Nonlinear State Space Approach Research Articles

Optimal Heartbeat Energy Harvesting using Electrostatic Energy Harvesters

Microelectromechanical system of electrostatic energy harvesters is modeled using a nonlinear state–space approach in this research. The analytical models of in‐plane overlap, in‐plane gap closing, and their compound structures are used to analyze the energy harvesting performance from heartbeats‐generated vibrations. The detailed models of both electrical and mechanical subsystems including stopper function, motion drag, parasitic capacitors, and energy converter capacitors are developed in the format of state–space equations. To reach the optimal heartbeat energy harvesting, typical 1D harvesters are developed and allowed to move in x–y and x–y–z directions. Accordingly, the optimal harvester combines the features of in‐plane overlap and in‐plane gap closing energy conversions, and so allows efficient absorption of energy released by heartbeat in different directions. This 3D feature gives a considerable rise to power generation to 35.038 μw at the same size compared to the new rate of the in‐plane overlap or in‐plane gap‐closing electrostatic harvesters individually.

Correction to “Impedance Spectra and Surface Coverages Simulated Directly from the Electrochemical Reaction Mechanism: A Nonlinear State-Space Approach”

ADVERTISEMENT RETURN TO ARTICLES ASAPPREVAddition/CorrectionNEXTORIGINAL ARTICLEThis notice is a correctionCorrection to “Impedance Spectra and Surface Coverages Simulated Directly from the Electrochemical Reaction Mechanism: A Nonlinear State-Space Approach”Kiran GeorgeKiran GeorgeMore by Kiran Georgehttps://orcid.org/0000-0003-2242-8190, Matthijs van BerkelMatthijs van BerkelMore by Matthijs van Berkelhttps://orcid.org/0000-0001-6574-3823, Xueqing ZhangXueqing ZhangMore by Xueqing Zhanghttps://orcid.org/0000-0002-7310-3858, Rochan SinhaRochan SinhaMore by Rochan Sinhahttps://orcid.org/0000-0001-9389-205X, and Anja Bieberle-Hütter*Anja Bieberle-HütterMore by Anja Bieberle-Hütterhttps://orcid.org/0000-0001-8794-9312Cite this: J. Phys. Chem. C 2022, XXXX, XXX, XXX-XXXPublication Date (Web):June 23, 2022Publication History Published online23 June 2022https://doi.org/10.1021/acs.jpcc.2c03604© 2022 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views17Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (498 KB) Get e-Alerts Get e-Alerts

Term structure estimation with liquidity-adjusted Ane Nelson Siegel model: A nonlinear state space approach applied to the Indian bond market

ABSTRACT Efficient term structure estimation in emerging markets is difficult not only because of overall lack of liquidity, but also because of the concentration of liquidity in a few securities. Using the arbitrage-free Affine Nelson-Siegel model, we explicitly incorporate this phenomenon using a proxy for liquidity based on observable data in the bond pricing function and estimate the term structure for Indian Government bond markets in a nonlinear state space setting using the Unscented Kalman Filter. We find strong empirical evidence in support of the extended model with both i) a better in-sample fit to bond prices, and ii) the likelihood ratio test rejecting the restrictions assumed in the standard AFNS specification. In an alternative specification, we also model liquidity as a latent risk factor within the AFNS framework. The estimated latent liquidity factor is found to be strongly correlated with the standard market benchmarks of overall liquidity and the India VIX index.

Impedance Spectra and Surface Coverages Simulated Directly from the Electrochemical Reaction Mechanism: A Nonlinear State-Space Approach

Current voltage curves and electrochemical impedance spectra (EIS) are crucial for investigating the performance and the electrochemical limitations of electrochemical cells. Therefore, we developed an approach which allows the direct simulation of such data based on microkinetic modeling. This approach allows us to assess the influence of various input parameters on the EIS and on the current–voltage curves and, hence, the overall performance of electrochemical cells. We develop our approach for the oxygen evolution reaction (OER) taking place at the semiconductor–electrolyte interface. At this interface, the microkinetic equations, that is, electrochemical reactions, for the multiple steps in OER are formulated and the resulting set of equations are modeled in a state-space form. As an input to the state-space model, we use the theoretical reaction rates calculated using density functional theory and Gerischer theory for semiconductors. Then, the electrochemical data are simulated as a function of applied potential. Next to the theory and the model development, a case study on the hematite–electrolyte interface which is a typical interface in photoelectrochemical cells is presented. Current voltage curves and EIS data for the hematite interface are simulated from the electrochemical model. The data are compared to experimental measurements. Apart from the current density and the EIS, the model can simulate the coverage of intermediate species as a function of applied potential which is highly demanded for identifying the limiting processes at the interface, but not available from experimental studies. This approach is generic and can be used for other electrochemical interfaces, such as present in fuel cells, electrolysers, or batteries.

Motion Tracking of the Carotid Artery Wall From Ultrasound Image Sequences: a Nonlinear State-Space Approach.

The motion of the common carotid artery (CCA) wall has been established to be useful in early diagnosis of atherosclerotic disease. However, tracking the CCA wall motion from ultrasound images remains a challenging task. In this paper, a nonlinear state-space approach has been developed to track CCA wall motion from ultrasound sequences. In this approach, a nonlinear state-space equation with a time-variant control signal was constructed from a mathematical model of the dynamics of the CCA wall. Then, the unscented Kalman filter (UKF) was adopted to solve the nonlinear state transfer function in order to evolve the state of the target tissue, which involves estimation of the motion trajectory of the CCA wall from noisy ultrasound images. The performance of this approach has been validated on 30 simulated ultrasound sequences and a real ultrasound dataset of 103 subjects by comparing the motion tracking results obtained in this study to those of three state-of-the-art methods and of the manual tracing method performed by two experienced ultrasound physicians. The experimental results demonstrated that the proposed approach is highly correlated with (intra-class correlation coefficient ≥ 0.9948 for the longitudinal motion and ≥ 0.9966 for the radial motion) and well agrees (the 95% confidence interval width is 0.8871 mm for the longitudinal motion and 0.4159 mm for the radial motion) with the manual tracing method on real data and also exhibits high accuracy on simulated data (0.1161 ~ 0.1260 mm). These results appear to demonstrate the effectiveness of the proposed approach for motion tracking of the CCA wall.

Multilevel models with stochastic volatility for repeated cross-sections: An application to tribal art prices

In this paper, we introduce a multilevel specification with stochastic volatility for repeated cross-sectional data. Modelling the time dynamics in repeated cross sections requires a suitable adaptation of the multilevel framework where the individuals/items are modelled at the first level whereas the time component appears at the second level. We perform maximum likelihood estimation by means of a nonlinear state space approach combined with Gauss–Legendre quadrature methods to approximate the likelihood function. We apply the model to the first database of tribal art items sold in the most important auction houses worldwide. The model allows to account properly for the heteroscedastic and autocorrelated volatility observed and has superior forecasting performance. Also, it provides valuable information on market trends and on predictability of prices that can be used by art markets stakeholders.

A dynamic environment-sensitive site index model for the prediction of site productivity potential under climate change

Accurate and reliable predictions of the future development of forest site productivity are crucial for the effective management of forest stands. Static models which simply extrapolate productivity into the future are inappropriate under conditions of environmental change since they lack a close link between fundamental environmental drivers and forest growth processes. Here we present a dynamic environment-sensitive site index model formulated in the framework of a nonlinear state space approach based on longitudinal data from long-term experimental plots. Estimation of the model parameters was carried out using the prediction error minimization method. Our aim was to identify dynamic relationships between site index and environmental variables and to make conditional predictions of the future development of site index under climate change scenarios. Nonlinear, interactive, as well as accumulative effects of environmental factors (climate/weather and nitrogen influx) on the growth response were considered in the model. In the study, we estimated the dynamic environment-sensitive site index model using data from 604 Norway spruce (Picea abies [L.] Karst.) long-term experimental plots in southwest Germany with measurement data covering a period of more than 100 years from the end of the 19th century until today. We used the calibrated model to project future site index changes under increasing growing season temperature scenarios. Conventional climate change impact studies usually utilize a gradient approach and apply space-for-time substitution for the parameterization of models that are calibrated using spatial variability in the data. In contrast, the approach presented here utilizes the longitudinal data structure of multiple real growth time series to simultaneously exploit spatial and temporal variation in the data to provide more reliable and robust projections. Limitations of the space-for-time substitution approach in forest growth modelling are discussed.

Identification of a Wiener–Hammerstein system using the polynomial nonlinear state space approach

In this paper, the Polynomial NonLinear State Space (PNLSS) approach is applied to model a nonlinear system with a Wiener–Hammerstein structure. To obtain good initial estimates, the best linear approximation of the system under test is first identified. Next, this linear model is extended to a polynomial nonlinear state space model to capture also the system's nonlinear behavior. The identification procedure is applied to measurement data.

Probabilistic forecasts of volatility and its risk premia

The object of this paper is to produce distributional forecasts of asset price volatility and its associated risk premia using a non-linear state space approach. Option and spot market information on the latent variance process is captured by using dual ‘model-free’ variance measures to define a bivariate observation equation in the state space model. The premium for variance diffusive risk is defined as linear in the latent variance (in the usual fashion) whilst the premium for variance jump risk is specified as a conditionally deterministic dynamic process, driven by a function of past measurements. The inferential approach adopted is Bayesian, implemented via a Markov chain Monte Carlo algorithm that caters for the multiple sources of non-linearity in the model and for the bivariate measure. The method is applied to spot and option price data on the S&P500 index from 1999 to 2008, with conclusions drawn about investors’ required compensation for variance risk during the recent financial turmoil. The accuracy of the probabilistic forecasts of the observable variance measures is demonstrated, and compared with that of forecasts yielded by alternative methods. To illustrate the benefits of the approach, it is used to produce forecasts of prices of derivatives on volatility itself. In addition, the posterior distribution is augmented by information on daily returns to produce value at risk predictions. Linking the variance risk premia to the risk aversion parameter in a representative agent model, probabilistic forecasts of (approximate) relative risk aversion are also produced.

Nonlinear state space approach for identifying electro-hydraulic servo control system

For the purpose of solving the problems in identification and modeling of nonlinear dynamic system using recurrent neural networks (RNN),a nonlinear state space model is investigated for RNN.The convergence criterion for networks training is discussed under minimum mean square error (MMSE).The stochastic variable in the Kalman filter formulations is researched.A parameter-augmented nonlinear state space equation for RNN is proposed.A derivative-free Kalman filter is employed to estimate the augmented parameters and to update weights of RNN by using artificial white noise to compel RNN to learn.Compared with the extended Kalman filter (EKF),computation of Jacobian information is avoided and the problem of slow convergence rate of algorithm based on gradient learning is also solved.The application of RNN in the identification and modeling of an electro-hydraulic servo system shows that RNN is capable of tracking the dynamic pressure of the hydraulic cylinder.The new algorithm has faster convergence and higher precision compared to the algorithm of extended Kalrnan filter.

A non-linear state space approach to model groundwater fluctuations

A non-linear state space model is developed for describing groundwater fluctuations. Non-linearity is introduced by modeling the (unobserved) degree of water saturation of the root zone. The non-linear relations are based on physical concepts describing the dependence of both the actual evapotranspiration and the percolation rate on the degree of saturation of the root zone. Precipitation and reference evaporation are the input variables. Recharge from the root zone is transferred through the percolation zone to the water table by means of a linear reservoir model. The flux in the percolation zone is assumed to be independent of the groundwater level. Errors due to model assumptions and parameter uncertainties are modeled as a noise process. The parameters of the resulting stochastic model are calibrated on time series by combining an extended Kalman filter with a maximum-likelihood criterion. The model is tested at two locations and compared with a linear time series model. It is shown that the non-linear model estimated extreme groundwater levels better than the linear time series model. The variance of the stochastic component reduced significantly. This implies that effects from natural influences can be separated from non-natural influences with less uncertainty. Another advantages of the non-linear model is that it also gives an indication of fluctuations in the degree of saturation in the root zone.