The use of dielectric elastomers as integrated actuators and strain sensors offers a simple approach for closed-loop control in a wide range of applications. While a number of approaches for self-sensing have been pro...
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The use of dielectric elastomers as integrated actuators and strain sensors offers a simple approach for closed-loop control in a wide range of applications. While a number of approaches for self-sensing have been proposed, the adaptive online algorithm offers an appealing combination of high accuracy and low computational cost. In this work, the recursive least squares algorithm will be applied to capacitive deformation sensing of dielectric elastomers. With the goal of minimizing sampling rate while achieving a set accuracy over a desired range of deformation frequencies, the probe frequency, sampling frequency, and forgetting factor will be optimized. It will be shown that the accuracy is primarily determined by a nondimensionalized variable, (W) over bar, which defines the proportion of a hypothetical deformation cycle that is weighted more heavily by the algorithm. Ultimately, this optimized algorithm will be validated by variably inflating a dielectric elastomer membrane and comparing the algorithm output to membrane deformation measured by video.
In order to model nonlinear systems with more accuracy,and to further exploit the potential capacities of recurrent neural networks,we propose a novel recursiveleast square(RLS) algorithm based on echo state network(...
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In order to model nonlinear systems with more accuracy,and to further exploit the potential capacities of recurrent neural networks,we propose a novel recursiveleast square(RLS) algorithm based on echo state network(ESN),and note it as RLSESN in this *** is a new paradigm for using recurrent neural networks(RNN) with a simpler training *** proposed RLSESN consists of three main components:an ESN,a recursiveleast square(RLS) algorithm with adaptive forgetting factor and a change detection *** first,the change detection module modifies the forgetting factor online according to ESN output *** then,the RLS algorithm regulates the ESN output connection *** simulation experiment results show that RLSESN can model nonlinear systems very well;the modeling performances are significantly better than those traditional ARMA model based filters.
A human-centered design of haptic aids aims at tuning the force feedback based on the effect it has on human behavior. For this goal, a better understanding of the influence of haptic aids on the pilot neuromuscular r...
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ISBN:
(纸本)9781479938407
A human-centered design of haptic aids aims at tuning the force feedback based on the effect it has on human behavior. For this goal, a better understanding of the influence of haptic aids on the pilot neuromuscular response becomes crucial. In realistic scenarios, the neuromuscular response can continuously vary depending on many factors, such as environmental factors or pilot fatigue. This paper presents a method that online estimates time-varying neuromuscular dynamics during force-related tasks. This method is based on a recursiveleastsquares (RLS) algorithm and assumes that the neuromuscular response can be approximated by a Finite Impulse Response filter. The reliability and the robustness of the method were investigated by performing a set of Monte-Carlo simulations with increasing level or remnant noise. Even with high level of remnant noise, the RLS algorithm provided accurate estimates when the neuromuscular dynamics were constant or changed slowly. With instantaneous changes, the RLS algorithm needed almost 8s to converge to a reliable estimate. These results seem to indicate that RLS algorithm is a valid tool for estimating online time-varying admittance.
Effectiveness of haptic guidance systems depends on how humans adapt their neuromuscular response to the force feedback. A quantitative insight into adaptation of neuromuscular response can be obtained by identifying ...
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ISBN:
(纸本)9781479986965
Effectiveness of haptic guidance systems depends on how humans adapt their neuromuscular response to the force feedback. A quantitative insight into adaptation of neuromuscular response can be obtained by identifying neuromuscular dynamics. Since humans are likely to vary their neuromuscular response during realistic control scenarios, there is a need for methods that can identify time-varying neuromuscular dynamics. In this work an identification method is developed which estimates the impulse response of time-varying neuromuscular system by using a recursiveleastsquares (RLS) method. The proposed method extends the commonly used RLS-based method by employing the pseudoinverse operator instead of the inverse operator. This results in improved robustness to external noise. The method was validated in a human in-the-loop experiment. The neuromuscular estimates given by the proposed method were more accurate than those obtained with the commonly used RLS-based method.
It is known that recursiveleastsquares (RLS) algorithm or least mean square (LMS) algorithm leads to biased FIR filter coefficients in the presence of input and output noise. In this paper, a new type of bias compen...
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It is known that recursiveleastsquares (RLS) algorithm or least mean square (LMS) algorithm leads to biased FIR filter coefficients in the presence of input and output noise. In this paper, a new type of bias compensated recursiveleastsquares (BCRLS) algorithm is proposed to produce consistent results for adaptive FIR filtering in the input and output noise case. The proposed algorithm introduces an auxiliary estimator to estimate unknown input noise variance. Owing to this, the bias of the RLS solution due to the input noise can be compensated to yield the consistent filter coefficients. Computational simulations indicate that the proposed algorithm is very efficient
This paper proposes a varying coefficient Susceptible-Exposed-Infected-Removed (vSEIR) model to dynamically simulate the early mpox epidemic. We incorporate a time-varying infection rate and smallpox vaccination prote...
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This paper proposes a varying coefficient Susceptible-Exposed-Infected-Removed (vSEIR) model to dynamically simulate the early mpox epidemic. We incorporate a time-varying infection rate and smallpox vaccination protection to capture real-time changes in transmission influenced by non-pharmaceutical interventions, setting our work apart from studies relying on fixed rates. To this end, we apply the recursive least squares algorithm with a forgetting factor for real-time identification of time-varying infection rate. The sparse Hodrick-Prescott (HP) filter, tuned with leave-one-out cross-validation, captures mpox epidemic kinks via the effective reproduction number RI obtained from the discrete vSEIR model. This allows for accurate segmentation of epidemic phases, better evaluation of intervention effectiveness, and insights that can guide preparedness for future possible outbreaks. We analyze the mpox and COVID19 outbreaks in four countries using the proposed kink-based framework. The results show that the mpox epidemic generally entered its decline phase earlier than COVID-19, despite weaker interventions. Additionally, the early mpox epidemic exhibited more inflection points compared to the early COVID-19 pandemic, reflecting stronger non-pharmacological controls during the latter. Sensitivity analyses further indicate that mpox infections would have increased by 12% without smallpox vaccination, and data uncertainty significantly impacts RI estimates. Finally, our proposed systematic framework can also be extended to other early outbreaks of human-to- human epidemics, especially in the absence of reliable medical data on contact rates.
Single-input and single-output (SISO) controlled autoregressive moving average system by using a scalar factor input-output data is considered. Through data scaling, a simple identification technique is obtained. Usin...
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ISBN:
(纸本)9781479967735
Single-input and single-output (SISO) controlled autoregressive moving average system by using a scalar factor input-output data is considered. Through data scaling, a simple identification technique is obtained. Using input-output scaling factors a data recursiveleastsquares (RLS) method for estimating the parameters of a linear model and contemporary sinusoidal disturbance detection is deduced. For estimating parameters of a model in nano range a very high frequency input signal with a very small sampling rate is needed. The main contribution of this work consists of the use of a scaled recursiveleast Square with a forgetting factor. Using this proposed technique, a low input signal frequency and a wider sampling rate can be used to identify the parameters. In the meantime, the scaling technique reduces the effect of the external disturbance so that RLS can be applied to identify the disturbance without considering a model of it. The proposed technique is quite general and can be applied to any kind of linear systems. The simulation results indicate that the proposed algorithm is effective.
A new recursivealgorithm for the leastsquares problem subject to linear equality and inequality constraints is presented. It is applicable for problems with a large number of inequalities. The algorithm combines thr...
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A new recursivealgorithm for the leastsquares problem subject to linear equality and inequality constraints is presented. It is applicable for problems with a large number of inequalities. The algorithm combines three types of recursion: time-, order-, and active-set-recursion. Each recursion step has time-complexity , where is the dimension of the data vectors. An -refreshment of the corresponding inverse matrices after each time-period of length makes the algorithm numerically very stable, such that it can handle arbitrarily many data vectors without significant rounding errors. Processing a new data vector (which usually only slightly changes the instance of the optimization problem) has time complexity , provided that the active set method only requires steps for the update until the optimum is found. In a series of examples with randomly generated data sets and with either convex constraints or with randomly generated linear constraints, the set of active constraints remains relatively stable after the inclusion of each new data vector.
This paper studies actuator fault detection and estimation for a quadrotor unmanned aerial vehicle. By combining a parity space approach and a recursive least squares algorithm, we propose a novel fault detection and ...
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This paper studies actuator fault detection and estimation for a quadrotor unmanned aerial vehicle. By combining a parity space approach and a recursive least squares algorithm, we propose a novel fault detection and estimation method strategy for a quadrotor unmanned aerial vehicle, which is described by a linear time-varying system. Specifically, the parity space approach is used to generate a residual in fault detection, and then the magnitude of the fault is estimated by a recursive least squares algorithm with a variable forgetting factor based on the parity. Numerical simulations of a quadrotor unmanned aerial vehicle are conducted to verify the effectiveness of the proposed method.
recursiveleast-squares (RLS) based prediction methods are very popular in lossless compression of hyperspectral imaging. Adaptive selection of number of bands used in the prediction in RLS based methods increases com...
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ISBN:
(纸本)9781509064946
recursiveleast-squares (RLS) based prediction methods are very popular in lossless compression of hyperspectral imaging. Adaptive selection of number of bands used in the prediction in RLS based methods increases compression performance significantly. However, this process brings additional computational load. In this work, a sample reduction based fast adaptive method to determine the number of bands required for prediction is proposed. Performance of the proposed method is compared to the-state-of-the-art methods in terms of bitrates and computation times and obtained results are discussed.
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