We investigate a magnetically coupled nonlinear piezoelectric energy harvester by altering the angular orientation of its external magnets for enhanced broadband frequency response. Electromechanical equations describing the nonlinear dynamic behavior include an experimentally identified polynomial for the transverse magnetic force that depends on magnet angle. Up- and down-sweep harmonic excitation tests are performed at constant acceleration over the range of 0–25 Hz. Very good agreement is observed between the numerical and experimental open-circuit voltage output frequency response curves. The nonlinear energy harvester proposed in this work can cover the broad low-frequency range of 4–22 Hz by changing the magnet orientation.
This paper presents an experiment using OPENBCI to collect data of two hand gestures and decoding the signal to distinguish gestures.The signal was extracted with three electrodes on the subject's forearm and transferred in one channel.After utilizing a Butterworth bandpass filter, we chose a novel way to detect gesture action segment.Instead of using moving average algorithm, which is based on the calculation of energy, We developed an algorithm based on the Hilbert transform to find a dynamic threshold and identified the action segment.Four features have been extracted from each activity section, generating feature vectors for classification.During the process of classification, we made a comparison between K-nearest-neighbors (KNN) and support vector machine (SVM), based on a relatively small amount of samples.Most common experiments are based on a large quantity of data to pursue a highly fitted model.But there are certain circumstances where we cannot obtain enough training data, so it makes the exploration of best method to do classification under small sample data imperative.Though KNN is known for its simplicity and practicability, it is a relatively time-consuming method.On the other hand, SVM has a better performance in terms of time requirement and recognition accuracy, due to its application of different Risk Minimization Principle.Experimental results show an average recognition rate for the SVM algorithm that is 1.25% higher than for KNN while SVM is 2.031 s shorter than that KNN.
Abstract Rotate vector (RV) reducers have advantages of high torque ratio, and extremely reliable functioning under dynamic load conditions, and so forth, and they are extensively used in precision transmissions, such as industrial robots. The torsional stiffness of RV reducers is a main parameter affecting the meshing vibration and transmission performance. In addition, the variable loads and tooth modifications have significant influence on the torsional stiffness of RV reducers. In this paper, a new method of calculating torsional stiffness for RV reducers considering variable loads and tooth modifications is presented. The dynamic stress and deformation of meshing teeth are calculated based on Hertz formulation, and the number of meshing teeth is determined by analyzing the dynamic stress of meshing teeth. Then, the torsional stiffness of RV reducers is obtained. Finite element method is applied to calculate the deformation of cycloidal‐pin gear in the maximum force position δ max . The influence of variable applied loads and different tooth modifications of cycloidal‐pin gear transmission on torsional stiffness are also studied. The results show that variable applied loads and different tooth modifications influence the torsional stiffness of RV reducers by changing teeth deformation and clearance, respectively.
The concept of gait synergy provides novel human–machine interfaces and has been applied to the control of lower limb assistive devices, such as powered prostheses and exoskeletons. Specifically, on the basis of gait synergy, the assistive device can generate/predict the appropriate reference trajectories precisely for the affected or missing parts from the motions of sound parts of the patients. Optimal modeling for gait synergy methods that involves optimal combinations of features (inputs) is required to achieve synergic trajectories that improve human–machine interaction. However, previous studies lack thorough discussions on the optimal methods for synergy modeling. In addition, feature selection (FS) that is crucial for reducing data dimensionality and improving modeling quality has often been neglected in previous studies. Here, we comprehensively investigated modeling methods and FS using 4 up-to-date neural networks: sequence-to-sequence (Seq2Seq), long short-term memory (LSTM), recurrent neural network (RNN), and gated recurrent unit (GRU). We also conducted complete FS using 3 commonly used methods: random forest, information gain, and Pearson correlation. Our findings reveal that Seq2Seq (mean absolute error: 0.404° and 0.596°, respectively) outperforms LSTM, RNN, and GRU for both interlimb and intralimb synergy modeling. Furthermore, FS is proven to significantly improve Seq2Seq’s modeling performance ( P < 0.05). FS-Seq2Seq even outperforms methods used in existing studies. Therefore, we propose FS-Seq2Seq as a 2-stage strategy for gait synergy modeling in lower limb assistive devices with the aim of achieving synergic and user-adaptive trajectories that improve human–machine interactions.
Wind energy is one of the most well-known renewable energies in the world, generating 6% of electricity throughout the world. In this journal, the main technology of a wind turbine is discussed, along with current implementations, its environmental impacts, and the economic influences of wind turbines. Most information in this article is based on former journals and data from businesses in the industry, allowing an accurate portrait of the current industry. This journal concludes the basic knowledges on wind turbines, providing a general insight of the wind energy system.
The complexity quantification of human gait time series has received considerable interest for wearable healthcare. Symbolic entropy is one of the most prevalent algorithms used to measure the complexity of a time series, but it fails to account for the multiple time scales and multi-channel statistical dependence inherent in such time series. To overcome this problem, multivariate multiscale symbolic entropy is proposed in this paper to distinguish the complexity of human gait signals in health and disease. The embedding dimension, time delay and quantization levels are appropriately designed to construct similarity of signals for calculating complexity of human gait. The proposed method can accurately detect healthy and pathologic group from realistic multivariate human gait time series on multiple scales. It strongly supports wearable healthcare with simplicity, robustness, and fast computation.
Aiming at the strong nonlinearity of switched reluctance motor during driving and regenerative braking,an adaptive robust control method was presented,which combined the fuzzy adaptive control with the H∞ robust control.Designed the adaptive robust controller to suppress the uncertainty of the system,put forward the fuzzy model based on regional linearity of the motor,and the stability and convergence of the controller was analyzed.The simulated and experimental results show that the adaptive robust controller can ensure the system performance and robust stability,and is superior to classical PI control in transition time,overshoot and stable error.
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