Nonlinear system fault diagnosis based on adaptive estimation

An approach to fault diagnosis for a class of nonlinear systems is proposed in this paper. It is based on a new adaptive estimation algorithm for recursive estimation of the parameters related to faults. This algorithm is designed in a constructive manner through a nontrivial combination of a high gain observer and a recently developed linear adaptive observer, without resort to any linearization. Its global exponential convergence is ensured by an easy-to-check persistent excitation condition. A numerical example is presented for illustration.     

Autoregressive Wiener filtering in a scanning electron microscopy imaging system

In this paper, we propose to use the autoregressive (AR)-based interpolator with Wiener filter and apply the idea to scanning electron microscope (SEM) images. The concept for combining the AR-based interpolator with Wiener filtering comes from the essential requirement of Wiener filtering for accurate and consistent estimation of the power of the noise in images prior to filter implementation. The resultant filter is called AR-Wiener filter. The proposed filter is embedded onto the frame grabber card of the scanning electron microscope (SEM) for real-time image processing. Different images are captured using SEM and used to compare the performances of the conventional Wiener and the proposed AR-Wiener technique.     

Image signal-to-noise ratio estimation using Shape-Preserving Piecewise Cubic Hermite Autoregressive Moving Average model

We propose to cascade the Shape-Preserving Piecewise Cubic Hermite model with the Autoregressive Moving Average (ARMA) interpolator; we call this technique the Shape-Preserving Piecewise Cubic Hermite Autoregressive Moving Average (SP2CHARMA) model. In a few test cases involving different images, this model is found to deliver an optimum solution for signal to noise ratio (SNR) estimation problems under different noise environments. The performance of the proposed estimator is compared with two existing methods: the autoregressive-based and autoregressive moving average estimators. Being more robust with noise, the SP2CHARMA estimator has efficiency that is significantly greater than those of the two methods.     

Blind colour separation of H&E stained histological images by linearly transforming the colour space

Blind source separation methods aim to split information into the original sources. In histology, each dye component attempts to specifically characterize different microscopic structures. In the case of the hematoxylin–eosin stain, universally used for routine examination, quantitative analysis may often require the inspection of different morphological signatures related mainly to nuclei patterns, but also to stroma distribution. Stain separation is usually a preprocessing operation that is transversal to different applications. This paper presents a novel colour separation method that finds the hematoxylin and eosin clusters by projecting the whole space to a folded surface connecting the distributions of a series of planes that divide the cloud of H&E tones. The proposed method produces density maps closer to those obtained with the colour mixing matrices set by an expert, when comparing with the density maps obtained using nonnegative matrix factorization (NMF), independent component analysis (ICA) and a state‐of‐the‐art method. The method has outperformed three baseline methods, NMF, Macenko and ICA, in about 8%, 12% and 52% for the eosin component, whereas this was about 4%, 8% and 26% for the hematoxylin component.     

Robust incremental compensation of the light attenuation with depth in 3D fluorescence microscopy

Fluorescent signal intensities from confocal laser scanning microscopes (CLSM) suffer from several distortions inherent to the method. Namely, layers which lie deeper within the specimen are relatively dark due to absorption and scattering of both excitation and fluorescent light, photobleaching and/or other factors. Because of these effects, a quantitative analysis of images is not always possible without correction. Under certain assumptions, the decay of intensities can be estimated and used for a partial depth intensity correction. In this paper we propose an original robust incremental method for compensating the attenuation of intensity signals. Most previous correction methods are more or less empirical and based on fitting a decreasing parametric function to the section mean intensity curve computed by summing all pixel values in each section. The fitted curve is then used for the calculation of correction factors for each section and a new compensated sections series is computed. However, these methods do not perfectly correct the images. Hence, the algorithm we propose for the automatic correction of intensities relies on robust estimation, which automatically ignores pixels where measurements deviate from the decay model. It is based on techniques adopted from the computer vision literature for image motion estimation. The resulting algorithm is used to correct volumes acquired in CLSM. An implementation of such a restoration filter is discussed and examples of successful restorations are given.     

An integrated system for process planning and cost estimation in hole making

The process planning function plays a critical role not only in computer integrated manufacture, but also in the economics of batch manufacturing. This paper describes the development of a generic knowledge-based system for process planning and cost estimation in the hole making process. The main function of the system, besides estimating the cost of production, is to recommend appropriate processes, their sequence and their respective machining conditions in order to obtain the required product specifications. The system has been implemented in a manufacturing organisation. The results obtained have indicated that in addition to cost savings the system also preserves the consistency in planning and cost estimation.     

车辆动力学稳定性系统变结构滑模控制研究

探讨了转弯车辆行驶在极限运动工况下时，依靠施加各车轮不同纵向制动力从而产生辅助横摆力矩来提高车辆动力学稳定性的基本原理，推导了两自由度车辆横向动力学方程，提出了车辆侧滑速度的3种实时估计方案（积分法，代数法和Luenberger观察器法），视实际车辆前后轮胎侧偏刚度为有界不确定性参数，为跟踪线性两自由度理想车辆模型的稳态输出响应，设计了车辆动力学稳定性变结构控制策略，通过仿真验证了该方案可行性。     

基于信道特征提取的物理层安全密钥生成方法

物理层密钥具有降低密钥分发、更新和维护的难度的特点,针对无线传感器网络中节点传输信息易遭受窃听攻击的问题,基于物理层安全中的"一次一密"理论,提出了一种物理层密钥生成方法,主要包含信道估计、特征提取、密钥生成与协商。在半双工通信模式下,根据信道估计阶段相干时间的长短,对训练序列的长度进行动态调整,提高信道估计的一致性。仿真试验与对比分析结果表明,窃听节点生成密钥与合法节点生成密钥的一致性始终维持在较低水平,降低了遭受窃听攻击的风险。     

Precision of stereological planar area predictors

Total planar area can be estimated based on sampling by a lattice of figures (e.g. point patterns, line segments, quadrats). General formulae are provided for the approximation of mean squared errors. The approximation formulae are products of the boundary length and of a parameter that depends only on the sampling scheme. An R package is provided by the authors for the numerical computation of the mean squared error formulae. The speed of convergence of the mean squared error approximation is assessed on the basis of several simulations. Several sampling schemes are compared in view of the approximated mean squared errors.     

密度峰值聚类的自适应社区发现算法

为减少社区发现算法中参数的选择对社区划分的影响,同时使算法能够自适应地进行社区划分,本文提出一种基于核密度估计的密度峰值聚类的社区发现算法KDED.首先,定义一种基于信任度的距离度量,将社交网络中的用户关系量化为距离矩阵,使用矩阵元素的大小度量用户关系的紧密程度;然后对距离矩阵进行核密度估计,统计各个节点在网络中的影响大小,结合热扩散模型改进计算流程,使其自适应不同规模的数据集以提高计算精度;结合密度峰值聚类原理和社区属性确定社区中心节点后,可根据节点间的距离得到社区内部层次结构和社区外部的自然结构;最后将剩余节点按距离分配到相应的社区当中以完成社区划分.仿真结果表明:通过可视化软件可观察到,通过KDED算法得到的社区划分结果具有清晰的自然结构和内部层次结构;随着社区规模的提升以及划分难度增加,KDED算法具有出色的稳定性;在真实数据集以及LFR基准网络上均得到较为接近真实划分结果的社区划分,自适应性良好,验证算法的可行性与有效性.