Kalman Filter Bibliography: Agriculture, Biology, and Medicine

27 pages, 1 article*Kalman Filter Bibliography: Agriculture, Biology, and Medicine* (Federer, Walter T.; Murty, B. R.) 27 page

Data cleaning and knowledge discovery in process data

This dissertation presents several methods for overcoming the Big Data challenges, with an emphasis on data cleaning and knowledge discovery in process data. Data cleaning and knowledge discovery is chosen as a main research area here due to its importance from both theoretical and practical points of view. Theoretical background and recent developments of data cleaning methods are reviewed from four aspects: missing data imputation, outlier detection, noise removal and time delay estimation. Moreover, the impact of contaminated data on model performance and corresponding improvement obtained by data cleaning methods are analyzed through both simulated and industrial case studies. The results provide a starting point for further advanced methodology development. It is hard to find a universally applicable method for data cleaning since every data set may have its own distinctive features. Thus, we have to customize available methods so that the quality of the data set is guaranteed. An integrated data cleaning scheme is proposed, which incorporates model building and performance evaluation, to provide guidance in tuning the parameters of data cleaning methods and prevent over-cleaning. A case study based on industrial data has been used to verify the feasibility and effectiveness of the proposed new method, during which a partial least squares (PLS) model was built and three univariate data cleaning procedures is tested. A time series Kalman filter (TSKF) is proposed that successfully handles outlier detection in dynamic systems, where normal process changes often mask the existence of outliers. The TSKF method combines a time series model fitting procedure with a modified Kalman filter to deal with additive outlier (AO) and innovational outlier (IO) detection problems in dynamic process data set. A comparative analysis of TSKF and available methods is performed on simulated and real chemical plant data. Root cause diagnosis of plant-wide oscillations, as a concrete example of data cleaning and knowledge discovery in the process data, is provided. Plant-wide oscillations can negatively influence the overall control performance of the process and the detection results are often affected by noise at different frequency ranges. To address such a problem, an information transfer method combining spectral envelope algorithm with spectral transfer entropy is proposed to detect and diagnose such oscillations within a specific frequency range, mitigating the effects from measurement noise. The feasibility and effectiveness of the proposed method are verified and compared with available methods through both simulated and industrial case studies.Chemical Engineerin

Detecting and locating electronic devices using their unintended electromagnetic emissions

Electronically-initiated explosives can have unintended electromagnetic emissions which propagate through walls and sealed containers. These emissions, if properly characterized, enable the prompt and accurate detection of explosive threats. The following dissertation develops and evaluates techniques for detecting and locating common electronic initiators. The unintended emissions of radio receivers and microcontrollers are analyzed. These emissions are low-power radio signals that result from the device\u27s normal operation. In the first section, it is demonstrated that arbitrary signals can be injected into a radio receiver\u27s unintended emissions using a relatively weak stimulation signal. This effect is called stimulated emissions. The performance of stimulated emissions is compared to passive detection techniques. The novel technique offers a 5 to 10 dB sensitivity improvement over passive methods for detecting radio receivers. The second section develops a radar-like technique for accurately locating radio receivers. The radar utilizes the stimulated emissions technique with wideband signals. A radar-like system is designed and implemented in hardware. Its accuracy tested in a noisy, multipath-rich, indoor environment. The proposed radar can locate superheterodyne radio receivers with a root mean square position error less than 5 meters when the SNR is 15 dB or above. In the third section, an analytic model is developed for the unintended emissions of microcontrollers. It is demonstrated that these emissions consist of a periodic train of impulses. Measurements of an 8051 microcontroller validate this model. The model is used to evaluate the noise performance of several existing algorithms. Results indicate that the pitch estimation techniques have a 4 dB sensitivity improvement over epoch folding algorithms --Abstract, page iii

Self-tuning robust adjustment within multivariate regression time series models with vector-autoregressive random errors

The iteratively reweighted least-squares approach to self-tuning robust adjustment of parameters in linear regression models with autoregressive (AR) and t-distributed random errors, previously established in Kargoll et al. (in J Geod 92(3):271–297, 2018. https://doi.org/10.1007/s00190-017-1062-6), is extended to multivariate approaches. Multivariate models are used to describe the behavior of multiple observables measured contemporaneously. The proposed approaches allow for the modeling of both auto- and cross-correlations through a vector-autoregressive (VAR) process, where the components of the white-noise input vector are modeled at every time instance either as stochastically independent t-distributed (herein called “stochastic model A”) or as multivariate t-distributed random variables (herein called “stochastic model B”). Both stochastic models are complementary in the sense that the former allows for group-specific degrees of freedom (df) of the t-distributions (thus, sensor-component-specific tail or outlier characteristics) but not for correlations within each white-noise vector, whereas the latter allows for such correlations but not for different dfs. Within the observation equations, nonlinear (differentiable) regression models are generally allowed for. Two different generalized expectation maximization (GEM) algorithms are derived to estimate the regression model parameters jointly with the VAR coefficients, the variance components (in case of stochastic model A) or the cofactor matrix (for stochastic model B), and the df(s). To enable the validation of the fitted VAR model and the selection of the best model order, the multivariate portmanteau test and Akaike’s information criterion are applied. The performance of the algorithms and of the white noise test is evaluated by means of Monte Carlo simulations. Furthermore, the suitability of one of the proposed models and the corresponding GEM algorithm is investigated within a case study involving the multivariate modeling and adjustment of time-series data at four GPS stations in the EUREF Permanent Network (EPN). © 2020, The Author(s)

Forecasting Nonlinear Systems with LSTM: Analysis and Comparison with EKF

This article belongs to the Special Issue Information Fusion and Machine Learning for Sensors.Certain difficulties in path forecasting and filtering problems are based in the initial hypothesis of estimation and filtering techniques. Common hypotheses include that the system can be modeled as linear, Markovian, Gaussian, or all at one time. Although, in many cases, there are strategies to tackle problems with approaches that show very good results, the associated engineering process can become highly complex, requiring a great deal of time or even becoming unapproachable. To have tools to tackle complex problems without starting from a previous hypothesis but to continue to solve classic challenges and sharpen the implementation of estimation and filtering systems is of high scientific interest. This paper addresses the forecast–filter problem from deep learning paradigms with a neural network architecture inspired by natural language processing techniques and data structure. Unlike Kalman, this proposal performs the process of prediction and filtering in the same phase, while Kalman requires two phases. We propose three different study cases of incremental conceptual difficulty. The experimentation is divided into five parts: the standardization effect in raw data, proposal validation, filtering, loss of measurements (forecasting), and, finally, robustness. The results are compared with a Kalman filter, showing that the proposal is comparable in terms of the error within the linear case, with improved performance when facing non-linear systems.This research was funded by public research projects of Spanish Ministry of Economy and Competitivity (MINECO), reference TEC2017-88048-C2-2-R and by the Madrid Government under the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M17), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation)

Analysis of Repeated Measures Data Under Circular Covariance

Circular covariance is important in modelling phenomena in epidemiological, communications and numerous physical contexts. We introduce and develop a variety of methods which make it a more versatile tool. First, we present two classes of estimators for use in the presence of missing observations. Using simulations, we show that the mean squared errors of the estimators of one of these classes are smaller than those of the Maximum Likelihood (ML) estimators under certain conditions. Next, we propose and discuss a parsimonious, autoregressive type of circular covariance structure which involves only two parameters. We specify ML and other types of estimators of these parameters, and present techniques for selection between various covariance structures related to circular covariance. Finally, we consider estimation assuming that observations on different individuals are correlated in various ways. This model is generalized for use when varying numbers of observations are taken on individuals. In all these contexts, we combine the measurements on individuals with covariates of varying dimensions, and consider estimation of the correlation between the observations and the covariates