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This paper presents a control framework of a vibrating flexible string system subject to input saturation and output constraint. By integrating the backstepping technique and Lyapunov theory, a smooth function, a Nussbaum function, a barrier function, and an auxiliary system are exploited to establish a constrained boundary control for tacking the output and input constraints as well as stabilizing the system. The derived control can assure the bounded stability in controlled systems depending on Lyapunov criteria. The simulation study and analysis verify the effectiveness of the presented scheme.

Underdetermined blind source separation (UBSS) is a hot and challenging problem in signal processing. In the traditional UBSS algorithm, the number of source signals is often assumed to be known, which is very inconvenient in practice. In addition, it is more difficult to obtain the accurate estimation of mixing matrix in the underdetermined case. However, this information has a great influence on the source separation results, which can easily lead to poor separation performance. In this paper, a novel UBSS algorithm is presented to carry out a combined source signal number estimation and source signal separation task. First, in the proposed algorithm, we design a gap-based detection method to detect the number of source signals by eigenvalue decomposition. Then, the estimation of the mixing matrix is processed using a higher-order cumulant-based method so that the uniqueness of the estimated mixing matrix is guaranteed. Furthermore, an improved l1 -norm minimization algorithm is proposed to estimate the source signals. Meanwhile, the pre-conditioned conjugate gradient technology is employed to accelerate the convergence rate such that the computational load is reduced. Finally, a series of simulation experiments with synthetic heart sound data and image reconstruction results demonstrate that the proposed algorithm achieves better separating property than the state-of-the-art algorithms.

As a promising choice of alternative energy, wind power will account for a major part of energy generation in future Energy Internet. With the exploitation of wind power, multiple wind turbines (WTs) are deployed at remote and harsh areas, in which the adverse working environment may lead to enormous WT operating and maintenance costs. Deploying unmanned aerial vehicles (UAVs) for WT detection and sensory data processing in wind farms has been considered as a promising technology to reduce the costs and improve inspection efficiency. In this paper, a mobile edge computing (MEC) driven UAV routine inspection scheme is proposed, in which the UAV not only detects WTs in multiple sorties, but also provides computing and offloading services. To provide seamless communication service, UAV can offload the sensory data to the ground station or satellite optimally. In order to minimize the total completion time, we jointly optimize the UAV trajectory and computation operations, while guaranteeing the data processing accuracy. In the proposed scheme, in order to overcome the influence of wind on UAV trajectory planning, a low complexity WT routine inspection trajectory and UAV scheduling approach is designed firstly. Then, we present an iterative optimization solution to minimize the energy consumption of computation processing, via finding the optimal offloading trajectory and computation offloading parameters. Finally, simulation results show that the proposed scheme can effectively improve the efficiency of UAV routine inspection system performance.

Energy hub integrates various energy conversion and storage technologies, which can yield complementarity among multiple energy and provide consumers with stable energy services, such as electricity, heating, and cooling. This enables energy hub to be an ideal energy system design for smart and green buildings. This paper proposes a distributed auction mechanism for multi-energy scheduling of an energy hub that serves numbers of building energy users. In the auction, users first submit their demand data to the hub manager. Then, the hub manager allocates energy to users via optimization of energy scheduling based on the users' data. The auction mechanism is designed to be incentive compatible, meaning that users are incentivized to truthfully submit their demand data. Next, to mitigate the computational burden of the hub manager, a distributed implementation of the auction is developed, in which an algorithm based on alternating direction method of multipliers (ADMM) is adopted to offload auction computation onto the users. Distributed computation offloading may bring in new chances for users to manipulate the auction outcome since the users participate part of the auction computation. It is proven that the proposed distributed auction mechanism can achieve incentive compatibility in a Nash equilibrium, which indicates that rational users will faithfully report demand data and complete the assigned computation as well. Finally, simulation results based on a household energy consumption dataset are presented to evaluate the energy scheduling performance and to verify the incentive compatibility of the auction mechanism.

Meter data collection and management in smart grid has the potential for underlying security risks, e.g., low-sparsity unobservable attacks. Thus, it is crucial to investigate the vulnerability of smart grid through various exposure tests associated with these unobservable attacks. Recently, much attention has been paid to low-sparsity unobservable attacks with complete knowledge of the system matrix. In this paper, the unobservable attack exposure analysis is based on a relaxed condition, i.e., an incomplete knowledge of the system matrix. Furthermore, a data-driven attack scheme is designed to demonstrate that such knowledge can be learned with a two-stage strategy. In the first stage, a sequence of intercepted meter data is utilized to learn about the incomplete system matrix with a blind identification approach. In the second stage, the estimated system matrix at hand is used for the attack vector construction with a sparsity-exploiting method. Finally, the validity of the proposed data-driven attack scheme is tested through various experiments. The proposed result reveals the potential risk of meter data leakage to the security of the smart grid.