Shape from focus based on 3D structure tensor using optical microscopy

Shape from focus (SFF) is a widely used passive optical method for 3D shape reconstruction. In SFF, a focus measure, which is used to estimate the relative focus level, plays a critical role in depth estimation. In this article, we present a new focus measure for accurate 3D shape estimation in optical microscopy based on the analysis of 3D structure tensor. First, the 3D tensors are computed from the input image sequence for each pixel. Then, each tensor is decomposed into point, curve, and surface tensors by decomposing tensors into eigenvalues and eigenvectors. Finally, the surfaceness is used to measure the quality of sharpness. The proposed focus measure provides accurate focus values and better resistance against noise. The proposed measure is evaluated by conducting experiments using image sequences of simulated and microscopic real objects. The comparative analysis demonstrates the effectiveness of the proposed focus measure in recovering 3D shape.     

A multi-tone central divided difference frequency tracker with adaptive process noise covariance tuning

The problem of real-time frequency estimation of nonstationary multi-harmonic signals is important in many applications. In this paper, we propose a novel multi-frequency tracker based on a state-space representation of the signal with Cartesian filters and the second-order central divided difference filter (CDDF), which improves the performance of the extended Kalman filter (EKF) by using Stirling's interpolation method to approximate the mean and covariance of the state vector. A crucial element of the method is the adaptive scaling of the process noise covariance matrix appearing in the filter equations, as a function of the innovation sequence, which tunes the accuracy-reactivity trade-off of the filter. The proposed solution is evaluated against two approaches from the literature, namely the factorized adaptive notch filter (FANF) and the extended Kalman filter frequency tracker (EKFFT). Several experiments emphasize the estimation accuracy of the proposed method as well as the improved robustness with respect to initial errors and input signal complexity. The presented method appears to be particularly efficient with rapidly varying frequencies, thanks to the update mechanism that adjusts the filter parameters based on the amplitude of the estimation error.     

基于改进交叉熵算法的电力系统随机生产模拟

蒙特卡洛模拟的计算效率与系统的可靠性密切相关，在其用于高可靠性系统的随机模拟时存在计算效率偏低的问题。为此，提出了一种基于多层交叉熵与对偶变数抽样技术相结合的随机模拟算法。首先使用多层交叉熵构造零方差概率密度函数的近似函数，提高小概率失效事件的抽取概率；其次基于已构造的近似概率函数，采用对偶变数抽样法进行抽样，进一步提高抽样的收敛速度。以IEEE RTS修改系统为例进行了算例分析，算例结果验证了所提出的基于改进交叉熵的电力系统随机生产模拟算法的有效性。     

Distributed data-driven observer for linear time invariant systems

This paper is concerned with distributed data-driven observer design problem. The existing data-driven observers rely on a common assumption that all the information about the system, and the calculations based upon this information are centralized. Therefore the resulting algorithms cannot be applied to the distributed systems in which each local observer receives only a part of the output signal. On the other hand, traditional model-based distributed state estimation methods generally assume that the processes are decomposed according to the known process models, while in data-driven approaches there is no such information available. The main goal of this paper is to extend the centralized data-driven observer design approach to the distributed framework. The stability of the proposed data-driven distributed observer is also proved analytically. A quadruple-tank process is simulated to demonstrate the performance of the proposed scheme.     

Outlier accommodation in moving-horizon state estimation: A risk-averse performance-specified approach

This paper presents a novel state estimation approach for linear dynamic systems when measurements are corrupted by outliers. Since outliers can degrade the performance of state estimation, outlier accommodation is critical. The standard approach combines outlier detection utilizing Neyman-Pearson (NP) type tests with a Kalman filter (KF). This approach ignores all residuals greater than a designer-specified threshold. When measurements with outliers are used (ie, missed detections), both the state estimate and the error covariance matrix become corrupted. This corrupted state and covariance estimate are then the basis for all subsequent outlier decisions. When valid measurements are rejected (ie, false alarms), potentially using the corrupted state estimate and error covariance, measurement information is lost. Either using invalid information or discarding too much valid information can result in divergence of the KF. An alternative approach is moving-horizon (MH) state estimation, which maintains all recent measurement data within a moving window with a time horizon of length L. In MH approaches, the number of measurements available for state estimation is affected by both the number of measurements per time step and the number of time steps L over which measurements are retained. Risk-averse performance-specified (RAPS) state estimation works within an optimization setting to choose a set of measurements that achieves a performance specification with minimum risk of outlier inclusion. This paper derives and formulates the MH-RAPS solution for outlier accommodation. The paper also presents implementation results. The MH-RAPS application uses Global Navigation Satellite Systems measurements to estimate the state of a moving platform using a position, velocity, and acceleration model. In this application, MH-RAPS performance is compared with MH-NP state estimation.     

Process noise covariance estimation via stochastic approximation

Kalman filtering for linear systems is known to provide the minimum variance estimation error, under the assumption that the model dynamics is known. While many system identification tools are available for computing the system matrices from experimental data, estimating the statistics of the output and process noises is still an open problem. Correlation-based approaches are very fast and sufficiently accurate, but there are typically restrictions on the number of noise covariance elements that can be estimated. On the other hand, maximum likelihood methods estimate all elements with high accuracy, but they are computationally expensive, and they require the use of external optimization solvers. In this paper, we propose an alternative solution, tailored for process noise covariance estimation and based on stochastic approximation and gradient-free optimization, that provides a good trade-off in terms of performance and computational load, and is also easy to implement. The effectiveness of the method as compared to the state of the art is shown on a number of recently proposed benchmark examples.     

基于多项式变换的参数估计方法

本文给出一种采用DPT估计SNCK信号时宽—带宽积的方法,并通过仿真该估计方法的性能与其它估计方法进行比较.首先给出SNCK信号参数估计的一般过程.为了便于计算和理论推导,根据估计出的中心频率将接收到的SNCK信号搬移到零频,从而进一步估计其它参数,如采用DPT估计SNCK信号时宽带宽积.本文将重点研究采用DPT算法估计SNCK信号值的方法.     

Efficacy of utilizing a hybrid algorithmic method in enhancing the functionality of multi-instance multi-label radial basis function neural networks

The facts show that multi-instance multi-label (MIML) learning plays a pivotal role in Artificial Intelligence studies. Evidently, the MIML learning introduces a framework in which data is described by a bag of instances associated with a set of labels. In this framework, the modeling of the connection is the challenging problem for MIML. The RBF neural network can explain the complex relations between the instances and labels in the MIMLRBF. The parameters estimation of the RBF network is a difficult task. In this paper, the computational convergence and the modeling accuracy of the RBF network has been improved. The present study aimed to investigate the impact of a novel hybrid algorithm consisting of Gases Brownian Motion optimization (GBMO) algorithm and the gradient based fast converging parameter estimation method on multi-instance multi-label learning. In the current study, a hybrid algorithm was developed to estimate the RBF neural network parameters (the weights, widths and centers of the hidden units) simultaneously. The algorithm uses the robustness of the GBMO to search the parameter space and the efficiency of the gradient. For this purpose, two real-world MIML tasks and a Corel dataset were utilized within a two-step experimental design. In the first step, the GBMO algorithm was used to determine the widths and centers of the network nodes. In the second step, for each molecule with fixed inputs and number of hidden nodes, the parameters were optimized by a structured nonlinear parameter optimization method (SNPOM). The findings demonstrated the superior performance of the hybrid algorithmic method. Additionally, the results for training and testing the dataset revealed that the hybrid method enhances RBF network learning more efficiently in comparison with other conventional RBF approaches. The results obtain better modeling accuracy than some other algorithms.     

Anti‐Sway Crane Control Considering Wind Disturbance and Container Mass

A method for estimating the sway angle using an observer has already been proposed. The state observer estimates the sway angle accurately and must use the detected sway angle value. However, the estimated sway angle has an error owing to rope length error, friction force, and wind. Moreover, the container mass cannot be determined, and therefore the observer parameter is not suitable. We already proposed robust antisway control for overcoming rope length error without adding a new sensor. Further, we designed a friction disturbance observer to cancel out the influence of the friction force. In this paper, we first propose a container mass estimation method when a crane system performs rolling up control. The observer parameter can be selected using the estimated mass value. Second, in crane parallel shift control, we propose a robust antisway control even when there is a wind disturbance. We design a wind disturbance observer and propose a wind disturbance estimator to separate the friction observer output from the wind disturbance observer output. We confirm through experiments that the proposed method can reduce vibration.     

A new class of nonlinear Rauch–Tung–Striebel cubature Kalman smoothers

In this paper, a new Rauch–Tung–Striebel type of nonlinear smoothing method is proposed based on a class of high-degree cubature integration rules. This new class of cubature Kalman smoothers generalizes the conventional third-degree cubature Kalman smoother using the combination of Genz׳s or Mysovskikh׳s high-degree spherical rule with the moment matching based arbitrary-degree radial rule, which considerably improves the estimation accuracy. A target tracking problem is utilized to demonstrate the performance of this new smoother and to compare it with other Gaussian approximation smoothers. It will be shown that this new cubature Kalman smoother enhances the filtering accuracy and outperforms the extended Kalman smoother, the unscented Kalman smoother, and the conventional third-degree cubature Kalman smoother. It also maintains close performance to the Gauss–Hermite quadrature smoother with much less computational cost.