.仿真实验结果表明该方法能够实现对未知系统参数的精确辨识以及驱动-响应系统的有效同步控制，验证了所提方法的可行性与有效性.     

基于长短时记忆神经网络的能谱核素识别方法

针对新兴的能谱核素识别方法在混合放射性核素的噪声环境中存在识别速度慢、准确率较低等问题,提出了基于长短时记忆神经网络（LSTM）的能谱核素识别方法。实验使用溴化镧（LaBr₃）晶体探测器,分别对环境中⁶⁰Co、¹³⁷Cs放射性源分组测量得到能谱数据集,首先使用数据平滑方法和归一化方法进行数据预处理,然后将能谱数据按时间序列分组以获得可用的输入序列数组,最后训练LSTM模型得到预测结果。通过基于BP神经网络和卷积神经网络（CNN）的两个能谱识别模型进行对比,得到在测试集中平均识别率分别为83.45%和86.21%,而LSTM能谱识别模型平均识别率为93.04%,实验结果表明,该能谱模型在核素识别效果中表现较好,可用于快速的能谱核素识别设备上。     

Experimental Investigation of the Effect of Purge Flow and Main Flow Interaction in a Low-Speed Turbine Cascade Passage

In order to protect the vulnerable turbine components from extreme high temperature, coolant flow is introduced from the compressor to the disk cavity, inevitably interacting with the main flow. This paper describes an experimental investigation of the interaction between the main flow and the purge flow in a low-speed turbine cascade with three purge flow rates, Cm = 0, Cm = 1%, and Cm = 2%. In order to study the effect of the interaction between the main flow and the purge flow on the secondary flows, a Rortex method developed by Liu Chaoquan is introduced to identify the vortex in the flow field. In the meantime, a method to calculate the mean entropy production rate based on the particle image velocimetry (PIV) result is adopted to investigate the flow loss. The PIV result indicates that the purge flow has a prominent impact on the flow field of the cascade passage, changing the velocity distribution that induces a local blockage area. The results of vortex identification show that the purge flow promotes the generation of the passage vortex near the suction side. In addition, the purge flow makes the passage vortex migrate to the tip wall direction, enlarging the region affected by the secondary flow. The mean entropy production (MEP) result shows that the flow loss is mainly caused by the passage vortex. The coincidence of the high-MEP region and the location of the passage vortex indicates that the purge flow increases the secondary flow loss by affecting the formation and the migration of the passage vortex.     

近红外光谱在转基因玉米检测识别中的应用

随着转基因食品的推广应用,人们越来越关心其食用安全性。以转基因玉米及其亲本为实验材料,借助于近红外光谱仪对转基因玉米及其亲本进行了识别分析：扫描区间为12 000～4 000 cm-1,分辨率为4 cm-1,扫描次数为64次;以三层误差反向传播算法(简称BP算法)进行数据处理。结果显示通过扫描光谱及数学和计算机软件分析,非常准确、方便地识别了转基因农产品。所以通过近红外光谱所建立的转基因BP-网络识别模型完全可用于实际应用。近红外分析还具有无污染、成本低等优点,是一种极具前景的转基因食品安全检测识别技术。     

Adaptive synchronization and lag synchronization of uncertain dynamical system with time delay based on parameter identification

In this paper, the adaptive synchronization and lag synchronization are considered for uncertain dynamical system with time delay based on parameter identification and a novel control method is then further given using the Lyapunov functional method. With this new and effective method, parameter identification and lag synchronization can be achieved simultaneously. Simulation results are given to justify the theoretical analysis in this paper.     

三维可穿透目标远场声波反演的一种指示器样本方法

提出了用时谐声散射场的远场信息来可视化三维可穿透目标的一种指示器样本方法,它是通过析取一个指示器函数在包含可穿透目标的某个样本区域中的支集来实现这种可视化的,其中,这个指示器函数在可穿透目标的内部和外部有显著不同的取值.这个算法的一个特别吸引人的性质是不需要关于障碍物的任何几何和物理的先验信息,并且只需要散射场在某个有限孔径中若干个入射和测量方向上的远场信息,即可得到可穿透目标的一个很理想的可视化.数值算例保证了这个可视化算法是有效和实用的 关键词： 声散射 反问题 物形反演     

基于激光共聚焦显微拉曼技术的藻种鉴别研究

微藻种类的鉴别和分类是研究微藻生理生化特性的基础和前提。微藻细胞中主要包含五种生物分子,包括蛋白质、糖类、油脂、核酸和色素,在不同藻种会有不同的比例含量,常常作为藻种鉴别的一种依据。文章探讨了采用激光共聚焦显微拉曼技术快速鉴别普通小球藻(Chlorella sp.)、莱茵衣藻(Chlamydomonas sp.)两种不同藻种的可行性。通过在相同光照时间、强度和相同培养基的条件下培养的两种微藻,利用琼脂固定法固定微藻细胞,在514.5 nm的激光下采集了不同藻种及其不同生长时期的拉曼光谱曲线,并通过rolling circle filter(RCF)算法去除荧光背景,然后采用去基线、卷积平滑等预处理方法得到两种藻种各40个样本的曲线。从80个样本中随机抽取50个样本训练建模,剩下的30个样本作为独立的验证集。对光谱数据采取不同的预处理方法,采用偏最小二乘(partial least squares,PLS)全波段建模建立光谱响应特征与普通小球藻、莱茵衣藻的关系模型,比较了不同预处理程度的效果。结果表明：利用激光共聚焦显微拉曼技术,基于不同藻种色素含量比的差异,同时结合化学计量学方法,可以快速、有效地将两种藻种鉴别出来。所提出的最大谱峰比值标准化法处理样本,当阈值为±0.5时,预测正确率达到100%,当阈值为±0.2时,预测正确率达到86.67%,表明所提出的新方法能在藻种鉴别和分类领域具有较高的可行性。