Various nonlinear models and their identification, equalization and linearization

System identification is a pre-requisite to analysis of a dynamic system and design of an appropriate controller for improving its performance. The more accurate the mathematical model identified for a system, the more effective will be the controller designed for it. The identification of nonlinear systems is a topic which has received considerable attention over the last two decades. Generally speaking, when it is difficult to model practical systems by mathematical analysis method, system identification may be an efficient way to overcome the shortage of mechanism analysis method. The goal of the modeling is to find a simple and efficient model which is in accord with the practical system. In many cases, linear models are not suitable to present these systems and nonlinear models have to be considered. Since there are nonlinear effects in practical systems, e.g. harmonic generation, intermediation, desensitization, gain expansion and chaos, we can infer that most control systems are nonlinear. Nonlinear models are more widely used in practice, because most phenomena are nonlinear in nature. Indeed, for many dynamic systems the use of nonlinear models is often of great interest and generally characterizes adequately physical processes over their whole operating range. Thus, accuracy and performance of the control law increase significantly. Therefore, nonlinear system modeling is much more important than linear system identification. We will deal with various nonlinear models and their processing

Multispectral Video Fusion for Non-contact Monitoring of Respiratory Rate and Apnea

Continuous monitoring of respiratory activity is desirable in many clinical applications to detect respiratory events. Non-contact monitoring of respiration can be achieved with near- and far-infrared spectrum cameras. However, current technologies are not sufficiently robust to be used in clinical applications. For example, they fail to estimate an accurate respiratory rate (RR) during apnea. We present a novel algorithm based on multispectral data fusion that aims at estimating RR also during apnea. The algorithm independently addresses the RR estimation and apnea detection tasks. Respiratory information is extracted from multiple sources and fed into an RR estimator and an apnea detector whose results are fused into a final respiratory activity estimation. We evaluated the system retrospectively using data from 30 healthy adults who performed diverse controlled breathing tasks while lying supine in a dark room and reproduced central and obstructive apneic events. Combining multiple respiratory information from multispectral cameras improved the root mean square error (RMSE) accuracy of the RR estimation from up to 4.64 monospectral data down to 1.60 breaths/min. The median F1 scores for classifying obstructive (0.75 to 0.86) and central apnea (0.75 to 0.93) also improved. Furthermore, the independent consideration of apnea detection led to a more robust system (RMSE of 4.44 vs. 7.96 breaths/min). Our findings may represent a step towards the use of cameras for vital sign monitoring in medical applications

PyCBC Live: Rapid Detection of Gravitational Waves from Compact Binary Mergers

We introduce an efficient and straightforward technique for rapidly detecting gravitational waves from compact binary mergers. We show that this method achieves the low latencies required to alert electromagnetic partners of candidate binary mergers, aids in data monitoring, and makes use of multidetector networks for sky localization. This approach was instrumental to the analysis of gravitational-wave candidates during the second observing run of Advanced LIGO, including the period of coincident operation with Advanced Virgo, and in particular the analysis of the first observed binary neutron star merger GW170817, where it led to the first tightly localized sky map ($31~\mathrm{deg}^2$) used to identify AT 2017gfo. Operation of this analysis also enabled the initial discovery of GW170104 and GW170608 despite non-nominal observing of the instrument.Comment: 10 pages, 5 figures, submitted to Physical Review

Robust EM kernel-based methods for linear system identification

Recent developments in system identification have brought attention to regularized kernel-based methods. This type of approach has been proven to compare favorably with classic parametric methods. However, current formulations are not robust with respect to outliers. In this paper, we introduce a novel method to robustify kernel-based system identification methods. To this end, we model the output measurement noise using random variables with heavy-tailed probability density functions (pdfs), focusing on the Laplacian and the Student's t distributions. Exploiting the representation of these pdfs as scale mixtures of Gaussians, we cast our system identification problem into a Gaussian process regression framework, which requires estimating a number of hyperparameters of the data size order. To overcome this difficulty, we design a new maximum a posteriori (MAP) estimator of the hyperparameters, and solve the related optimization problem with a novel iterative scheme based on the Expectation-Maximization (EM) method. In presence of outliers, tests on simulated data and on a real system show a substantial performance improvement compared to currently used kernel-based methods for linear system identification.Comment: Accepted for publication in Automatic

Synchrophasors: Multilevel Assessment and Data Quality Improvement for Enhanced System Reliability

. This study presents a comprehensive framework for testing and evaluation of Phasor Measurement Units (PMUs) and synchrophasor systems under normal power system operating conditions, as well as during disturbances such as faults and transients. The proposed framework suggests a performance assessment to be conducted in three steps: (a) type testing: conducted in the synchrophasor calibration laboratory according to accepted industrial standards; (b) application testing: conducted to evaluate the performance of the PMUs under faults, transients, and other disturbances in power systems; (c) end-to-end system testing: conducted to assess the risk and quantify the impact of measurement errors on the applications of interest. The suggested calibration toolset (type testing) enables performance characterization of different design alternatives in a standalone PMU (e.g., length of phasor estimation windows, filtering windows, reporting rates, etc.). In conjunction with the standard performance requirements, this work defines new metrics for PMU performance evaluations under any static and dynamic conditions that may unfold in the grid. The new metrics offer a more realistic understanding of the overall PMU performance and help users choose the appropriate device/settings for the target applications. Furthermore, the proposed probabilistic techniques quantify the PMU accuracy to various test performance thresholds specified by corresponding IEEE standards, rather than having only the pass/fail test outcome, as well as the probability of specific failures to meet the standard requirements defined in terms of the phasor, frequency, and rate of change of frequency accuracy. Application testing analysis encompasses PMU performance evaluation under faults and other prevailing conditions, and offers a realistic assessment of the PMU measurement errors in real-world field scenarios and reveals additional performance characteristics that are crucial for the overall application evaluation. End-to-end system tests quantify the impact of synchrophasor estimation errors and their propagation from the PMU towards the end-use applications and evaluate the associated risk. In this work, extensive experimental results demonstrate the advantages of the proposed framework and its applicability is verified through two synchrophasor applications, namely: Fault Location and Modal Analysis. Finally, a data-driven technique (Principal Component Pursuit) is proposed for the correction and completion of the synchrophasor data blocks, and its application and effectiveness is validated in modal analyzes