Narrow Results

This research focuses on stabilization challenges of a Power System (PS) described by a delayed conformable fractional-order nonlinear model. We adopt the Polynomial Fitting Approximation Algorithm (PFAA) to approximate the cardinal sine function by a Square Of Polynomial (SOP). A polynomial representation is made for PS with different behaviors by dividing the operating range into $r$ regions and then calculating an SOP approximation for each region. Thus, the PS is characterized by $r$ distinct models, each applicable within its respective region. An Observer Based Control (OBC) is designed to stabilize the considered PS. The proposed result ensures the stabilization of the different $r$ models by satisfying sufficient conditions based on the sum-of-squares (SOS) approach.

Taking into account the quarantine for an infectious disease, a susceptible-exposed-infected-quarantined-recovery-susceptible (SEIQRS) epidemic model with time delay on the finite scale-free network is given. The basic reproduction number $R_0$, which is dependent not only on all kinds of transfer rates, but also on the topology of the network, is derived. By constructing the Lyapunov function, it is asserted that the disease-free equilibrium of system is locally asymptotically stable if $R_0<1$, moreover, disease-free equilibrium of system is globally asymptotically stable when $R_0<1$. In addition, the influence of network nodes on the spread of diseases is discussed. Finally, the theoretical results are verified by corresponding numerical simulation.

In this work, we study the norovirus (NoV) epidemic model with random perturbations and a time delay. First of all, the existence and uniqueness of the global positive solution are obtained. Then, we derive sufficient conditions for the extinction of the disease. Moreover, by establishing appropriate Lyapunov function, the existence of a stationary distribution is discussed. Some numerical simulations are given to illustrate our analytical results.

The main aim of this paper is to analyze a mathematical model for malware dissemination on wireless sensor networks with time delay. Local stability and exhibition of the Hopf bifurcation are explored by means of analysis of the distribution of roots of the consequential characteristic equation. Moreover, global exponential stability is established with the help of linear matrix inequality techniques. Furthermore, properties of the Hopf bifurcation such as the direction and stability are studied by utilizing the normal form theory and the center manifold theorem. Finally, a computer numerical simulation example is presented to certify the rationality of our obtained results.

It is very useful in the human computer interface to quickly and accurately recognize human hand movements in real time. In this paper, we aimed to robustly recognize hand gestures in real time using Convolutional Recurrent Neural Network (CRNN) with pre-processing and overlapping window. The CRNN is a deep learning model that combines Long Short-Term Memory (LSTM) for time-series information classification and Convolutional Neural Network (CNN) for feature extraction. The sensor for hand gesture detection uses Myo-armband, and six hand gestures are recognized and classified, including two grips, three hand signs, and one rest. As the essential pre-processing due to the characteristics of EMG data, the existing Short Time Fourier Transform (STFT), Continuous-time Wavelet Transform (CWT), and newly proposed Scale Average Wavelet Transform (SAWT) are used, and thus, the SAWT showed relatively high accuracy in the stationary environmental test. The CRNN with overlapping window has been proposed that can improve the degradation of real-time prediction accuracy, which is caused by inconsistent start time and hand motion speed when acquiring the EMG signal. In the stationary environmental test, the CRNN model with SAWT and overlapping window showed the highest accuracy of 92.5%. In the real-time environmental test, for all subjects learning, 80% accuracy and 0.99 s time delay were obtained on average, and for individual learning, 91.5% accuracy and 0.32 s time delay were obtained on average. As a result, in both stationary and real-time tests, the CRNN with SAWT and overlapping window showed better performance than the other methods.