This study presents a new flatness-based control scheme for the quantum series–parallel LCC-type resonant converter with output voltage doubler for high-voltage pulse applications, like medical X-ray imaging. Based on a previously derived continuous time model of the system, a flat output of the system model is shown. Moreover, an optimised trajectory planning algorithm with limited resonant current and the controller design are addressed. Details on the experimental set-up and the real-time controller implementation on an FPGA are given. Measurement results show the accuracy of the presented control method.

This study introduces an efficient energy management system (EMS) for a wind–photovoltaic (PV)–fuel cell (FC)–battery energy scheme with an effective control strategy to integrate with the utility grid. The suggested technique utilises the multi-objective particle swarm optimisation (MOPSO) to minimise the operation cost of the microgrid (MG) and maximise the generated power by each source. The presented MOPSO has the ability to minimise the operation cost of the MG with respect to the renewable penetration, the fluctuation in the generated power, uncertainty in power demand, and continuous change of the utility market price. The proposed optimisation strategy makes the exact choice of sources in right planning and chooses the power that must be created by each source and the power required by the utility network. The price fluctuation data of power and the variance of the renewable energy produced by each unit will be sent to the EMS by a communication network using global positioning system. The dynamic performance and the operation cost of the MG have been tested under different weather conditions and the variance of the power demand through a whole simulation period of 24 h. The stability of the MG, power quality, and voltage regulation is verified by Matlab simulation and experimental results.

Information diffusion and virus propagation are fundamental processes, which achieve great attention in networks. While information spreads across the network, but the relations between the nodes, the underlying relation network, are usually unknown, and the authors only observe the time which transactions take place between nodes, called communication actions. Furthermore, the content of such transactions is unknown in many applications. They tackle these challenges by developing the NetMine algorithm to infer the relations between nodes and find out the underlying relation network, which is hidden from them. Given the times when nodes transmit a message to others (i.e. who sends a message to whom at some explicit time), they infer the optimal underlying relation network that best explains the observed communication actions. They demonstrate the effectiveness of their algorithm on synthetic and real networks, outperforming the art-of-date algorithms related to this problem.