Model Parameter Estimation Algorithm Research Articles

An improved adaptation algorithm for signer-independent sign language recognition

Sign language recognition is a technology that can present sign language in a understandable form, in order to achieve barrier-free communication between deaf and normal. To solve the differences in sign language data issues and the lack of training samples of manpower caused by low recognition of non-specific language, presents MLLR\MAP adaptive progressive non-specific integrated manpower language recognition framework. This approach optimises the division MLLR regression class to provide more accurate initial MAP model, which give full play to the rapidity and the MAP MLLR progressive. Then introduced MCE model parameter estimation algorithm to compensate for the limitations of the model parameters adaptive method to further reduce the system error rate and accelerate the recognition speed. Meanwhile, for the MCE algorithm computationally intensive problems proposed improvements. Experimental results show that the adaptive sign language data required for this algorithm is less than traditional MLLR and MAP methods, while improved average recognition rate by 15.6%.

A dynamical systems approach for the submaximal prediction of maximum heart rate and maximal oxygen uptake

This study examines the viability of utilizing a dynamical system model and heuristic parameter estimation algorithm to make predictions for maximum heart rate ( $$\mathrm {HR_{max}}$$ ) and maximal oxygen uptake ( $$\dot{\mathrm {V}}{\mathrm {O_{2max}}}$$ ) using data collected from a submaximal testing protocol. $$\dot{\mathrm {V}}{\mathrm {O_{2max}}}$$ is widely considered to be the best single measurement of overall fitness in humans. When a $$\dot{\mathrm {V}}{\mathrm {O_{2max}}}$$ assessment is not available, $$\mathrm {HR_{max}}$$ is often used to prescribe exercise intensities for training and rehabilitation. In the absence of maximal cardiopulmonary exercise testing (CPET), $$\mathrm {HR_{max}}$$ and $$\dot{\mathrm {V}}{\mathrm {O_{2max}}}$$ are typically estimated using traditional submaximal prediction methods with well-known limitations and inaccuracies. For this study, 12 regularly exercising healthy young adult males performed a bout of maximal CPET on a cycle ergometer to determine their true $$\mathrm {HR_{max}}$$ and $$\dot{\mathrm {V}}{\mathrm {O_{2max}}}$$ . Participants also performed a submaximal bout of exercise at varied intensities. A dynamical system model and heuristic parameter estimation algorithm were applied to the submaximal data to estimate the participants’ $$\mathrm {HR_{max}}$$ and $$\dot{\mathrm {V}}{\mathrm {O_{2max}}}$$ . The submaximal predictions were evaluated by computing the coefficient of determination $${R^2}$$ and the standard error of the estimate (SEE) through comparisons with the true maximal values for $$\mathrm {HR_{max}}$$ ( $${R^2 = 0.96}$$ , SEE = 2.4 bpm) and $$\dot{\mathrm {V}}{\mathrm {O_{2max}}}$$ ( $${R^2 = 0.93}$$ , SEE = 2.1 mL kg $$^{-1}$$ min $$^{-1}$$ ). The results from this study suggest that a dynamical system model and heuristic parameter estimation algorithm can provide accurate predictions for $$\mathrm {HR_{max}}$$ and $$\dot{\mathrm {V}}{\mathrm {O_{2max}}}$$ using data collected from a submaximal testing protocol.

Model selection and parameter estimation in structural dynamics using approximate Bayesian computation

This paper will introduce the use of the approximate Bayesian computation (ABC) algorithm for model selection and parameter estimation in structural dynamics. ABC is a likelihood-free method typically used when the likelihood function is either intractable or cannot be approached in a closed form. To circumvent the evaluation of the likelihood function, simulation from a forward model is at the core of the ABC algorithm. The algorithm offers the possibility to use different metrics and summary statistics representative of the data to carry out Bayesian inference. The efficacy of the algorithm in structural dynamics is demonstrated through three different illustrative examples of nonlinear system identification: cubic and cubic-quintic models, the Bouc-Wen model and the Duffing oscillator. The obtained results suggest that ABC is a promising alternative to deal with model selection and parameter estimation issues, specifically for systems with complex behaviours.

Bayesian inference of nonlinear unsteady aerodynamics from aeroelastic limit cycle oscillations

A Bayesian model selection and parameter estimation algorithm is applied to investigate the influence of nonlinear and unsteady aerodynamic loads on the limit cycle oscillation (LCO) of a pitching airfoil in the transitional Reynolds number regime. At small angles of attack, laminar boundary layer trailing edge separation causes negative aerodynamic damping leading to the LCO. The fluid-structure interaction of the rigid, but elastically mounted, airfoil and nonlinear unsteady aerodynamics is represented by two coupled nonlinear stochastic ordinary differential equations containing uncertain parameters and model approximation errors. Several plausible aerodynamic models with increasing complexity are proposed to describe the aeroelastic system leading to LCO. The likelihood in the posterior parameter probability density function (pdf) is available semi-analytically using the extended Kalman filter for the state estimation of the coupled nonlinear structural and unsteady aerodynamic model. The posterior parameter pdf is sampled using a parallel and adaptive Markov Chain Monte Carlo (MCMC) algorithm. The posterior probability of each model is estimated using the Chib-Jeliazkov method that directly uses the posterior MCMC samples for evidence (marginal likelihood) computation. The Bayesian algorithm is validated through a numerical study and then applied to model the nonlinear unsteady aerodynamic loads using wind-tunnel test data at various Reynolds numbers.

Multivariate Time-VaryingG-HCopula GARCH Model and Its Application in the Financial Market Risk Measurement

Taking full advantage of the strengths ofG-Hdistribution, Copula function, and GARCH model in depicting the return distribution of financial asset, we construct the multivariate time-varyingG-HCopula GARCH model which can comprehensively describe “asymmetric, leptokurtic, and heavy-tail” characteristics, the time-varying volatility characteristics, and the extreme-tail dependence characteristics of financial asset return. Based on the conditional maximum likelihood estimator and IFM method, we propose the estimation algorithm of model parameters. Using the quantile function and simulation method, we propose the calculation algorithm of VaR on the basis of this model. To apply this model on studying a real financial market risk, we select the SSCI (China), HSI (Hong Kong, China), TAIEX (Taiwan, China), and SP500 (USA) from January 3, 2000, to June 18, 2010, as the samples to estimate the model parameters and to measure the VaRs of various index risk portfolios under different confidence levels empirically. The results of the application example are in line with the actual situation and the risk diversification theory of portfolio. To a certain extent, these results also justify the feasibility and effectiveness of the multivariate time-varyingG-HCopula GARCH model in depicting the return distribution of financial assets.

Abstract CT330: Adjusting for confounding effects of treatment crossover in a randomized phase 2 study of dabrafenib plus trametinib in BRAF V600+ metastatic melanoma

Abstract In a randomized phase 2 study (BRF113220), dabrafenib (150 mg twice daily) plus trametinib (2 mg once daily) improved progression-free survival (HR=0.39; 95% CI, 0.25-0.62; P<.001) vs dabrafenib alone. 54 patients were randomized to each arm. At the most recent data cut (March 2013), median duration of follow-up was 24 months and 52% of patients had died. ITT analyses of overall survival (OS) are likely to underestimate the true benefit of treatment because patients in the control arm (83%) were able to cross over to combination treatment upon progression. ITT median OS, a secondary endpoint, was 23.8 vs 20.2 months (HR=0.73; 95% CI, 0.43-1.24). We adjusted for confounding due to this crossover using randomization-based crossover adjustment methods: the rank-preserving structural failure time model (RPSFTM) and iterative parameter estimation (IPE) algorithm. Different assumptions about the durability of treatment effect were tested: “treatment group” analyses allow for a treatment effect that continues beyond treatment discontinuation, while “on treatment_observed” analyses assumed that the treatment effect disappears upon discontinuation. Results are presented in the Table. Reestimation of median OS following adjustment with both RPSFTM and IPE (possible only for the treatment group analyses) suggested an OS gain of 9.1 months with combination treatment vs dabrafenib alone (compared with a 3.6-month gain under ITT assumptions). The RPSFTM estimation method performed marginally better than the IPE analyses and may provide more robust results. After adjusting for the effect of crossover, the reduction in HRs to 0.47-0.50 suggests that the ITT analysis underestimates the effect of dabrafenib plus trametinib on OS. Results are uncertain due to the limiting assumptions associated with the RPSFTM and IPE methodology, small trial size, low number of observed deaths, and low number of control group patients who did not cross over. Crossover Adjustment MethodAssumptionOS HR (95% CI)None (ITT analysis unadjusted for crossover)0.73 (0.43-1.24)RPSFTMTreatment group0.47 (0.13-1.66)On treatment–observed analyses0.49 (0.15-1.63)IPETreatment group0.50 (0.16-1.54)On treatment–observed analyses0.47 (0.14-1.61) Citation Format: Nicholas Latimer, Mayur Amonkar, Ceilidh Stapelkamp, Peng Sun. Adjusting for confounding effects of treatment crossover in a randomized phase 2 study of dabrafenib plus trametinib in BRAF V600+ metastatic melanoma. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr CT330. doi:10.1158/1538-7445.AM2014-CT330

An advanced algorithm for partitioning and parameter estimation in local model networks and its application to vehicle vertical dynamics

In this paper, advanced concepts for the identification of complex nonlinear systems are discussed. Three major problems are addressed: The nonlinearity of the system, noise in the data upon which the model has to be built, and the potential to incorporate qualitative and quantitative prior knowledge about the system. As an integrated solution approach, local model networks (LMNs) with appropriate parameter estimation schemes are proposed. LMNs generally offer a versatile structure for the identification of nonlinear dynamic systems. In order to account for a realistic situation when noise is present both in input and output data, an equality constrained generalised total least squares algorithm for the local model parameter estimation of the LMN is presented; the incorporation of equality constraints allows to mathematically enforce desired system properties. As an application and benchmark problem, the vertical dynamics of a vehicle is considered. After training the LMN on a rough road, excellent predictions of the behaviour of the vehicle at crossing a single obstacle are obtained, thus proving the effectiveness of the proposed algorithm. It is illustrated how both the application of a proper parameter estimation scheme and the integration of system constraints systematically improve the performance of the model.

유한요소 모델 변수의 역 추정법을 이용한 생체의 물성 규명

An inverse finite element (FE) model parameter estimation algorithm can be used to characterize mechanical properties of biological tissues. Using this algorithm, we can consider the influence of material nonlinearity, contact mechanics, complex boundary conditions, and geometrical constraints in the modeling. In this study, biomechanical experiments on macro and micro samples are conducted and characterized with the developed algorithm. Macro scale experiments were performed to measure the force response of porcine livers against mechanical loadings using one-dimensional indentation device. The force response of the human liver cancer cells was also measured by the atomic force microscope (AFM). The mechanical behavior of porcine livers (macro) and human liver cancer cells (micro) were characterized with the algorithm via hyperelastic and linear viscoelastic models. The developed models are suitable for computing accurate reaction force on tools and deformation of biomechanical tissues.

An Expectation-Maximization Algorithm Based Kalman Smoother Approach for Event-Related Desynchronization (ERD) Estimation from EEG

We consider the problem of event-related desynchronization (ERD) estimation. In existing approaches, model parameters are usually found manually through experimentation, a tedious task that often leads to suboptimal estimates. We propose an expectation-maximization (EM) algorithm for model parameter estimation that is fully automatic and gives optimal estimates. Further, we apply a Kalman smoother to obtain ERD estimates. Results show that the EM algorithm significantly improves the performance of the Kalman smoother. Application of the proposed approach to the motor-imagery EEG data shows that useful ERD patterns can be obtained even without careful selection of frequency bands.

Modeling and simulation of compound clutter for coastal environment

This paper introduces a novel clutter power model of coastal compound and deduces the statistic characteristics and the arithmetics of model parameter estimation algorithm. Then, a novel coastal compound distributed model of the clutter amplitude is proposed and its statistic characteristics are studied. Subsequently, the nonlinear relation of autocorrelation function is deduced between the coastal compound clutter random sequences and the correlated Gaussian random sequences, which is the important prerequisite for the clutter simulation based on the ZMNL (Zero Memory NonLinearity). Lastly, the advantage of the algorithm is demonstrated by a feasible simulation of the generation of the random coastal compound clutter sequences.

A neurofuzzy network knowledge extraction and extended gram-schmidt algorithm for model subspace decomposition

This paper introduces a new neurofuzzy model construction and parameter estimation algorithm from observed finite data sets, based on a Takagi-Sugeno (T-S) inference mechanism and a new extended Gram-Schmidt orthogonal decomposition algorithm, for the modeling of a priori unknown dynamical systems in the form of a set of fuzzy rules. The paper introduces a one to one mapping between a fuzzy rule-base and a model matrix feature subspace. Hence, rule-based knowledge can be extracted to enhance model transparency. Model transparency is explored by the derivation of an equivalence between an A-optimality experimental design criterion of the weighting matrix and the average model output sensitivity to the fuzzy rule. The A-optimality experimental design criterion of the weighting matrices of fuzzy rules is used to construct an initial model rule-base. An extended Gram-Schmidt algorithm is then developed to estimate the parameter vector for each rule. This new algorithm decomposes the model rule-bases via an orthogonal subspace decomposition approach, so as to enhance model transparency with the capability of interpreting the derived rule-base energy level.

Time-varying parametric system multiresolution identification by wavelets

In this paper, the problem of time-varying parametric system identification by wavelets is discussed. Employing wavelet operator matrix representation, we propose a new multiresolution least squares (MLS) algorithm for time-varying AR (ARX) system identification and a multiresolution least mean squares (MLMS) algorithm for the refinement of parameter estimation. These techniques can achieve the optimal tradeoff between the over-fitted solution and the poorly represented identification. The main features of time-varying model parameters are extracted in a multiresolution way, which can be used to represent the smooth trends as well as track the rapidly changing components of time-varying parameters simultaneously and adaptively. Further, a noisy time-varying AR (ARX) model can also be identified by combining the total least squares algorithm with the MLS algorithm. Based on the proposed AR (ARX) model parameter estimation algorithm, a novel identification scheme for time-varying ARMA (ARMAX) system is presented. A higher-order time-varying AR (ARX) model is used to approximate the time-varying ARMA (ARMAX) system and thus obtain an initial parameter estimation. Then an iterative algorithm is applied to obtain the consistent and efficient estimates of the ARMA (ARMAX) system parameters. This ARMA (ARMAX) identification algorithm requires linear operations only and thus greatly saves the computational load. In order to determine the time-varying model order, some modified AIC and MDL criterions are developed based on the proposed wavelet identification schemes. Simulation results verify that our methods can track the rapidly changing of time-varying system parameters and attain the best balance between parsimonious modelling and accurate identification.

A Dexterous Part-Holding Model for Handling Compliant Sheet Metal Parts

Material handling of compliant sheet metal parts significantly impacts both part dimensional quality and production rate in the stamping industry. This paper advances previously developed material handling end effector layout optimization methodology for rigid point end effectors [1] by developing a dexterous part-holding end effector model. This model overcomes the shortcomings of the rigid point part-holding end effector model by predicting part deformation more accurately for various modes of deformation and for a set of part-holding end effector locations. This is especially important for handling systems which utilize vacuum cup end effectors widely used for handling of large sheet metal parts. The dexterous end effector model design method and an algorithm for estimation of model parameters are developed. The algorithm combines data from design of computer simulations and from the set of experiments by integrating finite element analysis and a statistical data processing technique. Experimental studies are conducted to verify the developed model and the model parameter estimation algorithm. The developed methodology provides an analytical tool for product and process designers to accurately predict part deformation during handling, which further leads to minimization of part deformation, improvement of part dimensional quality and increase of production rate.

Development of parameter based fault detection and diagnosis technique for energy efficient building management system

This paper presents a complete methodology for detection and diagnosis of faults in variable air volume air handling units. Three cases are considered: (a) an off-line fault detection technique for existing buildings, (b) an automatic on-line fault detection technique for integration in building management systems (BMSs) of upcoming not very complex buildings and (c) an automatic on-line fault detection as well as diagnosis technique for BMSs of upcoming complex automated buildings. The method is based upon the auto regressive exogenous model and recursive parameter estimation algorithm. The proposed model and methodology have been trained by using several days of normal real time operational data and validated on data obtained by introducing faults artificially under normal operating conditions. It is concluded that the method is robust and can detect faults in dampers, sensors and PID control.

Time-varying parametric system multiresolution identification by wavelets

In this paper, the problem of time-varying parametric system identification by wavelets is discussed. Employing wavelet operator matrix representation, we propose a new multiresolution least squares (MLS) algorithm for time-varying AR (ARX) system identification and a multiresolution least mean squares (MLMS) algorithm for the refinement of parameter estimation. These techniques can achieve the optimal tradeoff between the over-fitted solution and the poorly represented identification. The main features of time-varying model parameters are extracted in a multiresolution way, which can be used to represent the smooth trends as well as track the rapidly changing components of time-varying parameters simultaneously and adaptively. Further, a noisy time-varying AR (ARX) model can also be identified by combining the total least squares algorithm with the MLS algorithm. Based on the proposed AR (ARX) model parameter estimation algorithm, a novel identification scheme for time-varying ARMA (ARMAX) system is presented. A higher-order time-varying AR (ARX) model is used to approximate the time-varying ARMA (ARMAX) system and thus obtain an initial parameter estimation. Then an iterative algorithm is applied to obtain the consistent and efficient estimates of the ARMA (ARMAX) system parameters. This ARMA (ARMAX) identification algorithm requires linear operations only and thus greatly saves the computational load. In order to determine the time-varying model order, some modified AIC and MDL criterions are developed based on the proposed wavelet identification schemes. Simulation results verify that our methods can track the rapidly changing of time-varying system parameters and attain the best balance between parsimonious modelling and accurate identification.