Adaptation Of Model Parameters Research Articles

TOAST

Touch typing on flat surfaces (e.g. interactive tabletop) is challenging due to lack of tactile feedback and hand drifting. In this paper, we present TOAST, an eyes-free keyboard technique for enabling efficient touch typing on touch-sensitive surfaces. We first formalized the problem of keyboard parameter (e.g. location and size) estimation based on users' typing data. Through a user study, we then examined users' eyes-free touch typing behavior on an interactive tabletop with only asterisk feedback. We fitted the keyboard model to the typing data, results suggested that the model parameters (keyboard location and size) changed not only between different users, but also within the same user along with time. Based on the results, we proposed a Markov-Bayesian algorithm for input prediction, which considers the relative location between successive touch points within each hand respectively. Simulation results showed that based on the pooled data from all users, this model improved the top-1 accuracy of the classical statistical decoding algorithm from 86.2% to 92.1%. In a second user study, we further improved TOAST with dynamical model parameter adaptation, and evaluated users' text entry performance with TOAST using realistic text entry tasks. Participants reached a pick-up speed of 41.4 WPM with a character-level error rate of 0.6%. And with less than 10 minutes of practice, they reached 44.6 WPM without sacrificing accuracy. Participants' subjective feedback also indicated that TOAST offered a natural and efficient typing experience.

Adaptive parametric model order reduction technique for optimization of vibro-acoustic models: Application to hearing aid design

Finite Element (FE) models of complex structural-acoustic coupled systems can require a large number of degrees of freedom in order to capture their physical behaviour. This is the case in the hearing aid field, where acoustic-mechanical feedback paths are a key factor in the overall system performance and modelling them accurately requires a precise description of the strong interaction between the light-weight parts and the internal and surrounding air over a wide frequency range. Parametric optimization of the FE model can be used to reduce the vibroacoustic feedback in a device during the design phase; however, it requires solving the model iteratively for multiple frequencies at different parameter values, which becomes highly time consuming when the system is large. Parametric Model Order Reduction (pMOR) techniques aim at reducing the computational cost associated with each analysis by projecting the full system into a reduced space. A drawback of most of the existing techniques is that the vector basis of the reduced space is built at an offline phase where the full system must be solved for a large sample of parameter values, which can also become highly time consuming. In this work, we present an adaptive pMOR technique where the construction of the projection basis is embedded in the optimization process and requires fewer full system analyses, while the accuracy of the reduced system is monitored by a cheap error indicator. The performance of the proposed method is evaluated for a 4-parameter optimization of a frequency response for a hearing aid model, evaluated at 300 frequencies, where the objective function evaluations become more than one order of magnitude faster than for the full system.

Scaling of the angle of repose test and its influence on the calibration of DEM parameters using upscaled particles

The numerical effort of computer simulations based on the Discrete Element Method (DEM) forces an idealisation of the DEM models especially when industrial problems are analysed. The idealisation often includes the scale-up of the particle sizes and therefore requires the adaption of contact model parameters to produce realistic simulation results. Hence, the calibration of contact parameters is the most crucial part in the development process of a DEM model. Due to the scale-up of particle size, a calibration test should provide results invariant to test dimensions and particle size. This paper shows how the results of one typical calibration test, the angle of repose (AoR) test using a lifting cylinder, are invariant regarding the dimensions of the test rig and the considered particle size distribution. The lifting velocity of the cylinder is indicated as a parameter which influences the AoR. Hence, the cylinder pull-up test delivers only size invariant results for materials in static stress state when slow lifting velocities are applied. The so called shear box test will be presented as an alternative AoR test to achieve invariant results even for a dynamic, fast flowing stress state and short test durations.

State estimation for a penicillin fed-batch process combining particle filtering methods with online and time delayed offline measurements

Real time monitoring of physiological characteristics during a cultivation process is of great importance in the pharmaceutical industry. Measuring biomass, product, substrate and precursor concentrations continuously however is limited due to time-consuming laboratory analysis or expensive and hard-to-handle devices. In this work, a particle filter algorithm for estimating these difficult-to-measure process states in a Penicillium chrysogenum fed-batch cultivation is presented. The implemented particle filter represents a new algorithmic framework, combining several already existing methods and techniques for state estimation. It is based on nonlinear process and measurement models and takes into account both online measurements for state estimation and time delayed offline measurements, ensuring the observability of the considered system and being essential for the adaptation of dynamic model parameters. The application on real experimental data showed the convincing performance of the algorithm, estimating biomass, precursor and product concentration, as well as the specific growth rate, requiring standard measurements only. Furthermore, the positive effect of parameter estimation with respect to estimation quality was analyzed and the effect of the time delay was highlighted exemplarily. Despite of being computationally expensive due to time delayed data, the algorithm can be considered as an alternative monitoring strategy for industrial applications. Thus, it can be used further for process understanding and control.

Statistical decision methods in the presence of linear nuisance parameters and despite imaging system heteroscedastic noise: Application to wheel surface inspection

This paper proposes a novel method for fully automatic anomaly detection on objects inspected using an imaging system. In order to address the inspection of a wide range of objects and to allow the detection of any anomaly, an original adaptive linear parametric model is proposed; The great flexibility of this adaptive model offers highest accuracy for a wide range of complex surfaces while preserving detection of small defects. In addition, because the proposed original model remains linear it allows the application of the hypothesis testing theory to design a test whose statistical performances are analytically known. Another important novelty of this paper is that it takes into account the specific heteroscedastic noise of imaging systems. Indeed, in such systems, the noise level depends on the pixels’ intensity which should be carefully taken into account for providing the proposed test with statistical properties. The proposed detection method is then applied for wheels surface inspection using an imaging system. Due to the nature of the wheels, the different elements are analyzed separately. Numerical results on a large set of real images show both the accuracy of the proposed adaptive model and the sharpness of the ensuing statistical test.

Uncertainty Management in Lebesgue-Sampling-Based Diagnosis and Prognosis for Lithium-Ion Battery

Lebesgue-sampling-based fault diagnosis and prognosis (LS-FDP) is developed with the advantage of less computation requirement and smaller uncertainty accumulation. Same as other diagnostic and prognostic approaches, the accuracy and precision of LS-FDP are significantly influenced by the parameters and uncertainties in the diagnostic and prognostic models. To improve performance of LS-FDP, this paper introduces an online model parameter adaptation scheme, which is realized by a recursive least square method with a forgetting factor. In addition, uncertainty of remaining useful life (RUL) prediction is managed by adjusting the model noises through a short-term prediction and correction loop. To verify the proposed parameter adaptation and noise adjustment methods, they are designed and implemented in a particle-filtering-based LS-FDP algorithm with applications to Li-ion batteries. Experimental results show that the proposed approach has significant improvement on both battery capacity estimation and RUL prediction.

Putting a price tag on temperature

Abstract A model for the evolution of daily average temperatures (DATs) is put forward to support the analysis of weather derivatives. The goal is to capture simultaneously the stochasticity, mean-reversion, and seasonality patterns of the DATs process. An Ornstein–Uhlenbeck (OU) process modulated by a hidden Markov chain (HMC) is proposed to model both the mean-reversion and stochasticity of a deseasonalised component. The seasonality part is modelled by a combination of linear and sinusoidal functions. Modified and more efficient OU–HMM filtering algorithms relative to the current ones in the literature are presented for the evolution of adaptive and switching model parameter estimates. Numerical implementation of the estimation technique using a 4-year Toronto temperature data set compiled by the National Climatic Data Center was conducted. A sensitivity analysis of the option prices with respect to model parameters is included.

Structural changes in economic growth models

The paper is devoted to analysis of one-sector growth models and corresponding control problems on optimal distribution of investments. The paper considers a model with a linear production function, which takes into account the feasibility of structural changes in an economy. By introducing dummy variables one can statistically indicate a period when the model undergoes changes. This provides the possibility to switch the model in different modes for providing more accurate forecasts of economic development. For the optimal control problems, the qualitative analysis of the Hamiltonian systems is implemented and solutions are constructed for each model mode. Continuous gluing of the obtained trajectories is obtained as a solution of the optimal control problem with different model modes on the infinite time interval. Comparison of the resulting model trajectory with statistical data reveals that the simulated trends provide sufficiently accurate matching with the real data. Adaptation of model parameters to a new economic mode can be considered as a learning process for the entire optimal control model. It makes the model more flexible with respect to the qualitative changes influencing forecasts of economic development.

Simplex‐based adaptive parametric model order reduction for applications in optimization

AbstractA new methodology for optimization using parametric reduced order models is introduced. An adaptive scheme to place the expansion points in the reduction step is combined with a smart parametric space sampling in the optimization step to produce very reliable reduced order models. The methodology is illustrated in a demonstrative electromagnetic problem with appreciable gain in computational time. This technique is of general application and can be used to speed up optimization processes in many areas of engineering.

Simultaneous model selection and parameter estimation: A superconducting qubit coupled to a bath of incoherent two-level systems

In characterization of quantum systems, adapting measurement settings based on data while it is collected can generally outperform in efficiency conventional measurements that are carried out independently of data. The existing methods for choosing measurement settings adaptively assume that the model, or the number of unknown parameters, is known. We introduce simultaneous adaptive model selection and parameter estimation. We apply our technique for characterization of a superconducting qubit and a bath of incoherent two-level systems, a leading decoherence mechanism in the state-of-the-art superconducting qubits.

A semi‐adaptive control approach to closed‐loop medication infusion

SummaryThis paper presents a semi‐adaptive control approach to closed‐loop medication infusion problems. The rationale underlying this approach is to design a controller that can adapt model parameters with a large impact on the model's fidelity while fixing the remaining parameters at nominal values. In this paper, a control‐oriented model for this purpose is derived via system identification and sensitivity analysis of a low‐order model capturing the direct dose‐response relationship Using the model thus derived, a model‐reference adaptive controller and a composite adaptive controller are designed and compared with each other. In‐silico simulation results using remifentanil's effect on respiratory rate as an example indicate that both controllers can regulate the output at commanded set points. Copyright © 2016 John Wiley & Sons, Ltd.

Batch-to-Batch Optimization of Chemical Processes in the Presence of Model-Plant Mismatch Using a Variable Subset of Model Parameters

In the presence of model-plant mismatch, a standard "two-step" approach, involving repeated identification and optimization steps, cannot guarantee convergence to the process optimum. Model parameter adaptation can be used for handling model error by correcting for mismatch between predicted and measured cost and constraint gradients while simultaneously satisfying both identification and optimization objectives. However, updating all model parameters at once is often impractical due to estimability and increased sensitivity to noise. This work presents a procedure for selecting, after each run, a particular subset of parameters based on parametric sensitivity of the model output and of cost and constraint gradients. The resulting improvements with respect to previous run-to-run studies are illustrated using a simulated case study of a penicillin fed-batch process.

Adaptive Active Fault Tolerant Control for Discrete‐Time Systems with Uncertainties

AbstractAn active fault‐tolerant control scheme for discrete‐time systems is proposed to solve a difficult problem of fault‐tolerant controller design in the presence of partial loss of actuator effectiveness faults and structural parameter uncertainties assumed to be matched, using adaptive control techniques to help a faster and more accurate compensation of failure and uncertainty. An automated fault estimation scheme is developed together with an adaptive model parameter identification to obtain system parameter estimates. With these estimates fed back to the system, a model reference adaptive controller is constructed to achieve a desired tracking performance. Since parameters are obtained and updated online, the control system has an automatic failure compensation capability so as to recognize or reconfigure the control law in real time in response to failure indications. The stability and convergence follow from discrete‐time Lyapunov arguments. Simulation results from the linearized lateral dynamics model of the Boeing 747 airplane are presented to show the efficiency of proposed methods.

Assessment of the Spatial and Seasonal Variation of the Error–Intensity Relationship in Satellite-Based Precipitation Measurements Using an Adaptive Parametric Model

AbstractStudies have been reported about the efficacy of satellites for measuring precipitation and about quantifying their errors. Based on these studies, the errors are associated with a number of factors, among them, intensity, location, climate, and season of the year. Several error models have been proposed to assess the relationship between the error and the rainfall intensity. However, it is unknown whether these models are adaptive to different seasons, different regions, or different types of satellite-based estimates. Therefore, how the error–intensity relationship varies with the season or region is unclear. To investigate these issues, a parametric joint pdf model is proposed to analyze and study the 9-yr satellite-derived precipitation datasets of Climate Prediction Center (CPC) morphing technique (CMORPH); PERSIANN; and the real-time TRMM product 3B42, version 7 (TRMM-3B42-RTV7). The NEXRAD Stage IV product is the ground reference. The adaptability of the proposed model is verified by applying it to three locations (Oklahoma, Montana, and Florida) and by applying it to cold season, warm season, and the entire year. Then, the heteroscedasticities in the errors of satellite-based precipitation measurements are investigated using the proposed model under those scenarios. The results show that the joint pdfs have the same formulation under these scenarios, whereas their parameter sets were adaptively adjusted. This parametric model reveals detailed information about the spatial and seasonal variations of the satellite-based precipitation measurements. It is found that the shape of the conditional pdf shifts across the intensity ranges. At the ~10–20 mm day−1 range, the conditional pdf is L shaped, while at the ~40–60 mm day−1 range, it becomes more bell shaped. It is also concluded that no single satellite-based precipitation product outperforms others with respect to the different scenarios (i.e., seasons, regions, and climates).

Defect Detection and Localization of Nonlinear System Based on Particle Filter with an Adaptive Parametric Model

A robust particle filter (PF) and its application to fault/defect detection of nonlinear system are investigated in this paper. First, an adaptive parametric model is exploited as the observation model for a nonlinear system. Second, by incorporating the parametric model, particle filter is employed to estimate more accurate hidden states for the nonlinear stochastic system. Third, by formulating the problem of defect detection within the hypothesis testing framework, the statistical properties of the proposed testing are established. Finally, experimental results demonstrate the effectiveness and robustness of the proposed detector on real defect detection and localization in images.

Binaural source separation based on spatial cues and maximum likelihood model adaptation

This paper describes a system for separating multiple moving sound sources from two-channel recordings based on spatial cues and a model adaptation technique. We employ a statistical model of observed interaural level and phase differences, where maximum likelihood estimation of model parameters is achieved through an expectation-maximization algorithm. This model is used to partition spectrogram points into several clusters (one cluster per source) and generate spectrogram masks accordingly for isolating individual sound sources. We follow a maximum likelihood linear regression (MLLR) approach for tracking source relocations and adapting model parameters accordingly. The proposed algorithm is able to separate more sources than input channels, i.e. in the underdetermined setting. In simulated anechoic and reverberant environments with two and three speakers, the proposed model-adaptation algorithm yields more than 10 dB gain in signal-to-noise-ratio-improvement for azimuthal source relocations of 15° or more. Moreover, this performance gain is achievable with only 0.6 seconds of input mixture received after relocation.

Predicting and Evaluating the Effect of Bivalirudin in Cardiac Surgical Patients

Bivalirudin, used in patients with heparin-induced thrombocytopenia, is a direct thrombin inhibitor. Since it is a rarely used drug, clinical experience with its dosing is sparse. We develop two approaches to predict the Partial Thromboplastin Time (PTT) based on bivalirudin infusion rates. The first approach is model free and utilizes regularized regression. It is flexible enough to be used as predictors bivalirudin infusion rates measured over several time instances before the time at which a PTT prediction is sought. The second approach is model based and proposes a specific model for obtaining PTT which uses a shorter history of the past measurements. We learn population-wide model parameters by solving a nonlinear optimization problem. We also devise an adaptive algorithm based on the extended Kalman filter that can adapt model parameters to individual patients. The latter adaptive model emerges as the most promising as it yields reduced mean error compared to the model-free approach. The model accuracy we demonstrate on actual patient measurements is sufficient to be useful in guiding the optimal therapy.

A Predictive Model for the Anticoagulant Bivalirudin Administered to Cardiac Surgical Patients.

Bivalirudin is used in patients with heparin-induced thrombocytopenia and is a direct thrombin inhibitor. Since it is a rarely used drug, clinical experience with its dosing is sparse. We develop a model that predicts the effect of bivalirudin, measured by the Partial Thromboplastin Time (PTT), based on its past fusion rates. We learn population-wide model parameters by solving a nonlinear optimization problem that uses a training set of patient data. More interestingly, we devise an adaptive algorithm based on the extended Kalman filter that can adapt model parameters to individual patients. The latter adaptive model emerges as the most promising as it reduces both the mean error and, drastically, the per-patient error variance. The model accuracy we demonstrate on actual patient measurements is sufficient to be useful in guiding optimal therapy.

Statistical detection of defects in radiographic images using an adaptive parametric model

In this paper, a new methodology is presented for detecting anomalies from radiographic images. This methodology exploits a statistical model adapted to the content of radiographic images together with the hypothesis testing theory. The main contributions are the following. First, by using a generic model of radiographies based on the acquisition pipeline, the whole non-destructive testing process is entirely automated and does not require any prior information on the inspected object. Second, by casting the problem of defects detection within the framework of testing theory, the statistical properties of the proposed test are analytically established. This particularly permits the guaranteeing of a prescribed false-alarm probability and allows us to show that the proposed test has a bounded loss of power compared to the optimal test which knows the content of inspected object. Experimental results show the sharpness of the established results and the relevance of the proposed approach.

Design and Implementation of an Online Tuning Adaptive Controller for Synchronous Reluctance Motor Drives

The paper proposes a new adaptive controller design for a synchronous reluctance motor drive system. In this paper, an online tuning adaptive controller, which is based on the least-mean-square algorithm, is proposed. The adaptive controller is constructed by using an adaptive model and an adaptive control. Both the adaptive-model parameters and the adaptive-control parameters are online tuned. A nonlinear programming optimization technique is used to improve the convergence rate of the online tuning method. By using the proposed method, the transient responses, load disturbance responses, and tracking responses of the drive system are improved. The experimental results show that the implemented drive system can perform a wide range in adjustable speed from 1 to 1800 r/min and a precise position control as well. A digital signal processor, TMS-320F-28335, is used to execute the adaptive current-loop, speed-loop, and position-loop controllers. Experimental results can validate the theoretical analysis and show the feasibility and correctness of the proposed method.