Metamaterials, as a new type of material for manufacturing electromagnetic and electronic devices, have become the focus of many studies. It can be used in many disciplines, such as chemistry, physics, materials science, optoelectronics, semiconductor science, and equipment manufacturing, and are currently a cutting-edge and strategically significant research topic. Electromagnetic metamaterials are a new type of artificial composite electromagnetic material with controllable dielectric constant and magnetic permeability. This paper establishes a theoretical framework for metamaterials, divides electromagnetic wave media into four quadrants using constitutive parameters, and introduces the calculation method of effective magnetic permeability. This paper is based on a square planar spiral structure. In order to reduce the resonant frequency, a quantitative analysis was conducted on the influence of double-layer mirror structure, patch resistance, and different stacking structures on the resonant frequency. The reference resonant frequency was reduced from 9.6MHz to 7.6MHz, providing a solid foundation for subsequent research.
In this paper, the combination of wavelet neural networks (WNN) and sliding mode control (SMC) is proposed and simulated to solve the problem of trajectory-tracking control of a two-link robot manipulator with periodic interference. The difficulties of designing control algorithm are mainly focused on achieving accurate trajectory tracking and good control performance with the guarantee of stability and robustness under uncertain cyclical interference. In order to deal with these issues, WNN is used to approximate the functions of control object and unknown periodic disturbance. In this three-layer neural networks design, a widely used Mexican hat wavelet as an activation function has been applied for hidden-layer neurons. Combined with the SMC theory, the adaptive learning laws of networks parameters are derived in the sense of Lyapunov stability analysis so that the tracking error and convergence of the weight can be guaranteed in this control system. The better effectiveness of proposed SMC and WNN control algorithm is demonstrated by numerical simulation on a two-link robot manipulator, as comparing with that of Gauss Radial Basis Function (GRBF) neural networks.
Diabetes is a serious chronic metabolic disease. In the recent years, more and more studies focus on the use of the non-invasive methods to achieve the blood glucose estimation. More and more consumer technology enterprises focusing on human health are committed to implementing accurate and non-invasive blood glucose algorithm in their products. The near infrared spectroscopy built in the wearable devices is one of the common approaches to achieve the non-invasive blood glucose estimation. However, due to the interference from the external environment, these wearable non-invasive methods yield the low estimation accuracy. Even if it is not medical equipment, as a consumer product, the detection accuracy will also be an important indicator for consumers. To address this issue, this paper employs different models based on different ranges of the blood glucose values for performing the blood glucose estimation. First the photoplethysmograms (PPGs) are acquired and they are denoised via the bit plane singular spectrum analysis (SSA) method. Second, the features are extracted. For the data in the training set, first the features are averaged across the measurements in the feature domain via the optimization approach. Second, the random forest is employed to sort the importance of each feature. Third, the training set is divided into three subsets according to the reference blood glucose values. Fourth, the feature vectors and the corresponding blood glucose values in the same group are employed to build an individual model. Fifth, for each feature, the average of the feature values for all the measurements in the same subset is computed. For the data in the test set, first, the sum of the weighted distances between the test feature values and the average values obtained in the above is computed for each model. Here, the weights are defined based on the importance sorted by the random forest obtained in the above. The model corresponding to the smallest sum is assigned. Finally, the blood glucose value is estimated based on the corresponding model. Compared to the state of arts methods, our proposed method can effectively improve the estimation accuracy. In particular, the mean absolute relative difference (MARD) and the percentage of the data fall in the zone A of the Clarke error grid yielded by our proposed method reaches 12.19%, and 87.0588%, respectively.
This research examines the bipartite synchronization of dynamical network with switching signed topologies, where the nodes are governed by certain nonlinear dynamics, and an event-triggered control strategy is applied by under periodic sampling communications. It is shown that when the topologies switch within a finite set of signed graph, all nodes are guaranteed to achieve bipartite synchronization if the time average of the algebraic connectivity of the corresponding unsigned graph over certain length of time is large enough, and the main theorem details the impacts of nodal dynamics, network structure on synchronization, and gives a criterion for selecting the involved control parameters. Finally, some numerical simulations are presented to show the validity of our theoretical results and the efficiency of the proposed controller.
In this paper, a new method, combined with stationary wavelet transform and Gaussian Radial Basis Function Neural Networks (GRBFNN), is proposed for solving the problem of human gait modelling. Firstly, the hardware system, consisting with MPU6050 sensor, wireless transform module, micro control unit and computer, is designed for collecting the gait signal. Secondly, stationary wavelet transform is applied for decomposing the gait signal with 5 scales. In order to remove the high frequency noise and baseline drift, the coefficients of high frequency and low frequency are set as zero. Thirdly, after wavelet denoising, setting a large enough space to cover the gait signal and establishing lattice points with equal intervals in this space, we take gait signal as input and use lattice points as mapping center in GRBFNN design. Fourthly, the identification equation of continuous dynamical system is rewritten into discrete one, and GRBFNN is used for modelling the dynamical function of gait signal. In order to ensure the stability of iteration, the chosen of gain parameter is proven by the Z transform. Finally, comparing with wavelet neural networks(WNN), the result of test in practice demonstrates the superiority of the proposed method for solving the problem of human gait modelling.
