This article investigates the quasi-synchronization for fractional multiweighted coupled neural networks (FMCNNs) with discontinuous activation functions and mismatched parameters. First, under the generalized Caputo fractional-order derivative operator, a novel piecewise fractional differential inequality is established to study the convergence of fractional systems, which significantly extends some related published results. Subsequently, by exploiting the new inequality and Lyapunov stability theory, some sufficient quasi-synchronization conditions of FMCNNs are presented by aperiodic intermittent control. Meanwhile, the exponential convergence rate and synchronization error’s bound are given explicitly. Finally, the validity of theoretical analysis is confirmed by numerical examples and simulations.

Since the dimension of combined feature set for partial discharge (PD) pattern recognition is higher, the corresponding sample size increases, as does the required amount of storage space and calculation, and there are features with less category-related characteristics in the feature parameters, which may contain redundant information between them. To solve the problem of higher feature dimension and complicated classification model required for the identification of partial discharge insulation defect type in this paper. Random forest sequential forward selection method based on variance analysis (RF-VA) is proposed for the optimal subset selection. This method is improved in two aspects. Firstly, a method based on variance analysis is proposed, which measures feature differences between categories, and obtains a modified arrangement displacement scheme to guide rearrangement of the order of values taken on data sample out of bag. Secondly, the sequence forward search method used to do feature selection could get iteration evaluation results, which solves randomness to determine the size of feature subset and instability of the results existing in the original algorithm. The results show RF-VA can obtain a better subset of features. It is feasible to reduce the dimension of partial discharge characteristic set, and effectively improve the identification rate of partial discharge defect type.

Induced-charge electroosmosis has attracted lots of attention from the microfluidic community over the past decade. Most previous researches on this subject focused on induced-charge electroosmosis (ICEO) vortex streaming actuated on ideally polarizable surfaces immersed in electrolyte solutions. Starting from this point, we conduct herein a linear asymptotic analysis on nonlinear electroosmotic flow next to leaky dielectric blocks of arbitrary electrical conductivity and dielectric permittivity in harmonic AC electric fields, and theoretically demonstrate that observable ICEO fluid motion can be generated at high field frequencies in the vicinity of nearly insulating semiconductors, a very low electrical conductivity, of which can evidently increase the double-layer relaxation frequency (inversely proportional to the solid permittivity) to be much higher than the typical reciprocal RC time constant for induced double-layer charging on ideally polarizable surfaces. A computational model is developed to study the feasibility of this high-frequency vortex flow field of ICEO for sample mixing in microfluidics, in which the usage of AC voltage signal at high field frequencies may be beneficial to suppress electrochemical reactions to some extent. The influence of various parameters for developing an efficient mixer is investigated, and an integrated arrangement of semiconductor block array is suggested for achieving a reliable mixing performance at relatively high sample fluxes. Our physical demonstration with high-frequency ICEO next to leaky dielectric blocks using a simple channel structure offers valuable insights into the design of high-throughput micromixers for a variety of lab-on-a-chip applications.

Identification of insulation defects in gas insulated metal-enclosed switchgear (GIS) is important for partial discharge (PD) evaluation. This article proposes a polar coordinate pattern approach to characterize the different kinds of defect types. These defect types include floating electrodes, a fixed protrusion on the enclosure, surface contamination on the spacer, metallic prominence on the high voltage electrode, a void in the insulator, and free metal particles on the enclosure. First, the physical models for the insulation defects in the established GIS model are designed. Second, the phase resolved pulse sequence (PRPS) data sets are obtained using ultra-high frequency (UHF) measurement. Then, the polar coordinate patterns are proposed to characterize the defects. Nine discharge parameters combined with the parameters based on quadrant statistical theory constitute the input feature vector to identify the PD types. The experimental results show that these new parameters could produce a clear, quantitative description of the characteristics of the defect types and could be used to distinguish between the different kinds of defect types.

In order to meet the highway guidance demand, this work studies the short-term traffic flow prediction method of highway. The Yu-Wu highway which is the main road in Chongqing, China, traffic flow time series is taken as the study object. It uses phase space reconstruction theory and Lyapunov exponent to analyze the nonlinear character of traffic flow. A new Volterra prediction method based on model order reduction via quadratic-linear systems (QLMOR) is applied to predict the traffic flow. Compared with Taylor-expansion-based methods, these QLMOR-reduced Volterra models retain more information of the system and more accuracy. The simulation results using this new Volterra model to predict short time traffic flow reveal that the accuracy of chaotic traffic flow prediction is enough for highway guidance and could be a new reference for intelligent highway management.