Automatic Tuning Methods for MPC Environments

Power system state estimation is one of the most important functions of power system control centers. In recent years, the complexity of power system state estimation has significantly increased due to the growing number of distributed, including renewable energy sources, electric vehicles, the demand response technologies, and the increased risk of cyber-attacks. Under these conditions, state estimation methods, which consider information about the time-correlation of the power system states have a great potential. The correlation is described by a transition model. The well-known state estimation methods usually use one single model. However, in case of stochastic behavior of the load and generation, it is impossible to assert the adequacy of the chosen model over the entire observation interval. Therefore, the paper proposes a multi-model forecasting power system state estimation method, which has lower errors in comparison with a single-model assessment at the moments of the lowest accuracy of the latter. Multi-model parameter estimation is used based on three procedures of the single-model Kalman filtering estimation and various transition models based on autoregressive and vector autoregressive analyzes, as well as Holt's exponential smoothing. Uniting the single-model estimation has been carried out according to the criterion of the minimum of variance of the resulting estimate. An algorithm of multi-model power system state estimation has been developed. Its version has been analyzed using a three-model forecasting-aided estimation using linear transition models. The state of the IEEE 30-bus test power system has been assessed by the means of simulation modeling. The maximum accuracy increase of the multi-model estimation in comparison with the single model is set up. 26,1 % is for autoregressive analysis; 16,9 % is for vector regression analysis and 37,7 % is for Holt’s exponential smoothing. The proposed method of multi-model state estimation has higher robustness and accuracy at the moments of the lowest accuracy of single-model estimation. It is advisable to use the method to solve control tasks of power systems with rapidly changing dynamic modes.

This paper presents a method for real time adaptive hybrid state estimation of the power system states using the SCADA as well as limited synchronous phasor measurements. The proposed method can track the power system dynamics by periodically updating the sensitivities to the unobservable buses during the intervals between successive outputs of state estimator with SCADA measurements. The performance of the proposed method is evaluated on a number of standard test systems for different sets of un-observability. Simulation result shows that the proposed method can track the electromechanical dynamics of the system during the large disturbances in the system, it also improves the accuracy of the estimates and enhances the numerical stability of the estimation.

Accurate segmentation of lung cancer in pathology slides is a critical step in improving patient care. We proposed the ACDC@LungHP (Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology) challenge for evaluating different computer-aided diagnosis (CADs) methods on the automatic diagnosis of lung cancer. The ACDC@LungHP 2019 focused on segmentation (pixel-wise detection) of cancer tissue in whole slide imaging (WSI), using an annotated dataset of 150 training images and 50 test images from 200 patients. This paper reviews this challenge and summarizes the top 10 submitted methods for lung cancer segmentation. All methods were evaluated using metrics using the precision, accuracy, sensitivity, specificity, and DICE coefficient (DC). The DC ranged from 0.7354 ±0.1149 to 0.8372 ±0.0858. The DC of the best method was close to the inter-observer agreement (0.8398 ±0.0890). All methods were based on deep learning and categorized into two groups: multi-model method and single model method. In general, multi-model methods were significantly better (p 0.01) than single model methods, with mean DC of 0.7966 and 0.7544, respectively. Deep learning based methods could potentially help pathologists find suspicious regions for further analysis of lung cancer in WSI.

Observing the state of an unknown linear system by means of a parametrized representation of the standard Luenberger observer with an additional adaptive loop (adaptive observer) is considered. A general technique on how to construct suitable adaptive laws for this additional loop is presented and it is proven that these adaptive laws always result in global and arbitrarily fast convergence of the adaptive process. Because the adaptive laws can assume a variety of different structures, both structural and parametric degrees of freedom in the adaptive law (rather than the so far available parametric degrees alone) are obtained, which can be used in a future optimization of the adaptive observer performance.

We consider belief propagation (BP) as an efficient and scalable tool for state estimation and optimization problems in supply networks such as power grids. BP algorithms make use of factor graph representations, whose assignment to the problem of interest is not unique. It depends on the state variables and their mutual interdependencies. Many short loops in factor graphs may impede the accuracy of BP. We propose a systematic way to cluster loops of naively assigned factor graphs such that the resulting transformed factor graphs have no additional loops as compared to the original network. They guarantee an accurate performance of BP with only slightly increased computational effort, as we demonstrate by a concrete and realistic implementation for power grids. The method outperforms existing alternatives to handle the loops. We point to other applications to supply networks such as gas-pipeline or other flow networks that share the structure of constraints in the form of analogues to Kirchhoff’s laws. Whenever small and abundant loops in factor graphs are systematically generated by constraints between variables in the original network, our factor-graph assignment in BP complements other approaches. It provides a fast and reliable algorithm to perform marginalization in tasks like state determination, estimation, or optimization issues in supply networks.Graphical abstract

State estimation and localization for the autonomous vehicle are essential for accurate navigation and safe maneuvers. The commonly used method is Kalman filtering, but its performance is affected by the noise covariance. An inappropriate set value will decrease the estimation accuracy and even makes the filter diverge. The noise covariance estimation problem has long been considered a tough issue because there is too much uncertainty in where the noise comes from and therefore unable to model it systematically. In recent years, Deep Reinforcement Learning (DRL) has made astonishing progress and is an excellent choice for tackling the problem that cannot be solved by conventional techniques, such as parameter estimation. By finely abstracting the problem as an MDP, we can use the DRL methods to solve it without too many prior assumptions. We propose an adaptive covariance tuning method applied to the Error State Extend Kalman Filter by taking advantage of DRL, called Reinforcement Learning Aided Covariance Tuning. The preliminary experiment result indicates that our method achieves a 14.73% estimation accuracy improvement on average compared with the vanilla fixed-covariance method and bound the estimation error within 0.4 m.

The estimation of states from input and output measurements using linear state-space models has been widely studied. In particular, the Kalman filtering algorithm has found many applications, such as time-series forecasting, control, parameter estimation, and fault diagnosis. In this paper we propose a new method for adaptive state estimation using feedforward and recurrent neural networks and, in particular, for state filtering that is applicable to general nonlinear systems. The developed method has been applied to the problem of estimating the parameters of an electromechanical system consisting of a DC motor and a centrifugal pump with the associated pumping system. Finally, the proposed algorithm is used in estimating the states and a critical parameter of a complex process system, namely a heat exchanger.

The objective of this paper is to provide a new estimation method for parametric models under progressive Type-I censoring. First, we propose a Kaplan-Meier nonparametric estimator of the reliability function taken at the censoring times. It is based on the observable number of failures, and the number of censored units occurring from the progressive censoring scheme at the censoring times. This estimator is then shown to asymptotically follow a normal distribution. Next, we propose a minimum-distance method to estimate the unknown Euclidean parameter of a given parametric model. This method leads to consistent, asymptotically normal estimators. The maximum likelihood estimation method based on group-censored samples is discussed next, and the efficiencies of these two methods are compared numerically. Then, based on the established results, we derive a method to obtain the optimal Type-I progressive censoring scheme, Finally we illustrate all these results through a Monte Carlo simulation study, and an illustrative example.

A semi-empirical growth estimation method for matrix models of endangered species

Nonlinear regression models are commonly used in various fields such as toxicology/pharmacology. When analyzing data using a nonlinear regression model the structure of error variance plays a key role in the estimation of parameters. Particularly, when data do not satisfy the homoscedasticity assumption, it is important to use an appropriate estimation method. In this paper, a robust M-estimation method against potential outliers in nonlinear regression under heteroscedasticity is considered. Under the heteroscedasticity assumption, three variance models are considered, and a weighted M-estimator is studied by the simulation to compare the performance of the estimator with three variance models. From the results of the simulation studies, even though not as well as proper estimators, WME using a nonlinear variance model generally shows good performances for homoscedastic data and heteroscedastic data with the variance models. The methods are also illustrated by analyzing real toxicological data.

Bandwidth plays an important role in determining the performance of nonparametric estimators, such as the local constant estimator. In this article, we propose a Bayesian approach to bandwidth estimation for local constant estimators of time-varying coefficients in time series models. We establish a large sample theory for the proposed bandwidth estimator and Bayesian estimators of the unknown parameters involved in the error density. A Monte Carlo simulation study shows that (i) the proposed Bayesian estimators for bandwidth and parameters in the error density have satisfactory finite sample performance; and (ii) our proposed Bayesian approach achieves better performance in estimating the bandwidths than the normal reference rule and cross-validation. Moreover, we apply our proposed Bayesian bandwidth estimation method for the time-varying coefficient models that explain Okun’s law and the relationship between consumption growth and income growth in the U.S. For each model, we also provide calibrated parametric forms of the time-varying coefficients. Supplementary materials for this article are available online.

We consider a simple yet flexible spline estimation method for quasi-likelihood models. We approximate the unknown function by B-splines and apply the Fisher scoring algorithm to compute the estimates. The spline estimate of the nonparametric component achieves the optimal rate of convergence under the smooth condition, and the estimate of the parametric part is shown to be asymptotically normal even if the variance function is misspecified. The semiparametric efficiency of the model can be established if the variance function is correctly specified. A direct and consistent variance estimation method based on the least-squares estimation is proposed. A simulation study is performed to evaluate the numerical performance of the spline estimate. The methodology is illustrated on a crab study.

Linear rational-expectations models with lagged expectations: A synthetic method

The paper is devoted to the research of the two-criteria estimation method for linear regressions. The first criterion corresponds to the least absolute deviations, the second – to the non-strict ordinary least squares. The implementation of the method requires solving a two-criteria linear programming problem, the solution of which involves the formation of a Pareto set. The main goal of the article was to research how the normalization of the initial variables affects the formation of the Pareto set. For this, two samples were used. The first was created artificially and contains an outlier. The second was formed on the basis of real economic data. In both cases, when normalizing the variables, the Pareto set turned out to be more representative than when working with non-normalized indicators. The example with an outlier illustrates the robustness of the least absolute deviations and the anti-robust ness of the ordinary least squares. It is shown how, based on the predicted values of the explained variable, it is possible to choose the optimal Pareto vertex.

We pose pattern classification as a density estimation problem where we consider mixtures of generative models under partially labeled data setups. Unlike traditional approaches that estimate density everywhere in data space, we focus on the density along the decision boundary that can yield more discriminative models with superior classification performance. We extend our earlier work on the recursive estimation method for discriminative mixture models to semi-supervised learning setups where some of the data points lack class labels. Our model exploits the mixture structure in the functional gradient framework: it searches for the base mixture component model in a greedy fashion, maximizing the conditional class likelihoods for the labeled data and at the same time minimizing the uncertainty of class label prediction for unlabeled data points. The objective can be effectively imposed as individual mixture component learning on weighted data, hence our mixture learning typically becomes highly efficient for popular base generative models like Gaussians or hidden Markov models. Moreover, apart from the expectation-maximization algorithm, the proposed recursive estimation has several advantages including the lack of need for a pre-determined mixture order and robustness to the choice of initial parameters. We demonstrate the benefits of the proposed approach on a comprehensive set of evaluations consisting of diverse time-series classification problems in semi-supervised scenarios.