34 research outputs found
A Hybrid Approach for Short-Term Forecasting of Wind Speed
We propose a hybrid method for forecasting the wind speed. The wind speed data is first decomposed into intrinsic mode functions (IMFs) with empirical mode decomposition. Based on the partial autocorrelation factor of the individual IMFs, adaptive methods are then employed for the prediction of IMFs. Least squares-support vector machines are employed for IMFs with weak correlation factor, and autoregressive model with Kalman filter is employed for IMFs with high correlation factor. Multistep prediction with the proposed hybrid method resulted in improved forecasting. Results with wind speed data show that the proposed method provides better forecasting compared to the existing methods
Motor Control Insights on Walking Planner and its Stability
The application of biomechanic and motor control models in the control of
bidedal robots (humanoids, and exoskeletons) has revealed limitations of our
understanding of human locomotion. A recently proposed model uses the potential
energy for bipedal structures to model the bipedal dynamics, and it allows to
predict the system dynamics from its kinematics. This work proposes a
task-space planner for human-like straight locomotion that target application
of in rehabilitation robotics and computational neuroscience. The proposed
architecture is based on the potential energy model and employs locomotor
strategies from human data as a reference for human behaviour. The model
generates Centre of Mass (CoM) trajectories, foot swing trajectories and the
Base of Support (BoS) over time. The data show that the proposed architecture
can generate behaviour in line with human walking strategies for both the CoM
and the foot swing. Despite the CoM vertical trajectory being not as smooth as
a human trajectory, yet the proposed model significantly reduces the error in
the estimation of the CoM vertical trajectory compared to the inverted pendulum
models. The proposed model is also able to asses the stability based on the
body kinematics embedding in currently used in the clinical practice. However,
the model also implies a shift in the interpretation of the spatiotemporal
parameters of the gait, which are now determined by the conditions for the
equilibrium and not \textit{vice versa}. In other words, locomotion is a
dynamic reaching where the motor primitives are also determined by gravity
Differential Evolution with Population and Strategy Parameter Adaptation
Differential evolution (DE) is simple and effective in solving numerous real-world global optimization problems. However, its effectiveness critically depends on the appropriate setting of population size and strategy parameters. Therefore, to obtain optimal performance the time-consuming preliminary tuning of parameters is needed. Recently, different strategy parameter adaptation techniques, which can automatically update the parameters to appropriate values to suit the characteristics of optimization problems, have been proposed. However, most of the works do not control the adaptation of the population size. In addition, they try to adapt each strategy parameters individually but do not take into account the interaction between the parameters that are being adapted. In this paper, we introduce a DE algorithm where both strategy parameters are self-adapted taking into account the parameter dependencies by means of a multivariate probabilistic technique based on Gaussian Adaptation working on the parameter space. In addition, the proposed DE algorithm starts by sampling a huge number of sample solutions in the search space and in each generation a constant number of individuals from huge sample set are adaptively selected to form the population that evolves. The proposed algorithm is evaluated on 14 benchmark problems of CEC 2005 with different dimensionality
Estimation of Physiological Tremor from Accelerometers for Real-Time Applications
Accurate filtering of physiological tremor is extremely important in robotics assisted surgical instruments and procedures. This paper focuses on developing single stage robust algorithms for accurate tremor filtering with accelerometers for real-time applications. Existing methods rely on estimating the tremor under the assumption that it has a single dominant frequency. Our time-frequency analysis on physiological tremor data revealed that tremor contains multiple dominant frequencies over the entire duration rather than a single dominant frequency. In this paper, the existing methods for tremor filtering are reviewed and two improved algorithms are presented. A comparative study is conducted on all the estimation methods with tremor data from microsurgeons and novice subjects under different conditions. Our results showed that the new improved algorithms performed better than the existing algorithms for tremor estimation. A procedure to separate the intended motion/drift from the tremor component is formulated
Time-Frequency Analysis of Non-Stationary Biological Signals with Sparse Linear Regression Based Fourier Linear Combiner
It is often difficult to analyze biological signals because of their nonlinear and non-stationary characteristics. This necessitates the usage of time-frequency decomposition methods for analyzing the subtle changes in these signals that are often connected to an underlying phenomena. This paper presents a new approach to analyze the time-varying characteristics of such signals by employing a simple truncated Fourier series model, namely the band-limited multiple Fourier linear combiner (BMFLC). In contrast to the earlier designs, we first identified the sparsity imposed on the signal model in order to reformulate the model to a sparse linear regression model. The coefficients of the proposed model are then estimated by a convex optimization algorithm. The performance of the proposed method was analyzed with benchmark test signals. An energy ratio metric is employed to quantify the spectral performance and results show that the proposed method Sparse-BMFLC has high mean energy (0.9976) ratio and outperforms existing methods such as short-time Fourier transfrom (STFT), continuous Wavelet transform (CWT) and BMFLC Kalman Smoother. Furthermore, the proposed method provides an overall 6.22% in reconstruction error