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Microscopic strategy update rules play an important role in the evolutionary dynamics of cooperation among interacting agents in structured populations. Many previous related works only consider one \emph{fixed} rule, while in reality, individuals may switch, sometimes periodically, between rules. It is of particular theoretical interest to investigate under what conditions the periodic switching of strategy update rules facilitates the emergence of cooperation. To answer this question, we study the evolutionary prisoner's dilemma game on structured networks where agents can periodically switch their strategy update rules. We accordingly develop a theoretical framework of this periodically switched system, where the replicator equation corresponding to each specific microscopic update rule is used for describing the subsystem, and all the subsystems are activated in sequence. By utilizing switched system theory, we identify the theoretical condition for the emergence of cooperative behavior. Under this condition, we have proved that the periodically switched system with different switching rules can converge to the full cooperation state. Finally, we consider an example where two strategy update rules, that is, the imitation and pairwise-comparison updating, are periodically switched, and find that our numerical calculations validate our theoretical results.

This paper focuses on the motion planning for mobile robots in 3D, which are modelled by 6-DOF rigid body systems with nonholonomic kinematics constraints. We not only specify the target position, but also bring in the requirement of the heading direction at the terminal time, which gives rise to a new and more challenging 3D motion planning problem. The proposed planning algorithm involves a novel velocity vector field (VF) over the workspace, and by following the VF, the robot can be navigated to the destination with the specified heading direction. In order to circumvent potential collisions with obstacles and other robots, a composite VF is designed by composing the navigation VF and an additional VF tangential to the boundary of the dangerous area. Moreover, we propose a priority-based algorithm to deal with the motion coupling issue among multiple robots. Finally, numerical simulations are conducted to verify the theoretical results.

Gas insulated switch-gear (GIS) is an important equipment in power systems and is widely used in high voltage and ultra-high voltage applications. Partial discharge (PD) is one of the main causes of the failure of GIS. Ultrasonic method is an effective means to detect PD of GIS. It can conduct live detection on GIS to evaluate its insulation state. The existing ultrasonic PD detection method generally needs the power supply and the electronic measuring instrument. The signal diagnosis is inconvenient, and the need of professional knowledge of the inspectors is relatively high. In this paper, based on ultrasonic PD detection method, combined with wireless communication technology, a novel portable PD detection device is developed. It is easy to carry, and has signal processing function, can upload data to the terminal, which is convenient for the inspectors to make diagnosis. The test results show that it is stable and reliable, and can locate the PD, which fully improves the efficiency of PD detection.

In this paper, a diagnosis method used for detecting insulation defects of GIL chamber filled with gas mixture of C 4 F 7 N/CO 2 was proposed and verified through experiments. Before extracting features of PRPD maps, perform Gabor transform on the figures to highlight details and characteristics. Optimized SVM was applied to identify the type of insulation defect. The results of experiments showed that the diagnosis method owns high accuracy. Higher accuracy indicates that the features extracted with Gabor transform can reflect more differences of insulation defects which can help identification.

Insulation defects that occur in gas-insulated switchgear (GIS), which is one of the most important types of equipment in the power grid, can lead to serious accidents. The ultrasonic detection method is commonly used to detect partial discharge (PD) signals in power equipment to discover defects. However, the traditional method to diagnose defects in GIS with ultrasonic PD signals is still based on the experience of testers. In this study, a classification system was proposed to identify insulation defects of GIS, based on voiceprint recognition technology. Twelve coefficients from mel frequency cepstral coefficient (MFCC) and 24 delta MFCC features were extracted as the acoustic features of the system. A support vector machine (SVM) multi-classifier was constructed to perform the classification and the sequential minimal optimization (SMO) algorithm was used to optimize the computational efficiency of the SVM. The experiments were conducted on a 110 kV GIS with different kinds of insulation defects. The results verified that the classification system with SMO-SVM achieved better identification accuracy and efficiency than the system with SVM. Therefore, it reveals the feasibility of the system to realize identification of insulation defects in GIS automatically and accurately.