光谱导数Kalman滤波同时测定苯酚-邻氯苯酚和2,4二氯苯酚

文中提出了一个新的、稳定的光谱导数Kalman滤波紫外分光光度法同时定量分析方法,并且成功地将其应用于极难分辨的苯酚-邻氯苯酚和2,4二氯苯酚三组分混合体系的同时测量.选择分析光谱导数的原因是其不仅包含着吸光度,还包括在指定波长位置变化趋向等更多的信息量,这样更利于在吸收峰重叠的位置捕捉有较大差异的信号.利用Kalman滤波可以解决由光谱导数而引起的噪声放大问题,也可以过滤源于实验的噪声以及来自传递模型误差.在260～290 nm区间测量了30组浓度各自在1～10 mg·L-1范围的苯酚-邻氯苯酚和2,4二氯苯酚标准混合溶液紫外吸收光谱图.利用分段延伸高次多项式回归模拟求导准确获得吸光度导数,并且利用偏最小二乘法将其制作成Kalman滤波标准工作系数矩阵.通过随机线性离散系统的Kalman滤波器最优平滑计算,对混合体系中的各组分进行定量分析.回收实验显示出,光谱导数Kalman滤波分析法应用于本实验的极难分辨的三组分体系,得到了非常高的回收率,并且在整个实验范围内光谱导数Kalman滤波分析法具有非常好的稳定性.     

小波域高斯混合模型与中值滤波的混合图像去噪研究

基于高斯混合模型的小波去噪方法并结合中值滤波法对脉冲噪音有较好滤除效果的特点,将这两种方法结合起来,对含有高斯脉冲混合噪音图像进行去噪处理.该算法采用Matlab语言进行仿真.实验结果表明,这种混合去噪方法的效果要优于单纯使用中值滤波或者小波去噪的效果.     

Detrending fluctuation analysis based on high-pass filtering

Detrended fluctuation analysis (DFA) is a scaling method commonly used for detecting long-range correlations in non-stationary time series. The DFA method uses a trend based on polynomial fitting to extract and quantify fluctuations at different time scales. Basically, such procedure acts as a (non-dynamical) high-pass filter that removes time series components below a given time scale. As an alternative to the polynomial fitting approach, this paper proposes a DFA method based on well-known high-pass filters (e.g., Butterworth, elliptic, etc.). Numerical results show that the proposed DFA approach yields results similar to traditional DFA method. Maybe, the main advantage of the proposed DFA method is that efficient implementations of high-pass filters are available commercially.     

Identification of a fault zone ahead of the tunnel excavation face using the extended Kalman filter

Simulation of mechanized tunneling and on-site excavation require very good knowledge of the geomechanical and material properties. Identification of the material must be fast and continuously performed during tunnel excavation for the best possible strategies for advancing the tunnel boring machine. We present in this work the use of the extended Kalman filter (EKF) for identification of the inclined fault zone ahead of the face. The EKF showed fast and stable convergence of the model parameters under study. In comparison with the particle swarm optimization technique applied to the same back analysis problem, faster convergence of the identified parameters as well as high robustness with respect to the choice of the initial parameter values have been observed.     

Lie-trotter product formulas for nonlinear filtering

The nonlinear filtering problem for a diffusion process whose drift and diffusion coefficients depend parametrically on a finite-state jump process involves the solution of a vector system of linear, stochastic partial differential equations. A Lie-Trotter product formula is proven to hold for this system and a recursive implementation is discussed.     

Information theory of optimization in chromatography: Analytical structure of optimization

Summary The analytical roles of chromatographic variables (column length, etc.) can be soundly comprehended and compared in terms of the precision (Φ) of measurements and efficiency (ϑ) of analysis which are described as Shannon information and information flow, respectively. The φϑ plots of the optimization process and the information Φ transmitted by a single peak are useful to understand the analytical structure of optimization. Variables treated here are mobile phase composition (X), column length (L), mobile phase velocity rate (u), detection wavelength (λ) and plate number (N).     

Quantised phase filters with binary low and high pass amplitude response: the effect of quantisation for scale and rotation input distortions

In the field of optical pattern recognition, there are a large number of filters that possess identification capabilities for certain kinds of input distortions. The resolution of the spectral components plays an important role in the overall processing time of a correlator that may employ such filters. In this paper, two different techniques are discussed: low pass phase filtering and high pass phase filtering. This is achieved by binarizing the amplitude information which then allows certain frequencies to pass and others to be blocked. Their response to different kinds of input distortions is presented and the effect of phase quantisation is considered in detail.     

Generation of turbulent inflow and initial conditions based on multi‐correlated random fields

Direct or large eddy simulation of a turbulent flow field is strongly influenced by its initial or inflow boundary condition. This paper presents a new stochastic approach to generate an artificial turbulent velocity field for initial or inflow boundary condition based on digital filtering. Each velocity component of the artificial turbulent velocity field is generated by linear combination of individual uncorrelated random fields. These uncorrelated random fields are obtained by filtering random white‐noise fields. Using common elements in these linear combinations results in multi‐correlation among different velocity components. The generated velocity field reproduces locally desired Reynolds stress components and integral length scales including cross‐integral length scales. The method appears to be simple, flexible and more accurate in comparison with previously developed methods. The accuracy and performance of the method are demonstrated by numerical simulation of a homogeneous turbulent shear flow with high and low shear rates. To assess the accuracy and performance of the method, simulation results are compared with a reference simulation. Copyright © 2007 John Wiley & Sons, Ltd.     

Cyclodextrin Functionalized Carbon Dots Using IFE and FRET Dual Mechanism for Sequential Detection of Morin and Al3+

Carbon dots (CDs) are emerging photoluminescent materials with excellent optical properties. However, the lack of active sites in primitive CDs has limited their development applications. Herein, functionalized carbon dots (Z-CDs) are successfully prepared by surface modification of CDs with mono (6-amino-6-deoxy) cyclodextrin (β-CD). The introduction of β-CD increases the spatial potential resistance between CDs, which effectively reduces the self-quenching effect. Moreover, the conjugated domains of Z-CDs are expanded, which improves the optical properties with a quantum yield of 48.74%. Z-CDs are able to be used in the sequential detection of morin and Al³⁺, and the fluorescence mechanisms are confirmed to be internal filtration effect and fluorescence resonance energy transfer, respectively. The limits of detection are 0.817 and 0.231 × 10⁻⁶ m . This study not only provides an idea to solve the problem of self-quenching of CDs but also enriches the detection means of flavonoids and ions, which is expected to be applied to biosensing and environmental monitoring.     

Probabilistic Predictions with Federated Learning

Probabilistic predictions with machine learning are important in many applications. These are commonly done with Bayesian learning algorithms. However, Bayesian learning methods are computationally expensive in comparison with non-Bayesian methods. Furthermore, the data used to train these algorithms are often distributed over a large group of end devices. Federated learning can be applied in this setting in a communication-efficient and privacy-preserving manner but does not include predictive uncertainty. To represent predictive uncertainty in federated learning, our suggestion is to introduce uncertainty in the aggregation step of the algorithm by treating the set of local weights as a posterior distribution for the weights of the global model. We compare our approach to state-of-the-art Bayesian and non-Bayesian probabilistic learning algorithms. By applying proper scoring rules to evaluate the predictive distributions, we show that our approach can achieve similar performance as the benchmark would achieve in a non-distributed setting.