Robust Estimation for Bivariate Poisson INGARCH Models

In the integer-valued generalized autoregressive conditional heteroscedastic (INGARCH) models, parameter estimation is conventionally based on the conditional maximum likelihood estimator (CMLE). However, because the CMLE is sensitive to outliers, we consider a robust estimation method for bivariate Poisson INGARCH models while using the minimum density power divergence estimator. We demonstrate the proposed estimator is consistent and asymptotically normal under certain regularity conditions. Monte Carlo simulations are conducted to evaluate the performance of the estimator in the presence of outliers. Finally, a real data analysis using monthly count series of crimes in New South Wales and an artificial data example are provided as an illustration.     

Bayesian compressive sensing for thermal imagery using Gaussian-Jeffreys prior

Recent advances have shown a great potential to explore compressive sensing (CS) theory for thermal imaging due to the capability of recovering high-resolution information from low-resolution measurements. In this paper, we present a Bayesian CS reconstruction algorithm that makes use of a new sparsity-inducing prior, referred as Gaussian-Jeffreys prior, and demonstrate performance gain of imposing this new prior on thermal imagery where the signal-to-noise ratio is low. We first derive a hierarchical representation of the Gaussian-Jeffreys prior that facilitates computational tractability, then propose an efficient evidence approximation inference algorithm. We show that the proposed estimator is able to provide stronger sparsity-inducing power comparing to the conventional choices. Extensive numerical examples are provided with performance comparisons of different CS estimators, in particular when the compressive measurements are available via thermal imaging.     

A Method for Unsupervised Semi-Quantification of Inmunohistochemical Staining with Beta Divergences

In many research laboratories, it is essential to determine the relative expression levels of some proteins of interest in tissue samples. The semi-quantitative scoring of a set of images consists of establishing a scale of scores ranging from zero or one to a maximum number set by the researcher and assigning a score to each image that should represent some predefined characteristic of the IHC staining, such as its intensity. However, manual scoring depends on the judgment of an observer and therefore exposes the assessment to a certain level of bias. In this work, we present a fully automatic and unsupervised method for comparative biomarker quantification in histopathological brightfield images. The method relies on a color separation method that discriminates between two chromogens expressed as brown and blue colors robustly, independent of color variation or biomarker expression level. For this purpose, we have adopted a two-stage stain separation approach in the optical density space. First, a preliminary separation is performed using a deconvolution method in which the color vectors of the stains are determined after an eigendecomposition of the data. Then, we adjust the separation using the non-negative matrix factorization method with beta divergences, initializing the algorithm with the matrices resulting from the previous step. After that, a feature vector of each image based on the intensity of the two chromogens is determined. Finally, the images are annotated using a systematically initialized k-means clustering algorithm with beta divergences. The method clearly defines the initial boundaries of the categories, although some flexibility is added. Experiments for the semi-quantitative scoring of images in five categories have been carried out by comparing the results with the scores of four expert researchers yielding accuracies that range between 76.60% and 94.58%. These results show that the proposed automatic scoring system, which is definable and reproducible, produces consistent results.     

Maximum Correntropy with Variable Center Unscented Kalman Filter for Robust Power System State Estimation

The robust Kalman filter with correntropy loss has received much attention in recent years for forecasting-aided state estimation in power systems, since it efficiently reduces the negative influence of various abnormal situations, such as non-Gaussian communication, changing environment, and instrument failures, and obviously improves the stability of power systems. However, the existing correntropy-based robust Kalman filters usually use the Gaussian function with a fixed center as the kernel function in correntropy, which may not be a suitable choice in practical applications of power system forecasting-aided state estimation (PSSE). To address this issue, a new and robust unscented Kalman filter, called the maximum correntropy with variable center unscented Kalman filter (MCVUKF), is proposed in this paper for PSSE. Specifically, MCVUKF adopts an extended version of correntropy, whose center can be located at any position, to replace the original correntropy in an unscented Kalman filter to improve the performance in PSSE. Moreover, by using an exponential function of the innovation vector to adjust a covariance matrix, an enhanced MCVUKF (En-MCVUKF) method is also developed for suppressing the influence of bad data to the innovation vector and further improving the accuracy of PSSE. Finally, extensive simulations have been conducted on IEEE 14-bus, 30-bus, and 57-bus test power systems, and the simulation results have shown the superiority of the proposed MCVUKF and En-MCVUKF methods compared with several related state-of-the-art Kalman filter methods.     

Bayesian Variable Selection and Estimation in Semiparametric Simplex Mixed-Effects Models with Longitudinal Proportional Data

In the development of simplex mixed-effects models, random effects in these mixed-effects models are generally distributed in normal distribution. The normality assumption may be violated in an analysis of skewed and multimodal longitudinal data. In this paper, we adopt the centered Dirichlet process mixture model (CDPMM) to specify the random effects in the simplex mixed-effects models. Combining the block Gibbs sampler and the Metropolis–Hastings algorithm, we extend a Bayesian Lasso (BLasso) to simultaneously estimate unknown parameters of interest and select important covariates with nonzero effects in semiparametric simplex mixed-effects models. Several simulation studies and a real example are employed to illustrate the proposed methodologies.     

Variational Sparse Bayesian Learning for Estimation of Gaussian Mixture Distributed Wireless Channels

In this paper, variational sparse Bayesian learning is utilized to estimate the multipath parameters for wireless channels. Due to its flexibility to fit any probability density function (PDF), the Gaussian mixture model (GMM) is introduced to represent the complicated fading phenomena in various communication scenarios. First, the expectation-maximization (EM) algorithm is applied to the parameter initialization. Then, the variational update scheme is proposed and implemented for the channel parameters’ posterior PDF approximation. Finally, in order to prevent the derived channel model from overfitting, an effective pruning criterion is designed to eliminate the virtual multipath components. The numerical results show that the proposed method outperforms the variational Bayesian scheme with Gaussian prior in terms of root mean squared error (RMSE) and selection accuracy of model order.     

Robust Aggregation for Federated Learning by Minimum γ-Divergence Estimation

Federated learning is a framework for multiple devices or institutions, called local clients, to collaboratively train a global model without sharing their data. For federated learning with a central server, an aggregation algorithm integrates model information sent from local clients to update the parameters for a global model. Sample mean is the simplest and most commonly used aggregation method. However, it is not robust for data with outliers or under the Byzantine problem, where Byzantine clients send malicious messages to interfere with the learning process. Some robust aggregation methods were introduced in literature including marginal median, geometric median and trimmed-mean. In this article, we propose an alternative robust aggregation method, named γ-mean, which is the minimum divergence estimation based on a robust density power divergence. This γ-mean aggregation mitigates the influence of Byzantine clients by assigning fewer weights. This weighting scheme is data-driven and controlled by the γ value. Robustness from the viewpoint of the influence function is discussed and some numerical results are presented.     

Robust fuzzy control for chaotic dynamics in Lorenz systems with uncertainties

This paper deals with the robust fuzzy control for chaotic systems in the presence of parametric uncertainties. An uncertain Takagi--Sugeno fuzzy model for a Lorenz chaotic system is first constructed. Then a robust fuzzy state feedback control scheme ensures the control for stable operations under bounded parametric uncertainties. For a chaotic system with known uncertainty bounds, a robust fuzzy regulator is designed by choosing the control parameters satisfying the linear matrix inequality. To verify the validity and effectiveness of the proposed controller design method, an analysis technique is suggested and applied to the control of an uncertain Lorenz chaotic system.     

Cramér-Rao Bound Expressions for Parametric Estimation of Overlapping Peaks: Influence of Prior Knowledge

We have derived analytical expressions of the Cramér-Rao lower bounds on spectral parameters for singlet, doublet, and triplet peaks in noise. We considered exponential damping (Lorentzian lineshape) and white Gaussian noise. The expressions, valid if a sufficiently large number of samples is used, were derived in the time domain for algebraic convenience. They enable one to judge the precision of any unbiased estimator as a function of the spectral and experimental parameters, which is useful for quantitation objectives and experimental design. The influence of constraints (chemical prior knowledge) on parameters of the peaks of doublets and triplets is demonstrated both analytically and numerically and the inherent benefits for quantitation are shown. Our expressions also enable analysis of spectra comprising many peaks.     

具有终端角度约束的自主水下航行器鲁棒制导律

针对具有末端角度约束的自主水下航行器(Automatic Underwater Vehicle,AUV)的制导问题,提出一种变结构导引方法。综合考虑自主航行器与目标的交会几何关系,采用近似零化视线角速度和随距离接近末端角度快速收敛向约束要求的准则,设计滑模控制器,实现对视线角速度和末端角度的控制。仿真证明了设计的制导算法可以实现水下自主航行器在具有末端角度约束条件下与目标的高精度交会,系统对目标机动具有较好的鲁棒性。     

观测平台转向时稀疏贝叶斯学习方位估计

针对观测平台转向时固定安装于其上的声呐线列阵指向快速变化导致的空间谱谱峰变宽问题,提出一种稀疏贝叶斯学习方位估计方法。该方法利用大地坐标系下不同接收快拍中的空域稀疏信号具有相同先验分布的特性,将转向过程中多个阵列指向分别对应的接收快拍信息联合处理,以求得目标方位。仿真分析与海试数据处理显示出,所提方法可以获得谱峰较尖锐的空间谱,具有较高的测向精度和角度分辨力,此外,对由左右舷模糊引起的伪峰有较强的抑制效果。所得结果表明,所提方法可以有效解决谱峰变宽问题,提升了平台转向时的方位估计性能,同时有效地利用阵列指向的变化提高了线阵抗左右舷模糊能力。     

Robust Trajectory Tracking Control for Continuous-Time Nonlinear Systems with State Constraints and Uncertain Disturbances

In this paper, a robust trajectory tracking control method with state constraints and uncertain disturbances on the ground of adaptive dynamic programming (ADP) is proposed for nonlinear systems. Firstly, the augmented system consists of the tracking error and the reference trajectory, and the tracking control problems with uncertain disturbances is described as the problem of robust control adjustment. In addition, considering the nominal system of the augmented system, the guaranteed cost tracking control problem is transformed into the optimal control problem by using the discount coefficient in the nominal system. A new safe Hamilton–Jacobi–Bellman (HJB) equation is proposed by combining the cost function with the control barrier function (CBF), so that the behavior of violating the safety regulations for the system states will be punished. In order to solve the new safe HJB equation, a critic neural network (NN) is used to approximate the solution of the safe HJB equation. According to the Lyapunov stability theory, in the case of state constraints and uncertain disturbances, the system states and the parameters of the critic neural network are guaranteed to be uniformly ultimately bounded (UUB). At the end of this paper, the feasibility of the proposed method is verified by a simulation example.     

From a Point Cloud to a Simulation Model—Bayesian Segmentation and Entropy Based Uncertainty Estimation for 3D Modelling

The 3D modelling of indoor environments and the generation of process simulations play an important role in factory and assembly planning. In brownfield planning cases, existing data are often outdated and incomplete especially for older plants, which were mostly planned in 2D. Thus, current environment models cannot be generated directly on the basis of existing data and a holistic approach on how to build such a factory model in a highly automated fashion is mostly non-existent. Major steps in generating an environment model of a production plant include data collection, data pre-processing and object identification as well as pose estimation. In this work, we elaborate on a methodical modelling approach, which starts with the digitalization of large-scale indoor environments and ends with the generation of a static environment or simulation model. The object identification step is realized using a Bayesian neural network capable of point cloud segmentation. We elaborate on the impact of the uncertainty information estimated by a Bayesian segmentation framework on the accuracy of the generated environment model. The steps of data collection and point cloud segmentation as well as the resulting model accuracy are evaluated on a real-world data set collected at the assembly line of a large-scale automotive production plant. The Bayesian segmentation network clearly surpasses the performance of the frequentist baseline and allows us to considerably increase the accuracy of the model placement in a simulation scene.     

A Robust Point Spread Function Estimation for Out-of-Focus Blurred and Noisy Images Based on a Distribution of Gradient Vectors on the Polar Plane

The estimation of the point spread function (PSF) is a very important and indispensable task for practical image restoration. Various PSF estimation algorithms have been developed, especially for the out-of-focus blur. However, a majority of them are useless in an extremely noisy environment. This paper describes a new robust PSF estimation algorithm based on a distribution of gradient vectors on the logarithmic amplitude spectrum mapped to the polar plane. The proposed algorithm can estimate the out-of-focus PSF accurately and robustly, even for an image highly corrupted by noise. The effectiveness of the proposed algorithm is verified by applying it to the PSF estimation for out-of-focus blurred and noisy images.     

Deep Coupling Recurrent Auto-Encoder with Multi-Modal EEG and EOG for Vigilance Estimation

Vigilance estimation of drivers is a hot research field of current traffic safety. Wearable devices can monitor information regarding the driver’s state in real time, which is then analyzed by a data analysis model to provide an estimation of vigilance. The accuracy of the data analysis model directly affects the effect of vigilance estimation. In this paper, we propose a deep coupling recurrent auto-encoder (DCRA) that combines electroencephalography (EEG) and electrooculography (EOG). This model uses a coupling layer to connect two single-modal auto-encoders to construct a joint objective loss function optimization model, which consists of single-modal loss and multi-modal loss. The single-modal loss is measured by Euclidean distance, and the multi-modal loss is measured by a Mahalanobis distance of metric learning, which can effectively reflect the distance between different modal data so that the distance between different modes can be described more accurately in the new feature space based on the metric matrix. In order to ensure gradient stability in the long sequence learning process, a multi-layer gated recurrent unit (GRU) auto-encoder model was adopted. The DCRA integrates data feature extraction and feature fusion. Relevant comparative experiments show that the DCRA is better than the single-modal method and the latest multi-modal fusion. The DCRA has a lower root mean square error (RMSE) and a higher Pearson correlation coefficient (PCC).     

IMDD-OFDM系统的低开支、高准确度信道估计方法

提出一种应用于强度调制直接检测光正交频分复用(IMDD-OFDM)传输系统的低开支、高准确度的信道估计算法.该算法充分考虑系统噪声特性,利用梳状导频插入结构,结合符号间平均与子载波间频域线性插值的思想,在低开支导频条件下实现较高的估计准确度.仿真和理论推导结果表明:与传统平均算法和直接线性插值算法相比,基于梳状导频先平均后线性插值的算法估计出来的信道特性更能接近实际信道的.实验结果表明:在误码率为3.8×10~(-3)处,本文所提出的算法仅使用0.78%导频开支即可与使用20%导频开支的平均算法获得相同的接收灵敏度;同时,与传统估计算法相比,该算法与导频开支无关,能较好抗系统中的高斯噪声,获得与真实信道较为接近的估计性能.     

Discrete Fourier Transform with Denoise Model Based Least Square Wiener Channel Estimator for Channel Estimation in MIMO-OFDM

Multiple-input Multiple-Output (MIMO) systems require orthogonal frequency division multiplexing to operate efficiently in multipath communication (OFDM). Channel estimation (C.E.) is used in channel conditions where time-varying features are required. The existing channel estimation techniques are highly complicated. A channel estimation algorithm is needed to estimate the received signal’s correctness. In order to resolve this complexity in C.E. methodologies, this paper developed an Improved Channel Estimation Algorithm integrated with DFT-LS-WIENER (ICEA-DA). The Least Square (L.S.) and Minimum Mean Square Error (MMSE) algorithms also use the Discrete Fourier Transform (DFT)-based channel estimation method. The DFT-LS-WIENER channel estimation approach is recommended for better BER performance. The input signal is modulated in the transmitter module using the Quadrature Phase Shift Keying (QPSK) technique, pulse modeling, and least squares concepts. The L.S. Estimation technique needs the channel consistent throughout the estimation period. DFT joined with L.S. gives higher estimation precision and limits M.S.E. and BER. Experimental analysis of the proposed state-of-the-art method shows that DFT-LS-WIENER provides superior performance in terms of symbol error rate (S.E.R.), bit error rate (BER), channel capacity (CC), and peak signal-to-noise (PSNR). At 15 dB SNR, the proposed DFT-LS-WIENER techniques reduce the BER of 48.19%, 38.19%, 14.8%, and 14.03% compared to L.S., LS-DFT, MMSE, and MMSE-DFT. Compared to the conventional algorithm, the proposed DFT-LS-WIENER outperform them.     

基于高储能密度电容退化数据的可靠性评估

根据强激光能源模块高储能密度电容器在工作过程中,受到连续冲击而使退化失效具有累积效应的特点,提出了利用产品运行过程中性能参数退化的信息,用复合Poisson过程对退化轨道建模并进行可靠性评估的方法。给出了模型参数的矩估计和电容器的平均退化量、可靠度、平均寿命等可靠性指标评估的Bootstrap仿真过程。并通过实例说明了该评估方法在工程中的应用。基于电容退化信息的可靠性评估方法,可以在极少甚至没有寿命数据的情况下给出客观可信的评估结果,在理论和应用上都具有重要的价值。     

On the effects on a Landau-type system for an atom with no permanent electric dipole moment due to a Coulomb-type potential

We analyse the bound states for a Landau-type system for an atom with no permanent electric dipole moment subject to a Coulomb-type potential. By comparing the energy levels for bound states of the system with the Landau quantization for an atom with no permanent electric dipole moment (Furtado et al., 2006), we show that the energy levels of the Landau-type system are modified, where the degeneracy of the energy levels is broken. Another quantum effect investigated is a dependence of the angular frequency of the system on the quantum numbers associated with the radial modes and the angular momentum. As examples, we obtain the angular frequency and the energy levels associated with the ground state and the first excited state of the system.